From 7c3464011512c2b9dc8262ec803ce1925fd0975d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Tobias=20Plo=CC=88tz?= Date: Tue, 7 Jul 2026 14:04:56 +0200 Subject: [PATCH 01/96] Add LLM optional dependency infrastructure Co-Authored-By: Claude Opus 4.6 (1M context) --- baybe/_optional/info.py | 3 + baybe/_optional/llm.py | 15 + pyproject.toml | 6 + uv.lock | 7122 +++++++++++++++++++++++++++------------ 4 files changed, 4976 insertions(+), 2170 deletions(-) create mode 100644 baybe/_optional/llm.py diff --git a/baybe/_optional/info.py b/baybe/_optional/info.py index 7608db4ff4..25da02bb32 100644 --- a/baybe/_optional/info.py +++ b/baybe/_optional/info.py @@ -25,7 +25,9 @@ def exclude_sys_path(path: str, /): # noqa: DOC402 # Individual packages with exclude_sys_path(os.getcwd()): FLAKE8_INSTALLED = find_spec("flake8") is not None + JINJA2_INSTALLED = find_spec("jinja2") is not None LIME_INSTALLED = find_spec("lime") is not None + LITELLM_INSTALLED = find_spec("litellm") is not None ONNX_INSTALLED = find_spec("onnxruntime") is not None NGBOOST_INSTALLED = find_spec("ngboost") is not None POLARS_INSTALLED = find_spec("polars") is not None @@ -49,6 +51,7 @@ def exclude_sys_path(path: str, /): # noqa: DOC402 # Information on whether all required packages for certain functionality are available CHEM_INSTALLED = SKFP_INSTALLED INSIGHTS_INSTALLED = SHAP_INSTALLED and LIME_INSTALLED +LLM_INSTALLED = JINJA2_INSTALLED and LITELLM_INSTALLED LINT_INSTALLED = all( ( FLAKE8_INSTALLED, diff --git a/baybe/_optional/llm.py b/baybe/_optional/llm.py new file mode 100644 index 0000000000..d7f194e4b8 --- /dev/null +++ b/baybe/_optional/llm.py @@ -0,0 +1,15 @@ +"""Optional LLM imports.""" + +from baybe.exceptions import OptionalImportError + +try: + from jinja2 import Template + from litellm import completion + +except ModuleNotFoundError as ex: + raise OptionalImportError(name="litellm", group="llm") from ex + +__all__ = [ + "Template", + "completion", +] diff --git a/pyproject.toml b/pyproject.toml index 04301d7ade..7edbf62869 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -62,6 +62,7 @@ Issues = "https://github.com/emdgroup/baybe/issues/" extras = [ "baybe[chem]", "baybe[insights]", + "baybe[llm]", "baybe[onnx]", "baybe[polars]", "baybe[simulation]", @@ -103,6 +104,11 @@ insights = [ "shap[others]>=0.47.0", ] +llm = [ + "jinja2>=3.1.0", + "litellm>=1.70.0", +] + docs = [ "baybe[examples]", # docs cannot be built without running examples "furo>=2023.09.10", diff --git a/uv.lock b/uv.lock index 548ba562c4..4b10fa6769 100644 --- a/uv.lock +++ b/uv.lock @@ -2,10 +2,22 @@ version = 1 revision = 3 requires-python = ">=3.10, <3.15" resolution-markers = [ - "python_full_version >= '3.14'", - "python_full_version == '3.13.*'", - "python_full_version == '3.12.*'", - "python_full_version == '3.11.*'", + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and platform_machine == 'x86_64' and sys_platform == 'darwin'", + "(python_full_version >= '3.14' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version >= '3.14' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')", + "python_full_version == '3.13.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.13.*' and sys_platform == 'emscripten'", + "python_full_version == '3.12.*' and sys_platform == 'emscripten'", + "python_full_version == '3.13.*' and platform_machine == 'x86_64' and sys_platform == 'darwin'", + "(python_full_version == '3.13.*' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')", + "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'darwin'", + 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index 407c88a26f..ea006881f7 100644 --- a/baybe/exceptions.py +++ b/baybe/exceptions.py @@ -90,6 +90,10 @@ class InfeasibilityError(Exception): """An optimization problem has no feasible solution.""" +class LLMResponseError(Exception): + """An error occurred while processing a language model response.""" + + class NotEnoughPointsLeftError(Exception): """ More recommendations are requested than there are viable parameter configurations From 0802026244c843d7e45740bd7453c7200fde3975 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Tobias=20Plo=CC=88tz?= Date: Tue, 7 Jul 2026 14:05:13 +0200 Subject: [PATCH 03/96] Add LLMRecommender for LLM-based experimental design Implements a recommender that uses LiteLLM to query language models for experimental design suggestions. Leverages the metadata system to auto-extract parameter descriptions from the search space. Co-Authored-By: Claude Opus 4.6 (1M context) --- baybe/recommenders/__init__.py | 2 + baybe/recommenders/pure/llm/__init__.py | 5 + baybe/recommenders/pure/llm/llm.py | 448 ++++++++++++++++++++++++ 3 files changed, 455 insertions(+) create mode 100644 baybe/recommenders/pure/llm/__init__.py create mode 100644 baybe/recommenders/pure/llm/llm.py diff --git a/baybe/recommenders/__init__.py b/baybe/recommenders/__init__.py index 88bc984337..2edda6e0fa 100644 --- a/baybe/recommenders/__init__.py +++ b/baybe/recommenders/__init__.py @@ -7,6 +7,7 @@ ) from baybe.recommenders.naive import NaiveHybridSpaceRecommender from baybe.recommenders.pure.bayesian.botorch import BotorchRecommender +from baybe.recommenders.pure.llm import LLMRecommender from baybe.recommenders.pure.nonpredictive.clustering import ( GaussianMixtureClusteringRecommender, KMeansClusteringRecommender, @@ -22,6 +23,7 @@ "FPSRecommender", "GaussianMixtureClusteringRecommender", "KMeansClusteringRecommender", + "LLMRecommender", "PAMClusteringRecommender", "NaiveHybridSpaceRecommender", "RandomRecommender", diff --git a/baybe/recommenders/pure/llm/__init__.py b/baybe/recommenders/pure/llm/__init__.py new file mode 100644 index 0000000000..fb6e13b07b --- /dev/null +++ b/baybe/recommenders/pure/llm/__init__.py @@ -0,0 +1,5 @@ +"""Recommender based on Large Language Models (LLMs).""" + +from baybe.recommenders.pure.llm.llm import LLMRecommender + +__all__ = ["LLMRecommender"] diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py new file mode 100644 index 0000000000..338d54ea93 --- /dev/null +++ b/baybe/recommenders/pure/llm/llm.py @@ -0,0 +1,448 @@ +"""LLM-based recommender for experimental design.""" + +from __future__ import annotations + +import gc +import json +from collections.abc import Callable +from json import JSONDecodeError +from types import SimpleNamespace +from typing import Any + +import pandas as pd +from attrs import define, field +from typing_extensions import override + +from baybe.exceptions import LLMResponseError +from baybe.objectives.base import Objective +from baybe.parameters.base import DiscreteParameter, Parameter +from baybe.parameters.numerical import NumericalContinuousParameter +from baybe.recommenders.base import RecommenderProtocol +from baybe.searchspace import SearchSpace +from baybe.utils.conversion import to_string + +_PROMPT_TEMPLATE = """\ +You are an expert experimental design assistant. Your task is to suggest new \ +experimental conditions based on the following information: + +EXPERIMENT DESCRIPTION: +{{ experiment_description }} + +OPTIMIZATION OBJECTIVE: +{{ objective_description }} + +PARAMETERS: +{% for param in parameters %} +Parameter: {{ param.name }} +{% if param.description is not none %} +Description: {{ param.description }} +{% endif %} +Type: {{ param.type }} +{% if param.type == 'continuous' %} +Bounds: [{{ param.bounds[0] }}, {{ param.bounds[1] }}] +{% else %} +Allowed values: {{ param.values }} +{% endif %} +{% if param.unit is not none %} +Unit: {{ param.unit }} +{% endif %} + +{% endfor %} + +{% if measurements is not none and not measurements.empty %} +PREVIOUS MEASUREMENTS: +{{ measurements.to_string(index=False) }} +{% endif %} + +{% if related_data is not none and not related_data.empty %} +RELATED DATA: +Here is data from other optimization campaigns. +It might be useful to learn from these experiments or not. +Use it as you see fit. +{{ related_data.to_string(index=False) }} +{% endif %} + +Please suggest {{ batch_size }} new experimental conditions that are likely to \ +improve the optimization objective. +For each suggestion, provide: +1. A brief explanation of why you chose these values +2. The values for each parameter + +{% if format_instructions is not none %} +{{ format_instructions }} +{% else %} +Format your response as a JSON array of objects with the following structure \ +(no backticks): +[ + { + "explanation": "Brief explanation of the suggestion", + "parameters": { + "param1": value1, + "param2": value2, + ... + } + }, + ... +] +{% endif %} +""" + +_RECOVERY_PROMPT_TEMPLATE = """\ +The previous response was malformed and could not be parsed as JSON. Please \ +correct the response to match the required format. + +ERROR: {{ error }} + +ORIGINAL RESPONSE: +{{ original_response }} + +PARAMETERS: +{% for param in parameters %} +Parameter: {{ param.name }} +Type: {{ param.type }} +{% if param.type == 'continuous' %} +Bounds: [{{ param.bounds[0] }}, {{ param.bounds[1] }}] +{% else %} +Allowed values: {{ param.values }} +{% endif %} +{% endfor %} + +Please provide a corrected JSON response that follows the required format: +{% if format_instructions is not none %} +{{ format_instructions }} +{% else %} +[ + { + "explanation": "Brief explanation of the suggestion", + "parameters": { + "param1": value1, + "param2": value2, + ... + } + }, + ... +] +{% endif %}\ +""" + + +def _extract_parameter_info( + parameters: tuple[Parameter, ...], +) -> list[SimpleNamespace]: + """Extract parameter information for prompt construction. + + Args: + parameters: The parameters from the search space. + + Returns: + A list of namespace objects containing parameter information. + """ + infos = [] + for param in parameters: + info: dict[str, Any] = { + "name": param.name, + "description": param.description, + "unit": param.unit, + } + + if isinstance(param, NumericalContinuousParameter): + info["type"] = "continuous" + info["bounds"] = param.bounds.to_tuple() + elif isinstance(param, DiscreteParameter): + info["type"] = "discrete_numeric" if param.is_numerical else "categorical" + info["values"] = list(param.values) + else: + info["type"] = "unknown" + + infos.append(SimpleNamespace(**info)) + + return infos + + +@define +class LLMRecommender(RecommenderProtocol): + """Recommender that uses a language model to suggest new experimental points.""" + + model: str = field() + """The LiteLLM model identifier to use for recommendations.""" + + experiment_description: str = field() + """Textual description of the experiment.""" + + objective_description: str = field() + """Textual description of the optimization objective.""" + + format_instructions: str | None = field(default=None) + """Optional custom instructions for formatting the LLM's response.""" + + recovery_model: str | None = field(default=None) + """Optional model to use for recovery attempts. + + If ``None``, uses the same model as the main recommendations. + """ + + litellm_args: dict = field(factory=dict) + """Additional arguments to pass to LiteLLM.""" + + recovery_litellm_args: dict | None = field(default=None) + """Optional arguments to pass to LiteLLM during recovery attempts. + + If ``None``, uses the same arguments as the main recommendations. + """ + + related_data: pd.DataFrame | None = field(default=None) + """Optional DataFrame containing data from similar optimization campaigns. + + This data can be used to inform the recommendations by learning from + similar experiments. + """ + + is_feasible_experiment: Callable[[pd.Series], bool] | None = field(default=None) + """Optional function to check if an experiment is feasible. + + If provided, the recommender will request additional experiments and + filter for feasibility. Only feasible experiments are returned. + """ + + overflow_experiments: int = field(default=0) + """Number of additional experiments to request from the LLM. + + The LLM will be asked to generate ``batch_size + overflow_experiments`` + experiments. After filtering for feasibility, the first ``batch_size`` + feasible experiments will be returned. + """ + + def _construct_prompt( + self, + searchspace: SearchSpace, + batch_size: int, + measurements: pd.DataFrame | None = None, + ) -> str: + """Construct the prompt for the language model. + + Args: + searchspace: The search space to generate recommendations for. + batch_size: The number of recommendations to generate. + measurements: Optional measurements to include in the prompt. + + Returns: + The constructed prompt. + """ + from baybe._optional.llm import Template + + total_experiments = batch_size + self.overflow_experiments + parameters = _extract_parameter_info(searchspace.parameters) + + template = Template(_PROMPT_TEMPLATE, trim_blocks=True, lstrip_blocks=True) + return template.render( + experiment_description=self.experiment_description, + objective_description=self.objective_description, + parameters=parameters, + measurements=measurements, + related_data=self.related_data, + batch_size=total_experiments, + format_instructions=self.format_instructions, + ) + + def _attempt_recovery( + self, + error: Exception, + original_response: str, + searchspace: SearchSpace, + ) -> pd.DataFrame: + """Attempt to recover from a malformed LLM response by asking for correction. + + Args: + error: The error that occurred during parsing. + original_response: The original malformed response. + searchspace: The search space to validate recommendations against. + + Returns: + A DataFrame containing the corrected recommendations. + + Raises: + LLMResponseError: If recovery fails. + """ + from baybe._optional.llm import Template, completion + + parameters = _extract_parameter_info(searchspace.parameters) + template = Template( + _RECOVERY_PROMPT_TEMPLATE, trim_blocks=True, lstrip_blocks=True + ) + recovery_prompt = template.render( + error=str(error), + original_response=original_response, + parameters=parameters, + format_instructions=self.format_instructions, + ) + + try: + litellm_args = self.recovery_litellm_args or self.litellm_args + response = completion( + model=self.recovery_model or self.model, + messages=[{"role": "user", "content": recovery_prompt}], + **litellm_args, + ) + return self._parse_llm_response( + response.choices[0].message.content, searchspace + ) + except Exception as e: + raise LLMResponseError( + f"Failed to recover from malformed response: {e}" + ) from e + + def _parse_llm_response( + self, response: str, searchspace: SearchSpace + ) -> pd.DataFrame: + """Parse the LLM response into a DataFrame of recommendations. + + Args: + response: The response from the language model. + searchspace: The search space to validate recommendations against. + + Returns: + A DataFrame containing the parsed recommendations. + + Raises: + LLMResponseError: If the response cannot be parsed or + contains invalid values. + """ + try: + suggestions = json.loads(response) + except JSONDecodeError as e: + raise LLMResponseError(f"Error parsing JSON output: {e}") from e + + if not isinstance(suggestions, list): + raise LLMResponseError("Response must be a JSON array") + + recommendations = [] + for suggestion in suggestions: + if not isinstance(suggestion, dict): + raise LLMResponseError("Each suggestion must be a JSON object") + + if "parameters" not in suggestion: + raise LLMResponseError( + "Each suggestion must contain a 'parameters' field" + ) + + if "explanation" not in suggestion: + raise LLMResponseError( + "Each suggestion must contain an 'explanation' field" + ) + + params = suggestion["parameters"] + if not isinstance(params, dict): + raise LLMResponseError("Parameters must be a JSON object") + + param_names = {p.name for p in searchspace.parameters} + unknown = set(params.keys()) - param_names + if unknown: + raise LLMResponseError( + f"Response contains unknown parameter names: {unknown}" + ) + + recommendations.append(params) + + df = pd.DataFrame(recommendations) + + for param in searchspace.parameters: + if param.name not in df.columns: + raise LLMResponseError(f"Missing parameter: {param.name}") + + values = df[param.name] + + if isinstance(param, NumericalContinuousParameter): + if not all(isinstance(v, (int, float)) for v in values): + raise LLMResponseError( + f"Non-numeric values for continuous parameter: {param.name}" + ) + bounds = param.bounds.to_tuple() + min_val, max_val = bounds + if not all(min_val <= v <= max_val for v in values): + raise LLMResponseError( + f"Values for {param.name} outside bounds [{min_val}, {max_val}]" + ) + + elif isinstance(param, DiscreteParameter): + allowed = list(param.values) + if not all(v in allowed for v in values): + raise LLMResponseError( + f"Invalid values for parameter: {param.name}" + ) + + return df + + @override + def recommend( + self, + batch_size: int, + searchspace: SearchSpace, + objective: Objective | None = None, + measurements: pd.DataFrame | None = None, + pending_experiments: pd.DataFrame | None = None, + ) -> pd.DataFrame: + """Generate recommendations using the language model. + + Args: + batch_size: The number of recommendations to generate. + searchspace: The search space to generate recommendations for. + objective: Optional objective to optimize for. + measurements: Optional measurements to include in the prompt. + pending_experiments: Optional pending experiments to consider. + + Returns: + A DataFrame containing the recommendations as individual rows. + """ + from baybe._optional.llm import completion + + prompt = self._construct_prompt(searchspace, batch_size, measurements) + response = completion( + model=self.model, + messages=[{"role": "user", "content": prompt}], + **self.litellm_args, + ) + try: + output = self._parse_llm_response( + response.choices[0].message.content, searchspace + ) + except LLMResponseError as e: + output = self._attempt_recovery( + e, response.choices[0].message.content, searchspace + ) + + if self.is_feasible_experiment is not None: + feasible_mask = output.apply(self.is_feasible_experiment, axis=1) + feasible_experiments = output[feasible_mask] + + if len(feasible_experiments) >= batch_size: + return feasible_experiments.head(batch_size) + return feasible_experiments + + return output + + @override + def __str__(self) -> str: + fields = [ + to_string("Model", self.model, single_line=True), + to_string("LiteLLM Args", self.litellm_args, single_line=True), + to_string( + "Experiment Description", self.experiment_description, single_line=True + ), + to_string( + "Optimization Objective", self.objective_description, single_line=True + ), + to_string("Related Data", self.related_data, single_line=True), + to_string( + "Overflow Experiments", self.overflow_experiments, single_line=True + ), + to_string( + "Feasibility Check", + "Enabled" if self.is_feasible_experiment is not None else "Disabled", + single_line=True, + ), + ] + return to_string(self.__class__.__name__, *fields) + + +# Collect leftover original slotted classes processed by `attrs.define` +gc.collect() From da937892cf593f1c9cee4b606a0d48109030c6c3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Tobias=20Plo=CC=88tz?= Date: Tue, 7 Jul 2026 14:05:18 +0200 Subject: [PATCH 04/96] Add tests for LLMRecommender Co-Authored-By: Claude Opus 4.6 (1M context) --- tests/test_llm_recommender.py | 294 ++++++++++++++++++++++++++++++++++ 1 file changed, 294 insertions(+) create mode 100644 tests/test_llm_recommender.py diff --git a/tests/test_llm_recommender.py b/tests/test_llm_recommender.py new file mode 100644 index 0000000000..349a8e9dff --- /dev/null +++ b/tests/test_llm_recommender.py @@ -0,0 +1,294 @@ +"""Tests for the LLM-based recommender.""" + +import json +from types import SimpleNamespace +from unittest.mock import patch + +import pandas as pd +import pytest + +from baybe._optional.info import LLM_INSTALLED +from baybe.exceptions import LLMResponseError +from baybe.parameters import ( + CategoricalParameter, + NumericalContinuousParameter, +) +from baybe.searchspace import SearchSpace + +pytestmark = pytest.mark.skipif( + not LLM_INSTALLED, reason="LLM dependencies not installed" +) + + +def _mock_response(content: str) -> SimpleNamespace: + """Create a mock LLM response.""" + return SimpleNamespace( + choices=[SimpleNamespace(message=SimpleNamespace(content=content))] + ) + + +@pytest.fixture(name="searchspace") +def fixture_searchspace(): + """A search space with continuous and categorical parameters.""" + parameters = [ + NumericalContinuousParameter( + name="temperature", + bounds=(0.0, 100.0), + metadata={"description": "Reaction temperature", "unit": "°C"}, + ), + NumericalContinuousParameter( + name="pressure", + bounds=(0.0, 5.0), + metadata={"description": "Reaction pressure", "unit": "bar"}, + ), + CategoricalParameter( + name="catalyst", + values=["A", "B", "C"], + metadata={"description": "Catalyst type"}, + ), + ] + return SearchSpace.from_product(parameters) + + +@pytest.fixture(name="recommender") +def fixture_recommender(): + """An LLMRecommender instance for testing.""" + from baybe.recommenders.pure.llm.llm import LLMRecommender + + return LLMRecommender( + model="gpt-3.5-turbo", + experiment_description="Test experiment", + objective_description="Maximize yield", + ) + + +@pytest.fixture(name="valid_response") +def fixture_valid_response(): + """A valid mock LLM response with two suggestions.""" + return _mock_response( + json.dumps( + [ + { + "explanation": "Test suggestion 1", + "parameters": { + "temperature": 25.0, + "pressure": 2.0, + "catalyst": "A", + }, + }, + { + "explanation": "Test suggestion 2", + "parameters": { + "temperature": 30.0, + "pressure": 1.5, + "catalyst": "B", + }, + }, + ] + ) + ) + + +@patch("baybe._optional.llm.completion") +def test_recommend_success(mock_completion, recommender, searchspace, valid_response): + """Successful recommendation returns a DataFrame with correct shape.""" + mock_completion.return_value = valid_response + + recommendations = recommender.recommend(batch_size=2, searchspace=searchspace) + + assert isinstance(recommendations, pd.DataFrame) + assert len(recommendations) == 2 + assert set(recommendations.columns) == {"temperature", "pressure", "catalyst"} + assert recommendations["temperature"].tolist() == [25.0, 30.0] + assert recommendations["pressure"].tolist() == [2.0, 1.5] + assert recommendations["catalyst"].tolist() == ["A", "B"] + + +@patch("baybe._optional.llm.completion") +def test_recommend_with_measurements( + mock_completion, recommender, searchspace, valid_response +): + """Recommendations can be generated with previous measurements.""" + mock_completion.return_value = valid_response + + measurements = pd.DataFrame( + { + "temperature": [20.0, 25.0], + "pressure": [1.0, 2.0], + "catalyst": ["A", "B"], + "yield": [0.5, 0.7], + } + ) + + recommendations = recommender.recommend( + batch_size=2, searchspace=searchspace, measurements=measurements + ) + + assert isinstance(recommendations, pd.DataFrame) + assert len(recommendations) == 2 + prompt_args = mock_completion.call_args + assert ( + "PREVIOUS MEASUREMENTS" + in prompt_args.kwargs.get("messages", prompt_args[1]["messages"])[0]["content"] + ) + + +@patch("baybe._optional.llm.completion") +def test_recommend_invalid_json(mock_completion, recommender, searchspace): + """Invalid JSON response raises LLMResponseError after failed recovery.""" + mock_completion.return_value = _mock_response("Invalid JSON") + + with pytest.raises(LLMResponseError, match="Failed to recover"): + recommender.recommend(batch_size=2, searchspace=searchspace) + + +@patch("baybe._optional.llm.completion") +def test_recommend_out_of_bounds(mock_completion, recommender, searchspace): + """Out-of-bounds values raise LLMResponseError after failed recovery.""" + mock_completion.return_value = _mock_response( + json.dumps( + [ + { + "explanation": "Test", + "parameters": { + "temperature": 150.0, + "pressure": 2.0, + "catalyst": "A", + }, + } + ] + ) + ) + + with pytest.raises(LLMResponseError, match="Failed to recover"): + recommender.recommend(batch_size=1, searchspace=searchspace) + + +@patch("baybe._optional.llm.completion") +def test_recommend_invalid_categorical(mock_completion, recommender, searchspace): + """Invalid categorical values raise LLMResponseError after failed recovery.""" + mock_completion.return_value = _mock_response( + json.dumps( + [ + { + "explanation": "Test", + "parameters": { + "temperature": 25.0, + "pressure": 2.0, + "catalyst": "D", + }, + } + ] + ) + ) + + with pytest.raises(LLMResponseError, match="Failed to recover"): + recommender.recommend(batch_size=1, searchspace=searchspace) + + +@patch("baybe._optional.llm.completion") +def test_recommend_missing_parameter(mock_completion, recommender, searchspace): + """Missing parameters raise LLMResponseError after failed recovery.""" + mock_completion.return_value = _mock_response( + json.dumps( + [ + { + "explanation": "Test", + "parameters": {"temperature": 25.0, "catalyst": "A"}, + } + ] + ) + ) + + with pytest.raises(LLMResponseError, match="Failed to recover"): + recommender.recommend(batch_size=1, searchspace=searchspace) + + +@patch("baybe._optional.llm.completion") +def test_recovery_success(mock_completion, recommender, searchspace): + """Successful recovery from a malformed initial response.""" + invalid = _mock_response("Invalid JSON") + valid = _mock_response( + json.dumps( + [ + { + "explanation": "Recovered", + "parameters": { + "temperature": 50.0, + "pressure": 3.0, + "catalyst": "C", + }, + } + ] + ) + ) + mock_completion.side_effect = [invalid, valid] + + recommendations = recommender.recommend(batch_size=1, searchspace=searchspace) + + assert len(recommendations) == 1 + assert mock_completion.call_count == 2 + + +@patch("baybe._optional.llm.completion") +def test_feasibility_filtering(mock_completion, searchspace): + """Feasibility filtering respects the is_feasible_experiment callback.""" + from baybe.recommenders.pure.llm.llm import LLMRecommender + + recommender = LLMRecommender( + model="gpt-3.5-turbo", + experiment_description="Test", + objective_description="Maximize yield", + is_feasible_experiment=lambda row: row["temperature"] > 20.0, + overflow_experiments=1, + ) + + mock_completion.return_value = _mock_response( + json.dumps( + [ + { + "explanation": "Low temp", + "parameters": { + "temperature": 10.0, + "pressure": 1.0, + "catalyst": "A", + }, + }, + { + "explanation": "High temp", + "parameters": { + "temperature": 50.0, + "pressure": 2.0, + "catalyst": "B", + }, + }, + { + "explanation": "Med temp", + "parameters": { + "temperature": 30.0, + "pressure": 3.0, + "catalyst": "C", + }, + }, + ] + ) + ) + + recommendations = recommender.recommend(batch_size=2, searchspace=searchspace) + + assert len(recommendations) == 2 + assert all(recommendations["temperature"] > 20.0) + + +def test_initialization(recommender): + """LLMRecommender initializes with correct attributes.""" + assert recommender.model == "gpt-3.5-turbo" + assert recommender.experiment_description == "Test experiment" + assert recommender.objective_description == "Maximize yield" + + +def test_str_representation(recommender): + """String representation includes key information.""" + s = str(recommender) + assert "LLMRecommender" in s + assert "gpt-3.5-turbo" in s From 597b9a452ddb5948c5bb815c591a794349789639 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Tobias=20Plo=CC=88tz?= Date: Tue, 7 Jul 2026 14:05:23 +0200 Subject: [PATCH 05/96] Update CHANGELOG Co-Authored-By: Claude Opus 4.6 (1M context) --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index a5b91f6979..214165d699 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -97,6 +97,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - `KernelFactory` now obeys the more general `GPComponentFactoryProtocol` ### Added +- `LLMRecommender` for LLM-based experimental design suggestions via LiteLLM - Support for Python 3.14 - Support for pandas 3 - `Settings` class for unified and streamlined settings management From 4eb850dc17e18b4f42b1f67c8285ff45ee6031c3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Tobias=20Plo=CC=88tz?= Date: Tue, 7 Jul 2026 14:16:50 +0200 Subject: [PATCH 06/96] Update LLMRecommender model version to gpt-5.4 in tests --- tests/test_llm_recommender.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tests/test_llm_recommender.py b/tests/test_llm_recommender.py index 349a8e9dff..eff9ea91df 100644 --- a/tests/test_llm_recommender.py +++ b/tests/test_llm_recommender.py @@ -56,7 +56,7 @@ def fixture_recommender(): from baybe.recommenders.pure.llm.llm import LLMRecommender return LLMRecommender( - model="gpt-3.5-turbo", + model="gpt-5.4", experiment_description="Test experiment", objective_description="Maximize yield", ) @@ -236,7 +236,7 @@ def test_feasibility_filtering(mock_completion, searchspace): from baybe.recommenders.pure.llm.llm import LLMRecommender recommender = LLMRecommender( - model="gpt-3.5-turbo", + model="gpt-5.4", experiment_description="Test", objective_description="Maximize yield", is_feasible_experiment=lambda row: row["temperature"] > 20.0, @@ -282,7 +282,7 @@ def test_feasibility_filtering(mock_completion, searchspace): def test_initialization(recommender): """LLMRecommender initializes with correct attributes.""" - assert recommender.model == "gpt-3.5-turbo" + assert recommender.model == "gpt-5.4" assert recommender.experiment_description == "Test experiment" assert recommender.objective_description == "Maximize yield" @@ -291,4 +291,4 @@ def test_str_representation(recommender): """String representation includes key information.""" s = str(recommender) assert "LLMRecommender" in s - assert "gpt-3.5-turbo" in s + assert "gpt-5.4" in s From b0756609efdaa1ff52c76fda53b7a55c7bbcf5d3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Tobias=20Plo=CC=88tz?= Date: Wed, 8 Jul 2026 09:14:36 +0200 Subject: [PATCH 07/96] Address PR review findings for LLMRecommender - Inherit from NonPredictiveRecommender instead of RecommenderProtocol - Add field validators for required string fields - Improve error handling in _attempt_recovery (split broad except) - Raise IncompatibilityError for unsupported parameter types - Add batch_size count validation with warning - Improve error messages with invalid/allowed values detail - Add Raises section to recommend() docstring - Fix dict type annotations to dict[str, Any] - Add LLMRecommender to pure/__init__.py for consistent hierarchy - Refactor tests: parametrize error cases, use batch_size=3, add NumericalDiscreteParameter, test related_data/recovery_model/ format_instructions, test feasibility edge cases Co-Authored-By: Claude Opus 4.6 (1M context) --- baybe/recommenders/pure/__init__.py | 2 + baybe/recommenders/pure/llm/llm.py | 123 +++++++-- tests/test_llm_recommender.py | 393 +++++++++++++++++++--------- 3 files changed, 370 insertions(+), 148 deletions(-) diff --git a/baybe/recommenders/pure/__init__.py b/baybe/recommenders/pure/__init__.py index 76340a57ad..97a65c579a 100644 --- a/baybe/recommenders/pure/__init__.py +++ b/baybe/recommenders/pure/__init__.py @@ -5,6 +5,7 @@ """ from baybe.recommenders.pure.bayesian.botorch import BotorchRecommender +from baybe.recommenders.pure.llm import LLMRecommender from baybe.recommenders.pure.nonpredictive import ( FPSRecommender, GaussianMixtureClusteringRecommender, @@ -18,6 +19,7 @@ "FPSRecommender", "GaussianMixtureClusteringRecommender", "KMeansClusteringRecommender", + "LLMRecommender", "PAMClusteringRecommender", "RandomRecommender", ] diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index 338d54ea93..5592423bca 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -4,21 +4,24 @@ import gc import json +import warnings from collections.abc import Callable from json import JSONDecodeError from types import SimpleNamespace -from typing import Any +from typing import Any, ClassVar import pandas as pd from attrs import define, field +from attrs.validators import instance_of, min_len from typing_extensions import override -from baybe.exceptions import LLMResponseError +from baybe.exceptions import IncompatibilityError, LLMResponseError from baybe.objectives.base import Objective from baybe.parameters.base import DiscreteParameter, Parameter from baybe.parameters.numerical import NumericalContinuousParameter -from baybe.recommenders.base import RecommenderProtocol +from baybe.recommenders.pure.nonpredictive.base import NonPredictiveRecommender from baybe.searchspace import SearchSpace +from baybe.searchspace.core import SearchSpaceType from baybe.utils.conversion import to_string _PROMPT_TEMPLATE = """\ @@ -136,6 +139,9 @@ def _extract_parameter_info( Returns: A list of namespace objects containing parameter information. + + Raises: + IncompatibilityError: If a parameter type is not supported. """ infos = [] for param in parameters: @@ -152,24 +158,34 @@ def _extract_parameter_info( info["type"] = "discrete_numeric" if param.is_numerical else "categorical" info["values"] = list(param.values) else: - info["type"] = "unknown" + raise IncompatibilityError( + f"Parameter '{param.name}' has unsupported type " + f"'{type(param).__name__}' for " + f"'{LLMRecommender.__name__}'. Only " + f"'{NumericalContinuousParameter.__name__}' and " + f"'{DiscreteParameter.__name__}' subclasses are supported." + ) infos.append(SimpleNamespace(**info)) return infos -@define -class LLMRecommender(RecommenderProtocol): +@define(slots=False) +class LLMRecommender(NonPredictiveRecommender): """Recommender that uses a language model to suggest new experimental points.""" - model: str = field() + # Class variables + compatibility: ClassVar[SearchSpaceType] = SearchSpaceType.HYBRID + # See base class. + + model: str = field(validator=(instance_of(str), min_len(1))) """The LiteLLM model identifier to use for recommendations.""" - experiment_description: str = field() + experiment_description: str = field(validator=(instance_of(str), min_len(1))) """Textual description of the experiment.""" - objective_description: str = field() + objective_description: str = field(validator=(instance_of(str), min_len(1))) """Textual description of the optimization objective.""" format_instructions: str | None = field(default=None) @@ -181,10 +197,10 @@ class LLMRecommender(RecommenderProtocol): If ``None``, uses the same model as the main recommendations. """ - litellm_args: dict = field(factory=dict) + litellm_args: dict[str, Any] = field(factory=dict) """Additional arguments to pass to LiteLLM.""" - recovery_litellm_args: dict | None = field(default=None) + recovery_litellm_args: dict[str, Any] | None = field(default=None) """Optional arguments to pass to LiteLLM during recovery attempts. If ``None``, uses the same arguments as the main recommendations. @@ -233,7 +249,11 @@ def _construct_prompt( total_experiments = batch_size + self.overflow_experiments parameters = _extract_parameter_info(searchspace.parameters) - template = Template(_PROMPT_TEMPLATE, trim_blocks=True, lstrip_blocks=True) + template = Template( + _PROMPT_TEMPLATE, + trim_blocks=True, + lstrip_blocks=True, + ) return template.render( experiment_description=self.experiment_description, objective_description=self.objective_description, @@ -276,19 +296,38 @@ def _attempt_recovery( format_instructions=self.format_instructions, ) + litellm_args = self.recovery_litellm_args or self.litellm_args try: - litellm_args = self.recovery_litellm_args or self.litellm_args response = completion( model=self.recovery_model or self.model, messages=[{"role": "user", "content": recovery_prompt}], **litellm_args, ) - return self._parse_llm_response( - response.choices[0].message.content, searchspace - ) except Exception as e: raise LLMResponseError( - f"Failed to recover from malformed response: {e}" + f"Recovery LLM call failed ({type(e).__name__}): {e}. " + f"Original error: {error}" + ) from e + + try: + content = response.choices[0].message.content + except (AttributeError, IndexError, TypeError) as e: + raise LLMResponseError( + f"Recovery response had unexpected structure: {e}. " + f"Original error: {error}" + ) from e + + if content is None: + raise LLMResponseError( + f"Recovery returned empty content (None). Original error: {error}" + ) + + try: + return self._parse_llm_response(content, searchspace) + except LLMResponseError as e: + raise LLMResponseError( + f"Recovery produced another malformed response: {e}. " + f"Original error: {error}" ) from e def _parse_llm_response( @@ -309,7 +348,7 @@ def _parse_llm_response( """ try: suggestions = json.loads(response) - except JSONDecodeError as e: + except (JSONDecodeError, TypeError) as e: raise LLMResponseError(f"Error parsing JSON output: {e}") from e if not isinstance(suggestions, list): @@ -365,9 +404,11 @@ def _parse_llm_response( elif isinstance(param, DiscreteParameter): allowed = list(param.values) - if not all(v in allowed for v in values): + invalid = [v for v in values if v not in allowed] + if invalid: raise LLMResponseError( - f"Invalid values for parameter: {param.name}" + f"Invalid values {invalid} for parameter '{param.name}'. " + f"Allowed values are: {allowed}" ) return df @@ -392,6 +433,10 @@ def recommend( Returns: A DataFrame containing the recommendations as individual rows. + + Raises: + LLMResponseError: If the LLM response cannot be parsed or + recovery fails. """ from baybe._optional.llm import completion @@ -401,24 +446,44 @@ def recommend( messages=[{"role": "user", "content": prompt}], **self.litellm_args, ) + try: - output = self._parse_llm_response( - response.choices[0].message.content, searchspace - ) + content = response.choices[0].message.content + except (AttributeError, IndexError, TypeError) as e: + raise LLMResponseError( + f"LLM returned an unexpected response structure: {e}" + ) from e + + if content is None: + raise LLMResponseError("LLM returned empty content (None).") + + try: + output = self._parse_llm_response(content, searchspace) except LLMResponseError as e: - output = self._attempt_recovery( - e, response.choices[0].message.content, searchspace + output = self._attempt_recovery(e, content, searchspace) + + if len(output) < batch_size + self.overflow_experiments: + warnings.warn( + f"LLM returned {len(output)} suggestions instead of the " + f"requested {batch_size + self.overflow_experiments}.", + stacklevel=2, ) if self.is_feasible_experiment is not None: feasible_mask = output.apply(self.is_feasible_experiment, axis=1) feasible_experiments = output[feasible_mask] - if len(feasible_experiments) >= batch_size: - return feasible_experiments.head(batch_size) - return feasible_experiments + if len(feasible_experiments) < batch_size: + warnings.warn( + f"Only {len(feasible_experiments)} of {batch_size} requested " + f"experiments passed the feasibility check. Consider increasing " + f"overflow_experiments (currently {self.overflow_experiments}).", + stacklevel=2, + ) + + return feasible_experiments.head(batch_size) - return output + return output.head(batch_size) @override def __str__(self) -> str: diff --git a/tests/test_llm_recommender.py b/tests/test_llm_recommender.py index eff9ea91df..97387a565e 100644 --- a/tests/test_llm_recommender.py +++ b/tests/test_llm_recommender.py @@ -12,6 +12,7 @@ from baybe.parameters import ( CategoricalParameter, NumericalContinuousParameter, + NumericalDiscreteParameter, ) from baybe.searchspace import SearchSpace @@ -27,9 +28,19 @@ def _mock_response(content: str) -> SimpleNamespace: ) +def _make_suggestions(params_list: list[dict]) -> str: + """Create a JSON string of suggestions from a list of parameter dicts.""" + return json.dumps( + [ + {"explanation": f"Suggestion {i}", "parameters": p} + for i, p in enumerate(params_list) + ] + ) + + @pytest.fixture(name="searchspace") def fixture_searchspace(): - """A search space with continuous and categorical parameters.""" + """A search space with continuous, discrete numeric, and categorical parameters.""" parameters = [ NumericalContinuousParameter( name="temperature", @@ -41,6 +52,11 @@ def fixture_searchspace(): bounds=(0.0, 5.0), metadata={"description": "Reaction pressure", "unit": "bar"}, ), + NumericalDiscreteParameter( + name="n_cycles", + values=[1, 2, 3, 4, 5], + metadata={"description": "Number of reaction cycles"}, + ), CategoricalParameter( name="catalyst", values=["A", "B", "C"], @@ -64,26 +80,13 @@ def fixture_recommender(): @pytest.fixture(name="valid_response") def fixture_valid_response(): - """A valid mock LLM response with two suggestions.""" + """A valid mock LLM response with three suggestions.""" return _mock_response( - json.dumps( + _make_suggestions( [ - { - "explanation": "Test suggestion 1", - "parameters": { - "temperature": 25.0, - "pressure": 2.0, - "catalyst": "A", - }, - }, - { - "explanation": "Test suggestion 2", - "parameters": { - "temperature": 30.0, - "pressure": 1.5, - "catalyst": "B", - }, - }, + {"temperature": 25.0, "pressure": 2.0, "n_cycles": 1, "catalyst": "A"}, + {"temperature": 30.0, "pressure": 1.5, "n_cycles": 3, "catalyst": "B"}, + {"temperature": 50.0, "pressure": 3.0, "n_cycles": 5, "catalyst": "C"}, ] ) ) @@ -94,114 +97,225 @@ def test_recommend_success(mock_completion, recommender, searchspace, valid_resp """Successful recommendation returns a DataFrame with correct shape.""" mock_completion.return_value = valid_response - recommendations = recommender.recommend(batch_size=2, searchspace=searchspace) + recommendations = recommender.recommend(batch_size=3, searchspace=searchspace) assert isinstance(recommendations, pd.DataFrame) - assert len(recommendations) == 2 - assert set(recommendations.columns) == {"temperature", "pressure", "catalyst"} - assert recommendations["temperature"].tolist() == [25.0, 30.0] - assert recommendations["pressure"].tolist() == [2.0, 1.5] - assert recommendations["catalyst"].tolist() == ["A", "B"] + assert len(recommendations) == 3 + assert set(recommendations.columns) == { + "temperature", + "pressure", + "n_cycles", + "catalyst", + } + assert recommendations["temperature"].tolist() == [25.0, 30.0, 50.0] + assert recommendations["catalyst"].tolist() == ["A", "B", "C"] + assert recommendations["n_cycles"].tolist() == [1, 3, 5] @patch("baybe._optional.llm.completion") def test_recommend_with_measurements( mock_completion, recommender, searchspace, valid_response ): - """Recommendations can be generated with previous measurements.""" + """Recommendations include previous measurements in prompt.""" mock_completion.return_value = valid_response measurements = pd.DataFrame( { "temperature": [20.0, 25.0], "pressure": [1.0, 2.0], + "n_cycles": [1, 2], "catalyst": ["A", "B"], "yield": [0.5, 0.7], } ) recommendations = recommender.recommend( - batch_size=2, searchspace=searchspace, measurements=measurements + batch_size=3, searchspace=searchspace, measurements=measurements ) assert isinstance(recommendations, pd.DataFrame) - assert len(recommendations) == 2 - prompt_args = mock_completion.call_args - assert ( - "PREVIOUS MEASUREMENTS" - in prompt_args.kwargs.get("messages", prompt_args[1]["messages"])[0]["content"] - ) + assert len(recommendations) == 3 + prompt_content = mock_completion.call_args.kwargs.get( + "messages", mock_completion.call_args[1]["messages"] + )[0]["content"] + assert "PREVIOUS MEASUREMENTS" in prompt_content @patch("baybe._optional.llm.completion") -def test_recommend_invalid_json(mock_completion, recommender, searchspace): - """Invalid JSON response raises LLMResponseError after failed recovery.""" - mock_completion.return_value = _mock_response("Invalid JSON") +def test_recommend_with_related_data(mock_completion, searchspace, valid_response): + """Related data is included in the prompt when provided.""" + from baybe.recommenders.pure.llm.llm import LLMRecommender + + related_data = pd.DataFrame( + {"temperature": [40.0], "pressure": [2.5], "n_cycles": [3], "yield": [0.9]} + ) + recommender = LLMRecommender( + model="gpt-5.4", + experiment_description="Test", + objective_description="Maximize yield", + related_data=related_data, + ) + mock_completion.return_value = valid_response + + recommender.recommend(batch_size=3, searchspace=searchspace) - with pytest.raises(LLMResponseError, match="Failed to recover"): - recommender.recommend(batch_size=2, searchspace=searchspace) + prompt_content = mock_completion.call_args.kwargs.get( + "messages", mock_completion.call_args[1]["messages"] + )[0]["content"] + assert "RELATED DATA" in prompt_content @patch("baybe._optional.llm.completion") -def test_recommend_out_of_bounds(mock_completion, recommender, searchspace): - """Out-of-bounds values raise LLMResponseError after failed recovery.""" - mock_completion.return_value = _mock_response( - json.dumps( - [ - { - "explanation": "Test", - "parameters": { - "temperature": 150.0, - "pressure": 2.0, - "catalyst": "A", - }, - } - ] - ) +def test_recommend_with_format_instructions( + mock_completion, searchspace, valid_response +): + """Custom format instructions are included in the prompt.""" + from baybe.recommenders.pure.llm.llm import LLMRecommender + + custom_instructions = "Return CSV format instead." + recommender = LLMRecommender( + model="gpt-5.4", + experiment_description="Test", + objective_description="Maximize yield", + format_instructions=custom_instructions, ) + mock_completion.return_value = valid_response + + recommender.recommend(batch_size=3, searchspace=searchspace) - with pytest.raises(LLMResponseError, match="Failed to recover"): - recommender.recommend(batch_size=1, searchspace=searchspace) + prompt_content = mock_completion.call_args.kwargs.get( + "messages", mock_completion.call_args[1]["messages"] + )[0]["content"] + assert custom_instructions in prompt_content @patch("baybe._optional.llm.completion") -def test_recommend_invalid_categorical(mock_completion, recommender, searchspace): - """Invalid categorical values raise LLMResponseError after failed recovery.""" - mock_completion.return_value = _mock_response( - json.dumps( +def test_recovery_with_distinct_model(mock_completion, recommender, searchspace): + """Recovery uses the specified recovery_model and recovery_litellm_args.""" + from baybe.recommenders.pure.llm.llm import LLMRecommender + + recommender = LLMRecommender( + model="gpt-5.4", + experiment_description="Test", + objective_description="Maximize yield", + recovery_model="gpt-4o-mini", + recovery_litellm_args={"temperature": 0.0}, + ) + + invalid = _mock_response("Invalid JSON") + valid = _mock_response( + _make_suggestions( [ - { - "explanation": "Test", - "parameters": { - "temperature": 25.0, - "pressure": 2.0, - "catalyst": "D", - }, - } + {"temperature": 50.0, "pressure": 3.0, "n_cycles": 2, "catalyst": "C"}, ] ) ) + mock_completion.side_effect = [invalid, valid] + + recommender.recommend(batch_size=1, searchspace=searchspace) + + recovery_call = mock_completion.call_args_list[1] + assert ( + recovery_call.kwargs.get("model", recovery_call[1].get("model")) + == "gpt-4o-mini" + ) + assert ( + recovery_call.kwargs.get("temperature", recovery_call[1].get("temperature")) + == 0.0 + ) - with pytest.raises(LLMResponseError, match="Failed to recover"): - recommender.recommend(batch_size=1, searchspace=searchspace) + +@pytest.mark.parametrize( + ("response_content", "error_match"), + [ + pytest.param( + "Invalid JSON", + "Error parsing JSON output", + id="invalid_json", + ), + pytest.param( + json.dumps( + [ + { + "explanation": "Test", + "parameters": { + "temperature": 150.0, + "pressure": 2.0, + "n_cycles": 1, + "catalyst": "A", + }, + } + ] + ), + "outside bounds", + id="out_of_bounds", + ), + pytest.param( + json.dumps( + [ + { + "explanation": "Test", + "parameters": { + "temperature": 25.0, + "pressure": 2.0, + "n_cycles": 1, + "catalyst": "D", + }, + } + ] + ), + "Invalid values", + id="invalid_categorical", + ), + pytest.param( + json.dumps( + [ + { + "explanation": "Test", + "parameters": {"temperature": 25.0, "catalyst": "A"}, + } + ] + ), + "Missing parameter", + id="missing_parameter", + ), + pytest.param( + json.dumps( + [ + { + "explanation": "Test", + "parameters": { + "temperature": 25.0, + "pressure": 2.0, + "n_cycles": 1, + "catalyst": "A", + "unknown": 1, + }, + } + ] + ), + "unknown parameter names", + id="unknown_parameter", + ), + ], +) +def test_parse_llm_response_errors( + response_content, error_match, recommender, searchspace +): + """Malformed responses raise LLMResponseError with descriptive messages.""" + with pytest.raises(LLMResponseError, match=error_match): + recommender._parse_llm_response(response_content, searchspace) @patch("baybe._optional.llm.completion") -def test_recommend_missing_parameter(mock_completion, recommender, searchspace): - """Missing parameters raise LLMResponseError after failed recovery.""" - mock_completion.return_value = _mock_response( - json.dumps( - [ - { - "explanation": "Test", - "parameters": {"temperature": 25.0, "catalyst": "A"}, - } - ] - ) - ) +def test_recommend_invalid_response_with_failed_recovery( + mock_completion, recommender, searchspace +): + """Invalid response that also fails recovery raises LLMResponseError.""" + mock_completion.return_value = _mock_response("Invalid JSON") - with pytest.raises(LLMResponseError, match="Failed to recover"): - recommender.recommend(batch_size=1, searchspace=searchspace) + with pytest.raises(LLMResponseError, match="Recovery produced another malformed"): + recommender.recommend(batch_size=3, searchspace=searchspace) @patch("baybe._optional.llm.completion") @@ -209,16 +323,9 @@ def test_recovery_success(mock_completion, recommender, searchspace): """Successful recovery from a malformed initial response.""" invalid = _mock_response("Invalid JSON") valid = _mock_response( - json.dumps( + _make_suggestions( [ - { - "explanation": "Recovered", - "parameters": { - "temperature": 50.0, - "pressure": 3.0, - "catalyst": "C", - }, - } + {"temperature": 50.0, "pressure": 3.0, "n_cycles": 2, "catalyst": "C"}, ] ) ) @@ -244,42 +351,70 @@ def test_feasibility_filtering(mock_completion, searchspace): ) mock_completion.return_value = _mock_response( - json.dumps( + _make_suggestions( [ - { - "explanation": "Low temp", - "parameters": { - "temperature": 10.0, - "pressure": 1.0, - "catalyst": "A", - }, - }, - { - "explanation": "High temp", - "parameters": { - "temperature": 50.0, - "pressure": 2.0, - "catalyst": "B", - }, - }, - { - "explanation": "Med temp", - "parameters": { - "temperature": 30.0, - "pressure": 3.0, - "catalyst": "C", - }, - }, + {"temperature": 10.0, "pressure": 1.0, "n_cycles": 1, "catalyst": "A"}, + {"temperature": 50.0, "pressure": 2.0, "n_cycles": 2, "catalyst": "B"}, + {"temperature": 30.0, "pressure": 3.0, "n_cycles": 3, "catalyst": "C"}, + {"temperature": 60.0, "pressure": 4.0, "n_cycles": 4, "catalyst": "A"}, ] ) ) - recommendations = recommender.recommend(batch_size=2, searchspace=searchspace) + recommendations = recommender.recommend(batch_size=3, searchspace=searchspace) - assert len(recommendations) == 2 + assert len(recommendations) == 3 assert all(recommendations["temperature"] > 20.0) +@patch("baybe._optional.llm.completion") +def test_feasibility_filtering_insufficient_feasible(mock_completion, searchspace): + """Warning is emitted when fewer feasible experiments than batch_size.""" + from baybe.recommenders.pure.llm.llm import LLMRecommender + + recommender = LLMRecommender( + model="gpt-5.4", + experiment_description="Test", + objective_description="Maximize yield", + is_feasible_experiment=lambda row: row["temperature"] > 90.0, + overflow_experiments=1, + ) + + mock_completion.return_value = _mock_response( + _make_suggestions( + [ + {"temperature": 10.0, "pressure": 1.0, "n_cycles": 1, "catalyst": "A"}, + {"temperature": 50.0, "pressure": 2.0, "n_cycles": 2, "catalyst": "B"}, + {"temperature": 95.0, "pressure": 3.0, "n_cycles": 3, "catalyst": "C"}, + {"temperature": 30.0, "pressure": 4.0, "n_cycles": 4, "catalyst": "A"}, + ] + ) + ) + + with pytest.warns(UserWarning, match="feasibility check"): + recommendations = recommender.recommend(batch_size=3, searchspace=searchspace) + + assert len(recommendations) == 1 + assert recommendations["temperature"].iloc[0] == 95.0 + + +@patch("baybe._optional.llm.completion") +def test_batch_size_warning_when_llm_returns_fewer( + mock_completion, recommender, searchspace +): + """Warning is emitted when LLM returns fewer suggestions than requested.""" + mock_completion.return_value = _mock_response( + _make_suggestions( + [ + {"temperature": 25.0, "pressure": 2.0, "n_cycles": 1, "catalyst": "A"}, + ] + ) + ) + + with pytest.warns(UserWarning, match="instead of the requested"): + recommender.recommend(batch_size=3, searchspace=searchspace) + + def test_initialization(recommender): """LLMRecommender initializes with correct attributes.""" assert recommender.model == "gpt-5.4" @@ -287,6 +422,26 @@ def test_initialization(recommender): assert recommender.objective_description == "Maximize yield" +def test_initialization_validation(): + """Empty required fields raise during construction.""" + from baybe.recommenders.pure.llm.llm import LLMRecommender + + with pytest.raises(ValueError, match="Length"): + LLMRecommender( + model="", experiment_description="desc", objective_description="obj" + ) + + with pytest.raises(ValueError, match="Length"): + LLMRecommender( + model="m", experiment_description="", objective_description="obj" + ) + + with pytest.raises(ValueError, match="Length"): + LLMRecommender( + model="m", experiment_description="desc", objective_description="" + ) + + def test_str_representation(recommender): """String representation includes key information.""" s = str(recommender) From 50a18c41ce035afd3dd0f87941984f05520cb461 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Tobias=20Plo=CC=88tz?= Date: Wed, 8 Jul 2026 09:42:48 +0200 Subject: [PATCH 08/96] Address review findings for LLMRecommender - Add pending_experiments rejection and objective warning in recommend() to match NonPredictiveRecommender contract - Guard test_naive_hybrid_serialization against classes with required args - Split optional imports to give accurate error for jinja2 vs litellm - Add overflow_experiments ge(0) validator - Add litellm_args defensive copy and reserved-key validation - Add empty suggestions guard in _parse_llm_response - Add math.isfinite check for continuous parameter values - Add discrete parameter canonical type coercion Co-Authored-By: Claude Opus 4.6 (1M context) --- baybe/_optional/llm.py | 5 +- baybe/recommenders/pure/llm/llm.py | 113 +++++++++++++++--- .../test_naive_hybrid_serialization.py | 12 +- 3 files changed, 114 insertions(+), 16 deletions(-) diff --git a/baybe/_optional/llm.py b/baybe/_optional/llm.py index d7f194e4b8..dd15cdb0b3 100644 --- a/baybe/_optional/llm.py +++ b/baybe/_optional/llm.py @@ -4,8 +4,11 @@ try: from jinja2 import Template - from litellm import completion +except ModuleNotFoundError as ex: + raise OptionalImportError(name="jinja2", group="llm") from ex +try: + from litellm import completion except ModuleNotFoundError as ex: raise OptionalImportError(name="litellm", group="llm") from ex diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index 5592423bca..7e728ef19c 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -4,6 +4,7 @@ import gc import json +import math import warnings from collections.abc import Callable from json import JSONDecodeError @@ -12,10 +13,15 @@ import pandas as pd from attrs import define, field -from attrs.validators import instance_of, min_len +from attrs.validators import ge, instance_of, min_len from typing_extensions import override -from baybe.exceptions import IncompatibilityError, LLMResponseError +from baybe.exceptions import ( + IncompatibilityError, + IncompatibleArgumentError, + LLMResponseError, + UnusedObjectWarning, +) from baybe.objectives.base import Objective from baybe.parameters.base import DiscreteParameter, Parameter from baybe.parameters.numerical import NumericalContinuousParameter @@ -129,6 +135,9 @@ """ +_RESERVED_LITELLM_KEYS = frozenset({"model", "messages"}) + + def _extract_parameter_info( parameters: tuple[Parameter, ...], ) -> list[SimpleNamespace]: @@ -197,7 +206,7 @@ class LLMRecommender(NonPredictiveRecommender): If ``None``, uses the same model as the main recommendations. """ - litellm_args: dict[str, Any] = field(factory=dict) + litellm_args: dict[str, Any] = field(factory=dict, converter=dict) """Additional arguments to pass to LiteLLM.""" recovery_litellm_args: dict[str, Any] | None = field(default=None) @@ -206,6 +215,28 @@ class LLMRecommender(NonPredictiveRecommender): If ``None``, uses the same arguments as the main recommendations. """ + @litellm_args.validator + def _validate_litellm_args(self, attribute, value): # noqa: DOC101, DOC103 + """Validate litellm_args does not contain reserved keys.""" + conflicts = _RESERVED_LITELLM_KEYS & set(value.keys()) + if conflicts: + raise ValueError( + f"'litellm_args' must not contain keys that are set explicitly: " + f"{conflicts}. Use the dedicated class attributes instead." + ) + + @recovery_litellm_args.validator + def _validate_recovery_litellm_args(self, attribute, value): # noqa: DOC101, DOC103 + """Validate recovery_litellm_args does not contain reserved keys.""" + if value is None: + return + conflicts = _RESERVED_LITELLM_KEYS & set(value.keys()) + if conflicts: + raise ValueError( + f"'recovery_litellm_args' must not contain keys that are set " + f"explicitly: {conflicts}. Use the dedicated class attributes instead." + ) + related_data: pd.DataFrame | None = field(default=None) """Optional DataFrame containing data from similar optimization campaigns. @@ -220,7 +251,7 @@ class LLMRecommender(NonPredictiveRecommender): filter for feasibility. Only feasible experiments are returned. """ - overflow_experiments: int = field(default=0) + overflow_experiments: int = field(default=0, validator=(instance_of(int), ge(0))) """Number of additional experiments to request from the LLM. The LLM will be asked to generate ``batch_size + overflow_experiments`` @@ -354,6 +385,11 @@ def _parse_llm_response( if not isinstance(suggestions, list): raise LLMResponseError("Response must be a JSON array") + if not suggestions: + raise LLMResponseError( + "Response contains an empty array with no suggestions." + ) + recommendations = [] for suggestion in suggestions: if not isinstance(suggestion, dict): @@ -391,9 +427,12 @@ def _parse_llm_response( values = df[param.name] if isinstance(param, NumericalContinuousParameter): - if not all(isinstance(v, (int, float)) for v in values): + if not all( + isinstance(v, (int, float)) and math.isfinite(v) for v in values + ): raise LLMResponseError( - f"Non-numeric values for continuous parameter: {param.name}" + f"Non-finite or non-numeric values for continuous parameter: " + f"{param.name}" ) bounds = param.bounds.to_tuple() min_val, max_val = bounds @@ -404,12 +443,40 @@ def _parse_llm_response( elif isinstance(param, DiscreteParameter): allowed = list(param.values) - invalid = [v for v in values if v not in allowed] - if invalid: - raise LLMResponseError( - f"Invalid values {invalid} for parameter '{param.name}'. " - f"Allowed values are: {allowed}" - ) + if param.is_numerical: + allowed_floats = [float(a) for a in allowed] + invalid = [] + canonical = [] + for v in values: + try: + fv = float(v) + except (TypeError, ValueError): + invalid.append(v) + canonical.append(v) + continue + if fv in allowed_floats: + canonical.append(allowed[allowed_floats.index(fv)]) + else: + invalid.append(v) + canonical.append(v) + if invalid: + raise LLMResponseError( + f"Invalid values {invalid} for parameter " + f"'{param.name}'. " + f"Allowed values are: {allowed}" + ) + df[param.name] = canonical + else: + invalid = [v for v in values if v not in allowed] + if invalid: + raise LLMResponseError( + f"Invalid values {invalid} for parameter " + f"'{param.name}'. " + f"Allowed values are: {allowed}" + ) + # Categorical values from JSON are strings; cast to canonical + allowed_map = {str(a): a for a in allowed} + df[param.name] = [allowed_map.get(str(v), v) for v in values] return df @@ -427,19 +494,37 @@ def recommend( Args: batch_size: The number of recommendations to generate. searchspace: The search space to generate recommendations for. - objective: Optional objective to optimize for. + objective: Not used by this recommender. measurements: Optional measurements to include in the prompt. - pending_experiments: Optional pending experiments to consider. + pending_experiments: Not supported by this recommender. Returns: A DataFrame containing the recommendations as individual rows. Raises: + IncompatibleArgumentError: If ``pending_experiments`` are provided. LLMResponseError: If the LLM response cannot be parsed or recovery fails. """ from baybe._optional.llm import completion + if pending_experiments is not None: + raise IncompatibleArgumentError( + f"Pending experiments were passed to " + f"'{self.__class__.__name__}.{self.recommend.__name__}' but " + f"'{self.__class__.__name__}' cannot use this information. If you " + f"want to exclude pending experiments, adjust the search space " + f"accordingly." + ) + if objective is not None: + warnings.warn( + f"'{self.recommend.__name__}' was called with an explicit objective " + f"but '{self.__class__.__name__}' does not consider objectives, " + f"meaning that the argument is ignored.", + UnusedObjectWarning, + stacklevel=2, + ) + prompt = self._construct_prompt(searchspace, batch_size, measurements) response = completion( model=self.model, diff --git a/tests/serialization/test_naive_hybrid_serialization.py b/tests/serialization/test_naive_hybrid_serialization.py index fcb9ec69e0..ade34529d7 100644 --- a/tests/serialization/test_naive_hybrid_serialization.py +++ b/tests/serialization/test_naive_hybrid_serialization.py @@ -14,11 +14,21 @@ from baybe.utils.basic import get_subclasses from tests.serialization.utils import assert_roundtrip_consistency + +def _try_default_construct(cls): + """Try to construct a recommender with no arguments, return None on failure.""" + try: + return cls() + except TypeError: + return None + + valid_discrete_non_predictive_recommenders = [ - cls() + rec for cls in get_subclasses(NonPredictiveRecommender) if cls.compatibility in [SearchSpaceType.DISCRETE, SearchSpaceType.EITHER, SearchSpaceType.HYBRID] + and (rec := _try_default_construct(cls)) is not None ] valid_discrete_bayesian_recommenders = [ cls() From 339993ba807638502540f7fb509f7f9f416ee534 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Tobias=20Plo=CC=88tz?= Date: Wed, 8 Jul 2026 13:09:19 +0200 Subject: [PATCH 09/96] fix mypy error and exclude problematic litellm versions --- mypy.ini | 3 +++ pyproject.toml | 2 +- uv.lock | 2 +- 3 files changed, 5 insertions(+), 2 deletions(-) diff --git a/mypy.ini b/mypy.ini index 184ba29f46..45bb8f27e1 100644 --- a/mypy.ini +++ b/mypy.ini @@ -75,3 +75,6 @@ ignore_missing_imports = True [mypy-shap.*] ignore_missing_imports = True + +[mypy-litellm.*] +ignore_missing_imports = True diff --git a/pyproject.toml b/pyproject.toml index 7edbf62869..02f6263026 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -106,7 +106,7 @@ insights = [ llm = [ "jinja2>=3.1.0", - "litellm>=1.70.0", + "litellm>=1.70.0,!=1.82.7,!=1.82.8", ] docs = [ diff --git a/uv.lock b/uv.lock index 4b10fa6769..7fb808cac7 100644 --- a/uv.lock +++ b/uv.lock @@ -740,7 +740,7 @@ requires-dist = [ { name = "joblib", specifier = ">1.4.0,<2" }, { name = "jupyter", marker = "extra == 'docs'", specifier = ">=1.1.1" }, { name = "jupytext", marker = "extra == 'docs'", specifier = ">=1.16.4" }, - { name = "litellm", marker = "extra == 'llm'", specifier = ">=1.70.0" }, + { name = "litellm", marker = "extra == 'llm'", specifier = ">=1.70.0,!=1.82.7,!=1.82.8" }, { name = "matplotlib", marker = "extra == 'examples'", specifier = ">=3.7.3,!=3.9.1" }, { name = "mypy", marker = "extra == 'mypy'", specifier = ">=1.19.1" }, { name = "myst-parser", marker = "extra == 'docs'", specifier = ">=4.0.0" }, From 028e45c1897163eef657dae5c3b39f28fc5db793 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Wed, 29 Jul 2026 13:08:02 +0200 Subject: [PATCH 10/96] Derive LLMRecommender from PureRecommender NonPredictiveRecommender rejects pending experiments and ignores measurements, but the LLMRecommender feeds both into the prompt. Inherit from PureRecommender instead and fully override recommend accordingly. --- baybe/recommenders/pure/llm/llm.py | 55 ++++++++++++++++++++++-------- tests/test_llm_recommender.py | 30 ++++++++++++++++ 2 files changed, 70 insertions(+), 15 deletions(-) diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index 7e728ef19c..0adce68495 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -18,17 +18,17 @@ from baybe.exceptions import ( IncompatibilityError, - IncompatibleArgumentError, LLMResponseError, UnusedObjectWarning, ) from baybe.objectives.base import Objective from baybe.parameters.base import DiscreteParameter, Parameter from baybe.parameters.numerical import NumericalContinuousParameter -from baybe.recommenders.pure.nonpredictive.base import NonPredictiveRecommender +from baybe.recommenders.pure.base import PureRecommender from baybe.searchspace import SearchSpace from baybe.searchspace.core import SearchSpaceType from baybe.utils.conversion import to_string +from baybe.utils.validation import preprocess_dataframe _PROMPT_TEMPLATE = """\ You are an expert experimental design assistant. Your task is to suggest new \ @@ -63,6 +63,13 @@ {{ measurements.to_string(index=False) }} {% endif %} +{% if pending_experiments is not none and not pending_experiments.empty %} +PENDING EXPERIMENTS: +The following experiments have already been proposed and are awaiting results. +Do not recommend these again. +{{ pending_experiments.to_string(index=False) }} +{% endif %} + {% if related_data is not none and not related_data.empty %} RELATED DATA: Here is data from other optimization campaigns. @@ -181,8 +188,16 @@ def _extract_parameter_info( @define(slots=False) -class LLMRecommender(NonPredictiveRecommender): - """Recommender that uses a language model to suggest new experimental points.""" +class LLMRecommender(PureRecommender): + """Recommender that uses a language model to suggest new experimental points. + + Unlike other pure recommenders, this recommender does not implement the + ``_recommend_discrete``/``_recommend_continuous``/``_recommend_hybrid`` hooks. + The language model returns a complete set of parameter configurations that already + constitutes the recommendation, so there is no per-subspace selection step to + delegate to those hooks. Instead, :meth:`recommend` is fully overridden and builds + the result directly from the model response. + """ # Class variables compatibility: ClassVar[SearchSpaceType] = SearchSpaceType.HYBRID @@ -264,6 +279,7 @@ def _construct_prompt( searchspace: SearchSpace, batch_size: int, measurements: pd.DataFrame | None = None, + pending_experiments: pd.DataFrame | None = None, ) -> str: """Construct the prompt for the language model. @@ -271,6 +287,7 @@ def _construct_prompt( searchspace: The search space to generate recommendations for. batch_size: The number of recommendations to generate. measurements: Optional measurements to include in the prompt. + pending_experiments: Optional pending experiments to include in the prompt. Returns: The constructed prompt. @@ -290,6 +307,7 @@ def _construct_prompt( objective_description=self.objective_description, parameters=parameters, measurements=measurements, + pending_experiments=pending_experiments, related_data=self.related_data, batch_size=total_experiments, format_instructions=self.format_instructions, @@ -496,26 +514,17 @@ def recommend( searchspace: The search space to generate recommendations for. objective: Not used by this recommender. measurements: Optional measurements to include in the prompt. - pending_experiments: Not supported by this recommender. + pending_experiments: Optional pending experiments to include in the prompt. Returns: A DataFrame containing the recommendations as individual rows. Raises: - IncompatibleArgumentError: If ``pending_experiments`` are provided. LLMResponseError: If the LLM response cannot be parsed or recovery fails. """ from baybe._optional.llm import completion - if pending_experiments is not None: - raise IncompatibleArgumentError( - f"Pending experiments were passed to " - f"'{self.__class__.__name__}.{self.recommend.__name__}' but " - f"'{self.__class__.__name__}' cannot use this information. If you " - f"want to exclude pending experiments, adjust the search space " - f"accordingly." - ) if objective is not None: warnings.warn( f"'{self.recommend.__name__}' was called with an explicit objective " @@ -525,7 +534,23 @@ def recommend( stacklevel=2, ) - prompt = self._construct_prompt(searchspace, batch_size, measurements) + if measurements is not None: + measurements = preprocess_dataframe( + measurements, + searchspace, + numerical_measurements_must_be_within_tolerance=False, + ) + + if pending_experiments is not None: + pending_experiments = preprocess_dataframe( + pending_experiments, + searchspace, + numerical_measurements_must_be_within_tolerance=False, + ) + + prompt = self._construct_prompt( + searchspace, batch_size, measurements, pending_experiments + ) response = completion( model=self.model, messages=[{"role": "user", "content": prompt}], diff --git a/tests/test_llm_recommender.py b/tests/test_llm_recommender.py index 97387a565e..7cbbe7a758 100644 --- a/tests/test_llm_recommender.py +++ b/tests/test_llm_recommender.py @@ -141,6 +141,36 @@ def test_recommend_with_measurements( assert "PREVIOUS MEASUREMENTS" in prompt_content +@patch("baybe._optional.llm.completion") +def test_recommend_with_pending_experiments( + mock_completion, recommender, searchspace, valid_response +): + """Pending experiments are accepted and included in the prompt.""" + mock_completion.return_value = valid_response + + pending_experiments = pd.DataFrame( + { + "temperature": [10.0, 15.0], + "pressure": [1.0, 2.0], + "n_cycles": [2, 4], + "catalyst": ["A", "C"], + } + ) + + recommendations = recommender.recommend( + batch_size=3, + searchspace=searchspace, + pending_experiments=pending_experiments, + ) + + assert isinstance(recommendations, pd.DataFrame) + assert len(recommendations) == 3 + prompt_content = mock_completion.call_args.kwargs.get( + "messages", mock_completion.call_args[1]["messages"] + )[0]["content"] + assert "PENDING EXPERIMENTS" in prompt_content + + @patch("baybe._optional.llm.completion") def test_recommend_with_related_data(mock_completion, searchspace, valid_response): """Related data is included in the prompt when provided.""" From 5ab2ac9368b3e424611c1ae9b724f09497672e96 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Wed, 29 Jul 2026 13:51:20 +0200 Subject: [PATCH 11/96] Remove concept of feasibility check from `LLMRecommender` --- baybe/recommenders/pure/llm/llm.py | 30 +---------------------- tests/test_llm_recommender.py | 39 +++--------------------------- 2 files changed, 5 insertions(+), 64 deletions(-) diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index 0adce68495..78adda74f2 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -6,7 +6,6 @@ import json import math import warnings -from collections.abc import Callable from json import JSONDecodeError from types import SimpleNamespace from typing import Any, ClassVar @@ -259,19 +258,11 @@ def _validate_recovery_litellm_args(self, attribute, value): # noqa: DOC101, DO similar experiments. """ - is_feasible_experiment: Callable[[pd.Series], bool] | None = field(default=None) - """Optional function to check if an experiment is feasible. - - If provided, the recommender will request additional experiments and - filter for feasibility. Only feasible experiments are returned. - """ - overflow_experiments: int = field(default=0, validator=(instance_of(int), ge(0))) """Number of additional experiments to request from the LLM. The LLM will be asked to generate ``batch_size + overflow_experiments`` - experiments. After filtering for feasibility, the first ``batch_size`` - feasible experiments will be returned. + experiments, of which the first ``batch_size`` are returned. """ def _construct_prompt( @@ -579,20 +570,6 @@ def recommend( stacklevel=2, ) - if self.is_feasible_experiment is not None: - feasible_mask = output.apply(self.is_feasible_experiment, axis=1) - feasible_experiments = output[feasible_mask] - - if len(feasible_experiments) < batch_size: - warnings.warn( - f"Only {len(feasible_experiments)} of {batch_size} requested " - f"experiments passed the feasibility check. Consider increasing " - f"overflow_experiments (currently {self.overflow_experiments}).", - stacklevel=2, - ) - - return feasible_experiments.head(batch_size) - return output.head(batch_size) @override @@ -610,11 +587,6 @@ def __str__(self) -> str: to_string( "Overflow Experiments", self.overflow_experiments, single_line=True ), - to_string( - "Feasibility Check", - "Enabled" if self.is_feasible_experiment is not None else "Disabled", - single_line=True, - ), ] return to_string(self.__class__.__name__, *fields) diff --git a/tests/test_llm_recommender.py b/tests/test_llm_recommender.py index 7cbbe7a758..954f7954fa 100644 --- a/tests/test_llm_recommender.py +++ b/tests/test_llm_recommender.py @@ -368,15 +368,14 @@ def test_recovery_success(mock_completion, recommender, searchspace): @patch("baybe._optional.llm.completion") -def test_feasibility_filtering(mock_completion, searchspace): - """Feasibility filtering respects the is_feasible_experiment callback.""" +def test_overflow_experiments(mock_completion, searchspace): + """Overflow experiments request extra suggestions but return only batch_size.""" from baybe.recommenders.pure.llm.llm import LLMRecommender recommender = LLMRecommender( model="gpt-5.4", experiment_description="Test", objective_description="Maximize yield", - is_feasible_experiment=lambda row: row["temperature"] > 20.0, overflow_experiments=1, ) @@ -393,39 +392,9 @@ def test_feasibility_filtering(mock_completion, searchspace): recommendations = recommender.recommend(batch_size=3, searchspace=searchspace) + # Four suggestions were requested (batch_size + overflow), but only the first + # batch_size are returned. assert len(recommendations) == 3 - assert all(recommendations["temperature"] > 20.0) - - -@patch("baybe._optional.llm.completion") -def test_feasibility_filtering_insufficient_feasible(mock_completion, searchspace): - """Warning is emitted when fewer feasible experiments than batch_size.""" - from baybe.recommenders.pure.llm.llm import LLMRecommender - - recommender = LLMRecommender( - model="gpt-5.4", - experiment_description="Test", - objective_description="Maximize yield", - is_feasible_experiment=lambda row: row["temperature"] > 90.0, - overflow_experiments=1, - ) - - mock_completion.return_value = _mock_response( - _make_suggestions( - [ - {"temperature": 10.0, "pressure": 1.0, "n_cycles": 1, "catalyst": "A"}, - {"temperature": 50.0, "pressure": 2.0, "n_cycles": 2, "catalyst": "B"}, - {"temperature": 95.0, "pressure": 3.0, "n_cycles": 3, "catalyst": "C"}, - {"temperature": 30.0, "pressure": 4.0, "n_cycles": 4, "catalyst": "A"}, - ] - ) - ) - - with pytest.warns(UserWarning, match="feasibility check"): - recommendations = recommender.recommend(batch_size=3, searchspace=searchspace) - - assert len(recommendations) == 1 - assert recommendations["temperature"].iloc[0] == 95.0 @patch("baybe._optional.llm.completion") From daefa4211083682e6ebf53b968f6ef5a957079da Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Wed, 29 Jul 2026 13:58:18 +0200 Subject: [PATCH 12/96] Remove overflow_experiments from LLMRecommender The field only existed to over-generate suggestions for feasibility filtering, which no longer exists. Request exactly `batch_size` suggestions. --- baybe/recommenders/pure/llm/llm.py | 19 ++++--------------- tests/test_llm_recommender.py | 30 ------------------------------ 2 files changed, 4 insertions(+), 45 deletions(-) diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index 78adda74f2..76102328cd 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -12,7 +12,7 @@ import pandas as pd from attrs import define, field -from attrs.validators import ge, instance_of, min_len +from attrs.validators import instance_of, min_len from typing_extensions import override from baybe.exceptions import ( @@ -258,13 +258,6 @@ def _validate_recovery_litellm_args(self, attribute, value): # noqa: DOC101, DO similar experiments. """ - overflow_experiments: int = field(default=0, validator=(instance_of(int), ge(0))) - """Number of additional experiments to request from the LLM. - - The LLM will be asked to generate ``batch_size + overflow_experiments`` - experiments, of which the first ``batch_size`` are returned. - """ - def _construct_prompt( self, searchspace: SearchSpace, @@ -285,7 +278,6 @@ def _construct_prompt( """ from baybe._optional.llm import Template - total_experiments = batch_size + self.overflow_experiments parameters = _extract_parameter_info(searchspace.parameters) template = Template( @@ -300,7 +292,7 @@ def _construct_prompt( measurements=measurements, pending_experiments=pending_experiments, related_data=self.related_data, - batch_size=total_experiments, + batch_size=batch_size, format_instructions=self.format_instructions, ) @@ -563,10 +555,10 @@ def recommend( except LLMResponseError as e: output = self._attempt_recovery(e, content, searchspace) - if len(output) < batch_size + self.overflow_experiments: + if len(output) < batch_size: warnings.warn( f"LLM returned {len(output)} suggestions instead of the " - f"requested {batch_size + self.overflow_experiments}.", + f"requested {batch_size}.", stacklevel=2, ) @@ -584,9 +576,6 @@ def __str__(self) -> str: "Optimization Objective", self.objective_description, single_line=True ), to_string("Related Data", self.related_data, single_line=True), - to_string( - "Overflow Experiments", self.overflow_experiments, single_line=True - ), ] return to_string(self.__class__.__name__, *fields) diff --git a/tests/test_llm_recommender.py b/tests/test_llm_recommender.py index 954f7954fa..615375f848 100644 --- a/tests/test_llm_recommender.py +++ b/tests/test_llm_recommender.py @@ -367,36 +367,6 @@ def test_recovery_success(mock_completion, recommender, searchspace): assert mock_completion.call_count == 2 -@patch("baybe._optional.llm.completion") -def test_overflow_experiments(mock_completion, searchspace): - """Overflow experiments request extra suggestions but return only batch_size.""" - from baybe.recommenders.pure.llm.llm import LLMRecommender - - recommender = LLMRecommender( - model="gpt-5.4", - experiment_description="Test", - objective_description="Maximize yield", - overflow_experiments=1, - ) - - mock_completion.return_value = _mock_response( - _make_suggestions( - [ - {"temperature": 10.0, "pressure": 1.0, "n_cycles": 1, "catalyst": "A"}, - {"temperature": 50.0, "pressure": 2.0, "n_cycles": 2, "catalyst": "B"}, - {"temperature": 30.0, "pressure": 3.0, "n_cycles": 3, "catalyst": "C"}, - {"temperature": 60.0, "pressure": 4.0, "n_cycles": 4, "catalyst": "A"}, - ] - ) - ) - - recommendations = recommender.recommend(batch_size=3, searchspace=searchspace) - - # Four suggestions were requested (batch_size + overflow), but only the first - # batch_size are returned. - assert len(recommendations) == 3 - - @patch("baybe._optional.llm.completion") def test_batch_size_warning_when_llm_returns_fewer( mock_completion, recommender, searchspace From 9a9bfd47ee31c3c8cada6134802f1ee3ec31b95b Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Wed, 29 Jul 2026 16:24:44 +0200 Subject: [PATCH 13/96] Feed objective into LLMRecommender prompt --- baybe/exceptions.py | 4 +++ baybe/recommenders/pure/llm/llm.py | 44 +++++++++++++++++------------- tests/test_llm_recommender.py | 33 ++++++++++++++++++++-- 3 files changed, 60 insertions(+), 21 deletions(-) diff --git a/baybe/exceptions.py b/baybe/exceptions.py index ea006881f7..4ef825e4a2 100644 --- a/baybe/exceptions.py +++ b/baybe/exceptions.py @@ -15,6 +15,10 @@ class InputDataTypeWarning(UserWarning): """An input has unexpected data type.""" +class LLMResponseWarning(UserWarning): + """A language model response did not fully meet the request.""" + + class UnusedObjectWarning(UserWarning): """ A method or function was called with undesired arguments which indicates an diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index 76102328cd..9c6425b292 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -18,7 +18,7 @@ from baybe.exceptions import ( IncompatibilityError, LLMResponseError, - UnusedObjectWarning, + LLMResponseWarning, ) from baybe.objectives.base import Objective from baybe.parameters.base import DiscreteParameter, Parameter @@ -39,6 +39,10 @@ OPTIMIZATION OBJECTIVE: {{ objective_description }} +{% if objective is not none %} +OPTIMIZATION TARGETS: +{{ objective }} +{% endif %} PARAMETERS: {% for param in parameters %} Parameter: {{ param.name }} @@ -262,6 +266,7 @@ def _construct_prompt( self, searchspace: SearchSpace, batch_size: int, + objective: Objective | None = None, measurements: pd.DataFrame | None = None, pending_experiments: pd.DataFrame | None = None, ) -> str: @@ -270,6 +275,7 @@ def _construct_prompt( Args: searchspace: The search space to generate recommendations for. batch_size: The number of recommendations to generate. + objective: Optional objective to include in the prompt. measurements: Optional measurements to include in the prompt. pending_experiments: Optional pending experiments to include in the prompt. @@ -288,6 +294,7 @@ def _construct_prompt( return template.render( experiment_description=self.experiment_description, objective_description=self.objective_description, + objective=objective, parameters=parameters, measurements=measurements, pending_experiments=pending_experiments, @@ -495,7 +502,7 @@ def recommend( Args: batch_size: The number of recommendations to generate. searchspace: The search space to generate recommendations for. - objective: Not used by this recommender. + objective: Optional objective to include in the prompt. measurements: Optional measurements to include in the prompt. pending_experiments: Optional pending experiments to include in the prompt. @@ -503,20 +510,11 @@ def recommend( A DataFrame containing the recommendations as individual rows. Raises: - LLMResponseError: If the LLM response cannot be parsed or - recovery fails. + LLMResponseError: If the call to the language model fails, or if its + response cannot be parsed and recovery fails. """ from baybe._optional.llm import completion - if objective is not None: - warnings.warn( - f"'{self.recommend.__name__}' was called with an explicit objective " - f"but '{self.__class__.__name__}' does not consider objectives, " - f"meaning that the argument is ignored.", - UnusedObjectWarning, - stacklevel=2, - ) - if measurements is not None: measurements = preprocess_dataframe( measurements, @@ -532,13 +530,20 @@ def recommend( ) prompt = self._construct_prompt( - searchspace, batch_size, measurements, pending_experiments - ) - response = completion( - model=self.model, - messages=[{"role": "user", "content": prompt}], - **self.litellm_args, + searchspace, batch_size, objective, measurements, pending_experiments ) + try: + response = completion( + model=self.model, + messages=[{"role": "user", "content": prompt}], + **self.litellm_args, + ) + except Exception as e: + raise LLMResponseError( + f"The call to the language model failed ({type(e).__name__}): {e}. " + f"Check your API credentials, network connection, and the model " + f"identifier '{self.model}'." + ) from e try: content = response.choices[0].message.content @@ -559,6 +564,7 @@ def recommend( warnings.warn( f"LLM returned {len(output)} suggestions instead of the " f"requested {batch_size}.", + LLMResponseWarning, stacklevel=2, ) diff --git a/tests/test_llm_recommender.py b/tests/test_llm_recommender.py index 615375f848..1c002e84c7 100644 --- a/tests/test_llm_recommender.py +++ b/tests/test_llm_recommender.py @@ -8,7 +8,7 @@ import pytest from baybe._optional.info import LLM_INSTALLED -from baybe.exceptions import LLMResponseError +from baybe.exceptions import LLMResponseError, LLMResponseWarning from baybe.parameters import ( CategoricalParameter, NumericalContinuousParameter, @@ -171,6 +171,26 @@ def test_recommend_with_pending_experiments( assert "PENDING EXPERIMENTS" in prompt_content +@patch("baybe._optional.llm.completion") +def test_recommend_with_objective( + mock_completion, recommender, searchspace, valid_response +): + """A passed objective is rendered into the prompt.""" + from baybe.objectives.single import SingleTargetObjective + from baybe.targets.numerical import NumericalTarget + + mock_completion.return_value = valid_response + objective = SingleTargetObjective(NumericalTarget("yield", minimize=False)) + + recommender.recommend(batch_size=3, searchspace=searchspace, objective=objective) + + prompt_content = mock_completion.call_args.kwargs.get( + "messages", mock_completion.call_args[1]["messages"] + )[0]["content"] + assert "OPTIMIZATION TARGETS" in prompt_content + assert "yield" in prompt_content + + @patch("baybe._optional.llm.completion") def test_recommend_with_related_data(mock_completion, searchspace, valid_response): """Related data is included in the prompt when provided.""" @@ -380,7 +400,16 @@ def test_batch_size_warning_when_llm_returns_fewer( ) ) - with pytest.warns(UserWarning, match="instead of the requested"): + with pytest.warns(LLMResponseWarning, match="instead of the requested"): + recommender.recommend(batch_size=3, searchspace=searchspace) + + +@patch("baybe._optional.llm.completion") +def test_completion_failure_wrapped(mock_completion, recommender, searchspace): + """A failing completion call is wrapped in an LLMResponseError.""" + mock_completion.side_effect = RuntimeError("boom") + + with pytest.raises(LLMResponseError, match="call to the language model failed"): recommender.recommend(batch_size=3, searchspace=searchspace) From 9ee446337cc7ecfea1b3dc40e09d43289ebdbcf8 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 30 Jul 2026 09:39:04 +0200 Subject: [PATCH 14/96] Exclude LLMRecommender from recommender auto-enumeration in tests The recommender has required constructor arguments and cannot be default-constructed, so route the PureRecommender enumerations through a dedicated exclusion helper --- tests/conftest.py | 24 ++++++++++++++++++- .../test_naive_hybrid_serialization.py | 12 +--------- tests/test_iterations.py | 12 ++++++---- 3 files changed, 32 insertions(+), 16 deletions(-) diff --git a/tests/conftest.py b/tests/conftest.py index 60dfc2ab98..837db34b3b 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -67,6 +67,7 @@ from baybe.recommenders.pure.bayesian.botorch import ( BotorchRecommender, ) +from baybe.recommenders.pure.llm.llm import LLMRecommender from baybe.recommenders.pure.nonpredictive.sampling import RandomRecommender from baybe.searchspace import SearchSpace from baybe.settings import Settings @@ -74,7 +75,7 @@ from baybe.surrogates.custom import CustomONNXSurrogate from baybe.targets import NumericalTarget from baybe.targets.binary import BinaryTarget -from baybe.utils.basic import hilberts_factory +from baybe.utils.basic import get_subclasses, hilberts_factory from baybe.utils.boolean import strtobool from baybe.utils.dataframe import ( add_fake_measurements, @@ -85,6 +86,27 @@ FAST_OPTIMIZATION_SETTINGS = {"n_restarts": 2, "n_raw_samples": 16} """Reduced acquisition optimization effort for tests not concerned with its quality.""" +# Recommenders excluded from automatic subclass enumeration because they cannot be +# default-constructed +RECOMMENDERS_EXCLUDED_FROM_AUTOTEST: frozenset[type] = frozenset({LLMRecommender}) + + +def default_constructible_recommenders(base: type) -> list[type]: + """Return subclasses of ``base`` that can be constructed without arguments. + + Args: + base: The recommender base class whose subclasses are enumerated. + + Returns: + The subclasses of ``base`` excluding those that cannot be default-constructed. + """ + return [ + cls + for cls in get_subclasses(base) + if cls not in RECOMMENDERS_EXCLUDED_FROM_AUTOTEST + ] + + # Hypothesis settings hypothesis_settings.register_profile("ci", deadline=500, max_examples=100) if strtobool(os.getenv("CI", "false")): diff --git a/tests/serialization/test_naive_hybrid_serialization.py b/tests/serialization/test_naive_hybrid_serialization.py index ade34529d7..fcb9ec69e0 100644 --- a/tests/serialization/test_naive_hybrid_serialization.py +++ b/tests/serialization/test_naive_hybrid_serialization.py @@ -14,21 +14,11 @@ from baybe.utils.basic import get_subclasses from tests.serialization.utils import assert_roundtrip_consistency - -def _try_default_construct(cls): - """Try to construct a recommender with no arguments, return None on failure.""" - try: - return cls() - except TypeError: - return None - - valid_discrete_non_predictive_recommenders = [ - rec + cls() for cls in get_subclasses(NonPredictiveRecommender) if cls.compatibility in [SearchSpaceType.DISCRETE, SearchSpaceType.EITHER, SearchSpaceType.HYBRID] - and (rec := _try_default_construct(cls)) is not None ] valid_discrete_bayesian_recommenders = [ cls() diff --git a/tests/test_iterations.py b/tests/test_iterations.py index c3491f46bc..b22be153d9 100644 --- a/tests/test_iterations.py +++ b/tests/test_iterations.py @@ -57,7 +57,11 @@ from baybe.targets.numerical import NumericalTarget from baybe.utils.basic import get_subclasses from baybe.utils.dataframe import create_fake_input -from tests.conftest import FAST_OPTIMIZATION_SETTINGS, run_iterations +from tests.conftest import ( + FAST_OPTIMIZATION_SETTINGS, + default_constructible_recommenders, + run_iterations, +) @pytest.fixture @@ -107,7 +111,7 @@ def _make_pure_recommender(cls: type[PureRecommender]) -> PureRecommender: # allows no training data valid_discrete_recommenders = [ TwoPhaseMetaRecommender(recommender=_make_pure_recommender(cls)) - for cls in get_subclasses(PureRecommender) + for cls in default_constructible_recommenders(PureRecommender) if cls.compatibility in [SearchSpaceType.DISCRETE, SearchSpaceType.HYBRID, SearchSpaceType.EITHER] ] @@ -115,7 +119,7 @@ def _make_pure_recommender(cls: type[PureRecommender]) -> PureRecommender: # allows no training data valid_continuous_recommenders = [ TwoPhaseMetaRecommender(recommender=_make_pure_recommender(cls)) - for cls in get_subclasses(PureRecommender) + for cls in default_constructible_recommenders(PureRecommender) if cls.compatibility in [SearchSpaceType.CONTINUOUS, SearchSpaceType.HYBRID, SearchSpaceType.EITHER] ] @@ -220,7 +224,7 @@ def _dedup_ids(ids: list[str]) -> list[str]: # allows no training data valid_hybrid_recommenders = [ TwoPhaseMetaRecommender(recommender=_make_pure_recommender(cls)) - for cls in get_subclasses(PureRecommender) + for cls in default_constructible_recommenders(PureRecommender) if cls.compatibility == SearchSpaceType.HYBRID ] # List of BotorchRecommenders with different sampling strategies. From 7b58e3f06a1a7460cd4b752771b23c30d8d223db Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 30 Jul 2026 10:13:02 +0200 Subject: [PATCH 15/96] Add parse-error test cases --- tests/test_llm_recommender.py | 53 +++++++++++++++++++++++++++++++++++ 1 file changed, 53 insertions(+) diff --git a/tests/test_llm_recommender.py b/tests/test_llm_recommender.py index 1c002e84c7..4f71bfc7d6 100644 --- a/tests/test_llm_recommender.py +++ b/tests/test_llm_recommender.py @@ -347,6 +347,59 @@ def test_recovery_with_distinct_model(mock_completion, recommender, searchspace) "unknown parameter names", id="unknown_parameter", ), + pytest.param( + json.dumps({"explanation": "Test", "parameters": {}}), + "Response must be a JSON array", + id="not_a_list", + ), + pytest.param( + json.dumps([]), + "empty array with no suggestions", + id="empty_array", + ), + pytest.param( + json.dumps(["a string"]), + "Each suggestion must be a JSON object", + id="suggestion_not_dict", + ), + pytest.param( + json.dumps([{"explanation": "Test", "parameters": [1, 2]}]), + "Parameters must be a JSON object", + id="parameters_not_dict", + ), + pytest.param( + json.dumps( + [ + { + "parameters": { + "temperature": 25.0, + "pressure": 2.0, + "n_cycles": 1, + "catalyst": "A", + } + } + ] + ), + "must contain an 'explanation' field", + id="missing_explanation", + ), + pytest.param( + json.dumps( + [ + { + "explanation": "Test", + "parameters": { + "temperature": "hot", + "pressure": 2.0, + "n_cycles": 1, + "catalyst": "A", + }, + } + ] + ), + "Non-finite or non-numeric values", + id="non_numeric_continuous", + ), ], ) def test_parse_llm_response_errors( From 7a50325c23bfab03df0b48be54c34e2158a1ba26 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 30 Jul 2026 12:00:28 +0200 Subject: [PATCH 16/96] Add helper for extracting JSON from response --- baybe/recommenders/pure/llm/llm.py | 26 +++++++++++++++++++++++++- 1 file changed, 25 insertions(+), 1 deletion(-) diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index 9c6425b292..bc7b711c18 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -190,6 +190,27 @@ def _extract_parameter_info( return infos +def _extract_json_array(response: str) -> str: + """Extract the JSON array payload from a raw language model response. + + Language models frequently wrap their output in Markdown code fences or add + surrounding prose despite instructions to the contrary. This helper isolates the + outermost ``[...]`` block so that such responses can still be parsed. + + Args: + response: The raw response text. + + Returns: + The substring spanning the outermost JSON array, or the original text if no + array delimiters are found. + """ + start = response.find("[") + end = response.rfind("]") + if start != -1 and end != -1 and start < end: + return response[start : end + 1] + return response + + @define(slots=False) class LLMRecommender(PureRecommender): """Recommender that uses a language model to suggest new experimental points. @@ -385,8 +406,11 @@ def _parse_llm_response( LLMResponseError: If the response cannot be parsed or contains invalid values. """ + payload = ( + _extract_json_array(response) if isinstance(response, str) else response + ) try: - suggestions = json.loads(response) + suggestions = json.loads(payload) except (JSONDecodeError, TypeError) as e: raise LLMResponseError(f"Error parsing JSON output: {e}") from e From 2057d4656316e5ea661a9840fd63a21d86126496 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 30 Jul 2026 12:43:13 +0200 Subject: [PATCH 17/96] Remove related_data field --- baybe/recommenders/pure/llm/llm.py | 17 ----------------- 1 file changed, 17 deletions(-) diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index bc7b711c18..5f6d385d68 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -73,14 +73,6 @@ {{ pending_experiments.to_string(index=False) }} {% endif %} -{% if related_data is not none and not related_data.empty %} -RELATED DATA: -Here is data from other optimization campaigns. -It might be useful to learn from these experiments or not. -Use it as you see fit. -{{ related_data.to_string(index=False) }} -{% endif %} - Please suggest {{ batch_size }} new experimental conditions that are likely to \ improve the optimization objective. For each suggestion, provide: @@ -276,13 +268,6 @@ def _validate_recovery_litellm_args(self, attribute, value): # noqa: DOC101, DO f"explicitly: {conflicts}. Use the dedicated class attributes instead." ) - related_data: pd.DataFrame | None = field(default=None) - """Optional DataFrame containing data from similar optimization campaigns. - - This data can be used to inform the recommendations by learning from - similar experiments. - """ - def _construct_prompt( self, searchspace: SearchSpace, @@ -319,7 +304,6 @@ def _construct_prompt( parameters=parameters, measurements=measurements, pending_experiments=pending_experiments, - related_data=self.related_data, batch_size=batch_size, format_instructions=self.format_instructions, ) @@ -605,7 +589,6 @@ def __str__(self) -> str: to_string( "Optimization Objective", self.objective_description, single_line=True ), - to_string("Related Data", self.related_data, single_line=True), ] return to_string(self.__class__.__name__, *fields) From 28351cb1ed9bf00052ef2b94a86af0c75ea45f71 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 30 Jul 2026 12:44:25 +0200 Subject: [PATCH 18/96] Add serialization test for LLMRecommender --- .../test_llm_recommender_serialization.py | 27 +++++++++++++++++++ 1 file changed, 27 insertions(+) create mode 100644 tests/serialization/test_llm_recommender_serialization.py diff --git a/tests/serialization/test_llm_recommender_serialization.py b/tests/serialization/test_llm_recommender_serialization.py new file mode 100644 index 0000000000..3476930efb --- /dev/null +++ b/tests/serialization/test_llm_recommender_serialization.py @@ -0,0 +1,27 @@ +"""Serialization tests for the LLM recommender.""" + +from baybe.recommenders import RandomRecommender, TwoPhaseMetaRecommender +from baybe.recommenders.pure.llm.llm import LLMRecommender +from tests.serialization.utils import assert_roundtrip_consistency + + +def test_llm_recommender_roundtrip(): + """An LLMRecommender survives a serialization roundtrip. + + Pure recommenders do not carry the serialization mix-in themselves; they are + serialized through an enclosing (meta) recommender, which is what we exercise here. + Serialization does not touch LiteLLM, so no optional dependencies are required. + """ + recommender = TwoPhaseMetaRecommender( + initial_recommender=LLMRecommender( + model="dummy-provider/dummy-model", + experiment_description="Optimize a direct arylation reaction.", + objective_description="Maximize the reaction yield.", + format_instructions="Return a JSON array only.", + recovery_model="dummy-provider/dummy-recovery-model", + litellm_args={"temperature": 0.2}, + recovery_litellm_args={"temperature": 0.0}, + ), + recommender=RandomRecommender(), + ) + assert_roundtrip_consistency(recommender) From eb7658ea5da88704392c84a62cbf90de889ea172 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 30 Jul 2026 12:54:09 +0200 Subject: [PATCH 19/96] Remove test for related data --- tests/test_llm_recommender.py | 24 ------------------------ 1 file changed, 24 deletions(-) diff --git a/tests/test_llm_recommender.py b/tests/test_llm_recommender.py index 4f71bfc7d6..b0c21887f6 100644 --- a/tests/test_llm_recommender.py +++ b/tests/test_llm_recommender.py @@ -191,30 +191,6 @@ def test_recommend_with_objective( assert "yield" in prompt_content -@patch("baybe._optional.llm.completion") -def test_recommend_with_related_data(mock_completion, searchspace, valid_response): - """Related data is included in the prompt when provided.""" - from baybe.recommenders.pure.llm.llm import LLMRecommender - - related_data = pd.DataFrame( - {"temperature": [40.0], "pressure": [2.5], "n_cycles": [3], "yield": [0.9]} - ) - recommender = LLMRecommender( - model="gpt-5.4", - experiment_description="Test", - objective_description="Maximize yield", - related_data=related_data, - ) - mock_completion.return_value = valid_response - - recommender.recommend(batch_size=3, searchspace=searchspace) - - prompt_content = mock_completion.call_args.kwargs.get( - "messages", mock_completion.call_args[1]["messages"] - )[0]["content"] - assert "RELATED DATA" in prompt_content - - @patch("baybe._optional.llm.completion") def test_recommend_with_format_instructions( mock_completion, searchspace, valid_response From a9d63dfc9b38dace7e12c0fd39986a3a8c9fc7c1 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 30 Jul 2026 12:54:23 +0200 Subject: [PATCH 20/96] Reorganize subpackage --- baybe/recommenders/pure/llm/_parsing.py | 147 ++++++++++ baybe/recommenders/pure/llm/_prompts.py | 242 ++++++++++++++++ baybe/recommenders/pure/llm/llm.py | 350 ++---------------------- 3 files changed, 415 insertions(+), 324 deletions(-) create mode 100644 baybe/recommenders/pure/llm/_parsing.py create mode 100644 baybe/recommenders/pure/llm/_prompts.py diff --git a/baybe/recommenders/pure/llm/_parsing.py b/baybe/recommenders/pure/llm/_parsing.py new file mode 100644 index 0000000000..63b6d74770 --- /dev/null +++ b/baybe/recommenders/pure/llm/_parsing.py @@ -0,0 +1,147 @@ +"""Parsing and validation of language model responses.""" + +from __future__ import annotations + +import json +import math +from json import JSONDecodeError + +import pandas as pd + +from baybe.exceptions import LLMResponseError +from baybe.parameters.base import DiscreteParameter +from baybe.parameters.numerical import NumericalContinuousParameter +from baybe.searchspace import SearchSpace + + +def extract_json_array(response: str) -> str: + """Extract the JSON array payload from a raw language model response. + + Language models frequently wrap their output in Markdown code fences or add + surrounding prose despite instructions to the contrary. This helper isolates the + outermost ``[...]`` block so that such responses can still be parsed. + + Args: + response: The raw response text. + + Returns: + The substring spanning the outermost JSON array, or the original text if no + array delimiters are found. + """ + start = response.find("[") + end = response.rfind("]") + if start != -1 and end != -1 and start < end: + return response[start : end + 1] + return response + + +def parse_llm_response(response: str, searchspace: SearchSpace) -> pd.DataFrame: + """Parse a language model response into a DataFrame of recommendations. + + Args: + response: The response from the language model. + searchspace: The search space to validate recommendations against. + + Returns: + A DataFrame containing the parsed recommendations. + + Raises: + LLMResponseError: If the response cannot be parsed or contains invalid values. + """ + payload = extract_json_array(response) if isinstance(response, str) else response + try: + suggestions = json.loads(payload) + except (JSONDecodeError, TypeError) as e: + raise LLMResponseError(f"Error parsing JSON output: {e}") from e + + if not isinstance(suggestions, list): + raise LLMResponseError("Response must be a JSON array") + + if not suggestions: + raise LLMResponseError("Response contains an empty array with no suggestions.") + + recommendations = [] + for suggestion in suggestions: + if not isinstance(suggestion, dict): + raise LLMResponseError("Each suggestion must be a JSON object") + + if "parameters" not in suggestion: + raise LLMResponseError("Each suggestion must contain a 'parameters' field") + + if "explanation" not in suggestion: + raise LLMResponseError( + "Each suggestion must contain an 'explanation' field" + ) + + params = suggestion["parameters"] + if not isinstance(params, dict): + raise LLMResponseError("Parameters must be a JSON object") + + param_names = {p.name for p in searchspace.parameters} + unknown = set(params.keys()) - param_names + if unknown: + raise LLMResponseError( + f"Response contains unknown parameter names: {unknown}" + ) + + recommendations.append(params) + + df = pd.DataFrame(recommendations) + + for param in searchspace.parameters: + if param.name not in df.columns: + raise LLMResponseError(f"Missing parameter: {param.name}") + + values = df[param.name] + + if isinstance(param, NumericalContinuousParameter): + if not all( + isinstance(v, (int, float)) and math.isfinite(v) for v in values + ): + raise LLMResponseError( + f"Non-finite or non-numeric values for continuous parameter: " + f"{param.name}" + ) + bounds = param.bounds.to_tuple() + min_val, max_val = bounds + if not all(min_val <= v <= max_val for v in values): + raise LLMResponseError( + f"Values for {param.name} outside bounds [{min_val}, {max_val}]" + ) + + elif isinstance(param, DiscreteParameter): + allowed = list(param.values) + if param.is_numerical: + allowed_floats = [float(a) for a in allowed] + invalid = [] + canonical = [] + for v in values: + try: + fv = float(v) + except (TypeError, ValueError): + invalid.append(v) + canonical.append(v) + continue + if fv in allowed_floats: + canonical.append(allowed[allowed_floats.index(fv)]) + else: + invalid.append(v) + canonical.append(v) + if invalid: + raise LLMResponseError( + f"Invalid values {invalid} for parameter '{param.name}'. " + f"Allowed values are: {allowed}" + ) + df[param.name] = canonical + else: + invalid = [v for v in values if v not in allowed] + if invalid: + raise LLMResponseError( + f"Invalid values {invalid} for parameter '{param.name}'. " + f"Allowed values are: {allowed}" + ) + # Categorical values from JSON are strings; cast to canonical + allowed_map = {str(a): a for a in allowed} + df[param.name] = [allowed_map.get(str(v), v) for v in values] + + return df diff --git a/baybe/recommenders/pure/llm/_prompts.py b/baybe/recommenders/pure/llm/_prompts.py new file mode 100644 index 0000000000..cd910f7247 --- /dev/null +++ b/baybe/recommenders/pure/llm/_prompts.py @@ -0,0 +1,242 @@ +"""Prompt construction for the LLM recommender.""" + +from __future__ import annotations + +from types import SimpleNamespace +from typing import TYPE_CHECKING, Any + +from baybe.exceptions import IncompatibilityError +from baybe.parameters.base import DiscreteParameter, Parameter +from baybe.parameters.numerical import NumericalContinuousParameter +from baybe.searchspace import SearchSpace + +if TYPE_CHECKING: + import pandas as pd + + from baybe.objectives.base import Objective + +_PROMPT_TEMPLATE = """\ +You are an expert experimental design assistant. Your task is to suggest new \ +experimental conditions based on the following information: + +EXPERIMENT DESCRIPTION: +{{ experiment_description }} + +OPTIMIZATION OBJECTIVE: +{{ objective_description }} + +{% if objective is not none %} +OPTIMIZATION TARGETS: +{{ objective }} +{% endif %} +PARAMETERS: +{% for param in parameters %} +Parameter: {{ param.name }} +{% if param.description is not none %} +Description: {{ param.description }} +{% endif %} +Type: {{ param.type }} +{% if param.type == 'continuous' %} +Bounds: [{{ param.bounds[0] }}, {{ param.bounds[1] }}] +{% else %} +Allowed values: {{ param.values }} +{% endif %} +{% if param.unit is not none %} +Unit: {{ param.unit }} +{% endif %} + +{% endfor %} + +{% if measurements is not none and not measurements.empty %} +PREVIOUS MEASUREMENTS: +{{ measurements.to_string(index=False) }} +{% endif %} + +{% if pending_experiments is not none and not pending_experiments.empty %} +PENDING EXPERIMENTS: +The following experiments have already been proposed and are awaiting results. +Do not recommend these again. +{{ pending_experiments.to_string(index=False) }} +{% endif %} + +Please suggest {{ batch_size }} new experimental conditions that are likely to \ +improve the optimization objective. +For each suggestion, provide: +1. A brief explanation of why you chose these values +2. The values for each parameter + +{% if format_instructions is not none %} +{{ format_instructions }} +{% else %} +Format your response as a JSON array of objects with the following structure \ +(no backticks): +[ + { + "explanation": "Brief explanation of the suggestion", + "parameters": { + "param1": value1, + "param2": value2, + ... + } + }, + ... +] +{% endif %} +""" + +_RECOVERY_PROMPT_TEMPLATE = """\ +The previous response was malformed and could not be parsed as JSON. Please \ +correct the response to match the required format. + +ERROR: {{ error }} + +ORIGINAL RESPONSE: +{{ original_response }} + +PARAMETERS: +{% for param in parameters %} +Parameter: {{ param.name }} +Type: {{ param.type }} +{% if param.type == 'continuous' %} +Bounds: [{{ param.bounds[0] }}, {{ param.bounds[1] }}] +{% else %} +Allowed values: {{ param.values }} +{% endif %} +{% endfor %} + +Please provide a corrected JSON response that follows the required format: +{% if format_instructions is not none %} +{{ format_instructions }} +{% else %} +[ + { + "explanation": "Brief explanation of the suggestion", + "parameters": { + "param1": value1, + "param2": value2, + ... + } + }, + ... +] +{% endif %}\ +""" + + +def _extract_parameter_info( + parameters: tuple[Parameter, ...], recommender_name: str +) -> list[SimpleNamespace]: + """Extract parameter information for prompt construction. + + Args: + parameters: The parameters from the search space. + recommender_name: The name of the recommender, used in error messages. + + Returns: + A list of namespace objects containing parameter information. + + Raises: + IncompatibilityError: If a parameter type is not supported. + """ + infos = [] + for param in parameters: + info: dict[str, Any] = { + "name": param.name, + "description": param.description, + "unit": param.unit, + } + + if isinstance(param, NumericalContinuousParameter): + info["type"] = "continuous" + info["bounds"] = param.bounds.to_tuple() + elif isinstance(param, DiscreteParameter): + info["type"] = "discrete_numeric" if param.is_numerical else "categorical" + info["values"] = list(param.values) + else: + raise IncompatibilityError( + f"Parameter '{param.name}' has unsupported type " + f"'{type(param).__name__}' for " + f"'{recommender_name}'. Only " + f"'{NumericalContinuousParameter.__name__}' and " + f"'{DiscreteParameter.__name__}' subclasses are supported." + ) + + infos.append(SimpleNamespace(**info)) + + return infos + + +def build_prompt( + searchspace: SearchSpace, + *, + recommender_name: str, + batch_size: int, + experiment_description: str, + objective_description: str, + objective: Objective | None, + measurements: pd.DataFrame | None, + pending_experiments: pd.DataFrame | None, + format_instructions: str | None, +) -> str: + """Construct the main prompt for the language model. + + Args: + searchspace: The search space to generate recommendations for. + recommender_name: The name of the recommender, used in error messages. + batch_size: The number of recommendations to generate. + experiment_description: Textual description of the experiment. + objective_description: Textual description of the optimization objective. + objective: Optional objective to include in the prompt. + measurements: Optional measurements to include in the prompt. + pending_experiments: Optional pending experiments to include in the prompt. + format_instructions: Optional custom response-formatting instructions. + + Returns: + The constructed prompt. + """ + from baybe._optional.llm import Template + + parameters = _extract_parameter_info(searchspace.parameters, recommender_name) + template = Template(_PROMPT_TEMPLATE, trim_blocks=True, lstrip_blocks=True) + return template.render( + experiment_description=experiment_description, + objective_description=objective_description, + objective=objective, + parameters=parameters, + measurements=measurements, + pending_experiments=pending_experiments, + batch_size=batch_size, + format_instructions=format_instructions, + ) + + +def build_recovery_prompt( + searchspace: SearchSpace, + *, + recommender_name: str, + error: Exception, + original_response: str, + format_instructions: str | None, +) -> str: + """Construct the recovery prompt asking the model to correct a malformed response. + + Args: + searchspace: The search space to generate recommendations for. + recommender_name: The name of the recommender, used in error messages. + error: The error that occurred during parsing. + original_response: The original malformed response. + format_instructions: Optional custom response-formatting instructions. + + Returns: + The constructed recovery prompt. + """ + from baybe._optional.llm import Template + + parameters = _extract_parameter_info(searchspace.parameters, recommender_name) + template = Template(_RECOVERY_PROMPT_TEMPLATE, trim_blocks=True, lstrip_blocks=True) + return template.render( + error=str(error), + original_response=original_response, + parameters=parameters, + format_instructions=format_instructions, + ) diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index 5f6d385d68..b2632e6c42 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -3,11 +3,7 @@ from __future__ import annotations import gc -import json -import math import warnings -from json import JSONDecodeError -from types import SimpleNamespace from typing import Any, ClassVar import pandas as pd @@ -15,194 +11,19 @@ from attrs.validators import instance_of, min_len from typing_extensions import override -from baybe.exceptions import ( - IncompatibilityError, - LLMResponseError, - LLMResponseWarning, -) +from baybe.exceptions import LLMResponseError, LLMResponseWarning from baybe.objectives.base import Objective -from baybe.parameters.base import DiscreteParameter, Parameter -from baybe.parameters.numerical import NumericalContinuousParameter from baybe.recommenders.pure.base import PureRecommender +from baybe.recommenders.pure.llm._parsing import parse_llm_response +from baybe.recommenders.pure.llm._prompts import build_prompt, build_recovery_prompt from baybe.searchspace import SearchSpace from baybe.searchspace.core import SearchSpaceType from baybe.utils.conversion import to_string from baybe.utils.validation import preprocess_dataframe -_PROMPT_TEMPLATE = """\ -You are an expert experimental design assistant. Your task is to suggest new \ -experimental conditions based on the following information: - -EXPERIMENT DESCRIPTION: -{{ experiment_description }} - -OPTIMIZATION OBJECTIVE: -{{ objective_description }} - -{% if objective is not none %} -OPTIMIZATION TARGETS: -{{ objective }} -{% endif %} -PARAMETERS: -{% for param in parameters %} -Parameter: {{ param.name }} -{% if param.description is not none %} -Description: {{ param.description }} -{% endif %} -Type: {{ param.type }} -{% if param.type == 'continuous' %} -Bounds: [{{ param.bounds[0] }}, {{ param.bounds[1] }}] -{% else %} -Allowed values: {{ param.values }} -{% endif %} -{% if param.unit is not none %} -Unit: {{ param.unit }} -{% endif %} - -{% endfor %} - -{% if measurements is not none and not measurements.empty %} -PREVIOUS MEASUREMENTS: -{{ measurements.to_string(index=False) }} -{% endif %} - -{% if pending_experiments is not none and not pending_experiments.empty %} -PENDING EXPERIMENTS: -The following experiments have already been proposed and are awaiting results. -Do not recommend these again. -{{ pending_experiments.to_string(index=False) }} -{% endif %} - -Please suggest {{ batch_size }} new experimental conditions that are likely to \ -improve the optimization objective. -For each suggestion, provide: -1. A brief explanation of why you chose these values -2. The values for each parameter - -{% if format_instructions is not none %} -{{ format_instructions }} -{% else %} -Format your response as a JSON array of objects with the following structure \ -(no backticks): -[ - { - "explanation": "Brief explanation of the suggestion", - "parameters": { - "param1": value1, - "param2": value2, - ... - } - }, - ... -] -{% endif %} -""" - -_RECOVERY_PROMPT_TEMPLATE = """\ -The previous response was malformed and could not be parsed as JSON. Please \ -correct the response to match the required format. - -ERROR: {{ error }} - -ORIGINAL RESPONSE: -{{ original_response }} - -PARAMETERS: -{% for param in parameters %} -Parameter: {{ param.name }} -Type: {{ param.type }} -{% if param.type == 'continuous' %} -Bounds: [{{ param.bounds[0] }}, {{ param.bounds[1] }}] -{% else %} -Allowed values: {{ param.values }} -{% endif %} -{% endfor %} - -Please provide a corrected JSON response that follows the required format: -{% if format_instructions is not none %} -{{ format_instructions }} -{% else %} -[ - { - "explanation": "Brief explanation of the suggestion", - "parameters": { - "param1": value1, - "param2": value2, - ... - } - }, - ... -] -{% endif %}\ -""" - - _RESERVED_LITELLM_KEYS = frozenset({"model", "messages"}) -def _extract_parameter_info( - parameters: tuple[Parameter, ...], -) -> list[SimpleNamespace]: - """Extract parameter information for prompt construction. - - Args: - parameters: The parameters from the search space. - - Returns: - A list of namespace objects containing parameter information. - - Raises: - IncompatibilityError: If a parameter type is not supported. - """ - infos = [] - for param in parameters: - info: dict[str, Any] = { - "name": param.name, - "description": param.description, - "unit": param.unit, - } - - if isinstance(param, NumericalContinuousParameter): - info["type"] = "continuous" - info["bounds"] = param.bounds.to_tuple() - elif isinstance(param, DiscreteParameter): - info["type"] = "discrete_numeric" if param.is_numerical else "categorical" - info["values"] = list(param.values) - else: - raise IncompatibilityError( - f"Parameter '{param.name}' has unsupported type " - f"'{type(param).__name__}' for " - f"'{LLMRecommender.__name__}'. Only " - f"'{NumericalContinuousParameter.__name__}' and " - f"'{DiscreteParameter.__name__}' subclasses are supported." - ) - - infos.append(SimpleNamespace(**info)) - - return infos - - -def _extract_json_array(response: str) -> str: - """Extract the JSON array payload from a raw language model response. - - Language models frequently wrap their output in Markdown code fences or add - surrounding prose despite instructions to the contrary. This helper isolates the - outermost ``[...]`` block so that such responses can still be parsed. - - Args: - response: The raw response text. - - Returns: - The substring spanning the outermost JSON array, or the original text if no - array delimiters are found. - """ - start = response.find("[") - end = response.rfind("]") - if start != -1 and end != -1 and start < end: - return response[start : end + 1] - return response - - @define(slots=False) class LLMRecommender(PureRecommender): """Recommender that uses a language model to suggest new experimental points. @@ -288,26 +109,32 @@ def _construct_prompt( Returns: The constructed prompt. """ - from baybe._optional.llm import Template - - parameters = _extract_parameter_info(searchspace.parameters) - - template = Template( - _PROMPT_TEMPLATE, - trim_blocks=True, - lstrip_blocks=True, - ) - return template.render( + return build_prompt( + searchspace, + recommender_name=self.__class__.__name__, + batch_size=batch_size, experiment_description=self.experiment_description, objective_description=self.objective_description, objective=objective, - parameters=parameters, measurements=measurements, pending_experiments=pending_experiments, - batch_size=batch_size, format_instructions=self.format_instructions, ) + def _parse_llm_response( + self, response: str, searchspace: SearchSpace + ) -> pd.DataFrame: + """Parse the LLM response into a DataFrame of recommendations. + + Args: + response: The response from the language model. + searchspace: The search space to validate recommendations against. + + Returns: + A DataFrame containing the parsed recommendations. + """ + return parse_llm_response(response, searchspace) + def _attempt_recovery( self, error: Exception, @@ -327,16 +154,13 @@ def _attempt_recovery( Raises: LLMResponseError: If recovery fails. """ - from baybe._optional.llm import Template, completion + from baybe._optional.llm import completion - parameters = _extract_parameter_info(searchspace.parameters) - template = Template( - _RECOVERY_PROMPT_TEMPLATE, trim_blocks=True, lstrip_blocks=True - ) - recovery_prompt = template.render( - error=str(error), + recovery_prompt = build_recovery_prompt( + searchspace, + recommender_name=self.__class__.__name__, + error=error, original_response=original_response, - parameters=parameters, format_instructions=self.format_instructions, ) @@ -374,128 +198,6 @@ def _attempt_recovery( f"Original error: {error}" ) from e - def _parse_llm_response( - self, response: str, searchspace: SearchSpace - ) -> pd.DataFrame: - """Parse the LLM response into a DataFrame of recommendations. - - Args: - response: The response from the language model. - searchspace: The search space to validate recommendations against. - - Returns: - A DataFrame containing the parsed recommendations. - - Raises: - LLMResponseError: If the response cannot be parsed or - contains invalid values. - """ - payload = ( - _extract_json_array(response) if isinstance(response, str) else response - ) - try: - suggestions = json.loads(payload) - except (JSONDecodeError, TypeError) as e: - raise LLMResponseError(f"Error parsing JSON output: {e}") from e - - if not isinstance(suggestions, list): - raise LLMResponseError("Response must be a JSON array") - - if not suggestions: - raise LLMResponseError( - "Response contains an empty array with no suggestions." - ) - - recommendations = [] - for suggestion in suggestions: - if not isinstance(suggestion, dict): - raise LLMResponseError("Each suggestion must be a JSON object") - - if "parameters" not in suggestion: - raise LLMResponseError( - "Each suggestion must contain a 'parameters' field" - ) - - if "explanation" not in suggestion: - raise LLMResponseError( - "Each suggestion must contain an 'explanation' field" - ) - - params = suggestion["parameters"] - if not isinstance(params, dict): - raise LLMResponseError("Parameters must be a JSON object") - - param_names = {p.name for p in searchspace.parameters} - unknown = set(params.keys()) - param_names - if unknown: - raise LLMResponseError( - f"Response contains unknown parameter names: {unknown}" - ) - - recommendations.append(params) - - df = pd.DataFrame(recommendations) - - for param in searchspace.parameters: - if param.name not in df.columns: - raise LLMResponseError(f"Missing parameter: {param.name}") - - values = df[param.name] - - if isinstance(param, NumericalContinuousParameter): - if not all( - isinstance(v, (int, float)) and math.isfinite(v) for v in values - ): - raise LLMResponseError( - f"Non-finite or non-numeric values for continuous parameter: " - f"{param.name}" - ) - bounds = param.bounds.to_tuple() - min_val, max_val = bounds - if not all(min_val <= v <= max_val for v in values): - raise LLMResponseError( - f"Values for {param.name} outside bounds [{min_val}, {max_val}]" - ) - - elif isinstance(param, DiscreteParameter): - allowed = list(param.values) - if param.is_numerical: - allowed_floats = [float(a) for a in allowed] - invalid = [] - canonical = [] - for v in values: - try: - fv = float(v) - except (TypeError, ValueError): - invalid.append(v) - canonical.append(v) - continue - if fv in allowed_floats: - canonical.append(allowed[allowed_floats.index(fv)]) - else: - invalid.append(v) - canonical.append(v) - if invalid: - raise LLMResponseError( - f"Invalid values {invalid} for parameter " - f"'{param.name}'. " - f"Allowed values are: {allowed}" - ) - df[param.name] = canonical - else: - invalid = [v for v in values if v not in allowed] - if invalid: - raise LLMResponseError( - f"Invalid values {invalid} for parameter " - f"'{param.name}'. " - f"Allowed values are: {allowed}" - ) - # Categorical values from JSON are strings; cast to canonical - allowed_map = {str(a): a for a in allowed} - df[param.name] = [allowed_map.get(str(v), v) for v in values] - - return df - @override def recommend( self, From 913da008040a53a7d19dccc1240f010de0a2adae Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 30 Jul 2026 13:03:31 +0200 Subject: [PATCH 21/96] Bump version of litellm --- pyproject.toml | 2 +- uv.lock | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 02f6263026..1208d3c900 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -106,7 +106,7 @@ insights = [ llm = [ "jinja2>=3.1.0", - "litellm>=1.70.0,!=1.82.7,!=1.82.8", + "litellm>=1.83", ] docs = [ diff --git a/uv.lock b/uv.lock index 7fb808cac7..0c8c6c35fc 100644 --- a/uv.lock +++ b/uv.lock @@ -740,7 +740,7 @@ requires-dist = [ { name = "joblib", specifier = ">1.4.0,<2" }, { name = "jupyter", marker = "extra == 'docs'", specifier = ">=1.1.1" }, { name = "jupytext", marker = "extra == 'docs'", specifier = ">=1.16.4" }, - { name = "litellm", marker = "extra == 'llm'", specifier = ">=1.70.0,!=1.82.7,!=1.82.8" }, + { name = "litellm", marker = "extra == 'llm'", specifier = ">=1.83" }, { name = "matplotlib", marker = "extra == 'examples'", specifier = ">=3.7.3,!=3.9.1" }, { name = "mypy", marker = "extra == 'mypy'", specifier = ">=1.19.1" }, { name = "myst-parser", marker = "extra == 'docs'", specifier = ">=4.0.0" }, From d75cd9a5616ebc1d4617df900069593782fe8125 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 30 Jul 2026 13:24:23 +0200 Subject: [PATCH 22/96] Add example --- examples/LLM/LLM_Header.md | 5 + examples/LLM/llm_recommender.py | 244 ++++ examples/LLM/llm_recommender_dark.svg | 1718 ++++++++++++++++++++++++ examples/LLM/llm_recommender_light.svg | 1718 ++++++++++++++++++++++++ 4 files changed, 3685 insertions(+) create mode 100644 examples/LLM/LLM_Header.md create mode 100644 examples/LLM/llm_recommender.py create mode 100644 examples/LLM/llm_recommender_dark.svg create mode 100644 examples/LLM/llm_recommender_light.svg diff --git a/examples/LLM/LLM_Header.md b/examples/LLM/LLM_Header.md new file mode 100644 index 0000000000..57164044f8 --- /dev/null +++ b/examples/LLM/LLM_Header.md @@ -0,0 +1,5 @@ +# LLM + +These examples demonstrate how to use BayBE's +{class}`~baybe.recommenders.pure.llm.llm.LLMRecommender` to let a large language model +propose experiments. diff --git a/examples/LLM/llm_recommender.py b/examples/LLM/llm_recommender.py new file mode 100644 index 0000000000..7549787af5 --- /dev/null +++ b/examples/LLM/llm_recommender.py @@ -0,0 +1,244 @@ +## Warm-Starting a Campaign with a Language Model + +# This example demonstrates the +# {class}`~baybe.recommenders.pure.llm.llm.LLMRecommender`, which queries a large +# language model (LLM) to propose experiments. We use it to guide the *first* iterations +# of a chemical reaction optimization, hand over to a regular Bayesian recommender once +# enough data has been collected, and compare this strategy against a random baseline and +# BayBE's default recommender. + +### How it works and why it helps early on + +# On each call, the recommender turns the search space, the experiment and objective +# descriptions, and any collected measurements into a prompt, sends it to an LLM via +# [LiteLLM](https://docs.litellm.ai/), and parses the returned JSON into validated +# experiments (retrying once if the response is malformed). + +# Bayesian optimization needs initial data before its surrogate becomes informative, so +# early experiments are usually random. An LLM can do better: needing no training data, it +# exploits literature and chemistry priors and understands the parameter semantics (names, +# units, allowed values) to propose sensible starting points. Once enough data exists, we +# switch to a {class}`~baybe.recommenders.pure.bayesian.botorch.BotorchRecommender` via a +# {class}`~baybe.recommenders.meta.sequential.TwoPhaseMetaRecommender`. + + +### Imports + +import json +import os +from contextlib import nullcontext +from types import SimpleNamespace +from unittest.mock import patch + +import pandas as pd +import seaborn as sns + +from baybe import Campaign +from baybe.objectives import SingleTargetObjective +from baybe.parameters import NumericalDiscreteParameter, SubstanceParameter +from baybe.recommenders import ( + BotorchRecommender, + LLMRecommender, + RandomRecommender, + TwoPhaseMetaRecommender, +) +from baybe.searchspace import SearchSpace +from baybe.simulation import simulate_scenarios +from baybe.targets import NumericalTarget +from examples.utils import create_example_plots + +### Settings + +# Let's define some general settings for our example: + +SMOKE_TEST = "SMOKE_TEST" in os.environ + +BATCH_SIZE = 1 +N_WARMSTART_EXPERIMENTS = 10 # experiments handled by the LLM before switching +N_DOE_ITERATIONS = 2 if SMOKE_TEST else 3 +# Kept low to bound the number of (potentially costly) LLM calls. +N_MC_ITERATIONS = 2 if SMOKE_TEST else 3 + +# LiteLLM reads the provider credentials from the environment. In CI (``SMOKE_TEST``) we +# fall back to a mocked LLM so the example runs offline; otherwise a real model is queried. + + +### Lookup Data + +# We optimize a direct arylation reaction, the same chemical example used in other BayBE +# examples. Since all parameter combinations have been measured, we can use a lookup table +# to obtain the reaction yield for any recommendation. The data set was obtained from +# [Shields, B.J., Stevens et al. Nature 590, 89-96 (2021)](https://doi.org/10.1038/s41586-021-03213-y). + +try: + lookup = pd.read_csv("benchmarks/data/direct_arylation/data.csv") +except FileNotFoundError: + lookup = pd.read_csv("../../../benchmarks/data/direct_arylation/data.csv") + + +### Defining the Optimization Problem + +# The reaction is described by three chemical substances (solvent, base, and ligand) and +# two numerical process parameters (temperature and concentration). The parameter names +# must match the columns of the lookup table. + +dict_solvent = { + "DMAc": r"CC(N(C)C)=O", + "Butyornitrile": r"CCCC#N", + "Butyl Ester": r"CCCCOC(C)=O", + "p-Xylene": r"CC1=CC=C(C)C=C1", +} + +dict_base = { + "Potassium acetate": r"O=C([O-])C.[K+]", + "Potassium pivalate": r"O=C([O-])C(C)(C)C.[K+]", + "Cesium acetate": r"O=C([O-])C.[Cs+]", + "Cesium pivalate": r"O=C([O-])C(C)(C)C.[Cs+]", +} + +dict_ligand = { + "BrettPhos": r"CC(C)C1=CC(C(C)C)=C(C(C(C)C)=C1)C2=C(P(C3CCCCC3)C4CCCCC4)C(OC)=" + "CC=C2OC", + "Di-tert-butylphenylphosphine": r"CC(C)(C)P(C1=CC=CC=C1)C(C)(C)C", + "(t-Bu)PhCPhos": r"CN(C)C1=CC=CC(N(C)C)=C1C2=CC=CC=C2P(C(C)(C)C)C3=CC=CC=C3", +} + +# We define the substance parameters using the dictionaries above and add descriptive +# metadata to the numerical parameters. The metadata (descriptions and units) is picked +# up automatically by the ``LLMRecommender`` and rendered into the prompt, giving the +# language model additional context. + +solvent = SubstanceParameter("Solvent", data=dict_solvent, encoding="MORDRED") +base = SubstanceParameter("Base", data=dict_base, encoding="MORDRED") +ligand = SubstanceParameter("Ligand", data=dict_ligand, encoding="MORDRED") + +temperature = NumericalDiscreteParameter( + "Temp_C", + values=[90, 105, 120], + tolerance=2, + metadata={"description": "Reaction temperature", "unit": "°C"}, +) +concentration = NumericalDiscreteParameter( + "Concentration", + values=[0.057, 0.1, 0.153], + tolerance=0.005, + metadata={"description": "Substrate concentration", "unit": "mol/L"}, +) + +searchspace = SearchSpace.from_product( + [solvent, base, ligand, temperature, concentration] +) + +# We maximize the reaction yield: + +objective = SingleTargetObjective(target=NumericalTarget(name="yield")) + + +### Setting Up the Recommenders + +# The ``LLMRecommender`` needs the model identifier plus textual descriptions of the +# experiment and objective, which convey the domain context to the language model. + +llm_recommender = LLMRecommender( + model="anthropic/claude-haiku-4-5", + experiment_description=( + "Optimization of a direct C-H arylation reaction. Each experiment selects a " + "solvent, a base, and a ligand (given by their names) together with a reaction " + "temperature and a substrate concentration." + ), + objective_description="Maximize the reaction yield in percent. Higher is better.", +) + +# We compare three strategies: +# * **LLM + Bayesian**: the language model warm-starts the campaign and the meta +# recommender switches to the Bayesian recommender after ``N_WARMSTART_EXPERIMENTS``, +# * **Default**: BayBE's default recommender (random initialization, then Bayesian), and +# * **Random**: a pure random baseline. + +scenarios = { + "LLM + Bayesian": Campaign( + searchspace=searchspace, + objective=objective, + recommender=TwoPhaseMetaRecommender( + initial_recommender=llm_recommender, + recommender=BotorchRecommender(), + switch_after=N_WARMSTART_EXPERIMENTS, + ), + ), + "Default": Campaign(searchspace=searchspace, objective=objective), + "Random": Campaign( + searchspace=searchspace, + objective=objective, + recommender=RandomRecommender(), + ), +} + + +### Mocked Fallback + +# Without a credential, we patch the LiteLLM call to return random valid configurations, +# keeping the example runnable offline. With a real token, this is skipped. + + +def _make_mock_completion(space: SearchSpace): + """Create a fake LiteLLM ``completion`` returning random valid suggestions.""" + candidates = space.discrete.exp_rep + + def _mock_completion(*args, **kwargs): + rows = candidates.sample(min(len(candidates), 4 * BATCH_SIZE)) + suggestions = [ + {"explanation": "Mocked suggestion.", "parameters": row.to_dict()} + for _, row in rows.iterrows() + ] + content = json.dumps(suggestions, default=str) + message = SimpleNamespace(content=content) + return SimpleNamespace(choices=[SimpleNamespace(message=message)]) + + return _mock_completion + + +llm_context = ( + patch("baybe._optional.llm.completion", _make_mock_completion(searchspace)) + if SMOKE_TEST + else nullcontext() +) + + +### Running the Comparison + +# We use `simulate_scenarios` to run a full optimization loop for each strategy, tracking +# the best yield found so far. The language model is queried through the (real or mocked) +# LiteLLM call for the duration of the simulation. + + +with llm_context: + results = simulate_scenarios( + scenarios, + lookup, + batch_size=BATCH_SIZE, + n_doe_iterations=N_DOE_ITERATIONS, + n_mc_iterations=N_MC_ITERATIONS, + ) + +print(results) + + +### Visualization + +# We plot the best yield found so far against the number of conducted experiments. The +# language model gives the "LLM + Bayesian" strategy a head start in the early +# iterations, before the Bayesian recommender takes over. + +results.rename(columns={"Scenario": "Strategy"}, inplace=True) +ax = sns.lineplot( + data=results, + marker="o", + markersize=10, + x="Num_Experiments", + y="yield_CumBest", + hue="Strategy", +) + +# Note that the displayed images were generated from a real run with 30 MC and 30 DoE +# iterations to demonstrate the effect of the LLM warm-start. +create_example_plots(ax=ax, base_name="llm_recommender") diff --git a/examples/LLM/llm_recommender_dark.svg b/examples/LLM/llm_recommender_dark.svg new file mode 100644 index 0000000000..542707e9c3 --- /dev/null +++ b/examples/LLM/llm_recommender_dark.svg @@ -0,0 +1,1718 @@ + + + + + + + + 2026-07-30T13:20:29.258180 + image/svg+xml + + + Matplotlib v3.10.9, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/examples/LLM/llm_recommender_light.svg b/examples/LLM/llm_recommender_light.svg new file mode 100644 index 0000000000..48f4758882 --- /dev/null +++ b/examples/LLM/llm_recommender_light.svg @@ -0,0 +1,1718 @@ + + + + + + + + 2026-07-30T13:20:29.274463 + image/svg+xml + + + Matplotlib v3.10.9, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + From 9ba0086cb8baa4a870cc6e6f1db4dfeb0f06ddd6 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 30 Jul 2026 13:24:31 +0200 Subject: [PATCH 23/96] Update user guide --- docs/components/recommenders.md | 29 +++++++++++++++++++++++++++++ 1 file changed, 29 insertions(+) diff --git a/docs/components/recommenders.md b/docs/components/recommenders.md index d8e415f27e..1afa7d7e68 100644 --- a/docs/components/recommenders.md +++ b/docs/components/recommenders.md @@ -88,6 +88,35 @@ BayBE provides two recommenders that recommend by sampling form the search space this recommender can be found [here](./../../examples/Custom_Surrogates/surrogate_params). +### LLM Recommenders + +The **[`LLMRecommender`](baybe.recommenders.pure.llm.llm.LLMRecommender)** queries a large +language model to propose experiments. This is particularly useful for warm-starting a +campaign, where a language model can inject prior knowledge to suggest more sensible +points than random sampling before enough data has been collected to train a surrogate +model. It builds a prompt from the search space and the provided ``experiment_description`` +and ``objective_description``, queries the model through +[LiteLLM](https://docs.litellm.ai/), and validates the returned suggestions against the +search space. The ``model`` field accepts any LiteLLM model identifier (e.g. +``"openai/gpt-4o"`` or ``"anthropic/claude-sonnet-4-6"``). + +This recommender requires the optional ``llm`` dependency group: + +~~~bash +pip install "baybe[llm]" +~~~ + +```{admonition} Credentials +:class: note +The recommender does not take an API key. LiteLLM reads the provider credentials from +environment variables (e.g. ``OPENAI_API_KEY``, ``ANTHROPIC_API_KEY``) selected by the +provider prefix of the ``model`` identifier. +``` + +A walk-through that warm-starts a campaign with the ``LLMRecommender`` before switching to +a Bayesian recommender can be found +[here](./../../examples/LLM/llm_recommender). + ## Meta Recommenders In analogy to meta studies, meta recommenders are wrappers that operate on a sequence From 35c7f5787ee8f053e6e3f929d997bed2c617f712 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 30 Jul 2026 13:24:59 +0200 Subject: [PATCH 24/96] Add test for JSON parsing --- tests/test_llm_recommender.py | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/tests/test_llm_recommender.py b/tests/test_llm_recommender.py index b0c21887f6..0c905c4566 100644 --- a/tests/test_llm_recommender.py +++ b/tests/test_llm_recommender.py @@ -386,6 +386,25 @@ def test_parse_llm_response_errors( recommender._parse_llm_response(response_content, searchspace) +@pytest.mark.parametrize( + "wrapper", + [ + "```json\n{payload}\n```", + "```\n{payload}\n```", + "Here are the suggestions:\n{payload}\nHope this helps!", + ], + ids=["json_fence", "bare_fence", "surrounding_prose"], +) +def test_parse_llm_response_strips_wrappers(wrapper, recommender, searchspace): + """Markdown fences and surrounding prose are stripped before JSON parsing.""" + payload = _make_suggestions( + [{"temperature": 25.0, "pressure": 2.0, "n_cycles": 1, "catalyst": "A"}] + ) + df = recommender._parse_llm_response(wrapper.format(payload=payload), searchspace) + assert len(df) == 1 + assert df["catalyst"].iloc[0] == "A" + + @patch("baybe._optional.llm.completion") def test_recommend_invalid_response_with_failed_recovery( mock_completion, recommender, searchspace From 522049898f6d8cd0cfd2da18e54c1ae2fc083726 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 30 Jul 2026 13:25:09 +0200 Subject: [PATCH 25/96] Update CHANGELOG --- CHANGELOG.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 214165d699..93e2c66ebb 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -97,7 +97,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - `KernelFactory` now obeys the more general `GPComponentFactoryProtocol` ### Added -- `LLMRecommender` for LLM-based experimental design suggestions via LiteLLM +- `LLMRecommender` for LLM-based experimental design suggestions via LiteLLM, + available through the optional `llm` dependency group - Support for Python 3.14 - Support for pandas 3 - `Settings` class for unified and streamlined settings management From db4a130e2656f29136e439f647db8c33af044545 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 30 Jul 2026 13:45:50 +0200 Subject: [PATCH 26/96] Reduce number of warmstart experiments --- examples/LLM/llm_recommender.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/LLM/llm_recommender.py b/examples/LLM/llm_recommender.py index 7549787af5..8fb5ed7db4 100644 --- a/examples/LLM/llm_recommender.py +++ b/examples/LLM/llm_recommender.py @@ -54,7 +54,7 @@ SMOKE_TEST = "SMOKE_TEST" in os.environ BATCH_SIZE = 1 -N_WARMSTART_EXPERIMENTS = 10 # experiments handled by the LLM before switching +N_WARMSTART_EXPERIMENTS = 1 # experiments handled by the LLM before switching N_DOE_ITERATIONS = 2 if SMOKE_TEST else 3 # Kept low to bound the number of (potentially costly) LLM calls. N_MC_ITERATIONS = 2 if SMOKE_TEST else 3 From 388431af95ac2afdb6296256fbcc7493b4327954 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 30 Jul 2026 14:32:15 +0200 Subject: [PATCH 27/96] Reduce number of MC iterations to 1 --- examples/LLM/llm_recommender.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/LLM/llm_recommender.py b/examples/LLM/llm_recommender.py index 8fb5ed7db4..4ec3336a2a 100644 --- a/examples/LLM/llm_recommender.py +++ b/examples/LLM/llm_recommender.py @@ -57,7 +57,7 @@ N_WARMSTART_EXPERIMENTS = 1 # experiments handled by the LLM before switching N_DOE_ITERATIONS = 2 if SMOKE_TEST else 3 # Kept low to bound the number of (potentially costly) LLM calls. -N_MC_ITERATIONS = 2 if SMOKE_TEST else 3 +N_MC_ITERATIONS = 1 if SMOKE_TEST else 3 # LiteLLM reads the provider credentials from the environment. In CI (``SMOKE_TEST``) we # fall back to a mocked LLM so the example runs offline; otherwise a real model is queried. From 4f26687287d76ee4d06a56f6898fbbf975af3404 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 30 Jul 2026 14:42:51 +0200 Subject: [PATCH 28/96] Deactivate parallel simulation for LLMRecommender example --- examples/LLM/llm_recommender.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/examples/LLM/llm_recommender.py b/examples/LLM/llm_recommender.py index 4ec3336a2a..403dea03b3 100644 --- a/examples/LLM/llm_recommender.py +++ b/examples/LLM/llm_recommender.py @@ -33,7 +33,7 @@ import pandas as pd import seaborn as sns -from baybe import Campaign +from baybe import Campaign, Settings from baybe.objectives import SingleTargetObjective from baybe.parameters import NumericalDiscreteParameter, SubstanceParameter from baybe.recommenders import ( @@ -55,7 +55,7 @@ BATCH_SIZE = 1 N_WARMSTART_EXPERIMENTS = 1 # experiments handled by the LLM before switching -N_DOE_ITERATIONS = 2 if SMOKE_TEST else 3 +N_DOE_ITERATIONS = 1 if SMOKE_TEST else 3 # Kept low to bound the number of (potentially costly) LLM calls. N_MC_ITERATIONS = 1 if SMOKE_TEST else 3 @@ -203,6 +203,9 @@ def _mock_completion(*args, **kwargs): else nullcontext() ) +if SMOKE_TEST: + Settings(parallelize_simulation_runs=False).activate() + ### Running the Comparison From f56307d6c7a3d7de8cf92561d7933c8388a85b54 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 30 Jul 2026 16:37:23 +0200 Subject: [PATCH 29/96] Fix broken link in example --- examples/LLM/llm_recommender.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/LLM/llm_recommender.py b/examples/LLM/llm_recommender.py index 403dea03b3..0bb8209819 100644 --- a/examples/LLM/llm_recommender.py +++ b/examples/LLM/llm_recommender.py @@ -18,7 +18,7 @@ # early experiments are usually random. An LLM can do better: needing no training data, it # exploits literature and chemistry priors and understands the parameter semantics (names, # units, allowed values) to propose sensible starting points. Once enough data exists, we -# switch to a {class}`~baybe.recommenders.pure.bayesian.botorch.BotorchRecommender` via a +# switch to a {class}`~baybe.recommenders.pure.bayesian.botorch.core.BotorchRecommender` via a # {class}`~baybe.recommenders.meta.sequential.TwoPhaseMetaRecommender`. From f3cc3302e49173f3304bd04a881ce49d10eed89c Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 20 Aug 2026 16:36:59 +0200 Subject: [PATCH 30/96] Replace frozenset by set --- tests/conftest.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/conftest.py b/tests/conftest.py index 837db34b3b..b6497f9702 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -88,7 +88,7 @@ # Recommenders excluded from automatic subclass enumeration because they cannot be # default-constructed -RECOMMENDERS_EXCLUDED_FROM_AUTOTEST: frozenset[type] = frozenset({LLMRecommender}) +RECOMMENDERS_EXCLUDED_FROM_AUTOTEST: set[type] = {LLMRecommender} def default_constructible_recommenders(base: type) -> list[type]: From 7a5fae2202e98a8febded2efa89e74d5567c3f77 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 20 Aug 2026 16:40:11 +0200 Subject: [PATCH 31/96] Rename helper for autotestable recommenders --- tests/conftest.py | 7 ++++--- tests/test_iterations.py | 8 ++++---- 2 files changed, 8 insertions(+), 7 deletions(-) diff --git a/tests/conftest.py b/tests/conftest.py index b6497f9702..5503649098 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -91,14 +91,15 @@ RECOMMENDERS_EXCLUDED_FROM_AUTOTEST: set[type] = {LLMRecommender} -def default_constructible_recommenders(base: type) -> list[type]: - """Return subclasses of ``base`` that can be constructed without arguments. +def autotestable_recommenders(base: type) -> list[type]: + """Return subclasses of ``base`` eligible for automatic testing. Args: base: The recommender base class whose subclasses are enumerated. Returns: - The subclasses of ``base`` excluding those that cannot be default-constructed. + The subclasses of ``base`` excluding those in + :data:`RECOMMENDERS_EXCLUDED_FROM_AUTOTEST`. """ return [ cls diff --git a/tests/test_iterations.py b/tests/test_iterations.py index b22be153d9..b143cd95d1 100644 --- a/tests/test_iterations.py +++ b/tests/test_iterations.py @@ -59,7 +59,7 @@ from baybe.utils.dataframe import create_fake_input from tests.conftest import ( FAST_OPTIMIZATION_SETTINGS, - default_constructible_recommenders, + autotestable_recommenders, run_iterations, ) @@ -111,7 +111,7 @@ def _make_pure_recommender(cls: type[PureRecommender]) -> PureRecommender: # allows no training data valid_discrete_recommenders = [ TwoPhaseMetaRecommender(recommender=_make_pure_recommender(cls)) - for cls in default_constructible_recommenders(PureRecommender) + for cls in autotestable_recommenders(PureRecommender) if cls.compatibility in [SearchSpaceType.DISCRETE, SearchSpaceType.HYBRID, SearchSpaceType.EITHER] ] @@ -119,7 +119,7 @@ def _make_pure_recommender(cls: type[PureRecommender]) -> PureRecommender: # allows no training data valid_continuous_recommenders = [ TwoPhaseMetaRecommender(recommender=_make_pure_recommender(cls)) - for cls in default_constructible_recommenders(PureRecommender) + for cls in autotestable_recommenders(PureRecommender) if cls.compatibility in [SearchSpaceType.CONTINUOUS, SearchSpaceType.HYBRID, SearchSpaceType.EITHER] ] @@ -224,7 +224,7 @@ def _dedup_ids(ids: list[str]) -> list[str]: # allows no training data valid_hybrid_recommenders = [ TwoPhaseMetaRecommender(recommender=_make_pure_recommender(cls)) - for cls in default_constructible_recommenders(PureRecommender) + for cls in autotestable_recommenders(PureRecommender) if cls.compatibility == SearchSpaceType.HYBRID ] # List of BotorchRecommenders with different sampling strategies. From 3de8504fe0cd7d75c41db626d64b4e015777ebec Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 20 Aug 2026 16:46:52 +0200 Subject: [PATCH 32/96] Use `is not None` instead of `or` for checking recovery_litellm_args --- baybe/recommenders/pure/llm/llm.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index b2632e6c42..73b66cc1ac 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -164,7 +164,11 @@ def _attempt_recovery( format_instructions=self.format_instructions, ) - litellm_args = self.recovery_litellm_args or self.litellm_args + litellm_args = ( + self.recovery_litellm_args + if self.recovery_litellm_args is not None + else self.litellm_args + ) try: response = completion( model=self.recovery_model or self.model, From a83907a7970c52dc29dbbb228dae960e12231d51 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 20 Aug 2026 16:56:32 +0200 Subject: [PATCH 33/96] Remove option to provide format constructions We de facto require JSON format, so the code should not give the impression that other formats are supported. --- baybe/recommenders/pure/llm/_prompts.py | 16 +------------ baybe/recommenders/pure/llm/llm.py | 5 ---- .../test_llm_recommender_serialization.py | 1 - tests/test_llm_recommender.py | 24 ------------------- 4 files changed, 1 insertion(+), 45 deletions(-) diff --git a/baybe/recommenders/pure/llm/_prompts.py b/baybe/recommenders/pure/llm/_prompts.py index cd910f7247..fa2c5e1adf 100644 --- a/baybe/recommenders/pure/llm/_prompts.py +++ b/baybe/recommenders/pure/llm/_prompts.py @@ -65,9 +65,6 @@ 1. A brief explanation of why you chose these values 2. The values for each parameter -{% if format_instructions is not none %} -{{ format_instructions }} -{% else %} Format your response as a JSON array of objects with the following structure \ (no backticks): [ @@ -81,7 +78,6 @@ }, ... ] -{% endif %} """ _RECOVERY_PROMPT_TEMPLATE = """\ @@ -105,9 +101,6 @@ {% endfor %} Please provide a corrected JSON response that follows the required format: -{% if format_instructions is not none %} -{{ format_instructions }} -{% else %} [ { "explanation": "Brief explanation of the suggestion", @@ -118,8 +111,7 @@ } }, ... -] -{% endif %}\ +]\ """ @@ -176,7 +168,6 @@ def build_prompt( objective: Objective | None, measurements: pd.DataFrame | None, pending_experiments: pd.DataFrame | None, - format_instructions: str | None, ) -> str: """Construct the main prompt for the language model. @@ -189,7 +180,6 @@ def build_prompt( objective: Optional objective to include in the prompt. measurements: Optional measurements to include in the prompt. pending_experiments: Optional pending experiments to include in the prompt. - format_instructions: Optional custom response-formatting instructions. Returns: The constructed prompt. @@ -206,7 +196,6 @@ def build_prompt( measurements=measurements, pending_experiments=pending_experiments, batch_size=batch_size, - format_instructions=format_instructions, ) @@ -216,7 +205,6 @@ def build_recovery_prompt( recommender_name: str, error: Exception, original_response: str, - format_instructions: str | None, ) -> str: """Construct the recovery prompt asking the model to correct a malformed response. @@ -225,7 +213,6 @@ def build_recovery_prompt( recommender_name: The name of the recommender, used in error messages. error: The error that occurred during parsing. original_response: The original malformed response. - format_instructions: Optional custom response-formatting instructions. Returns: The constructed recovery prompt. @@ -238,5 +225,4 @@ def build_recovery_prompt( error=str(error), original_response=original_response, parameters=parameters, - format_instructions=format_instructions, ) diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index 73b66cc1ac..5340e45ba2 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -49,9 +49,6 @@ class LLMRecommender(PureRecommender): objective_description: str = field(validator=(instance_of(str), min_len(1))) """Textual description of the optimization objective.""" - format_instructions: str | None = field(default=None) - """Optional custom instructions for formatting the LLM's response.""" - recovery_model: str | None = field(default=None) """Optional model to use for recovery attempts. @@ -118,7 +115,6 @@ def _construct_prompt( objective=objective, measurements=measurements, pending_experiments=pending_experiments, - format_instructions=self.format_instructions, ) def _parse_llm_response( @@ -161,7 +157,6 @@ def _attempt_recovery( recommender_name=self.__class__.__name__, error=error, original_response=original_response, - format_instructions=self.format_instructions, ) litellm_args = ( diff --git a/tests/serialization/test_llm_recommender_serialization.py b/tests/serialization/test_llm_recommender_serialization.py index 3476930efb..c4482d8f79 100644 --- a/tests/serialization/test_llm_recommender_serialization.py +++ b/tests/serialization/test_llm_recommender_serialization.py @@ -17,7 +17,6 @@ def test_llm_recommender_roundtrip(): model="dummy-provider/dummy-model", experiment_description="Optimize a direct arylation reaction.", objective_description="Maximize the reaction yield.", - format_instructions="Return a JSON array only.", recovery_model="dummy-provider/dummy-recovery-model", litellm_args={"temperature": 0.2}, recovery_litellm_args={"temperature": 0.0}, diff --git a/tests/test_llm_recommender.py b/tests/test_llm_recommender.py index 0c905c4566..d9172f1a5b 100644 --- a/tests/test_llm_recommender.py +++ b/tests/test_llm_recommender.py @@ -191,30 +191,6 @@ def test_recommend_with_objective( assert "yield" in prompt_content -@patch("baybe._optional.llm.completion") -def test_recommend_with_format_instructions( - mock_completion, searchspace, valid_response -): - """Custom format instructions are included in the prompt.""" - from baybe.recommenders.pure.llm.llm import LLMRecommender - - custom_instructions = "Return CSV format instead." - recommender = LLMRecommender( - model="gpt-5.4", - experiment_description="Test", - objective_description="Maximize yield", - format_instructions=custom_instructions, - ) - mock_completion.return_value = valid_response - - recommender.recommend(batch_size=3, searchspace=searchspace) - - prompt_content = mock_completion.call_args.kwargs.get( - "messages", mock_completion.call_args[1]["messages"] - )[0]["content"] - assert custom_instructions in prompt_content - - @patch("baybe._optional.llm.completion") def test_recovery_with_distinct_model(mock_completion, recommender, searchspace): """Recovery uses the specified recovery_model and recovery_litellm_args.""" From 692e0090d01b9bea1d1e8f676f3a4b5cff5183c2 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 20 Aug 2026 17:05:27 +0200 Subject: [PATCH 34/96] Prevent the usage of preserved key in construction If users would pass credential keys to the recommender, those would be stored in plain text in the object. This is prevented by checking the keys in the constructor and raising an error if any of the reserved keys are used. --- baybe/recommenders/pure/llm/llm.py | 36 +++++++++++++++++++++--- tests/test_llm_recommender.py | 44 ++++++++++++++++++++++++++++++ 2 files changed, 76 insertions(+), 4 deletions(-) diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index 5340e45ba2..68b3e57497 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -21,8 +21,15 @@ from baybe.utils.conversion import to_string from baybe.utils.validation import preprocess_dataframe +# Keys that are wired in by the recommender itself and must not be overridden. _RESERVED_LITELLM_KEYS = frozenset({"model", "messages"}) +# Credential keys that LiteLLM accepts inline but must be supplied via environment +# variables instead (e.g. OPENAI_API_KEY, ANTHROPIC_API_KEY). Blocking them at +# construction time prevents accidental exposure in logs, __str__, and serialized +# campaign files. +_CREDENTIAL_LITELLM_KEYS = frozenset({"api_key", "api_base", "api_version"}) + @define(slots=False) class LLMRecommender(PureRecommender): @@ -56,27 +63,41 @@ class LLMRecommender(PureRecommender): """ litellm_args: dict[str, Any] = field(factory=dict, converter=dict) - """Additional arguments to pass to LiteLLM.""" + """Additional arguments to pass to LiteLLM (e.g. ``temperature``, ``max_tokens``). + + API credentials must **not** be passed here — they would be stored in plain text + and appear in logs and serialized campaign files. Configure them via the + environment variables that LiteLLM reads automatically based on the model prefix + (e.g. ``OPENAI_API_KEY``, ``ANTHROPIC_API_KEY``). + """ recovery_litellm_args: dict[str, Any] | None = field(default=None) """Optional arguments to pass to LiteLLM during recovery attempts. - If ``None``, uses the same arguments as the main recommendations. + If ``None``, uses the same arguments as the main recommendations. The same + credential restriction as for :attr:`litellm_args` applies. """ @litellm_args.validator def _validate_litellm_args(self, attribute, value): # noqa: DOC101, DOC103 - """Validate litellm_args does not contain reserved keys.""" + """Validate litellm_args does not contain reserved or credential keys.""" conflicts = _RESERVED_LITELLM_KEYS & set(value.keys()) if conflicts: raise ValueError( f"'litellm_args' must not contain keys that are set explicitly: " f"{conflicts}. Use the dedicated class attributes instead." ) + cred_conflicts = _CREDENTIAL_LITELLM_KEYS & set(value.keys()) + if cred_conflicts: + raise ValueError( + f"'litellm_args' must not contain credential keys {cred_conflicts}. " + f"Supply credentials via environment variables instead " + f"(e.g. OPENAI_API_KEY, ANTHROPIC_API_KEY)." + ) @recovery_litellm_args.validator def _validate_recovery_litellm_args(self, attribute, value): # noqa: DOC101, DOC103 - """Validate recovery_litellm_args does not contain reserved keys.""" + """Validate recovery_litellm_args has no reserved or credential keys.""" if value is None: return conflicts = _RESERVED_LITELLM_KEYS & set(value.keys()) @@ -85,6 +106,13 @@ def _validate_recovery_litellm_args(self, attribute, value): # noqa: DOC101, DO f"'recovery_litellm_args' must not contain keys that are set " f"explicitly: {conflicts}. Use the dedicated class attributes instead." ) + cred_conflicts = _CREDENTIAL_LITELLM_KEYS & set(value.keys()) + if cred_conflicts: + raise ValueError( + f"'recovery_litellm_args' must not contain credential keys " + f"{cred_conflicts}. Supply credentials via environment variables " + f"instead (e.g. OPENAI_API_KEY, ANTHROPIC_API_KEY)." + ) def _construct_prompt( self, diff --git a/tests/test_llm_recommender.py b/tests/test_llm_recommender.py index d9172f1a5b..ed720b1e34 100644 --- a/tests/test_llm_recommender.py +++ b/tests/test_llm_recommender.py @@ -464,6 +464,50 @@ def test_initialization_validation(): ) +@pytest.mark.parametrize( + "key", + ["model", "messages"], + ids=["reserved_model", "reserved_messages"], +) +def test_litellm_args_rejects_reserved_keys(key): + """Reserved LiteLLM keys raise during construction.""" + from baybe.recommenders.pure.llm.llm import LLMRecommender + + with pytest.raises(ValueError, match="must not contain keys that are set"): + LLMRecommender( + model="m", + experiment_description="desc", + objective_description="obj", + litellm_args={key: "value"}, + ) + + +@pytest.mark.parametrize( + "key", + ["api_key", "api_base", "api_version"], + ids=["api_key", "api_base", "api_version"], +) +def test_litellm_args_rejects_credential_keys(key): + """Credential keys in litellm_args raise during construction.""" + from baybe.recommenders.pure.llm.llm import LLMRecommender + + with pytest.raises(ValueError, match="must not contain credential keys"): + LLMRecommender( + model="m", + experiment_description="desc", + objective_description="obj", + litellm_args={key: "secret"}, + ) + + with pytest.raises(ValueError, match="must not contain credential keys"): + LLMRecommender( + model="m", + experiment_description="desc", + objective_description="obj", + recovery_litellm_args={key: "secret"}, + ) + + def test_str_representation(recommender): """String representation includes key information.""" s = str(recommender) From f7c1d48f1e793307008cb9f7975d439cd2b5f56d Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 20 Aug 2026 17:17:52 +0200 Subject: [PATCH 35/96] Add missing validation --- baybe/recommenders/pure/llm/llm.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index 68b3e57497..85347e7f44 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -19,7 +19,7 @@ from baybe.searchspace import SearchSpace from baybe.searchspace.core import SearchSpaceType from baybe.utils.conversion import to_string -from baybe.utils.validation import preprocess_dataframe +from baybe.utils.validation import preprocess_dataframe, validate_object_names # Keys that are wired in by the recommender itself and must not be overridden. _RESERVED_LITELLM_KEYS = frozenset({"model", "messages"}) @@ -252,10 +252,14 @@ def recommend( """ from baybe._optional.llm import completion + if objective is not None: + validate_object_names(searchspace.parameters + objective.targets) + if measurements is not None: measurements = preprocess_dataframe( measurements, searchspace, + objective, numerical_measurements_must_be_within_tolerance=False, ) From c2a6cc47a2210d8f10739bbbb6cfb44759c6f338 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Fri, 21 Aug 2026 09:40:05 +0200 Subject: [PATCH 36/96] Unify tests for handling protected keys --- tests/test_llm_recommender.py | 38 ++++++++++++----------------------- 1 file changed, 13 insertions(+), 25 deletions(-) diff --git a/tests/test_llm_recommender.py b/tests/test_llm_recommender.py index ed720b1e34..4673745cf1 100644 --- a/tests/test_llm_recommender.py +++ b/tests/test_llm_recommender.py @@ -465,33 +465,21 @@ def test_initialization_validation(): @pytest.mark.parametrize( - "key", - ["model", "messages"], - ids=["reserved_model", "reserved_messages"], -) -def test_litellm_args_rejects_reserved_keys(key): - """Reserved LiteLLM keys raise during construction.""" - from baybe.recommenders.pure.llm.llm import LLMRecommender - - with pytest.raises(ValueError, match="must not contain keys that are set"): - LLMRecommender( - model="m", - experiment_description="desc", - objective_description="obj", - litellm_args={key: "value"}, - ) - - -@pytest.mark.parametrize( - "key", - ["api_key", "api_base", "api_version"], - ids=["api_key", "api_base", "api_version"], + ("key", "phrase"), + [ + ("api_key", "credential keys"), + ("api_base", "credential keys"), + ("api_version", "credential keys"), + ("model", "keys that are set"), + ("messages", "keys that are set"), + ], + ids=["api_key", "api_base", "api_version", "model", "messages"], ) -def test_litellm_args_rejects_credential_keys(key): - """Credential keys in litellm_args raise during construction.""" +def test_construction_rejects_protected_keys(key, phrase): + """Credential or preset keys raise during construction.""" from baybe.recommenders.pure.llm.llm import LLMRecommender - with pytest.raises(ValueError, match="must not contain credential keys"): + with pytest.raises(ValueError, match=f"must not contain {phrase}"): LLMRecommender( model="m", experiment_description="desc", @@ -499,7 +487,7 @@ def test_litellm_args_rejects_credential_keys(key): litellm_args={key: "secret"}, ) - with pytest.raises(ValueError, match="must not contain credential keys"): + with pytest.raises(ValueError, match=f"must not contain {phrase}"): LLMRecommender( model="m", experiment_description="desc", From 304ce9ed374c0faff52f9f3e6b68b8f1bb200216 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Fri, 21 Aug 2026 10:33:09 +0200 Subject: [PATCH 37/96] Add `litellm` to list for `replace-imports-with-any` Necessary since there is a `# type: ignore` inside of the `__init__.py` file of `litellm`. This has been fixed in `litellm` version 1.97.0, so this can be removed once we bump to that version. --- pyproject.toml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 1208d3c900..f4322b7ba3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -210,7 +210,8 @@ project-excludes = [ "baybe/utils/dataframe.py", "baybe/utils/clustering_algorithms/third_party/kmedoids.py", ] -replace-imports-with-any = ["sklearn.*", "shap"] +replace-imports-with-any = ["sklearn.*", "shap", "litellm"] +# Note: For litellm, this could be removed by bumping to a version >=1.97.0 [tool.pyrefly.errors] unused-ignore = "error" From 289e55307991f89516dc11d007ab249f7e945749 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Fri, 21 Aug 2026 10:35:56 +0200 Subject: [PATCH 38/96] Raise instead of warn if too few recommendations are returned --- baybe/recommenders/pure/llm/llm.py | 14 ++++++-------- 1 file changed, 6 insertions(+), 8 deletions(-) diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index 85347e7f44..8b7ad0efd5 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -3,7 +3,6 @@ from __future__ import annotations import gc -import warnings from typing import Any, ClassVar import pandas as pd @@ -11,7 +10,7 @@ from attrs.validators import instance_of, min_len from typing_extensions import override -from baybe.exceptions import LLMResponseError, LLMResponseWarning +from baybe.exceptions import LLMResponseError from baybe.objectives.base import Objective from baybe.recommenders.pure.base import PureRecommender from baybe.recommenders.pure.llm._parsing import parse_llm_response @@ -247,8 +246,9 @@ def recommend( A DataFrame containing the recommendations as individual rows. Raises: - LLMResponseError: If the call to the language model fails, or if its - response cannot be parsed and recovery fails. + LLMResponseError: If the call to the language model fails, if its + response cannot be parsed and recovery fails, or if the number of + suggestions is less than the requested batch size. """ from baybe._optional.llm import completion @@ -302,11 +302,9 @@ def recommend( output = self._attempt_recovery(e, content, searchspace) if len(output) < batch_size: - warnings.warn( + raise LLMResponseError( f"LLM returned {len(output)} suggestions instead of the " - f"requested {batch_size}.", - LLMResponseWarning, - stacklevel=2, + f"requested {batch_size}." ) return output.head(batch_size) From 3dfd74e109a71e842a3499eaab2296c4ec045698 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Fri, 21 Aug 2026 11:04:45 +0200 Subject: [PATCH 39/96] Add hypothesis strategies and tests for `LLMRecommender` Since none of our current recommenders currently uses our `SerialMixin`, the recommender is wrapped in a meta recommender. --- tests/hypothesis_strategies/recommenders.py | 41 +++++++++++++++++++ .../test_llm_recommender_serialization.py | 23 +++++------ 2 files changed, 51 insertions(+), 13 deletions(-) create mode 100644 tests/hypothesis_strategies/recommenders.py diff --git a/tests/hypothesis_strategies/recommenders.py b/tests/hypothesis_strategies/recommenders.py new file mode 100644 index 0000000000..7881eef146 --- /dev/null +++ b/tests/hypothesis_strategies/recommenders.py @@ -0,0 +1,41 @@ +"""Hypothesis strategies for recommenders.""" + +import hypothesis.strategies as st +from hypothesis import assume + +from baybe.recommenders.pure.llm.llm import ( + _CREDENTIAL_LITELLM_KEYS, + _RESERVED_LITELLM_KEYS, + LLMRecommender, +) + +_FORBIDDEN_LITELLM_KEYS = _RESERVED_LITELLM_KEYS | _CREDENTIAL_LITELLM_KEYS + +_litellm_values = st.one_of( + st.text(), + st.integers(), + st.floats(allow_nan=False, allow_infinity=False), + st.booleans(), +) +"""A strategy generating JSON-serializable values suitable for litellm args.""" + + +@st.composite +def _safe_litellm_args(draw: st.DrawFn) -> dict: + """Generate a litellm args dict containing no reserved or credential keys.""" + args = draw(st.dictionaries(st.text(min_size=1), _litellm_values, max_size=3)) + assume(not set(args).intersection(_FORBIDDEN_LITELLM_KEYS)) + return args + + +def llm_recommenders() -> st.SearchStrategy[LLMRecommender]: + """Strategy for :class:`~baybe.recommenders.pure.llm.llm.LLMRecommender`.""" + return st.builds( + LLMRecommender, + model=st.text(min_size=1), + experiment_description=st.text(min_size=1), + objective_description=st.text(min_size=1), + recovery_model=st.one_of(st.none(), st.text(min_size=1)), + litellm_args=_safe_litellm_args(), + recovery_litellm_args=st.one_of(st.none(), _safe_litellm_args()), + ) diff --git a/tests/serialization/test_llm_recommender_serialization.py b/tests/serialization/test_llm_recommender_serialization.py index c4482d8f79..b8af422e5c 100644 --- a/tests/serialization/test_llm_recommender_serialization.py +++ b/tests/serialization/test_llm_recommender_serialization.py @@ -1,26 +1,23 @@ """Serialization tests for the LLM recommender.""" +from hypothesis import given + from baybe.recommenders import RandomRecommender, TwoPhaseMetaRecommender from baybe.recommenders.pure.llm.llm import LLMRecommender +from tests.hypothesis_strategies.recommenders import llm_recommenders from tests.serialization.utils import assert_roundtrip_consistency -def test_llm_recommender_roundtrip(): - """An LLMRecommender survives a serialization roundtrip. +@given(llm_recommenders()) +def test_llm_recommender_roundtrip(recommender: LLMRecommender): + """An LLMRecommender survives a serialization roundtrip across all configurations. Pure recommenders do not carry the serialization mix-in themselves; they are - serialized through an enclosing (meta) recommender, which is what we exercise here. + serialized through an enclosing meta recommender, which is what we exercise here. Serialization does not touch LiteLLM, so no optional dependencies are required. """ - recommender = TwoPhaseMetaRecommender( - initial_recommender=LLMRecommender( - model="dummy-provider/dummy-model", - experiment_description="Optimize a direct arylation reaction.", - objective_description="Maximize the reaction yield.", - recovery_model="dummy-provider/dummy-recovery-model", - litellm_args={"temperature": 0.2}, - recovery_litellm_args={"temperature": 0.0}, - ), + wrapped = TwoPhaseMetaRecommender( + initial_recommender=recommender, recommender=RandomRecommender(), ) - assert_roundtrip_consistency(recommender) + assert_roundtrip_consistency(wrapped) From 262a3dd3111cf8113aef31b2281234ebba79b996 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Fri, 21 Aug 2026 11:44:33 +0200 Subject: [PATCH 40/96] Fix test about raising errors --- tests/test_llm_recommender.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tests/test_llm_recommender.py b/tests/test_llm_recommender.py index 4673745cf1..a0d8a0b887 100644 --- a/tests/test_llm_recommender.py +++ b/tests/test_llm_recommender.py @@ -8,7 +8,7 @@ import pytest from baybe._optional.info import LLM_INSTALLED -from baybe.exceptions import LLMResponseError, LLMResponseWarning +from baybe.exceptions import LLMResponseError from baybe.parameters import ( CategoricalParameter, NumericalContinuousParameter, @@ -412,10 +412,10 @@ def test_recovery_success(mock_completion, recommender, searchspace): @patch("baybe._optional.llm.completion") -def test_batch_size_warning_when_llm_returns_fewer( +def test_batch_size_error_when_llm_returns_fewer( mock_completion, recommender, searchspace ): - """Warning is emitted when LLM returns fewer suggestions than requested.""" + """An error is raised when LLM returns fewer suggestions than requested.""" mock_completion.return_value = _mock_response( _make_suggestions( [ @@ -424,7 +424,7 @@ def test_batch_size_warning_when_llm_returns_fewer( ) ) - with pytest.warns(LLMResponseWarning, match="instead of the requested"): + with pytest.raises(LLMResponseError, match="instead of the requested"): recommender.recommend(batch_size=3, searchspace=searchspace) From e6f0a0ba3bd94b48d6a6c6da708fb4ade25a3846 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Fri, 21 Aug 2026 11:47:13 +0200 Subject: [PATCH 41/96] Improve metadata usage in `LLMRecommender` The recommender now uses the description of the objective itself insted of exponsing an additional field for it. Furthermore, the targets as well as their metadata is now being used within the recommender. --- baybe/recommenders/pure/llm/_prompts.py | 25 +++++++++++++++------ baybe/recommenders/pure/llm/llm.py | 22 ++++++++++++------ examples/LLM/llm_recommender.py | 1 - tests/hypothesis_strategies/recommenders.py | 1 - tests/test_llm_recommender.py | 25 +++++++-------------- 5 files changed, 41 insertions(+), 33 deletions(-) diff --git a/baybe/recommenders/pure/llm/_prompts.py b/baybe/recommenders/pure/llm/_prompts.py index fa2c5e1adf..1a82c8defd 100644 --- a/baybe/recommenders/pure/llm/_prompts.py +++ b/baybe/recommenders/pure/llm/_prompts.py @@ -22,12 +22,23 @@ EXPERIMENT DESCRIPTION: {{ experiment_description }} +{% if objective is not none %} +{% if objective.metadata.description is not none %} OPTIMIZATION OBJECTIVE: -{{ objective_description }} +{{ objective.metadata.description }} -{% if objective is not none %} +{% endif %} OPTIMIZATION TARGETS: -{{ objective }} +{% for target in objective.targets %} +Target: {{ target.name }} +{% if target.metadata.description is not none %} +Description: {{ target.metadata.description }} +{% endif %} +{% if target.metadata.unit is not none %} +Unit: {{ target.metadata.unit }} +{% endif %} + +{% endfor %} {% endif %} PARAMETERS: {% for param in parameters %} @@ -164,7 +175,6 @@ def build_prompt( recommender_name: str, batch_size: int, experiment_description: str, - objective_description: str, objective: Objective | None, measurements: pd.DataFrame | None, pending_experiments: pd.DataFrame | None, @@ -176,8 +186,10 @@ def build_prompt( recommender_name: The name of the recommender, used in error messages. batch_size: The number of recommendations to generate. experiment_description: Textual description of the experiment. - objective_description: Textual description of the optimization objective. - objective: Optional objective to include in the prompt. + objective: Optional objective to include in the prompt. Set + :attr:`baybe.objectives.base.Objective.metadata` to provide the + language model with a description of what to optimize and per-target + context such as units and descriptions. measurements: Optional measurements to include in the prompt. pending_experiments: Optional pending experiments to include in the prompt. @@ -190,7 +202,6 @@ def build_prompt( template = Template(_PROMPT_TEMPLATE, trim_blocks=True, lstrip_blocks=True) return template.render( experiment_description=experiment_description, - objective_description=objective_description, objective=objective, parameters=parameters, measurements=measurements, diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index 8b7ad0efd5..037dad2022 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -3,6 +3,7 @@ from __future__ import annotations import gc +import warnings from typing import Any, ClassVar import pandas as pd @@ -50,10 +51,12 @@ class LLMRecommender(PureRecommender): """The LiteLLM model identifier to use for recommendations.""" experiment_description: str = field(validator=(instance_of(str), min_len(1))) - """Textual description of the experiment.""" + """Textual description of the experiment. - objective_description: str = field(validator=(instance_of(str), min_len(1))) - """Textual description of the optimization objective.""" + For best results, also set :attr:`baybe.objectives.base.Objective.metadata` + on the objective passed to :meth:`recommend` to give the language model a + description of what to optimize and per-target context such as units. + """ recovery_model: str | None = field(default=None) """Optional model to use for recovery attempts. @@ -138,7 +141,6 @@ def _construct_prompt( recommender_name=self.__class__.__name__, batch_size=batch_size, experiment_description=self.experiment_description, - objective_description=self.objective_description, objective=objective, measurements=measurements, pending_experiments=pending_experiments, @@ -254,6 +256,15 @@ def recommend( if objective is not None: validate_object_names(searchspace.parameters + objective.targets) + if objective.metadata.is_empty: + warnings.warn( + "The objective has no metadata description. Without context on " + "what to optimize, the language model may produce suboptimal " + "suggestions. Set the 'description' field on the objective's " + "metadata to guide the LLM.", + UserWarning, + stacklevel=2, + ) if measurements is not None: measurements = preprocess_dataframe( @@ -317,9 +328,6 @@ def __str__(self) -> str: to_string( "Experiment Description", self.experiment_description, single_line=True ), - to_string( - "Optimization Objective", self.objective_description, single_line=True - ), ] return to_string(self.__class__.__name__, *fields) diff --git a/examples/LLM/llm_recommender.py b/examples/LLM/llm_recommender.py index 0bb8209819..cee97555b2 100644 --- a/examples/LLM/llm_recommender.py +++ b/examples/LLM/llm_recommender.py @@ -146,7 +146,6 @@ "solvent, a base, and a ligand (given by their names) together with a reaction " "temperature and a substrate concentration." ), - objective_description="Maximize the reaction yield in percent. Higher is better.", ) # We compare three strategies: diff --git a/tests/hypothesis_strategies/recommenders.py b/tests/hypothesis_strategies/recommenders.py index 7881eef146..90da123696 100644 --- a/tests/hypothesis_strategies/recommenders.py +++ b/tests/hypothesis_strategies/recommenders.py @@ -34,7 +34,6 @@ def llm_recommenders() -> st.SearchStrategy[LLMRecommender]: LLMRecommender, model=st.text(min_size=1), experiment_description=st.text(min_size=1), - objective_description=st.text(min_size=1), recovery_model=st.one_of(st.none(), st.text(min_size=1)), litellm_args=_safe_litellm_args(), recovery_litellm_args=st.one_of(st.none(), _safe_litellm_args()), diff --git a/tests/test_llm_recommender.py b/tests/test_llm_recommender.py index a0d8a0b887..514c7e707d 100644 --- a/tests/test_llm_recommender.py +++ b/tests/test_llm_recommender.py @@ -74,7 +74,6 @@ def fixture_recommender(): return LLMRecommender( model="gpt-5.4", experiment_description="Test experiment", - objective_description="Maximize yield", ) @@ -182,7 +181,12 @@ def test_recommend_with_objective( mock_completion.return_value = valid_response objective = SingleTargetObjective(NumericalTarget("yield", minimize=False)) - recommender.recommend(batch_size=3, searchspace=searchspace, objective=objective) + with pytest.warns(UserWarning, match="objective has no metadata description"): + recommender.recommend( + batch_size=3, + searchspace=searchspace, + objective=objective, + ) prompt_content = mock_completion.call_args.kwargs.get( "messages", mock_completion.call_args[1]["messages"] @@ -199,7 +203,6 @@ def test_recovery_with_distinct_model(mock_completion, recommender, searchspace) recommender = LLMRecommender( model="gpt-5.4", experiment_description="Test", - objective_description="Maximize yield", recovery_model="gpt-4o-mini", recovery_litellm_args={"temperature": 0.0}, ) @@ -441,7 +444,6 @@ def test_initialization(recommender): """LLMRecommender initializes with correct attributes.""" assert recommender.model == "gpt-5.4" assert recommender.experiment_description == "Test experiment" - assert recommender.objective_description == "Maximize yield" def test_initialization_validation(): @@ -449,19 +451,10 @@ def test_initialization_validation(): from baybe.recommenders.pure.llm.llm import LLMRecommender with pytest.raises(ValueError, match="Length"): - LLMRecommender( - model="", experiment_description="desc", objective_description="obj" - ) + LLMRecommender(model="", experiment_description="desc") with pytest.raises(ValueError, match="Length"): - LLMRecommender( - model="m", experiment_description="", objective_description="obj" - ) - - with pytest.raises(ValueError, match="Length"): - LLMRecommender( - model="m", experiment_description="desc", objective_description="" - ) + LLMRecommender(model="m", experiment_description="") @pytest.mark.parametrize( @@ -483,7 +476,6 @@ def test_construction_rejects_protected_keys(key, phrase): LLMRecommender( model="m", experiment_description="desc", - objective_description="obj", litellm_args={key: "secret"}, ) @@ -491,7 +483,6 @@ def test_construction_rejects_protected_keys(key, phrase): LLMRecommender( model="m", experiment_description="desc", - objective_description="obj", recovery_litellm_args={key: "secret"}, ) From 4362484fe463840c74db658050b86298a3ea0584 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Fri, 21 Aug 2026 15:00:27 +0200 Subject: [PATCH 42/96] Pass full dict of metadata to the prompt As a consequence, the prompt template was also extended such that it contains whatever is in the `misc` field of the metadata dict. --- baybe/recommenders/pure/llm/_prompts.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/baybe/recommenders/pure/llm/_prompts.py b/baybe/recommenders/pure/llm/_prompts.py index 1a82c8defd..ae0ef26d4f 100644 --- a/baybe/recommenders/pure/llm/_prompts.py +++ b/baybe/recommenders/pure/llm/_prompts.py @@ -5,6 +5,8 @@ from types import SimpleNamespace from typing import TYPE_CHECKING, Any +from attrs import asdict as attrs_asdict + from baybe.exceptions import IncompatibilityError from baybe.parameters.base import DiscreteParameter, Parameter from baybe.parameters.numerical import NumericalContinuousParameter @@ -55,6 +57,11 @@ {% if param.unit is not none %} Unit: {{ param.unit }} {% endif %} +{% if param.misc %} +{% for key, value in param.misc.items() %} +{{ key }}: {{ value }} +{% endfor %} +{% endif %} {% endfor %} @@ -145,8 +152,7 @@ def _extract_parameter_info( for param in parameters: info: dict[str, Any] = { "name": param.name, - "description": param.description, - "unit": param.unit, + **attrs_asdict(param.metadata), } if isinstance(param, NumericalContinuousParameter): From f1927da535e260e8e3a365311a8a2c2cc8462929 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Fri, 21 Aug 2026 15:59:34 +0200 Subject: [PATCH 43/96] Handle constraints for LLM responses For discrete constraints, we check for validity using the existing mechanisms and raise an `LLMResponseError` if violated. Continuous constraints can currently not be validated, so an `LLMResponseWarning` is issued if any such constraint is present. --- baybe/recommenders/pure/llm/_parsing.py | 45 +++++- tests/test_llm_recommender.py | 200 +++++++++++++++++++++++- 2 files changed, 242 insertions(+), 3 deletions(-) diff --git a/baybe/recommenders/pure/llm/_parsing.py b/baybe/recommenders/pure/llm/_parsing.py index 63b6d74770..e521f7f846 100644 --- a/baybe/recommenders/pure/llm/_parsing.py +++ b/baybe/recommenders/pure/llm/_parsing.py @@ -4,11 +4,12 @@ import json import math +import warnings from json import JSONDecodeError import pandas as pd -from baybe.exceptions import LLMResponseError +from baybe.exceptions import LLMResponseError, LLMResponseWarning from baybe.parameters.base import DiscreteParameter from baybe.parameters.numerical import NumericalContinuousParameter from baybe.searchspace import SearchSpace @@ -46,7 +47,14 @@ def parse_llm_response(response: str, searchspace: SearchSpace) -> pd.DataFrame: A DataFrame containing the parsed recommendations. Raises: - LLMResponseError: If the response cannot be parsed or contains invalid values. + LLMResponseError: If the response cannot be parsed, contains invalid parameter + values, or violates any discrete constraint (including batch constraints) + present in the search space. + + Warns: + LLMResponseWarning: If the search space contains continuous constraints. + Continuous constraints cannot be validated after the fact, so compliance + of the LLM suggestions with such constraints is not guaranteed. """ payload = extract_json_array(response) if isinstance(response, str) else response try: @@ -144,4 +152,37 @@ def parse_llm_response(response: str, searchspace: SearchSpace) -> pd.DataFrame: allowed_map = {str(a): a for a in allowed} df[param.name] = [allowed_map.get(str(v), v) for v in values] + continuous_constraints = ( + *searchspace.continuous.constraints_lin_eq, + *searchspace.continuous.constraints_lin_ineq, + *searchspace.continuous.constraints_nonlin, + ) + if continuous_constraints: + names = ", ".join(f"'{type(c).__name__}'" for c in continuous_constraints) + warnings.warn( + f"The search space contains continuous constraints ({names}) that cannot " + f"be validated. The LLM suggestions may violate these constraints.", + LLMResponseWarning, + stacklevel=2, + ) + + for constraint in searchspace.discrete.constraints: + invalid_idx = constraint.get_invalid(df) + if not invalid_idx.empty: + raise LLMResponseError( + f"{len(invalid_idx)} suggestion(s) violate the " + f"'{type(constraint).__name__}' constraint on parameters " + f"{constraint.parameters}." + ) + + for constraint in searchspace.discrete.constraints_batch: + param_name = constraint.parameters[0] + unique_values = df[param_name].unique() + if len(unique_values) > 1: + raise LLMResponseError( + f"Suggestions violate 'DiscreteBatchConstraint' on parameter " + f"'{param_name}': all suggestions in a batch must share the same " + f"value, but received {list(unique_values)}." + ) + return df diff --git a/tests/test_llm_recommender.py b/tests/test_llm_recommender.py index 514c7e707d..a7a615cb46 100644 --- a/tests/test_llm_recommender.py +++ b/tests/test_llm_recommender.py @@ -8,13 +8,28 @@ import pytest from baybe._optional.info import LLM_INSTALLED -from baybe.exceptions import LLMResponseError +from baybe.constraints.conditions import SubSelectionCondition, ThresholdCondition +from baybe.constraints.discrete import ( + DiscreteBatchConstraint, + DiscreteCardinalityConstraint, + DiscreteConstraint, + DiscreteCustomConstraint, + DiscreteDependenciesConstraint, + DiscreteExcludeConstraint, + DiscreteLinkedParametersConstraint, + DiscreteNoLabelDuplicatesConstraint, + DiscretePermutationInvarianceConstraint, + DiscreteProductConstraint, + DiscreteSumConstraint, +) +from baybe.exceptions import LLMResponseError, LLMResponseWarning from baybe.parameters import ( CategoricalParameter, NumericalContinuousParameter, NumericalDiscreteParameter, ) from baybe.searchspace import SearchSpace +from baybe.utils.basic import get_subclasses pytestmark = pytest.mark.skipif( not LLM_INSTALLED, reason="LLM dependencies not installed" @@ -365,6 +380,189 @@ def test_parse_llm_response_errors( recommender._parse_llm_response(response_content, searchspace) +# --------------------------------------------------------------------------- +# Constraint violation test cases — one per concrete DiscreteConstraint class +# (except DiscreteBatchConstraint, which is tested separately below). +# +# Each entry maps a constraint class to (parameters, constraints, violations) +# where violations is a list of suggestion dicts that individually satisfy all +# parameter bounds/values but violate the constraint as a combination or batch. +# --------------------------------------------------------------------------- +_ROW_CONSTRAINT_VIOLATION_CASES = { + DiscreteExcludeConstraint: ( + [ + NumericalDiscreteParameter("x", values=[1, 2, 3]), + CategoricalParameter("y", values=["a", "b", "c"]), + ], + [ + DiscreteExcludeConstraint( + parameters=["x", "y"], + conditions=[ + SubSelectionCondition(selection=[2]), + SubSelectionCondition(selection=["b"]), + ], + combiner="AND", + ) + ], + [{"x": 2, "y": "b"}], + ), + DiscreteSumConstraint: ( + [ + NumericalDiscreteParameter("a", values=[1, 2, 3]), + NumericalDiscreteParameter("b", values=[1, 2, 3]), + ], + [ + DiscreteSumConstraint( + parameters=["a", "b"], + condition=ThresholdCondition(threshold=6.0, operator="="), + ) + ], + [{"a": 1, "b": 1}], # sum=2, required sum=6 + ), + DiscreteProductConstraint: ( + [ + NumericalDiscreteParameter("a", values=[1, 2, 3]), + NumericalDiscreteParameter("b", values=[1, 2, 3]), + ], + [ + DiscreteProductConstraint( + parameters=["a", "b"], + condition=ThresholdCondition(threshold=6.0, operator=">="), + ) + ], + [{"a": 1, "b": 1}], # product=1, required product>=6 + ), + DiscreteNoLabelDuplicatesConstraint: ( + [ + CategoricalParameter("x", values=["A", "B", "C"]), + CategoricalParameter("y", values=["A", "B", "C"]), + ], + [DiscreteNoLabelDuplicatesConstraint(parameters=["x", "y"])], + [{"x": "A", "y": "A"}], # duplicate label across parameters + ), + DiscreteLinkedParametersConstraint: ( + [ + NumericalDiscreteParameter("x", values=[1, 2, 3]), + NumericalDiscreteParameter("y", values=[1, 2, 3]), + ], + [DiscreteLinkedParametersConstraint(parameters=["x", "y"])], + [{"x": 1, "y": 2}], # x and y must be equal + ), + DiscreteDependenciesConstraint: ( + [ + CategoricalParameter("switch", values=["on", "off"]), + CategoricalParameter("mode", values=["fast", "slow"]), + ], + [ + DiscreteDependenciesConstraint( + parameters=["switch"], + conditions=[SubSelectionCondition(selection=["on"])], + affected_parameters=[["mode"]], + ) + ], + # when switch="off" mode is irrelevant: two "off" rows are duplicates + [{"switch": "off", "mode": "fast"}, {"switch": "off", "mode": "slow"}], + ), + DiscretePermutationInvarianceConstraint: ( + [ + NumericalDiscreteParameter("a", values=[1, 2, 3]), + NumericalDiscreteParameter("b", values=[1, 2, 3]), + ], + [DiscretePermutationInvarianceConstraint(parameters=["a", "b"])], + # (1,2) and (2,1) are permutation-equivalent; the second is a duplicate + [{"a": 1, "b": 2}, {"a": 2, "b": 1}], + ), + DiscreteCustomConstraint: ( + [NumericalDiscreteParameter("x", values=[1, 2, 3])], + [DiscreteCustomConstraint(parameters=["x"], validator=lambda df: df["x"] != 2)], + [{"x": 2}], # custom validator rejects x=2 + ), + DiscreteCardinalityConstraint: ( + [ + NumericalDiscreteParameter("a", values=[0, 1, 2]), + NumericalDiscreteParameter("b", values=[0, 1, 2]), + ], + [DiscreteCardinalityConstraint(parameters=["a", "b"], max_cardinality=1)], + [{"a": 1, "b": 1}], # 2 nonzero values exceeds max_cardinality=1 + ), +} + +# Verify all concrete DiscreteConstraint subclasses (except DiscreteBatchConstraint) +# have a violation test case — fails at collection time if coverage lapses. +_ALL_ROW_CONSTRAINT_CLASSES = frozenset( + cls + for cls in get_subclasses(DiscreteConstraint) + if cls is not DiscreteBatchConstraint +) +assert frozenset(_ROW_CONSTRAINT_VIOLATION_CASES) == _ALL_ROW_CONSTRAINT_CLASSES, ( + "Missing constraint violation cases for: " + f"{_ALL_ROW_CONSTRAINT_CLASSES - frozenset(_ROW_CONSTRAINT_VIOLATION_CASES)}" +) + + +@pytest.mark.parametrize( + ("parameters", "constraints", "violation_suggestions"), + [ + pytest.param(*_ROW_CONSTRAINT_VIOLATION_CASES[cls], id=cls.__name__) + for cls in get_subclasses(DiscreteConstraint) + if cls is not DiscreteBatchConstraint + ], +) +def test_parse_llm_response_rejects_row_constraint_violations( + parameters, constraints, violation_suggestions +): + """Suggestions valid per-parameter but violating a discrete constraint raise.""" + from baybe.recommenders.pure.llm.llm import LLMRecommender + + space = SearchSpace.from_product(parameters=parameters, constraints=constraints) + rec = LLMRecommender(model="m", experiment_description="test") + response = _make_suggestions(violation_suggestions) + with pytest.raises(LLMResponseError, match="violate the.*constraint"): + rec._parse_llm_response(response, space) + + +def test_parse_llm_response_rejects_batch_constraint_violation(): + """Batch suggestions with mixed values for a DiscreteBatchConstraint param raise.""" + from baybe.recommenders.pure.llm.llm import LLMRecommender + + parameters = [ + NumericalDiscreteParameter("x", values=[1, 2, 3]), + CategoricalParameter("y", values=["a", "b"]), + ] + space = SearchSpace.from_product( + parameters=parameters, + constraints=[DiscreteBatchConstraint(parameters=["x"])], + ) + rec = LLMRecommender(model="m", experiment_description="test") + # x values differ across suggestions — violates the batch constraint + response = _make_suggestions([{"x": 1, "y": "a"}, {"x": 2, "y": "b"}]) + with pytest.raises(LLMResponseError, match="DiscreteBatchConstraint"): + rec._parse_llm_response(response, space) + + +def test_parse_llm_response_warns_for_continuous_constraints(): + """A warning is issued when the search space has continuous constraints.""" + from baybe.constraints.continuous import ContinuousLinearConstraint + from baybe.recommenders.pure.llm.llm import LLMRecommender + + parameters = [ + NumericalContinuousParameter("x", bounds=(0, 1)), + NumericalContinuousParameter("y", bounds=(0, 1)), + ] + space = SearchSpace.from_product( + parameters=parameters, + constraints=[ + ContinuousLinearConstraint( + parameters=["x", "y"], coefficients=[1, 1], rhs=1.5, operator="<=" + ) + ], + ) + rec = LLMRecommender(model="m", experiment_description="test") + response = _make_suggestions([{"x": 0.3, "y": 0.4}]) + with pytest.warns(LLMResponseWarning, match="continuous constraints"): + rec._parse_llm_response(response, space) + + @pytest.mark.parametrize( "wrapper", [ From 0b4e056596a8b1e63b08b03e76b0b65340aae5ca Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Fri, 21 Aug 2026 16:19:11 +0200 Subject: [PATCH 44/96] Align index of returned recommendations with search space Inspired by how the same issue is handled in the hybrid recommender. --- baybe/recommenders/pure/llm/_parsing.py | 21 +++++++++++++++++++++ tests/test_llm_recommender.py | 19 +++++++++++++++++++ 2 files changed, 40 insertions(+) diff --git a/baybe/recommenders/pure/llm/_parsing.py b/baybe/recommenders/pure/llm/_parsing.py index e521f7f846..b7baa5581a 100644 --- a/baybe/recommenders/pure/llm/_parsing.py +++ b/baybe/recommenders/pure/llm/_parsing.py @@ -185,4 +185,25 @@ def parse_llm_response(response: str, searchspace: SearchSpace) -> pd.DataFrame: f"value, but received {list(unique_values)}." ) + # Recover exp_rep index so campaign metadata tracking works correctly, + # analogous to the merge-based alignment in the Botorch hybrid recommender. + discrete_param_names = [p.name for p in searchspace.discrete.parameters] + if discrete_param_names: + exp_rep = searchspace.discrete.exp_rep + _IDX_COL = "__exp_rep_idx__" + exp_lookup = exp_rep[discrete_param_names].copy() + exp_lookup[_IDX_COL] = exp_rep.index + aligned_index = pd.Index( + df[discrete_param_names] + .merge(exp_lookup, on=discrete_param_names, how="left")[_IDX_COL] + .values + ) + continuous_param_names = [p.name for p in searchspace.continuous.parameters] + if continuous_param_names: + rec_disc = exp_rep.loc[aligned_index] + rec_cont = df[continuous_param_names].set_axis(aligned_index) + return pd.concat([rec_disc, rec_cont], axis=1) + else: + return exp_rep.loc[aligned_index] + return df diff --git a/tests/test_llm_recommender.py b/tests/test_llm_recommender.py index a7a615cb46..4bfe9ddcc1 100644 --- a/tests/test_llm_recommender.py +++ b/tests/test_llm_recommender.py @@ -540,6 +540,25 @@ def test_parse_llm_response_rejects_batch_constraint_violation(): rec._parse_llm_response(response, space) +def test_parse_llm_response_aligns_index_with_exp_rep(): + """Returned DataFrame index matches the exp_rep index of the search space.""" + from baybe.recommenders.pure.llm.llm import LLMRecommender + + parameters = [ + NumericalDiscreteParameter("x", values=[1, 2, 3]), + NumericalDiscreteParameter("y", values=[10, 20, 30]), + ] + space = SearchSpace.from_product(parameters=parameters) + rec = LLMRecommender(model="m", experiment_description="test") + + # Suggest the last row of exp_rep — its index is not 0 + last_row = space.discrete.exp_rep.iloc[-1] + response = _make_suggestions([{"x": last_row["x"], "y": last_row["y"]}]) + result = rec._parse_llm_response(response, space) + + assert list(result.index) == [space.discrete.exp_rep.index[-1]] + + def test_parse_llm_response_warns_for_continuous_constraints(): """A warning is issued when the search space has continuous constraints.""" from baybe.constraints.continuous import ContinuousLinearConstraint From 4ad39703cbaeb78abf70785a31f403c8a97aa4c6 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 3 Sep 2026 07:53:03 +0200 Subject: [PATCH 45/96] Re-use existing validation mechanisms for LLM response The LLM response is now handled like a potentially faulty user input. We thus re-use the `validate_parameter_input` function to check the LLM response for validity and also normalize it. --- baybe/recommenders/pure/llm/_parsing.py | 95 ++++++------------------- tests/test_llm_recommender.py | 39 ++++++++-- 2 files changed, 58 insertions(+), 76 deletions(-) diff --git a/baybe/recommenders/pure/llm/_parsing.py b/baybe/recommenders/pure/llm/_parsing.py index b7baa5581a..c7785b258b 100644 --- a/baybe/recommenders/pure/llm/_parsing.py +++ b/baybe/recommenders/pure/llm/_parsing.py @@ -3,19 +3,18 @@ from __future__ import annotations import json -import math import warnings from json import JSONDecodeError import pandas as pd from baybe.exceptions import LLMResponseError, LLMResponseWarning -from baybe.parameters.base import DiscreteParameter -from baybe.parameters.numerical import NumericalContinuousParameter from baybe.searchspace import SearchSpace +from baybe.utils.dataframe import fuzzy_row_match, normalize_input_dtypes +from baybe.utils.validation import validate_parameter_input -def extract_json_array(response: str) -> str: +def extract_json_array(response: str, /) -> str: """Extract the JSON array payload from a raw language model response. Language models frequently wrap their output in Markdown code fences or add @@ -36,7 +35,7 @@ def extract_json_array(response: str) -> str: return response -def parse_llm_response(response: str, searchspace: SearchSpace) -> pd.DataFrame: +def parse_llm_response(response: str, /, searchspace: SearchSpace) -> pd.DataFrame: """Parse a language model response into a DataFrame of recommendations. Args: @@ -96,61 +95,20 @@ def parse_llm_response(response: str, searchspace: SearchSpace) -> pd.DataFrame: df = pd.DataFrame(recommendations) - for param in searchspace.parameters: - if param.name not in df.columns: - raise LLMResponseError(f"Missing parameter: {param.name}") - - values = df[param.name] - - if isinstance(param, NumericalContinuousParameter): - if not all( - isinstance(v, (int, float)) and math.isfinite(v) for v in values - ): - raise LLMResponseError( - f"Non-finite or non-numeric values for continuous parameter: " - f"{param.name}" - ) - bounds = param.bounds.to_tuple() - min_val, max_val = bounds - if not all(min_val <= v <= max_val for v in values): - raise LLMResponseError( - f"Values for {param.name} outside bounds [{min_val}, {max_val}]" - ) - - elif isinstance(param, DiscreteParameter): - allowed = list(param.values) - if param.is_numerical: - allowed_floats = [float(a) for a in allowed] - invalid = [] - canonical = [] - for v in values: - try: - fv = float(v) - except (TypeError, ValueError): - invalid.append(v) - canonical.append(v) - continue - if fv in allowed_floats: - canonical.append(allowed[allowed_floats.index(fv)]) - else: - invalid.append(v) - canonical.append(v) - if invalid: - raise LLMResponseError( - f"Invalid values {invalid} for parameter '{param.name}'. " - f"Allowed values are: {allowed}" - ) - df[param.name] = canonical - else: - invalid = [v for v in values if v not in allowed] - if invalid: - raise LLMResponseError( - f"Invalid values {invalid} for parameter '{param.name}'. " - f"Allowed values are: {allowed}" - ) - # Categorical values from JSON are strings; cast to canonical - allowed_map = {str(a): a for a in allowed} - df[param.name] = [allowed_map.get(str(v), v) for v in values] + # Validate parameter columns as for measurement input. Called directly, not via + # `preprocess_dataframe`, which is a no-op under `preprocess_dataframes=False`. + # TODO: Sharpen the caught errors — different validation failures (e.g. missing + # columns vs. out-of-range values) will require different reactions (hard error + # vs. recovery vs. eligibility drop) rather than a single wrapped error. + try: + validate_parameter_input( + df, + searchspace.parameters, + numerical_measurements_must_be_within_tolerance=True, + ) + except (ValueError, TypeError) as e: + raise LLMResponseError(str(e)) from e + df = normalize_input_dtypes(df, searchspace.parameters) continuous_constraints = ( *searchspace.continuous.constraints_lin_eq, @@ -185,19 +143,12 @@ def parse_llm_response(response: str, searchspace: SearchSpace) -> pd.DataFrame: f"value, but received {list(unique_values)}." ) - # Recover exp_rep index so campaign metadata tracking works correctly, - # analogous to the merge-based alignment in the Botorch hybrid recommender. - discrete_param_names = [p.name for p in searchspace.discrete.parameters] - if discrete_param_names: + # Recover the exp_rep index (for campaign metadata tracking) via the same fuzzy + # matching used for measurement input: exact for categorical, nearest numerical. + discrete_params = searchspace.discrete.parameters + if discrete_params: exp_rep = searchspace.discrete.exp_rep - _IDX_COL = "__exp_rep_idx__" - exp_lookup = exp_rep[discrete_param_names].copy() - exp_lookup[_IDX_COL] = exp_rep.index - aligned_index = pd.Index( - df[discrete_param_names] - .merge(exp_lookup, on=discrete_param_names, how="left")[_IDX_COL] - .values - ) + aligned_index = fuzzy_row_match(exp_rep, df, discrete_params) continuous_param_names = [p.name for p in searchspace.continuous.parameters] if continuous_param_names: rec_disc = exp_rep.loc[aligned_index] diff --git a/tests/test_llm_recommender.py b/tests/test_llm_recommender.py index 4bfe9ddcc1..dd156134e4 100644 --- a/tests/test_llm_recommender.py +++ b/tests/test_llm_recommender.py @@ -267,7 +267,7 @@ def test_recovery_with_distinct_model(mock_completion, recommender, searchspace) } ] ), - "outside bounds", + "has invalid values in parameter", id="out_of_bounds", ), pytest.param( @@ -284,7 +284,7 @@ def test_recovery_with_distinct_model(mock_completion, recommender, searchspace) } ] ), - "Invalid values", + "has invalid values in parameter", id="invalid_categorical", ), pytest.param( @@ -296,7 +296,7 @@ def test_recovery_with_distinct_model(mock_completion, recommender, searchspace) } ] ), - "Missing parameter", + "missing columns for the following parameters", id="missing_parameter", ), pytest.param( @@ -367,7 +367,7 @@ def test_recovery_with_distinct_model(mock_completion, recommender, searchspace) } ] ), - "Non-finite or non-numeric values", + "has non-numeric entries", id="non_numeric_continuous", ), ], @@ -380,6 +380,37 @@ def test_parse_llm_response_errors( recommender._parse_llm_response(response_content, searchspace) +def test_parse_llm_response_numerical_tolerance_snaps_to_nearest(): + """A numerical-discrete value within tolerance is accepted and snapped. + + Matches how user measurement input is handled: ``validate_parameter_input`` + accepts values within the parameter tolerance and ``fuzzy_row_match`` snaps + them to the nearest allowed value. + """ + from baybe.recommenders.pure.llm.llm import LLMRecommender + + space = SearchSpace.from_product( + [NumericalDiscreteParameter("x", values=[1.0, 2.0, 3.0], tolerance=0.4)] + ) + rec = LLMRecommender(model="m", experiment_description="test") + # 1.3 is within tolerance 0.4 of 1.0 but not an exact allowed value. + result = rec._parse_llm_response(_make_suggestions([{"x": 1.3}]), space) + assert result["x"].tolist() == [1.0] + + +def test_parse_llm_response_numerical_out_of_tolerance_rejected(): + """A numerical-discrete value outside tolerance is rejected.""" + from baybe.recommenders.pure.llm.llm import LLMRecommender + + space = SearchSpace.from_product( + [NumericalDiscreteParameter("x", values=[1.0, 2.0, 3.0], tolerance=0.1)] + ) + rec = LLMRecommender(model="m", experiment_description="test") + # 1.3 is outside tolerance 0.1 of every allowed value. + with pytest.raises(LLMResponseError, match="has invalid values in parameter"): + rec._parse_llm_response(_make_suggestions([{"x": 1.3}]), space) + + # --------------------------------------------------------------------------- # Constraint violation test cases — one per concrete DiscreteConstraint class # (except DiscreteBatchConstraint, which is tested separately below). From 7bbb8a073931ab1418354941c2c47c85f1224fa4 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 3 Sep 2026 08:46:26 +0200 Subject: [PATCH 46/96] Match against actual candidates, not the full space Previously, we matched the LLM response against the full search space instead of the candidates. This could have caused incorrect recommendations to be returned. --- baybe/recommenders/pure/llm/_parsing.py | 9 ++- baybe/recommenders/pure/llm/llm.py | 16 +++++- tests/test_llm_recommender.py | 74 +++++++++++++++++++++++++ 3 files changed, 94 insertions(+), 5 deletions(-) diff --git a/baybe/recommenders/pure/llm/_parsing.py b/baybe/recommenders/pure/llm/_parsing.py index c7785b258b..5e58fba094 100644 --- a/baybe/recommenders/pure/llm/_parsing.py +++ b/baybe/recommenders/pure/llm/_parsing.py @@ -43,7 +43,9 @@ def parse_llm_response(response: str, /, searchspace: SearchSpace) -> pd.DataFra searchspace: The search space to validate recommendations against. Returns: - A DataFrame containing the parsed recommendations. + A DataFrame containing the parsed recommendations, restricted to the eligible + candidate set of the search space (as returned by + :meth:`baybe.searchspace.discrete.SubspaceDiscrete.get_candidates`). Raises: LLMResponseError: If the response cannot be parsed, contains invalid parameter @@ -145,9 +147,12 @@ def parse_llm_response(response: str, /, searchspace: SearchSpace) -> pd.DataFra # Recover the exp_rep index (for campaign metadata tracking) via the same fuzzy # matching used for measurement input: exact for categorical, nearest numerical. + # Matching against get_candidates() (not exp_rep directly) ensures that suggestions + # snap to eligible points only, respecting the allow_recommending_* filters applied + # by the campaign via FilteredSubspaceDiscrete. discrete_params = searchspace.discrete.parameters if discrete_params: - exp_rep = searchspace.discrete.exp_rep + exp_rep, _ = searchspace.discrete.get_candidates() aligned_index = fuzzy_row_match(exp_rep, df, discrete_params) continuous_param_names = [p.name for p in searchspace.continuous.parameters] if continuous_param_names: diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index 037dad2022..450e794a4b 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -250,10 +250,17 @@ def recommend( Raises: LLMResponseError: If the call to the language model fails, if its response cannot be parsed and recovery fails, or if the number of - suggestions is less than the requested batch size. + eligible suggestions is less than the requested batch size. + ValueError: If ``batch_size`` is smaller than 1. """ from baybe._optional.llm import completion + if batch_size < 1: + raise ValueError( + f"You must at least request one recommendation per batch, but " + f"provided {batch_size=}." + ) + if objective is not None: validate_object_names(searchspace.parameters + objective.targets) if objective.metadata.is_empty: @@ -314,10 +321,13 @@ def recommend( if len(output) < batch_size: raise LLMResponseError( - f"LLM returned {len(output)} suggestions instead of the " - f"requested {batch_size}." + f"Only {len(output)} eligible suggestion(s) remained instead of the " + f"requested {batch_size}. The language model may have returned too " + f"few suggestions or proposed points excluded by the current " + f"candidate filters." ) + # NOTE: Duplicate configurations within a batch are permitted return output.head(batch_size) @override diff --git a/tests/test_llm_recommender.py b/tests/test_llm_recommender.py index dd156134e4..e8b2104ee4 100644 --- a/tests/test_llm_recommender.py +++ b/tests/test_llm_recommender.py @@ -53,6 +53,28 @@ def _make_suggestions(params_list: list[dict]) -> str: ) +def _filtered_discrete_space(exclude: dict): + """Build a 3x3 discrete space with one candidate filtered from the eligible set.""" + from attrs import evolve + + from baybe.searchspace._filtered import FilteredSubspaceDiscrete + + space = SearchSpace.from_product( + [ + NumericalDiscreteParameter("x", values=[1, 2, 3]), + NumericalDiscreteParameter("y", values=[1, 2, 3]), + ] + ) + exp_rep = space.discrete.exp_rep + mask_keep = ~( + (exp_rep["x"] == exclude["x"]) & (exp_rep["y"] == exclude["y"]) + ).to_numpy() + return evolve( + space, + discrete=FilteredSubspaceDiscrete.from_subspace(space.discrete, mask_keep), + ) + + @pytest.fixture(name="searchspace") def fixture_searchspace(): """A search space with continuous, discrete numeric, and categorical parameters.""" @@ -688,6 +710,58 @@ def test_completion_failure_wrapped(mock_completion, recommender, searchspace): recommender.recommend(batch_size=3, searchspace=searchspace) +def test_recommend_rejects_nonpositive_batch_size(recommender, searchspace): + """A batch size below 1 raises ValueError before any model call.""" + with pytest.raises(ValueError, match="at least request one recommendation"): + recommender.recommend(batch_size=0, searchspace=searchspace) + + +@patch("baybe._optional.llm.completion") +def test_recommend_allows_duplicate_configurations( + mock_completion, recommender, searchspace +): + """Duplicate configurations within a batch are permitted.""" + dup = {"temperature": 25.0, "pressure": 2.0, "n_cycles": 1, "catalyst": "A"} + mock_completion.return_value = _mock_response(_make_suggestions([dup, dup, dup])) + + recommendations = recommender.recommend(batch_size=3, searchspace=searchspace) + + assert len(recommendations) == 3 + assert recommendations["catalyst"].tolist() == ["A", "A", "A"] + + +@patch("baybe._optional.llm.completion") +def test_recommend_excludes_filtered_candidates(mock_completion): + """Suggestions matching filtered-out (ineligible) candidates are dropped.""" + from baybe.recommenders.pure.llm.llm import LLMRecommender + + filtered_space = _filtered_discrete_space(exclude={"x": 1, "y": 1}) + rec = LLMRecommender(model="m", experiment_description="test") + # The first suggestion targets the excluded candidate; the rest are eligible. + mock_completion.return_value = _mock_response( + _make_suggestions([{"x": 1, "y": 1}, {"x": 2, "y": 2}, {"x": 3, "y": 3}]) + ) + + result = rec.recommend(batch_size=2, searchspace=filtered_space) + + assert len(result) == 2 + assert not ((result["x"] == 1) & (result["y"] == 1)).any() + + +@patch("baybe._optional.llm.completion") +def test_recommend_errors_when_too_few_eligible(mock_completion): + """An error is raised when eligibility filtering leaves fewer than requested.""" + from baybe.recommenders.pure.llm.llm import LLMRecommender + + filtered_space = _filtered_discrete_space(exclude={"x": 1, "y": 1}) + rec = LLMRecommender(model="m", experiment_description="test") + # Only the excluded candidate is suggested, so nothing eligible remains. + mock_completion.return_value = _mock_response(_make_suggestions([{"x": 1, "y": 1}])) + + with pytest.raises(LLMResponseError, match="eligible suggestion"): + rec.recommend(batch_size=1, searchspace=filtered_space) + + def test_initialization(recommender): """LLMRecommender initializes with correct attributes.""" assert recommender.model == "gpt-5.4" From dd679f4218113bdab8ea0006191ce2381ac881cd Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 3 Sep 2026 14:16:55 +0200 Subject: [PATCH 47/96] Add `SerialMixin` to `LLMRecommender` Note that we cannot use the `roundtrip` helper in the test. The reason is that `roundtrip` calls `_get_highest_baseclass(LLMRecommender)` which returns `PureRecommender` - and this class does not have serialization support.. --- baybe/recommenders/pure/llm/llm.py | 3 ++- .../test_llm_recommender_serialization.py | 10 +--------- 2 files changed, 3 insertions(+), 10 deletions(-) diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index 450e794a4b..d08b0b9abd 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -18,6 +18,7 @@ from baybe.recommenders.pure.llm._prompts import build_prompt, build_recovery_prompt from baybe.searchspace import SearchSpace from baybe.searchspace.core import SearchSpaceType +from baybe.serialization import SerialMixin from baybe.utils.conversion import to_string from baybe.utils.validation import preprocess_dataframe, validate_object_names @@ -32,7 +33,7 @@ @define(slots=False) -class LLMRecommender(PureRecommender): +class LLMRecommender(PureRecommender, SerialMixin): """Recommender that uses a language model to suggest new experimental points. Unlike other pure recommenders, this recommender does not implement the diff --git a/tests/serialization/test_llm_recommender_serialization.py b/tests/serialization/test_llm_recommender_serialization.py index b8af422e5c..370cd5544e 100644 --- a/tests/serialization/test_llm_recommender_serialization.py +++ b/tests/serialization/test_llm_recommender_serialization.py @@ -2,22 +2,14 @@ from hypothesis import given -from baybe.recommenders import RandomRecommender, TwoPhaseMetaRecommender from baybe.recommenders.pure.llm.llm import LLMRecommender from tests.hypothesis_strategies.recommenders import llm_recommenders -from tests.serialization.utils import assert_roundtrip_consistency @given(llm_recommenders()) def test_llm_recommender_roundtrip(recommender: LLMRecommender): """An LLMRecommender survives a serialization roundtrip across all configurations. - Pure recommenders do not carry the serialization mix-in themselves; they are - serialized through an enclosing meta recommender, which is what we exercise here. Serialization does not touch LiteLLM, so no optional dependencies are required. """ - wrapped = TwoPhaseMetaRecommender( - initial_recommender=recommender, - recommender=RandomRecommender(), - ) - assert_roundtrip_consistency(wrapped) + assert LLMRecommender.from_json(recommender.to_json()) == recommender From d78b33e38255e66c40c9afea290fc286bba2ad40 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 3 Sep 2026 14:30:29 +0200 Subject: [PATCH 48/96] Simplify methods for parsing responses and building prompts Those were previously part of the `_prompts.py` file and methods of the `LLMRecommender` class. To simplify, the private functions on `LLMRecommender` have been removed. The class now uses the functions from `_prompts.py` directly. --- baybe/recommenders/pure/llm/_prompts.py | 14 ++---- baybe/recommenders/pure/llm/llm.py | 58 ++++--------------------- tests/test_llm_recommender.py | 41 +++++------------ 3 files changed, 24 insertions(+), 89 deletions(-) diff --git a/baybe/recommenders/pure/llm/_prompts.py b/baybe/recommenders/pure/llm/_prompts.py index ae0ef26d4f..63813a4a95 100644 --- a/baybe/recommenders/pure/llm/_prompts.py +++ b/baybe/recommenders/pure/llm/_prompts.py @@ -134,13 +134,12 @@ def _extract_parameter_info( - parameters: tuple[Parameter, ...], recommender_name: str + parameters: tuple[Parameter, ...], ) -> list[SimpleNamespace]: """Extract parameter information for prompt construction. Args: parameters: The parameters from the search space. - recommender_name: The name of the recommender, used in error messages. Returns: A list of namespace objects containing parameter information. @@ -164,8 +163,7 @@ def _extract_parameter_info( else: raise IncompatibilityError( f"Parameter '{param.name}' has unsupported type " - f"'{type(param).__name__}' for " - f"'{recommender_name}'. Only " + f"'{type(param).__name__}'. Only " f"'{NumericalContinuousParameter.__name__}' and " f"'{DiscreteParameter.__name__}' subclasses are supported." ) @@ -178,7 +176,6 @@ def _extract_parameter_info( def build_prompt( searchspace: SearchSpace, *, - recommender_name: str, batch_size: int, experiment_description: str, objective: Objective | None, @@ -189,7 +186,6 @@ def build_prompt( Args: searchspace: The search space to generate recommendations for. - recommender_name: The name of the recommender, used in error messages. batch_size: The number of recommendations to generate. experiment_description: Textual description of the experiment. objective: Optional objective to include in the prompt. Set @@ -204,7 +200,7 @@ def build_prompt( """ from baybe._optional.llm import Template - parameters = _extract_parameter_info(searchspace.parameters, recommender_name) + parameters = _extract_parameter_info(searchspace.parameters) template = Template(_PROMPT_TEMPLATE, trim_blocks=True, lstrip_blocks=True) return template.render( experiment_description=experiment_description, @@ -219,7 +215,6 @@ def build_prompt( def build_recovery_prompt( searchspace: SearchSpace, *, - recommender_name: str, error: Exception, original_response: str, ) -> str: @@ -227,7 +222,6 @@ def build_recovery_prompt( Args: searchspace: The search space to generate recommendations for. - recommender_name: The name of the recommender, used in error messages. error: The error that occurred during parsing. original_response: The original malformed response. @@ -236,7 +230,7 @@ def build_recovery_prompt( """ from baybe._optional.llm import Template - parameters = _extract_parameter_info(searchspace.parameters, recommender_name) + parameters = _extract_parameter_info(searchspace.parameters) template = Template(_RECOVERY_PROMPT_TEMPLATE, trim_blocks=True, lstrip_blocks=True) return template.render( error=str(error), diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index d08b0b9abd..77012e531f 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -117,50 +117,6 @@ def _validate_recovery_litellm_args(self, attribute, value): # noqa: DOC101, DO f"instead (e.g. OPENAI_API_KEY, ANTHROPIC_API_KEY)." ) - def _construct_prompt( - self, - searchspace: SearchSpace, - batch_size: int, - objective: Objective | None = None, - measurements: pd.DataFrame | None = None, - pending_experiments: pd.DataFrame | None = None, - ) -> str: - """Construct the prompt for the language model. - - Args: - searchspace: The search space to generate recommendations for. - batch_size: The number of recommendations to generate. - objective: Optional objective to include in the prompt. - measurements: Optional measurements to include in the prompt. - pending_experiments: Optional pending experiments to include in the prompt. - - Returns: - The constructed prompt. - """ - return build_prompt( - searchspace, - recommender_name=self.__class__.__name__, - batch_size=batch_size, - experiment_description=self.experiment_description, - objective=objective, - measurements=measurements, - pending_experiments=pending_experiments, - ) - - def _parse_llm_response( - self, response: str, searchspace: SearchSpace - ) -> pd.DataFrame: - """Parse the LLM response into a DataFrame of recommendations. - - Args: - response: The response from the language model. - searchspace: The search space to validate recommendations against. - - Returns: - A DataFrame containing the parsed recommendations. - """ - return parse_llm_response(response, searchspace) - def _attempt_recovery( self, error: Exception, @@ -184,7 +140,6 @@ def _attempt_recovery( recovery_prompt = build_recovery_prompt( searchspace, - recommender_name=self.__class__.__name__, error=error, original_response=original_response, ) @@ -220,7 +175,7 @@ def _attempt_recovery( ) try: - return self._parse_llm_response(content, searchspace) + return parse_llm_response(content, searchspace) except LLMResponseError as e: raise LLMResponseError( f"Recovery produced another malformed response: {e}. " @@ -289,8 +244,13 @@ def recommend( numerical_measurements_must_be_within_tolerance=False, ) - prompt = self._construct_prompt( - searchspace, batch_size, objective, measurements, pending_experiments + prompt = build_prompt( + searchspace, + batch_size=batch_size, + experiment_description=self.experiment_description, + objective=objective, + measurements=measurements, + pending_experiments=pending_experiments, ) try: response = completion( @@ -316,7 +276,7 @@ def recommend( raise LLMResponseError("LLM returned empty content (None).") try: - output = self._parse_llm_response(content, searchspace) + output = parse_llm_response(content, searchspace) except LLMResponseError as e: output = self._attempt_recovery(e, content, searchspace) diff --git a/tests/test_llm_recommender.py b/tests/test_llm_recommender.py index e8b2104ee4..5b123aef0c 100644 --- a/tests/test_llm_recommender.py +++ b/tests/test_llm_recommender.py @@ -28,6 +28,7 @@ NumericalContinuousParameter, NumericalDiscreteParameter, ) +from baybe.recommenders.pure.llm._parsing import parse_llm_response from baybe.searchspace import SearchSpace from baybe.utils.basic import get_subclasses @@ -394,12 +395,10 @@ def test_recovery_with_distinct_model(mock_completion, recommender, searchspace) ), ], ) -def test_parse_llm_response_errors( - response_content, error_match, recommender, searchspace -): +def test_parse_llm_response_errors(response_content, error_match, searchspace): """Malformed responses raise LLMResponseError with descriptive messages.""" with pytest.raises(LLMResponseError, match=error_match): - recommender._parse_llm_response(response_content, searchspace) + parse_llm_response(response_content, searchspace) def test_parse_llm_response_numerical_tolerance_snaps_to_nearest(): @@ -409,28 +408,22 @@ def test_parse_llm_response_numerical_tolerance_snaps_to_nearest(): accepts values within the parameter tolerance and ``fuzzy_row_match`` snaps them to the nearest allowed value. """ - from baybe.recommenders.pure.llm.llm import LLMRecommender - space = SearchSpace.from_product( [NumericalDiscreteParameter("x", values=[1.0, 2.0, 3.0], tolerance=0.4)] ) - rec = LLMRecommender(model="m", experiment_description="test") # 1.3 is within tolerance 0.4 of 1.0 but not an exact allowed value. - result = rec._parse_llm_response(_make_suggestions([{"x": 1.3}]), space) + result = parse_llm_response(_make_suggestions([{"x": 1.3}]), space) assert result["x"].tolist() == [1.0] def test_parse_llm_response_numerical_out_of_tolerance_rejected(): """A numerical-discrete value outside tolerance is rejected.""" - from baybe.recommenders.pure.llm.llm import LLMRecommender - space = SearchSpace.from_product( [NumericalDiscreteParameter("x", values=[1.0, 2.0, 3.0], tolerance=0.1)] ) - rec = LLMRecommender(model="m", experiment_description="test") # 1.3 is outside tolerance 0.1 of every allowed value. with pytest.raises(LLMResponseError, match="has invalid values in parameter"): - rec._parse_llm_response(_make_suggestions([{"x": 1.3}]), space) + parse_llm_response(_make_suggestions([{"x": 1.3}]), space) # --------------------------------------------------------------------------- @@ -565,19 +558,14 @@ def test_parse_llm_response_rejects_row_constraint_violations( parameters, constraints, violation_suggestions ): """Suggestions valid per-parameter but violating a discrete constraint raise.""" - from baybe.recommenders.pure.llm.llm import LLMRecommender - space = SearchSpace.from_product(parameters=parameters, constraints=constraints) - rec = LLMRecommender(model="m", experiment_description="test") response = _make_suggestions(violation_suggestions) with pytest.raises(LLMResponseError, match="violate the.*constraint"): - rec._parse_llm_response(response, space) + parse_llm_response(response, space) def test_parse_llm_response_rejects_batch_constraint_violation(): """Batch suggestions with mixed values for a DiscreteBatchConstraint param raise.""" - from baybe.recommenders.pure.llm.llm import LLMRecommender - parameters = [ NumericalDiscreteParameter("x", values=[1, 2, 3]), CategoricalParameter("y", values=["a", "b"]), @@ -586,28 +574,23 @@ def test_parse_llm_response_rejects_batch_constraint_violation(): parameters=parameters, constraints=[DiscreteBatchConstraint(parameters=["x"])], ) - rec = LLMRecommender(model="m", experiment_description="test") # x values differ across suggestions — violates the batch constraint response = _make_suggestions([{"x": 1, "y": "a"}, {"x": 2, "y": "b"}]) with pytest.raises(LLMResponseError, match="DiscreteBatchConstraint"): - rec._parse_llm_response(response, space) + parse_llm_response(response, space) def test_parse_llm_response_aligns_index_with_exp_rep(): """Returned DataFrame index matches the exp_rep index of the search space.""" - from baybe.recommenders.pure.llm.llm import LLMRecommender - parameters = [ NumericalDiscreteParameter("x", values=[1, 2, 3]), NumericalDiscreteParameter("y", values=[10, 20, 30]), ] space = SearchSpace.from_product(parameters=parameters) - rec = LLMRecommender(model="m", experiment_description="test") - # Suggest the last row of exp_rep — its index is not 0 last_row = space.discrete.exp_rep.iloc[-1] response = _make_suggestions([{"x": last_row["x"], "y": last_row["y"]}]) - result = rec._parse_llm_response(response, space) + result = parse_llm_response(response, space) assert list(result.index) == [space.discrete.exp_rep.index[-1]] @@ -615,7 +598,6 @@ def test_parse_llm_response_aligns_index_with_exp_rep(): def test_parse_llm_response_warns_for_continuous_constraints(): """A warning is issued when the search space has continuous constraints.""" from baybe.constraints.continuous import ContinuousLinearConstraint - from baybe.recommenders.pure.llm.llm import LLMRecommender parameters = [ NumericalContinuousParameter("x", bounds=(0, 1)), @@ -629,10 +611,9 @@ def test_parse_llm_response_warns_for_continuous_constraints(): ) ], ) - rec = LLMRecommender(model="m", experiment_description="test") response = _make_suggestions([{"x": 0.3, "y": 0.4}]) with pytest.warns(LLMResponseWarning, match="continuous constraints"): - rec._parse_llm_response(response, space) + parse_llm_response(response, space) @pytest.mark.parametrize( @@ -644,12 +625,12 @@ def test_parse_llm_response_warns_for_continuous_constraints(): ], ids=["json_fence", "bare_fence", "surrounding_prose"], ) -def test_parse_llm_response_strips_wrappers(wrapper, recommender, searchspace): +def test_parse_llm_response_strips_wrappers(wrapper, searchspace): """Markdown fences and surrounding prose are stripped before JSON parsing.""" payload = _make_suggestions( [{"temperature": 25.0, "pressure": 2.0, "n_cycles": 1, "catalyst": "A"}] ) - df = recommender._parse_llm_response(wrapper.format(payload=payload), searchspace) + df = parse_llm_response(wrapper.format(payload=payload), searchspace) assert len(df) == 1 assert df["catalyst"].iloc[0] == "A" From 3ddb673d07e4f6ee7672f2acfa994998285d29c1 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 3 Sep 2026 14:34:03 +0200 Subject: [PATCH 49/96] Rename `build_*`-named functions to `make_*`-named functions --- baybe/recommenders/pure/llm/_prompts.py | 4 ++-- baybe/recommenders/pure/llm/llm.py | 6 +++--- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/baybe/recommenders/pure/llm/_prompts.py b/baybe/recommenders/pure/llm/_prompts.py index 63813a4a95..51e057646f 100644 --- a/baybe/recommenders/pure/llm/_prompts.py +++ b/baybe/recommenders/pure/llm/_prompts.py @@ -173,7 +173,7 @@ def _extract_parameter_info( return infos -def build_prompt( +def make_prompt( searchspace: SearchSpace, *, batch_size: int, @@ -212,7 +212,7 @@ def build_prompt( ) -def build_recovery_prompt( +def make_recovery_prompt( searchspace: SearchSpace, *, error: Exception, diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index 77012e531f..a402cee8e8 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -15,7 +15,7 @@ from baybe.objectives.base import Objective from baybe.recommenders.pure.base import PureRecommender from baybe.recommenders.pure.llm._parsing import parse_llm_response -from baybe.recommenders.pure.llm._prompts import build_prompt, build_recovery_prompt +from baybe.recommenders.pure.llm._prompts import make_prompt, make_recovery_prompt from baybe.searchspace import SearchSpace from baybe.searchspace.core import SearchSpaceType from baybe.serialization import SerialMixin @@ -138,7 +138,7 @@ def _attempt_recovery( """ from baybe._optional.llm import completion - recovery_prompt = build_recovery_prompt( + recovery_prompt = make_recovery_prompt( searchspace, error=error, original_response=original_response, @@ -244,7 +244,7 @@ def recommend( numerical_measurements_must_be_within_tolerance=False, ) - prompt = build_prompt( + prompt = make_prompt( searchspace, batch_size=batch_size, experiment_description=self.experiment_description, From baaf23720979f20b420fb09ae78bf04adb7e920d Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 3 Sep 2026 14:56:08 +0200 Subject: [PATCH 50/96] Replace hardcoded names --- baybe/recommenders/pure/llm/_parsing.py | 6 +++--- baybe/recommenders/pure/llm/llm.py | 14 +++++++------- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/baybe/recommenders/pure/llm/_parsing.py b/baybe/recommenders/pure/llm/_parsing.py index 5e58fba094..a8e3bcd7a7 100644 --- a/baybe/recommenders/pure/llm/_parsing.py +++ b/baybe/recommenders/pure/llm/_parsing.py @@ -140,9 +140,9 @@ def parse_llm_response(response: str, /, searchspace: SearchSpace) -> pd.DataFra unique_values = df[param_name].unique() if len(unique_values) > 1: raise LLMResponseError( - f"Suggestions violate 'DiscreteBatchConstraint' on parameter " - f"'{param_name}': all suggestions in a batch must share the same " - f"value, but received {list(unique_values)}." + f"Suggestions violate the '{type(constraint).__name__}' constraint on " + f"parameter '{param_name}': all suggestions in a batch must share the " + f"same value, but received {list(unique_values)}." ) # Recover the exp_rep index (for campaign metadata tracking) via the same fuzzy diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index a402cee8e8..6c724f626e 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -87,15 +87,15 @@ def _validate_litellm_args(self, attribute, value): # noqa: DOC101, DOC103 conflicts = _RESERVED_LITELLM_KEYS & set(value.keys()) if conflicts: raise ValueError( - f"'litellm_args' must not contain keys that are set explicitly: " + f"'{attribute.name}' must not contain keys that are set explicitly: " f"{conflicts}. Use the dedicated class attributes instead." ) cred_conflicts = _CREDENTIAL_LITELLM_KEYS & set(value.keys()) if cred_conflicts: raise ValueError( - f"'litellm_args' must not contain credential keys {cred_conflicts}. " - f"Supply credentials via environment variables instead " - f"(e.g. OPENAI_API_KEY, ANTHROPIC_API_KEY)." + f"'{attribute.name}' must not contain credential keys " + f"{cred_conflicts}. Supply credentials via environment variables " + f"instead (e.g. OPENAI_API_KEY, ANTHROPIC_API_KEY)." ) @recovery_litellm_args.validator @@ -106,13 +106,13 @@ def _validate_recovery_litellm_args(self, attribute, value): # noqa: DOC101, DO conflicts = _RESERVED_LITELLM_KEYS & set(value.keys()) if conflicts: raise ValueError( - f"'recovery_litellm_args' must not contain keys that are set " - f"explicitly: {conflicts}. Use the dedicated class attributes instead." + f"'{attribute.name}' must not contain keys that are set explicitly: " + f"{conflicts}. Use the dedicated class attributes instead." ) cred_conflicts = _CREDENTIAL_LITELLM_KEYS & set(value.keys()) if cred_conflicts: raise ValueError( - f"'recovery_litellm_args' must not contain credential keys " + f"'{attribute.name}' must not contain credential keys " f"{cred_conflicts}. Supply credentials via environment variables " f"instead (e.g. OPENAI_API_KEY, ANTHROPIC_API_KEY)." ) From 01d8ca28cf5d0377ae0ec087948f0c3d6cd229d2 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 3 Sep 2026 14:59:41 +0200 Subject: [PATCH 51/96] Simplify comments and docstring --- baybe/recommenders/pure/llm/llm.py | 14 +++----------- 1 file changed, 3 insertions(+), 11 deletions(-) diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index 6c724f626e..89bb90bfcf 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -27,22 +27,14 @@ # Credential keys that LiteLLM accepts inline but must be supplied via environment # variables instead (e.g. OPENAI_API_KEY, ANTHROPIC_API_KEY). Blocking them at -# construction time prevents accidental exposure in logs, __str__, and serialized -# campaign files. +# construction time prevents accidental exposure in logs, __str__, and serialization +# strings. _CREDENTIAL_LITELLM_KEYS = frozenset({"api_key", "api_base", "api_version"}) @define(slots=False) class LLMRecommender(PureRecommender, SerialMixin): - """Recommender that uses a language model to suggest new experimental points. - - Unlike other pure recommenders, this recommender does not implement the - ``_recommend_discrete``/``_recommend_continuous``/``_recommend_hybrid`` hooks. - The language model returns a complete set of parameter configurations that already - constitutes the recommendation, so there is no per-subspace selection step to - delegate to those hooks. Instead, :meth:`recommend` is fully overridden and builds - the result directly from the model response. - """ + """Recommender that uses a language model to suggest new experimental points.""" # Class variables compatibility: ClassVar[SearchSpaceType] = SearchSpaceType.HYBRID From 59d7befbc2a9b4810154e975eedc82ed8e15b2b8 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 3 Sep 2026 15:02:21 +0200 Subject: [PATCH 52/96] Remove unnecessary check --- baybe/recommenders/pure/llm/_parsing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/baybe/recommenders/pure/llm/_parsing.py b/baybe/recommenders/pure/llm/_parsing.py index a8e3bcd7a7..eae88eed59 100644 --- a/baybe/recommenders/pure/llm/_parsing.py +++ b/baybe/recommenders/pure/llm/_parsing.py @@ -57,7 +57,7 @@ def parse_llm_response(response: str, /, searchspace: SearchSpace) -> pd.DataFra Continuous constraints cannot be validated after the fact, so compliance of the LLM suggestions with such constraints is not guaranteed. """ - payload = extract_json_array(response) if isinstance(response, str) else response + payload = extract_json_array(response) try: suggestions = json.loads(payload) except (JSONDecodeError, TypeError) as e: From 8fa684147b26cd7ce02c2ab9b9520f4a8293d473 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 3 Sep 2026 15:05:45 +0200 Subject: [PATCH 53/96] Generalize type --- baybe/recommenders/pure/llm/_prompts.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/baybe/recommenders/pure/llm/_prompts.py b/baybe/recommenders/pure/llm/_prompts.py index 51e057646f..3ced3e1b1b 100644 --- a/baybe/recommenders/pure/llm/_prompts.py +++ b/baybe/recommenders/pure/llm/_prompts.py @@ -2,6 +2,7 @@ from __future__ import annotations +from collections.abc import Sequence from types import SimpleNamespace from typing import TYPE_CHECKING, Any @@ -134,7 +135,7 @@ def _extract_parameter_info( - parameters: tuple[Parameter, ...], + parameters: Sequence[Parameter], ) -> list[SimpleNamespace]: """Extract parameter information for prompt construction. From 676e6052ed9781afab19aad9624991133e0449b3 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 3 Sep 2026 16:49:22 +0200 Subject: [PATCH 54/96] Introduce more fine-grained LLM error handling Instead of a generic error, we use more fine-grained errors. The corresponding exceptions also carry information that will be passed to the recovery prompt. --- baybe/exceptions.py | 157 ++++++++++++++++++++++++ baybe/recommenders/pure/llm/_parsing.py | 95 ++++++++++---- tests/test_llm_recommender.py | 74 +++++------ 3 files changed, 264 insertions(+), 62 deletions(-) diff --git a/baybe/exceptions.py b/baybe/exceptions.py index 4ef825e4a2..096da7f47f 100644 --- a/baybe/exceptions.py +++ b/baybe/exceptions.py @@ -1,6 +1,7 @@ """Custom exceptions and warnings.""" import gc +from collections.abc import Collection from typing import Any import pandas as pd @@ -97,6 +98,162 @@ class InfeasibilityError(Exception): class LLMResponseError(Exception): """An error occurred while processing a language model response.""" + @property + def recovery_instruction(self) -> str: + """Guidance for the language model on how to correct its response.""" + return ( + "Your previous response could not be used. Please provide a new " + "recommendation that follows the required format and respects the search " + "space." + ) + + +class MalformedLLMResponseError(LLMResponseError): + """A language model response could not be parsed into the expected structure.""" + + @property + @override + def recovery_instruction(self) -> str: + return ( + "Your previous response was not valid JSON in the required format. Return " + "a JSON array of objects, each with an 'explanation' string and a " + "'parameters' object mapping parameter names to values, and nothing else." + ) + + +class UnknownParameterError(LLMResponseError): + """A language model response referenced parameters outside the search space.""" + + def __init__( + self, + *args: Any, + unknown_names: Collection[str], + valid_names: Collection[str], + ): + super().__init__(*args) + self.unknown_names = unknown_names + self.valid_names = valid_names + + @property + @override + def recovery_instruction(self) -> str: + return ( + f"Your previous response used unknown parameter names " + f"{sorted(self.unknown_names)}. Use only the following parameters: " + f"{sorted(self.valid_names)}." + ) + + +class MissingParameterError(LLMResponseError): + """A language model response omitted required search space parameters.""" + + def __init__(self, *args: Any, parameters: Collection[str]): + super().__init__(*args) + self.parameters = parameters + + @property + @override + def recovery_instruction(self) -> str: + return ( + f"Your previous response did not specify values for the required " + f"parameters {sorted(self.parameters)}. Every suggestion must provide a " + f"value for all parameters." + ) + + +class NonNumericParameterError(LLMResponseError): + """A language model response gave non-numeric values for a numerical parameter.""" + + def __init__(self, *args: Any, detail: str): + super().__init__(*args) + self.detail = detail + + @property + @override + def recovery_instruction(self) -> str: + return ( + f"Your previous response provided non-numeric values for a numerical " + f"parameter: {self.detail} Provide numeric values for all numerical " + f"parameters." + ) + + +class InvalidParameterValueError(LLMResponseError): + """A language model response contained invalid parameter values.""" + + def __init__(self, *args: Any, detail: str): + super().__init__(*args) + self.detail = detail + + @property + @override + def recovery_instruction(self) -> str: + return ( + f"Your previous response contained invalid parameter values: {self.detail} " + f"Provide values that lie within the allowed range (for numerical " + f"parameters) or match an allowed choice exactly (for categorical " + f"parameters)." + ) + + +class ConstraintViolationError(LLMResponseError): + """A language model response violated a search space constraint.""" + + def __init__( + self, + *args: Any, + constraint_name: str, + parameters: Collection[str], + ): + super().__init__(*args) + self.constraint_name = constraint_name + self.parameters = parameters + + @property + @override + def recovery_instruction(self) -> str: + return ( + f"Your previous response violated the '{self.constraint_name}' constraint " + f"on parameters {list(self.parameters)}. Ensure all suggestions satisfy " + f"this constraint." + ) + + +class IneligiblePointsError(LLMResponseError): + """A language model response proposed points outside the eligible candidate set.""" + + def __init__(self, *args: Any, n_ineligible: int): + super().__init__(*args) + self.n_ineligible = n_ineligible + + @property + @override + def recovery_instruction(self) -> str: + return ( + f"{self.n_ineligible} of your proposed configurations are not eligible " + f"candidates (they may already have been measured or recommended, or they " + f"do not exist in the search space). Propose different points from the " + f"available search space." + ) + + +class BatchSizeError(LLMResponseError): + """A language model response contained the wrong number of recommendations.""" + + def __init__(self, *args: Any, requested: int, received: int): + super().__init__(*args) + self.requested = requested + self.received = received + + @property + @override + def recovery_instruction(self) -> str: + return ( + f"Your previous response provided {self.received} valid recommendation(s), " + f"but exactly {self.requested} are required. Provide {self.requested} " + f"distinct, valid recommendations." + ) + class NotEnoughPointsLeftError(Exception): """ diff --git a/baybe/recommenders/pure/llm/_parsing.py b/baybe/recommenders/pure/llm/_parsing.py index eae88eed59..123daee66a 100644 --- a/baybe/recommenders/pure/llm/_parsing.py +++ b/baybe/recommenders/pure/llm/_parsing.py @@ -8,7 +8,16 @@ import pandas as pd -from baybe.exceptions import LLMResponseError, LLMResponseWarning +from baybe.exceptions import ( + ConstraintViolationError, + IneligiblePointsError, + InvalidParameterValueError, + LLMResponseWarning, + MalformedLLMResponseError, + MissingParameterError, + NonNumericParameterError, + UnknownParameterError, +) from baybe.searchspace import SearchSpace from baybe.utils.dataframe import fuzzy_row_match, normalize_input_dtypes from baybe.utils.validation import validate_parameter_input @@ -48,9 +57,18 @@ def parse_llm_response(response: str, /, searchspace: SearchSpace) -> pd.DataFra :meth:`baybe.searchspace.discrete.SubspaceDiscrete.get_candidates`). Raises: - LLMResponseError: If the response cannot be parsed, contains invalid parameter - values, or violates any discrete constraint (including batch constraints) - present in the search space. + MalformedLLMResponseError: If the response cannot be parsed into the expected + JSON structure. + UnknownParameterError: If a suggestion references parameters not in the search + space. + MissingParameterError: If a suggestion omits required search space parameters. + NonNumericParameterError: If a suggestion gives non-numeric values for a + numerical parameter. + InvalidParameterValueError: If a suggestion contains invalid parameter values. + ConstraintViolationError: If a suggestion violates a discrete constraint + (including batch constraints) present in the search space. + IneligiblePointsError: If a suggestion does not correspond to an eligible + candidate of the search space. Warns: LLMResponseWarning: If the search space contains continuous constraints. @@ -61,55 +79,69 @@ def parse_llm_response(response: str, /, searchspace: SearchSpace) -> pd.DataFra try: suggestions = json.loads(payload) except (JSONDecodeError, TypeError) as e: - raise LLMResponseError(f"Error parsing JSON output: {e}") from e + raise MalformedLLMResponseError(f"Error parsing JSON output: {e}.") from e if not isinstance(suggestions, list): - raise LLMResponseError("Response must be a JSON array") + raise MalformedLLMResponseError("Response must be a JSON array.") if not suggestions: - raise LLMResponseError("Response contains an empty array with no suggestions.") + raise MalformedLLMResponseError( + "Response contains an empty array with no suggestions." + ) recommendations = [] for suggestion in suggestions: if not isinstance(suggestion, dict): - raise LLMResponseError("Each suggestion must be a JSON object") + raise MalformedLLMResponseError("Each suggestion must be a JSON object.") if "parameters" not in suggestion: - raise LLMResponseError("Each suggestion must contain a 'parameters' field") + raise MalformedLLMResponseError( + "Each suggestion must contain a 'parameters' field." + ) if "explanation" not in suggestion: - raise LLMResponseError( - "Each suggestion must contain an 'explanation' field" + raise MalformedLLMResponseError( + "Each suggestion must contain an 'explanation' field." ) params = suggestion["parameters"] if not isinstance(params, dict): - raise LLMResponseError("Parameters must be a JSON object") + raise MalformedLLMResponseError("Parameters must be a JSON object.") param_names = {p.name for p in searchspace.parameters} unknown = set(params.keys()) - param_names if unknown: - raise LLMResponseError( - f"Response contains unknown parameter names: {unknown}" + raise UnknownParameterError( + f"Response contains unknown parameter names: {unknown}.", + unknown_names=unknown, + valid_names=param_names, ) recommendations.append(params) df = pd.DataFrame(recommendations) - # Validate parameter columns as for measurement input. Called directly, not via - # `preprocess_dataframe`, which is a no-op under `preprocess_dataframes=False`. - # TODO: Sharpen the caught errors — different validation failures (e.g. missing - # columns vs. out-of-range values) will require different reactions (hard error - # vs. recovery vs. eligibility drop) rather than a single wrapped error. + # Detect missing columns up front so they surface as a distinct error. + missing = {p.name for p in searchspace.parameters}.difference(df.columns) + if missing: + raise MissingParameterError( + f"Response is missing values for the following parameters: {missing}.", + parameters=missing, + ) + + # Validate parameter columns as for measurement input. `validate_parameter_input` + # raises `TypeError` for non-numeric entries and `ValueError` for other invalid + # values, which we surface as distinct error types. try: validate_parameter_input( df, searchspace.parameters, numerical_measurements_must_be_within_tolerance=True, ) - except (ValueError, TypeError) as e: - raise LLMResponseError(str(e)) from e + except TypeError as e: + raise NonNumericParameterError(str(e), detail=str(e)) from e + except ValueError as e: + raise InvalidParameterValueError(str(e), detail=str(e)) from e df = normalize_input_dtypes(df, searchspace.parameters) continuous_constraints = ( @@ -129,20 +161,24 @@ def parse_llm_response(response: str, /, searchspace: SearchSpace) -> pd.DataFra for constraint in searchspace.discrete.constraints: invalid_idx = constraint.get_invalid(df) if not invalid_idx.empty: - raise LLMResponseError( + raise ConstraintViolationError( f"{len(invalid_idx)} suggestion(s) violate the " f"'{type(constraint).__name__}' constraint on parameters " - f"{constraint.parameters}." + f"{constraint.parameters}.", + constraint_name=type(constraint).__name__, + parameters=constraint.parameters, ) for constraint in searchspace.discrete.constraints_batch: param_name = constraint.parameters[0] unique_values = df[param_name].unique() if len(unique_values) > 1: - raise LLMResponseError( + raise ConstraintViolationError( f"Suggestions violate the '{type(constraint).__name__}' constraint on " f"parameter '{param_name}': all suggestions in a batch must share the " - f"same value, but received {list(unique_values)}." + f"same value, but received {list(unique_values)}.", + constraint_name=type(constraint).__name__, + parameters=constraint.parameters, ) # Recover the exp_rep index (for campaign metadata tracking) via the same fuzzy @@ -154,6 +190,15 @@ def parse_llm_response(response: str, /, searchspace: SearchSpace) -> pd.DataFra if discrete_params: exp_rep, _ = searchspace.discrete.get_candidates() aligned_index = fuzzy_row_match(exp_rep, df, discrete_params) + # `fuzzy_row_match` silently drops suggestions with no eligible candidate, so + # detect the shortfall explicitly rather than let them vanish. + n_ineligible = len(df) - len(aligned_index) + if n_ineligible > 0: + raise IneligiblePointsError( + f"{n_ineligible} suggestion(s) do not correspond to eligible " + f"candidates of the search space.", + n_ineligible=n_ineligible, + ) continuous_param_names = [p.name for p in searchspace.continuous.parameters] if continuous_param_names: rec_disc = exp_rep.loc[aligned_index] diff --git a/tests/test_llm_recommender.py b/tests/test_llm_recommender.py index 5b123aef0c..5d7e0a6c6a 100644 --- a/tests/test_llm_recommender.py +++ b/tests/test_llm_recommender.py @@ -22,7 +22,17 @@ DiscreteProductConstraint, DiscreteSumConstraint, ) -from baybe.exceptions import LLMResponseError, LLMResponseWarning +from baybe.exceptions import ( + ConstraintViolationError, + IneligiblePointsError, + InvalidParameterValueError, + LLMResponseError, + LLMResponseWarning, + MalformedLLMResponseError, + MissingParameterError, + NonNumericParameterError, + UnknownParameterError, +) from baybe.parameters import ( CategoricalParameter, NumericalContinuousParameter, @@ -269,10 +279,11 @@ def test_recovery_with_distinct_model(mock_completion, recommender, searchspace) @pytest.mark.parametrize( - ("response_content", "error_match"), + ("response_content", "error_type", "error_match"), [ pytest.param( "Invalid JSON", + MalformedLLMResponseError, "Error parsing JSON output", id="invalid_json", ), @@ -290,6 +301,7 @@ def test_recovery_with_distinct_model(mock_completion, recommender, searchspace) } ] ), + InvalidParameterValueError, "has invalid values in parameter", id="out_of_bounds", ), @@ -307,6 +319,7 @@ def test_recovery_with_distinct_model(mock_completion, recommender, searchspace) } ] ), + InvalidParameterValueError, "has invalid values in parameter", id="invalid_categorical", ), @@ -319,7 +332,8 @@ def test_recovery_with_distinct_model(mock_completion, recommender, searchspace) } ] ), - "missing columns for the following parameters", + MissingParameterError, + "missing values for the following parameters", id="missing_parameter", ), pytest.param( @@ -337,26 +351,31 @@ def test_recovery_with_distinct_model(mock_completion, recommender, searchspace) } ] ), + UnknownParameterError, "unknown parameter names", id="unknown_parameter", ), pytest.param( json.dumps({"explanation": "Test", "parameters": {}}), + MalformedLLMResponseError, "Response must be a JSON array", id="not_a_list", ), pytest.param( json.dumps([]), + MalformedLLMResponseError, "empty array with no suggestions", id="empty_array", ), pytest.param( json.dumps(["a string"]), + MalformedLLMResponseError, "Each suggestion must be a JSON object", id="suggestion_not_dict", ), pytest.param( json.dumps([{"explanation": "Test", "parameters": [1, 2]}]), + MalformedLLMResponseError, "Parameters must be a JSON object", id="parameters_not_dict", ), @@ -373,6 +392,7 @@ def test_recovery_with_distinct_model(mock_completion, recommender, searchspace) } ] ), + MalformedLLMResponseError, "must contain an 'explanation' field", id="missing_explanation", ), @@ -390,14 +410,17 @@ def test_recovery_with_distinct_model(mock_completion, recommender, searchspace) } ] ), + NonNumericParameterError, "has non-numeric entries", id="non_numeric_continuous", ), ], ) -def test_parse_llm_response_errors(response_content, error_match, searchspace): - """Malformed responses raise LLMResponseError with descriptive messages.""" - with pytest.raises(LLMResponseError, match=error_match): +def test_parse_llm_response_errors( + response_content, error_type, error_match, searchspace +): + """Malformed responses raise the specific error subtype with a clear message.""" + with pytest.raises(error_type, match=error_match): parse_llm_response(response_content, searchspace) @@ -560,7 +583,7 @@ def test_parse_llm_response_rejects_row_constraint_violations( """Suggestions valid per-parameter but violating a discrete constraint raise.""" space = SearchSpace.from_product(parameters=parameters, constraints=constraints) response = _make_suggestions(violation_suggestions) - with pytest.raises(LLMResponseError, match="violate the.*constraint"): + with pytest.raises(ConstraintViolationError, match="violate the.*constraint"): parse_llm_response(response, space) @@ -576,7 +599,7 @@ def test_parse_llm_response_rejects_batch_constraint_violation(): ) # x values differ across suggestions — violates the batch constraint response = _make_suggestions([{"x": 1, "y": "a"}, {"x": 2, "y": "b"}]) - with pytest.raises(LLMResponseError, match="DiscreteBatchConstraint"): + with pytest.raises(ConstraintViolationError, match="DiscreteBatchConstraint"): parse_llm_response(response, space) @@ -711,36 +734,13 @@ def test_recommend_allows_duplicate_configurations( assert recommendations["catalyst"].tolist() == ["A", "A", "A"] -@patch("baybe._optional.llm.completion") -def test_recommend_excludes_filtered_candidates(mock_completion): - """Suggestions matching filtered-out (ineligible) candidates are dropped.""" - from baybe.recommenders.pure.llm.llm import LLMRecommender - - filtered_space = _filtered_discrete_space(exclude={"x": 1, "y": 1}) - rec = LLMRecommender(model="m", experiment_description="test") - # The first suggestion targets the excluded candidate; the rest are eligible. - mock_completion.return_value = _mock_response( - _make_suggestions([{"x": 1, "y": 1}, {"x": 2, "y": 2}, {"x": 3, "y": 3}]) - ) - - result = rec.recommend(batch_size=2, searchspace=filtered_space) - - assert len(result) == 2 - assert not ((result["x"] == 1) & (result["y"] == 1)).any() - - -@patch("baybe._optional.llm.completion") -def test_recommend_errors_when_too_few_eligible(mock_completion): - """An error is raised when eligibility filtering leaves fewer than requested.""" - from baybe.recommenders.pure.llm.llm import LLMRecommender - +def test_parse_llm_response_rejects_ineligible_points(): + """Suggestions matching filtered-out (ineligible) candidates raise.""" filtered_space = _filtered_discrete_space(exclude={"x": 1, "y": 1}) - rec = LLMRecommender(model="m", experiment_description="test") - # Only the excluded candidate is suggested, so nothing eligible remains. - mock_completion.return_value = _mock_response(_make_suggestions([{"x": 1, "y": 1}])) - - with pytest.raises(LLMResponseError, match="eligible suggestion"): - rec.recommend(batch_size=1, searchspace=filtered_space) + # The first suggestion targets the excluded candidate; the second is eligible. + response = _make_suggestions([{"x": 1, "y": 1}, {"x": 2, "y": 2}]) + with pytest.raises(IneligiblePointsError, match="do not correspond to eligible"): + parse_llm_response(response, filtered_space) def test_initialization(recommender): From 1d3b01e31b7b1428dbe4cea1d755a86b8460d897 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 3 Sep 2026 16:59:52 +0200 Subject: [PATCH 55/96] Remove user-configurable recovery functionality It was agreed to have an automated recovery mechanism based on the more fine-grained analysis of LLM responses --- baybe/recommenders/pure/llm/llm.py | 41 +------------------- tests/hypothesis_strategies/recommenders.py | 2 - tests/test_llm_recommender.py | 42 --------------------- 3 files changed, 2 insertions(+), 83 deletions(-) diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index 89bb90bfcf..f98587bb9a 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -51,12 +51,6 @@ class LLMRecommender(PureRecommender, SerialMixin): description of what to optimize and per-target context such as units. """ - recovery_model: str | None = field(default=None) - """Optional model to use for recovery attempts. - - If ``None``, uses the same model as the main recommendations. - """ - litellm_args: dict[str, Any] = field(factory=dict, converter=dict) """Additional arguments to pass to LiteLLM (e.g. ``temperature``, ``max_tokens``). @@ -66,13 +60,6 @@ class LLMRecommender(PureRecommender, SerialMixin): (e.g. ``OPENAI_API_KEY``, ``ANTHROPIC_API_KEY``). """ - recovery_litellm_args: dict[str, Any] | None = field(default=None) - """Optional arguments to pass to LiteLLM during recovery attempts. - - If ``None``, uses the same arguments as the main recommendations. The same - credential restriction as for :attr:`litellm_args` applies. - """ - @litellm_args.validator def _validate_litellm_args(self, attribute, value): # noqa: DOC101, DOC103 """Validate litellm_args does not contain reserved or credential keys.""" @@ -90,25 +77,6 @@ def _validate_litellm_args(self, attribute, value): # noqa: DOC101, DOC103 f"instead (e.g. OPENAI_API_KEY, ANTHROPIC_API_KEY)." ) - @recovery_litellm_args.validator - def _validate_recovery_litellm_args(self, attribute, value): # noqa: DOC101, DOC103 - """Validate recovery_litellm_args has no reserved or credential keys.""" - if value is None: - return - conflicts = _RESERVED_LITELLM_KEYS & set(value.keys()) - if conflicts: - raise ValueError( - f"'{attribute.name}' must not contain keys that are set explicitly: " - f"{conflicts}. Use the dedicated class attributes instead." - ) - cred_conflicts = _CREDENTIAL_LITELLM_KEYS & set(value.keys()) - if cred_conflicts: - raise ValueError( - f"'{attribute.name}' must not contain credential keys " - f"{cred_conflicts}. Supply credentials via environment variables " - f"instead (e.g. OPENAI_API_KEY, ANTHROPIC_API_KEY)." - ) - def _attempt_recovery( self, error: Exception, @@ -136,16 +104,11 @@ def _attempt_recovery( original_response=original_response, ) - litellm_args = ( - self.recovery_litellm_args - if self.recovery_litellm_args is not None - else self.litellm_args - ) try: response = completion( - model=self.recovery_model or self.model, + model=self.model, messages=[{"role": "user", "content": recovery_prompt}], - **litellm_args, + **self.litellm_args, ) except Exception as e: raise LLMResponseError( diff --git a/tests/hypothesis_strategies/recommenders.py b/tests/hypothesis_strategies/recommenders.py index 90da123696..fbae798b67 100644 --- a/tests/hypothesis_strategies/recommenders.py +++ b/tests/hypothesis_strategies/recommenders.py @@ -34,7 +34,5 @@ def llm_recommenders() -> st.SearchStrategy[LLMRecommender]: LLMRecommender, model=st.text(min_size=1), experiment_description=st.text(min_size=1), - recovery_model=st.one_of(st.none(), st.text(min_size=1)), litellm_args=_safe_litellm_args(), - recovery_litellm_args=st.one_of(st.none(), _safe_litellm_args()), ) diff --git a/tests/test_llm_recommender.py b/tests/test_llm_recommender.py index 5d7e0a6c6a..eb254a67ca 100644 --- a/tests/test_llm_recommender.py +++ b/tests/test_llm_recommender.py @@ -243,41 +243,6 @@ def test_recommend_with_objective( assert "yield" in prompt_content -@patch("baybe._optional.llm.completion") -def test_recovery_with_distinct_model(mock_completion, recommender, searchspace): - """Recovery uses the specified recovery_model and recovery_litellm_args.""" - from baybe.recommenders.pure.llm.llm import LLMRecommender - - recommender = LLMRecommender( - model="gpt-5.4", - experiment_description="Test", - recovery_model="gpt-4o-mini", - recovery_litellm_args={"temperature": 0.0}, - ) - - invalid = _mock_response("Invalid JSON") - valid = _mock_response( - _make_suggestions( - [ - {"temperature": 50.0, "pressure": 3.0, "n_cycles": 2, "catalyst": "C"}, - ] - ) - ) - mock_completion.side_effect = [invalid, valid] - - recommender.recommend(batch_size=1, searchspace=searchspace) - - recovery_call = mock_completion.call_args_list[1] - assert ( - recovery_call.kwargs.get("model", recovery_call[1].get("model")) - == "gpt-4o-mini" - ) - assert ( - recovery_call.kwargs.get("temperature", recovery_call[1].get("temperature")) - == 0.0 - ) - - @pytest.mark.parametrize( ("response_content", "error_type", "error_match"), [ @@ -782,13 +747,6 @@ def test_construction_rejects_protected_keys(key, phrase): litellm_args={key: "secret"}, ) - with pytest.raises(ValueError, match=f"must not contain {phrase}"): - LLMRecommender( - model="m", - experiment_description="desc", - recovery_litellm_args={key: "secret"}, - ) - def test_str_representation(recommender): """String representation includes key information.""" From 084327705df7f0f9fc6c0c58e6c73d2e686d6eeb Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 3 Sep 2026 17:05:47 +0200 Subject: [PATCH 56/96] Use recovery instructions from errors --- baybe/recommenders/pure/llm/_prompts.py | 20 ++++++++++-------- baybe/recommenders/pure/llm/llm.py | 2 +- tests/test_llm_recommender.py | 28 +++++++++++++++++++++++++ 3 files changed, 40 insertions(+), 10 deletions(-) diff --git a/baybe/recommenders/pure/llm/_prompts.py b/baybe/recommenders/pure/llm/_prompts.py index 3ced3e1b1b..5eb3bbcbe6 100644 --- a/baybe/recommenders/pure/llm/_prompts.py +++ b/baybe/recommenders/pure/llm/_prompts.py @@ -8,7 +8,7 @@ from attrs import asdict as attrs_asdict -from baybe.exceptions import IncompatibilityError +from baybe.exceptions import IncompatibilityError, LLMResponseError from baybe.parameters.base import DiscreteParameter, Parameter from baybe.parameters.numerical import NumericalContinuousParameter from baybe.searchspace import SearchSpace @@ -100,10 +100,10 @@ """ _RECOVERY_PROMPT_TEMPLATE = """\ -The previous response was malformed and could not be parsed as JSON. Please \ -correct the response to match the required format. +Your previous recommendation could not be used and needs to be corrected. -ERROR: {{ error }} +WHAT WENT WRONG: +{{ recovery_instruction }} ORIGINAL RESPONSE: {{ original_response }} @@ -216,15 +216,17 @@ def make_prompt( def make_recovery_prompt( searchspace: SearchSpace, *, - error: Exception, + error: LLMResponseError, original_response: str, ) -> str: - """Construct the recovery prompt asking the model to correct a malformed response. + """Construct the recovery prompt asking the model to correct its response. Args: searchspace: The search space to generate recommendations for. - error: The error that occurred during parsing. - original_response: The original malformed response. + error: The error that occurred while processing the previous response. Its + :attr:`~baybe.exceptions.LLMResponseError.recovery_instruction` provides the + error-specific guidance embedded in the prompt. + original_response: The original response that could not be used. Returns: The constructed recovery prompt. @@ -234,7 +236,7 @@ def make_recovery_prompt( parameters = _extract_parameter_info(searchspace.parameters) template = Template(_RECOVERY_PROMPT_TEMPLATE, trim_blocks=True, lstrip_blocks=True) return template.render( - error=str(error), + recovery_instruction=error.recovery_instruction, original_response=original_response, parameters=parameters, ) diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index f98587bb9a..eca3445e99 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -79,7 +79,7 @@ def _validate_litellm_args(self, attribute, value): # noqa: DOC101, DOC103 def _attempt_recovery( self, - error: Exception, + error: LLMResponseError, original_response: str, searchspace: SearchSpace, ) -> pd.DataFrame: diff --git a/tests/test_llm_recommender.py b/tests/test_llm_recommender.py index eb254a67ca..c20de3e613 100644 --- a/tests/test_llm_recommender.py +++ b/tests/test_llm_recommender.py @@ -39,6 +39,7 @@ NumericalDiscreteParameter, ) from baybe.recommenders.pure.llm._parsing import parse_llm_response +from baybe.recommenders.pure.llm._prompts import make_recovery_prompt from baybe.searchspace import SearchSpace from baybe.utils.basic import get_subclasses @@ -634,6 +635,33 @@ def test_recommend_invalid_response_with_failed_recovery( recommender.recommend(batch_size=3, searchspace=searchspace) +@pytest.mark.parametrize( + "error", + [ + MalformedLLMResponseError("bad"), + UnknownParameterError( + "bad", unknown_names={"foo"}, valid_names={"temperature"} + ), + MissingParameterError("bad", parameters={"pressure"}), + NonNumericParameterError("bad", detail="detail."), + InvalidParameterValueError("bad", detail="detail."), + ConstraintViolationError( + "bad", constraint_name="DiscreteSumConstraint", parameters=["a", "b"] + ), + IneligiblePointsError("bad", n_ineligible=2), + ], + ids=lambda e: type(e).__name__, +) +def test_make_recovery_prompt_embeds_error_guidance(error, searchspace): + """The recovery prompt embeds the error-specific recovery instruction.""" + prompt = make_recovery_prompt( + searchspace, error=error, original_response="the original response" + ) + assert "WHAT WENT WRONG" in prompt + assert error.recovery_instruction in prompt + assert "the original response" in prompt + + @patch("baybe._optional.llm.completion") def test_recovery_success(mock_completion, recommender, searchspace): """Successful recovery from a malformed initial response.""" From f4e39c22bc9baf4a18ebe8275140d0a5c9dd800a Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 3 Sep 2026 17:18:34 +0200 Subject: [PATCH 57/96] Rework LLM recovery into a single error-specific retry Split the response handling into `_query_model` (model call and content extraction) and `_validate_response` (parsing plus batch-size check), so the initial and recovery attempts share one path instead of duplicating it. A recoverable error now triggers exactly one recovery attempt; a second failure raises a single error reporting both the initial and remaining problem. Model call failures propagate without a retry, and batch-size shortfalls now go through recovery like other response issues. --- baybe/recommenders/pure/llm/llm.py | 137 +++++++++++++---------------- tests/test_llm_recommender.py | 84 ++++++++++++++++-- 2 files changed, 140 insertions(+), 81 deletions(-) diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index eca3445e99..310784fd4d 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -11,7 +11,7 @@ from attrs.validators import instance_of, min_len from typing_extensions import override -from baybe.exceptions import LLMResponseError +from baybe.exceptions import BatchSizeError, LLMResponseError from baybe.objectives.base import Objective from baybe.recommenders.pure.base import PureRecommender from baybe.recommenders.pure.llm._parsing import parse_llm_response @@ -77,65 +77,72 @@ def _validate_litellm_args(self, attribute, value): # noqa: DOC101, DOC103 f"instead (e.g. OPENAI_API_KEY, ANTHROPIC_API_KEY)." ) - def _attempt_recovery( - self, - error: LLMResponseError, - original_response: str, - searchspace: SearchSpace, - ) -> pd.DataFrame: - """Attempt to recover from a malformed LLM response by asking for correction. + def _query_model(self, prompt: str) -> str: + """Query the language model and return the raw response text. Args: - error: The error that occurred during parsing. - original_response: The original malformed response. - searchspace: The search space to validate recommendations against. + prompt: The prompt to send to the language model. Returns: - A DataFrame containing the corrected recommendations. + The raw text content of the model response. Raises: - LLMResponseError: If recovery fails. + LLMResponseError: If the model call fails or returns no usable content. """ from baybe._optional.llm import completion - recovery_prompt = make_recovery_prompt( - searchspace, - error=error, - original_response=original_response, - ) - try: response = completion( model=self.model, - messages=[{"role": "user", "content": recovery_prompt}], + messages=[{"role": "user", "content": prompt}], **self.litellm_args, ) except Exception as e: raise LLMResponseError( - f"Recovery LLM call failed ({type(e).__name__}): {e}. " - f"Original error: {error}" + f"The call to the language model failed ({type(e).__name__}): {e}. " + f"Check your API credentials, network connection, and the model " + f"identifier '{self.model}'." ) from e try: content = response.choices[0].message.content except (AttributeError, IndexError, TypeError) as e: raise LLMResponseError( - f"Recovery response had unexpected structure: {e}. " - f"Original error: {error}" + f"The language model returned an unexpected response structure: {e}." ) from e if content is None: - raise LLMResponseError( - f"Recovery returned empty content (None). Original error: {error}" - ) + raise LLMResponseError("The language model returned empty content (None).") - try: - return parse_llm_response(content, searchspace) - except LLMResponseError as e: - raise LLMResponseError( - f"Recovery produced another malformed response: {e}. " - f"Original error: {error}" - ) from e + return content + + def _validate_response( + self, content: str, searchspace: SearchSpace, batch_size: int + ) -> pd.DataFrame: + """Parse and validate a raw response into a recommendation batch. + + Args: + content: The raw text content of the model response. + searchspace: The search space to validate recommendations against. + batch_size: The number of recommendations to generate. + + Returns: + A DataFrame with exactly ``batch_size`` recommendations. + + Raises: + LLMResponseError: If the response cannot be parsed/validated, or if it + contains fewer than ``batch_size`` recommendations. + """ + output = parse_llm_response(content, searchspace) + if len(output) < batch_size: + raise BatchSizeError( + f"The language model returned {len(output)} valid recommendation(s) " + f"instead of the requested {batch_size}.", + requested=batch_size, + received=len(output), + ) + # NOTE: Duplicate configurations within a batch are permitted. + return output.head(batch_size) @override def recommend( @@ -159,13 +166,11 @@ def recommend( A DataFrame containing the recommendations as individual rows. Raises: - LLMResponseError: If the call to the language model fails, if its - response cannot be parsed and recovery fails, or if the number of - eligible suggestions is less than the requested batch size. + LLMResponseError: If the call to the language model fails, or if its + response cannot be turned into a valid recommendation batch even after + a recovery attempt. ValueError: If ``batch_size`` is smaller than 1. """ - from baybe._optional.llm import completion - if batch_size < 1: raise ValueError( f"You must at least request one recommendation per batch, but " @@ -207,44 +212,26 @@ def recommend( measurements=measurements, pending_experiments=pending_experiments, ) + content = self._query_model(prompt) try: - response = completion( - model=self.model, - messages=[{"role": "user", "content": prompt}], - **self.litellm_args, + return self._validate_response(content, searchspace, batch_size) + except LLMResponseError as initial_error: + # The recommendation had an issue. Make a single, error-specific recovery + # attempt, informing the model what went wrong. + recovery_prompt = make_recovery_prompt( + searchspace, error=initial_error, original_response=content ) - except Exception as e: - raise LLMResponseError( - f"The call to the language model failed ({type(e).__name__}): {e}. " - f"Check your API credentials, network connection, and the model " - f"identifier '{self.model}'." - ) from e - - try: - content = response.choices[0].message.content - except (AttributeError, IndexError, TypeError) as e: - raise LLMResponseError( - f"LLM returned an unexpected response structure: {e}" - ) from e - - if content is None: - raise LLMResponseError("LLM returned empty content (None).") - - try: - output = parse_llm_response(content, searchspace) - except LLMResponseError as e: - output = self._attempt_recovery(e, content, searchspace) - - if len(output) < batch_size: - raise LLMResponseError( - f"Only {len(output)} eligible suggestion(s) remained instead of the " - f"requested {batch_size}. The language model may have returned too " - f"few suggestions or proposed points excluded by the current " - f"candidate filters." - ) - - # NOTE: Duplicate configurations within a batch are permitted - return output.head(batch_size) + recovery_content = self._query_model(recovery_prompt) + try: + return self._validate_response( + recovery_content, searchspace, batch_size + ) + except LLMResponseError as recovery_error: + raise LLMResponseError( + f"The language model failed to produce a valid recommendation, " + f"even after a recovery attempt. Initial problem: {initial_error} " + f"Remaining problem after recovery: {recovery_error}" + ) from recovery_error @override def __str__(self) -> str: diff --git a/tests/test_llm_recommender.py b/tests/test_llm_recommender.py index c20de3e613..5da8b25f5c 100644 --- a/tests/test_llm_recommender.py +++ b/tests/test_llm_recommender.py @@ -628,11 +628,12 @@ def test_parse_llm_response_strips_wrappers(wrapper, searchspace): def test_recommend_invalid_response_with_failed_recovery( mock_completion, recommender, searchspace ): - """Invalid response that also fails recovery raises LLMResponseError.""" + """An invalid response whose recovery also fails raises an informative error.""" mock_completion.return_value = _mock_response("Invalid JSON") - with pytest.raises(LLMResponseError, match="Recovery produced another malformed"): + with pytest.raises(LLMResponseError, match="even after a recovery attempt"): recommender.recommend(batch_size=3, searchspace=searchspace) + assert mock_completion.call_count == 2 @pytest.mark.parametrize( @@ -681,21 +682,92 @@ def test_recovery_success(mock_completion, recommender, searchspace): assert mock_completion.call_count == 2 +@pytest.mark.parametrize( + "bad_parameters", + [ + pytest.param("not json at all", id="malformed"), + pytest.param( + { + "temperature": 25.0, + "pressure": 2.0, + "n_cycles": 1, + "catalyst": "A", + "unknown": 1, + }, + id="unknown_parameter", + ), + pytest.param({"temperature": 25.0, "catalyst": "A"}, id="missing_parameter"), + pytest.param( + {"temperature": "hot", "pressure": 2.0, "n_cycles": 1, "catalyst": "A"}, + id="non_numeric", + ), + pytest.param( + {"temperature": 150.0, "pressure": 2.0, "n_cycles": 1, "catalyst": "A"}, + id="invalid_value", + ), + ], +) +@patch("baybe._optional.llm.completion") +def test_recovery_per_error_category( + mock_completion, bad_parameters, recommender, searchspace +): + """Each response issue triggers exactly one recovery attempt that can succeed.""" + bad_content = ( + bad_parameters + if isinstance(bad_parameters, str) + else _make_suggestions([bad_parameters]) + ) + valid = _make_suggestions( + [{"temperature": 50.0, "pressure": 3.0, "n_cycles": 2, "catalyst": "C"}] + ) + mock_completion.side_effect = [_mock_response(bad_content), _mock_response(valid)] + + recommendations = recommender.recommend(batch_size=1, searchspace=searchspace) + + assert len(recommendations) == 1 + assert mock_completion.call_count == 2 + + @patch("baybe._optional.llm.completion") -def test_batch_size_error_when_llm_returns_fewer( +def test_batch_size_shortfall_triggers_recovery( mock_completion, recommender, searchspace ): - """An error is raised when LLM returns fewer suggestions than requested.""" - mock_completion.return_value = _mock_response( + """Too few valid suggestions trigger a recovery attempt that can succeed.""" + too_few = _mock_response( + _make_suggestions( + [{"temperature": 25.0, "pressure": 2.0, "n_cycles": 1, "catalyst": "A"}] + ) + ) + enough = _mock_response( _make_suggestions( [ {"temperature": 25.0, "pressure": 2.0, "n_cycles": 1, "catalyst": "A"}, + {"temperature": 50.0, "pressure": 3.0, "n_cycles": 2, "catalyst": "B"}, ] ) ) + mock_completion.side_effect = [too_few, enough] + + recommendations = recommender.recommend(batch_size=2, searchspace=searchspace) + + assert len(recommendations) == 2 + assert mock_completion.call_count == 2 + + +@patch("baybe._optional.llm.completion") +def test_batch_size_shortfall_failed_recovery( + mock_completion, recommender, searchspace +): + """A persistent batch-size shortfall raises after a single recovery attempt.""" + mock_completion.return_value = _mock_response( + _make_suggestions( + [{"temperature": 25.0, "pressure": 2.0, "n_cycles": 1, "catalyst": "A"}] + ) + ) - with pytest.raises(LLMResponseError, match="instead of the requested"): + with pytest.raises(LLMResponseError, match="even after a recovery attempt"): recommender.recommend(batch_size=3, searchspace=searchspace) + assert mock_completion.call_count == 2 @patch("baybe._optional.llm.completion") From a62df065ad81e69b92b1fdb362270f336a09741e Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Fri, 4 Sep 2026 11:16:01 +0200 Subject: [PATCH 58/96] Improve type safety of LLM prompt construction and parsing Replace the dynamically-shaped `SimpleNamespace` prompt context with a typed `TypedDict` view of each parameter, so the code that assembles the prompt is checked by `mypy`/`Pyrefly` instead of flowing through `dict[str, Any]`. Flatten the value domain into a single string to give the template one stable shape, and render substance parameters specially so their SMILES are shown next to the substance names. Enable Jinja's `StrictUndefined` so a missing or misspelled template variable fails immediately instead of silently rendering as empty. Define the response field names and the response-format example once in a shared module, consumed by both prompt construction and the parser, so the prompt and parser can no longer drift apart. --- baybe/_optional/llm.py | 3 +- baybe/recommenders/pure/llm/_parsing.py | 14 +- baybe/recommenders/pure/llm/_prompts.py | 227 ++++++++++++++---------- baybe/recommenders/pure/llm/_schema.py | 30 ++++ tests/test_llm_recommender.py | 105 ++++++++++- 5 files changed, 274 insertions(+), 105 deletions(-) create mode 100644 baybe/recommenders/pure/llm/_schema.py diff --git a/baybe/_optional/llm.py b/baybe/_optional/llm.py index dd15cdb0b3..49e7612307 100644 --- a/baybe/_optional/llm.py +++ b/baybe/_optional/llm.py @@ -3,7 +3,7 @@ from baybe.exceptions import OptionalImportError try: - from jinja2 import Template + from jinja2 import StrictUndefined, Template except ModuleNotFoundError as ex: raise OptionalImportError(name="jinja2", group="llm") from ex @@ -13,6 +13,7 @@ raise OptionalImportError(name="litellm", group="llm") from ex __all__ = [ + "StrictUndefined", "Template", "completion", ] diff --git a/baybe/recommenders/pure/llm/_parsing.py b/baybe/recommenders/pure/llm/_parsing.py index 123daee66a..8fcd950a2f 100644 --- a/baybe/recommenders/pure/llm/_parsing.py +++ b/baybe/recommenders/pure/llm/_parsing.py @@ -18,6 +18,10 @@ NonNumericParameterError, UnknownParameterError, ) +from baybe.recommenders.pure.llm._schema import ( + _EXPLANATION_FIELD, + _PARAMETERS_FIELD, +) from baybe.searchspace import SearchSpace from baybe.utils.dataframe import fuzzy_row_match, normalize_input_dtypes from baybe.utils.validation import validate_parameter_input @@ -94,17 +98,17 @@ def parse_llm_response(response: str, /, searchspace: SearchSpace) -> pd.DataFra if not isinstance(suggestion, dict): raise MalformedLLMResponseError("Each suggestion must be a JSON object.") - if "parameters" not in suggestion: + if _PARAMETERS_FIELD not in suggestion: raise MalformedLLMResponseError( - "Each suggestion must contain a 'parameters' field." + f"Each suggestion must contain a '{_PARAMETERS_FIELD}' field." ) - if "explanation" not in suggestion: + if _EXPLANATION_FIELD not in suggestion: raise MalformedLLMResponseError( - "Each suggestion must contain an 'explanation' field." + f"Each suggestion must contain an '{_EXPLANATION_FIELD}' field." ) - params = suggestion["parameters"] + params = suggestion[_PARAMETERS_FIELD] if not isinstance(params, dict): raise MalformedLLMResponseError("Parameters must be a JSON object.") diff --git a/baybe/recommenders/pure/llm/_prompts.py b/baybe/recommenders/pure/llm/_prompts.py index 5eb3bbcbe6..1c9bcf4a76 100644 --- a/baybe/recommenders/pure/llm/_prompts.py +++ b/baybe/recommenders/pure/llm/_prompts.py @@ -2,15 +2,13 @@ from __future__ import annotations -from collections.abc import Sequence -from types import SimpleNamespace -from typing import TYPE_CHECKING, Any - -from attrs import asdict as attrs_asdict +from typing import TYPE_CHECKING, Literal, TypedDict from baybe.exceptions import IncompatibilityError, LLMResponseError from baybe.parameters.base import DiscreteParameter, Parameter from baybe.parameters.numerical import NumericalContinuousParameter +from baybe.parameters.substance import SubstanceParameter +from baybe.recommenders.pure.llm._schema import _response_format from baybe.searchspace import SearchSpace if TYPE_CHECKING: @@ -49,33 +47,27 @@ {% if param.description is not none %} Description: {{ param.description }} {% endif %} -Type: {{ param.type }} -{% if param.type == 'continuous' %} -Bounds: [{{ param.bounds[0] }}, {{ param.bounds[1] }}] -{% else %} -Allowed values: {{ param.values }} -{% endif %} +Type: {{ param.kind }} +{{ param.domain }} {% if param.unit is not none %} Unit: {{ param.unit }} {% endif %} -{% if param.misc %} -{% for key, value in param.misc.items() %} +{% for key, value in param.misc %} {{ key }}: {{ value }} {% endfor %} -{% endif %} {% endfor %} -{% if measurements is not none and not measurements.empty %} +{% if measurements is not none %} PREVIOUS MEASUREMENTS: -{{ measurements.to_string(index=False) }} +{{ measurements }} {% endif %} -{% if pending_experiments is not none and not pending_experiments.empty %} +{% if pending_experiments is not none %} PENDING EXPERIMENTS: The following experiments have already been proposed and are awaiting results. Do not recommend these again. -{{ pending_experiments.to_string(index=False) }} +{{ pending_experiments }} {% endif %} Please suggest {{ batch_size }} new experimental conditions that are likely to \ @@ -86,17 +78,7 @@ Format your response as a JSON array of objects with the following structure \ (no backticks): -[ - { - "explanation": "Brief explanation of the suggestion", - "parameters": { - "param1": value1, - "param2": value2, - ... - } - }, - ... -] +{{ response_format }} """ _RECOVERY_PROMPT_TEMPLATE = """\ @@ -111,67 +93,98 @@ PARAMETERS: {% for param in parameters %} Parameter: {{ param.name }} -Type: {{ param.type }} -{% if param.type == 'continuous' %} -Bounds: [{{ param.bounds[0] }}, {{ param.bounds[1] }}] -{% else %} -Allowed values: {{ param.values }} -{% endif %} +Type: {{ param.kind }} +{{ param.domain }} {% endfor %} Please provide a corrected JSON response that follows the required format: -[ - { - "explanation": "Brief explanation of the suggestion", - "parameters": { - "param1": value1, - "param2": value2, - ... - } - }, - ... -]\ +{{ response_format }}\ """ -def _extract_parameter_info( - parameters: Sequence[Parameter], -) -> list[SimpleNamespace]: - """Extract parameter information for prompt construction. +class _ParameterPromptInfo(TypedDict): + """Typed, presentation-only view of a parameter for prompt rendering. + + Gives the template a single stable shape (instead of the previous + dynamically-shaped ``SimpleNamespace``); the value domain is flattened into one + ``domain`` string so no field is conditionally present. + """ + + name: str + description: str | None + kind: Literal["continuous", "discrete_numeric", "categorical", "substance"] + domain: str + unit: str | None + misc: tuple[tuple[str, str], ...] + + +class _PromptContext(TypedDict): + """Typed render context for the main prompt.""" + + experiment_description: str + objective: Objective | None + parameters: tuple[_ParameterPromptInfo, ...] + measurements: str | None + pending_experiments: str | None + batch_size: int + response_format: str + + +class _RecoveryPromptContext(TypedDict): + """Typed render context for the recovery prompt.""" + + parameters: tuple[_ParameterPromptInfo, ...] + recovery_instruction: str + original_response: str + response_format: str + + +def _parameter_prompt_info(parameter: Parameter) -> _ParameterPromptInfo: + """Build the prompt view of a parameter. Args: - parameters: The parameters from the search space. + parameter: The parameter to describe. Returns: - A list of namespace objects containing parameter information. + A typed, presentation-only view consumed by the prompt template. Raises: - IncompatibilityError: If a parameter type is not supported. + IncompatibilityError: If the parameter type is not supported. """ - infos = [] - for param in parameters: - info: dict[str, Any] = { - "name": param.name, - **attrs_asdict(param.metadata), - } - - if isinstance(param, NumericalContinuousParameter): - info["type"] = "continuous" - info["bounds"] = param.bounds.to_tuple() - elif isinstance(param, DiscreteParameter): - info["type"] = "discrete_numeric" if param.is_numerical else "categorical" - info["values"] = list(param.values) - else: - raise IncompatibilityError( - f"Parameter '{param.name}' has unsupported type " - f"'{type(param).__name__}'. Only " - f"'{NumericalContinuousParameter.__name__}' and " - f"'{DiscreteParameter.__name__}' subclasses are supported." - ) - - infos.append(SimpleNamespace(**info)) - - return infos + if isinstance(parameter, NumericalContinuousParameter): + lower, upper = parameter.bounds.to_tuple() + kind: Literal["continuous", "discrete_numeric", "categorical", "substance"] = ( + "continuous" + ) + domain = f"Bounds: [{lower}, {upper}]" + elif isinstance(parameter, SubstanceParameter): + # Substances are chemical compounds: expose their SMILES so the model can + # reason about structure, while still choosing by substance name. + kind = "substance" + substances = ", ".join( + f"{name} ({smiles})" for name, smiles in parameter.data.items() + ) + domain = f"Allowed values (choose by name; SMILES in parentheses): {substances}" + elif isinstance(parameter, DiscreteParameter): + kind = "discrete_numeric" if parameter.is_numerical else "categorical" + domain = f"Allowed values: {list(parameter.values)}" + else: + raise IncompatibilityError( + f"Parameter '{parameter.name}' has unsupported type " + f"'{type(parameter).__name__}'. Only " + f"'{NumericalContinuousParameter.__name__}' and " + f"'{DiscreteParameter.__name__}' subclasses are supported." + ) + return { + "name": parameter.name, + "description": parameter.description, + "kind": kind, + "domain": domain, + "unit": parameter.unit, + "misc": tuple( + (key, str(value)) for key, value in parameter.metadata.misc.items() + ), + } def make_prompt( @@ -199,18 +212,34 @@ def make_prompt( Returns: The constructed prompt. """ - from baybe._optional.llm import Template - - parameters = _extract_parameter_info(searchspace.parameters) - template = Template(_PROMPT_TEMPLATE, trim_blocks=True, lstrip_blocks=True) - return template.render( - experiment_description=experiment_description, - objective=objective, - parameters=parameters, - measurements=measurements, - pending_experiments=pending_experiments, - batch_size=batch_size, + from baybe._optional.llm import StrictUndefined, Template + + measurements_text = ( + measurements.to_string(index=False) + if measurements is not None and not measurements.empty + else None + ) + pending_text = ( + pending_experiments.to_string(index=False) + if pending_experiments is not None and not pending_experiments.empty + else None + ) + context: _PromptContext = { + "experiment_description": experiment_description, + "objective": objective, + "parameters": tuple(_parameter_prompt_info(p) for p in searchspace.parameters), + "measurements": measurements_text, + "pending_experiments": pending_text, + "batch_size": batch_size, + "response_format": _response_format(), + } + template = Template( + _PROMPT_TEMPLATE, + trim_blocks=True, + lstrip_blocks=True, + undefined=StrictUndefined, ) + return template.render(context) def make_recovery_prompt( @@ -231,12 +260,18 @@ def make_recovery_prompt( Returns: The constructed recovery prompt. """ - from baybe._optional.llm import Template - - parameters = _extract_parameter_info(searchspace.parameters) - template = Template(_RECOVERY_PROMPT_TEMPLATE, trim_blocks=True, lstrip_blocks=True) - return template.render( - recovery_instruction=error.recovery_instruction, - original_response=original_response, - parameters=parameters, + from baybe._optional.llm import StrictUndefined, Template + + context: _RecoveryPromptContext = { + "parameters": tuple(_parameter_prompt_info(p) for p in searchspace.parameters), + "recovery_instruction": error.recovery_instruction, + "original_response": original_response, + "response_format": _response_format(), + } + template = Template( + _RECOVERY_PROMPT_TEMPLATE, + trim_blocks=True, + lstrip_blocks=True, + undefined=StrictUndefined, ) + return template.render(context) diff --git a/baybe/recommenders/pure/llm/_schema.py b/baybe/recommenders/pure/llm/_schema.py new file mode 100644 index 0000000000..925cb6e017 --- /dev/null +++ b/baybe/recommenders/pure/llm/_schema.py @@ -0,0 +1,30 @@ +"""Shared definitions for the LLM response wire contract. + +The language model is asked to return a JSON array of suggestion objects. The field +names of those objects are defined here once, so that prompt construction and response +parsing consume the same definitions and cannot silently drift apart. +""" + +_EXPLANATION_FIELD = "explanation" +"""JSON field holding a suggestion's free-text explanation.""" + +_PARAMETERS_FIELD = "parameters" +"""JSON field holding a suggestion's parameter assignments.""" + + +def _response_format() -> str: + """Render the response-format example embedded in the prompts. + + Returns: + A JSON example illustrating the expected response structure, using the field + names defined in this module as the single source of truth. + """ + return f"""\ +[ + {{ + "{_EXPLANATION_FIELD}": "Brief explanation of the suggestion", + "{_PARAMETERS_FIELD}": {{ + "": + }} + }} +]""" diff --git a/tests/test_llm_recommender.py b/tests/test_llm_recommender.py index 5da8b25f5c..2cf4f4422f 100644 --- a/tests/test_llm_recommender.py +++ b/tests/test_llm_recommender.py @@ -7,7 +7,7 @@ import pandas as pd import pytest -from baybe._optional.info import LLM_INSTALLED +from baybe._optional.info import CHEM_INSTALLED, LLM_INSTALLED from baybe.constraints.conditions import SubSelectionCondition, ThresholdCondition from baybe.constraints.discrete import ( DiscreteBatchConstraint, @@ -39,7 +39,12 @@ NumericalDiscreteParameter, ) from baybe.recommenders.pure.llm._parsing import parse_llm_response -from baybe.recommenders.pure.llm._prompts import make_recovery_prompt +from baybe.recommenders.pure.llm._prompts import make_prompt, make_recovery_prompt +from baybe.recommenders.pure.llm._schema import ( + _EXPLANATION_FIELD, + _PARAMETERS_FIELD, + _response_format, +) from baybe.searchspace import SearchSpace from baybe.utils.basic import get_subclasses @@ -59,7 +64,7 @@ def _make_suggestions(params_list: list[dict]) -> str: """Create a JSON string of suggestions from a list of parameter dicts.""" return json.dumps( [ - {"explanation": f"Suggestion {i}", "parameters": p} + {_EXPLANATION_FIELD: f"Suggestion {i}", _PARAMETERS_FIELD: p} for i, p in enumerate(params_list) ] ) @@ -244,6 +249,100 @@ def test_recommend_with_objective( assert "yield" in prompt_content +def test_response_contract_is_single_sourced(searchspace): + """Both prompts and the parser share one definition of the response fields.""" + response_format = _response_format() + assert _EXPLANATION_FIELD in response_format + assert _PARAMETERS_FIELD in response_format + + prompt = make_prompt( + searchspace, + batch_size=1, + experiment_description="Test", + objective=None, + measurements=None, + pending_experiments=None, + ) + assert response_format in prompt + + recovery_prompt = make_recovery_prompt( + searchspace, + error=MalformedLLMResponseError("boom"), + original_response="[]", + ) + assert response_format in recovery_prompt + + # A response built from the shared field names parses successfully. + suggestions = _make_suggestions( + [{"temperature": 25.0, "pressure": 2.0, "n_cycles": 1, "catalyst": "A"}] + ) + assert len(parse_llm_response(suggestions, searchspace)) == 1 + + +def test_parameter_prompt_info(): + """The typed prompt view maps each parameter type to a stable shape.""" + from baybe.exceptions import IncompatibilityError + from baybe.recommenders.pure.llm._prompts import _parameter_prompt_info + + cont = _parameter_prompt_info( + NumericalContinuousParameter( + "x", bounds=(0.0, 1.0), metadata={"unit": "m", "description": "d"} + ) + ) + assert cont["kind"] == "continuous" + assert cont["domain"] == "Bounds: [0.0, 1.0]" + assert cont["unit"] == "m" + assert cont["description"] == "d" + assert cont["misc"] == () + + disc = _parameter_prompt_info(NumericalDiscreteParameter("n", values=[1, 2, 3])) + assert disc["kind"] == "discrete_numeric" + assert disc["domain"].startswith("Allowed values:") + assert disc["unit"] is None + + # Non-string misc values are normalized to strings at the prompt boundary. + cat = _parameter_prompt_info( + CategoricalParameter("c", values=["A", "B"], metadata={"note": 1}) + ) + assert cat["kind"] == "categorical" + assert cat["domain"] == "Allowed values: ['A', 'B']" + assert cat["misc"] == (("note", "1"),) + + with pytest.raises(IncompatibilityError, match="unsupported type"): + _parameter_prompt_info(SimpleNamespace(name="weird")) + + +@pytest.mark.skipif(not CHEM_INSTALLED, reason="Chemistry dependencies not installed") +def test_parameter_prompt_info_substance(): + """Substance parameters expose their SMILES alongside the substance names.""" + from baybe.parameters.substance import SubstanceParameter + from baybe.recommenders.pure.llm._prompts import _parameter_prompt_info + + info = _parameter_prompt_info( + SubstanceParameter("solvent", data={"water": "O", "ethanol": "CCO"}) + ) + assert info["kind"] == "substance" + assert "water (O)" in info["domain"] + assert "ethanol (CCO)" in info["domain"] + + +def test_prompt_templates_use_strict_undefined(): + """Undefined template variables fail fast instead of rendering empty.""" + from jinja2 import UndefinedError + + from baybe._optional.llm import StrictUndefined, Template + from baybe.recommenders.pure.llm._prompts import _PROMPT_TEMPLATE + + template = Template( + _PROMPT_TEMPLATE, + trim_blocks=True, + lstrip_blocks=True, + undefined=StrictUndefined, + ) + with pytest.raises(UndefinedError): + template.render() # empty context -> first undefined access raises + + @pytest.mark.parametrize( ("response_content", "error_type", "error_match"), [ From fada0fab4ba43466d209b70b989443497876793a Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Mon, 7 Sep 2026 08:29:57 +0200 Subject: [PATCH 59/96] Add convenience functions for LLM recommender --- baybe/recommenders/__init__.py | 8 ++- baybe/recommenders/pure/llm/__init__.py | 12 +++- baybe/recommenders/pure/llm/llm.py | 90 ++++++++++++++++++++++++- tests/test_llm_recommender.py | 47 +++++++++++++ 4 files changed, 153 insertions(+), 4 deletions(-) diff --git a/baybe/recommenders/__init__.py b/baybe/recommenders/__init__.py index 2edda6e0fa..c0446e2c15 100644 --- a/baybe/recommenders/__init__.py +++ b/baybe/recommenders/__init__.py @@ -7,7 +7,11 @@ ) from baybe.recommenders.naive import NaiveHybridSpaceRecommender from baybe.recommenders.pure.bayesian.botorch import BotorchRecommender -from baybe.recommenders.pure.llm import LLMRecommender +from baybe.recommenders.pure.llm import ( + LLMRecommender, + make_llm_alternating_recommender, + make_llm_two_phase_recommender, +) from baybe.recommenders.pure.nonpredictive.clustering import ( GaussianMixtureClusteringRecommender, KMeansClusteringRecommender, @@ -30,4 +34,6 @@ "TwoPhaseMetaRecommender", "SequentialMetaRecommender", "StreamingSequentialMetaRecommender", + "make_llm_alternating_recommender", + "make_llm_two_phase_recommender", ] diff --git a/baybe/recommenders/pure/llm/__init__.py b/baybe/recommenders/pure/llm/__init__.py index fb6e13b07b..1a5db0936e 100644 --- a/baybe/recommenders/pure/llm/__init__.py +++ b/baybe/recommenders/pure/llm/__init__.py @@ -1,5 +1,13 @@ """Recommender based on Large Language Models (LLMs).""" -from baybe.recommenders.pure.llm.llm import LLMRecommender +from baybe.recommenders.pure.llm.llm import ( + LLMRecommender, + make_llm_alternating_recommender, + make_llm_two_phase_recommender, +) -__all__ = ["LLMRecommender"] +__all__ = [ + "LLMRecommender", + "make_llm_alternating_recommender", + "make_llm_two_phase_recommender", +] diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index 310784fd4d..d269482539 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -4,7 +4,7 @@ import gc import warnings -from typing import Any, ClassVar +from typing import TYPE_CHECKING, Any, ClassVar import pandas as pd from attrs import define, field @@ -22,6 +22,13 @@ from baybe.utils.conversion import to_string from baybe.utils.validation import preprocess_dataframe, validate_object_names +if TYPE_CHECKING: + from baybe.recommenders.base import RecommenderProtocol + from baybe.recommenders.meta.sequential import ( + SequentialMetaRecommender, + TwoPhaseMetaRecommender, + ) + # Keys that are wired in by the recommender itself and must not be overridden. _RESERVED_LITELLM_KEYS = frozenset({"model", "messages"}) @@ -245,5 +252,86 @@ def __str__(self) -> str: return to_string(self.__class__.__name__, *fields) +def make_llm_two_phase_recommender( + model: str, + experiment_description: str, + *, + switch_after: int = 1, + litellm_args: dict[str, Any] | None = None, + recommender: RecommenderProtocol | None = None, +) -> TwoPhaseMetaRecommender: + """Create a recommender that warm-starts with an LLM, then switches to Bayesian. + + The returned meta recommender uses an :class:`LLMRecommender` for the initial + experiments and switches to ``recommender`` once ``switch_after`` measurements have + been collected. + + Args: + model: The LiteLLM model identifier for the initial LLM recommender. + experiment_description: Textual description of the experiment for the LLM. + switch_after: The number of collected experiments after which the recommender + switches from the LLM to ``recommender``. + litellm_args: Optional additional arguments passed to LiteLLM. + recommender: The recommender used after the switch. Defaults to a + :class:`~baybe.recommenders.pure.bayesian.botorch.core.BotorchRecommender`. + + Returns: + A :class:`~baybe.recommenders.meta.sequential.TwoPhaseMetaRecommender` using the + LLM as its initial recommender. + """ + from baybe.recommenders.meta.sequential import TwoPhaseMetaRecommender + from baybe.recommenders.pure.bayesian.botorch import BotorchRecommender + + return TwoPhaseMetaRecommender( + initial_recommender=LLMRecommender( + model=model, + experiment_description=experiment_description, + litellm_args=litellm_args or {}, + ), + recommender=BotorchRecommender() if recommender is None else recommender, + switch_after=switch_after, + ) + + +def make_llm_alternating_recommender( + model: str, + experiment_description: str, + *, + litellm_args: dict[str, Any] | None = None, + recommender: RecommenderProtocol | None = None, +) -> SequentialMetaRecommender: + """Create a recommender that alternates each round between an LLM and Bayesian. + + The returned meta recommender cycles indefinitely between an + :class:`LLMRecommender` and ``recommender``, advancing to the next one whenever new + measurements become available. + + Args: + model: The LiteLLM model identifier for the LLM recommender. + experiment_description: Textual description of the experiment for the LLM. + litellm_args: Optional additional arguments passed to LiteLLM. + recommender: The recommender alternated with the LLM. Defaults to a + :class:`~baybe.recommenders.pure.bayesian.botorch.core.BotorchRecommender`. + + Returns: + A :class:`~baybe.recommenders.meta.sequential.SequentialMetaRecommender` in + cyclic mode, alternating between the LLM and ``recommender``. + """ + from baybe.recommenders.meta.sequential import SequentialMetaRecommender + from baybe.recommenders.pure.bayesian.botorch import BotorchRecommender + + return SequentialMetaRecommender( + recommenders=[ + LLMRecommender( + model=model, + experiment_description=experiment_description, + litellm_args=litellm_args or {}, + ), + BotorchRecommender() if recommender is None else recommender, + ], + mode="cyclic", + ) + + # Collect leftover original slotted classes processed by `attrs.define` gc.collect() diff --git a/tests/test_llm_recommender.py b/tests/test_llm_recommender.py index 2cf4f4422f..edc59d4a99 100644 --- a/tests/test_llm_recommender.py +++ b/tests/test_llm_recommender.py @@ -326,6 +326,53 @@ def test_parameter_prompt_info_substance(): assert "ethanol (CCO)" in info["domain"] +def test_make_llm_two_phase_recommender(): + """The two-phase factory wires an LLM warm-start into a TwoPhaseMetaRecommender.""" + from baybe.recommenders import ( + BotorchRecommender, + TwoPhaseMetaRecommender, + make_llm_two_phase_recommender, + ) + from baybe.recommenders.pure.llm.llm import LLMRecommender + + rec = make_llm_two_phase_recommender( + model="gpt-5.4", + experiment_description="Test", + switch_after=3, + litellm_args={"temperature": 0.1}, + ) + assert isinstance(rec, TwoPhaseMetaRecommender) + assert isinstance(rec.initial_recommender, LLMRecommender) + assert rec.initial_recommender.model == "gpt-5.4" + assert rec.initial_recommender.litellm_args == {"temperature": 0.1} + assert isinstance(rec.recommender, BotorchRecommender) + assert rec.switch_after == 3 + + # The returned recommender is a standard serializable type. + assert type(rec).from_json(rec.to_json()) == rec + + +def test_make_llm_alternating_recommender(): + """The alternating factory yields a cyclic LLM/Bayesian sequential recommender.""" + from baybe.recommenders import ( + RandomRecommender, + SequentialMetaRecommender, + make_llm_alternating_recommender, + ) + from baybe.recommenders.pure.llm.llm import LLMRecommender + + rec = make_llm_alternating_recommender( + model="gpt-5.4", + experiment_description="Test", + recommender=RandomRecommender(), + ) + assert isinstance(rec, SequentialMetaRecommender) + assert rec.mode == "cyclic" + assert len(rec.recommenders) == 2 + assert isinstance(rec.recommenders[0], LLMRecommender) + assert isinstance(rec.recommenders[1], RandomRecommender) + + def test_prompt_templates_use_strict_undefined(): """Undefined template variables fail fast instead of rendering empty.""" from jinja2 import UndefinedError From 59f2f2578227daea4a384cf857058b1fb6d9d141 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Mon, 7 Sep 2026 08:33:33 +0200 Subject: [PATCH 60/96] Fix JSON extraction for multi-block LLM responses Extract the last complete suggestion array so responses containing prose or a reconsidered second block parse correctly instead of raising a malformed-response error. --- baybe/recommenders/pure/llm/_parsing.py | 35 ++++++++++++++++++------- tests/test_llm_recommender.py | 25 ++++++++++++++++++ 2 files changed, 50 insertions(+), 10 deletions(-) diff --git a/baybe/recommenders/pure/llm/_parsing.py b/baybe/recommenders/pure/llm/_parsing.py index 8fcd950a2f..ce850ac2d5 100644 --- a/baybe/recommenders/pure/llm/_parsing.py +++ b/baybe/recommenders/pure/llm/_parsing.py @@ -30,22 +30,37 @@ def extract_json_array(response: str, /) -> str: """Extract the JSON array payload from a raw language model response. - Language models frequently wrap their output in Markdown code fences or add - surrounding prose despite instructions to the contrary. This helper isolates the - outermost ``[...]`` block so that such responses can still be parsed. + Models often wrap the array in Markdown fences or prose, or emit several blocks + (e.g. after reconsidering). Return the *last* array that is a list of objects (the + intended answer), ignoring prose and stray brackets like ``x[0]``. If none is found, + fall back to the last complete array, else the original text. Args: response: The raw response text. Returns: - The substring spanning the outermost JSON array, or the original text if no - array delimiters are found. + The substring spanning the extracted JSON array, or the original text if none. """ - start = response.find("[") - end = response.rfind("]") - if start != -1 and end != -1 and start < end: - return response[start : end + 1] - return response + decoder = json.JSONDecoder() + last_array: str | None = None + last_object_array: str | None = None + search_start = 0 + while (start := response.find("[", search_start)) != -1: + try: + value, end = decoder.raw_decode(response, start) + except JSONDecodeError: + search_start = start + 1 # not a valid array here; try the next "[" + continue + last_array = response[start:end] + is_object_list = ( + isinstance(value, list) + and bool(value) + and all(isinstance(x, dict) for x in value) + ) + if is_object_list: + last_object_array = last_array + search_start = end # keep scanning; a later block wins + return last_object_array or last_array or response def parse_llm_response(response: str, /, searchspace: SearchSpace) -> pd.DataFrame: diff --git a/tests/test_llm_recommender.py b/tests/test_llm_recommender.py index edc59d4a99..c98ab1ebf8 100644 --- a/tests/test_llm_recommender.py +++ b/tests/test_llm_recommender.py @@ -770,6 +770,31 @@ def test_parse_llm_response_strips_wrappers(wrapper, searchspace): assert df["catalyst"].iloc[0] == "A" +def test_extract_json_array_handles_multiple_blocks_and_prose(): + """The last suggestion-like array wins; stray brackets in prose are ignored.""" + from baybe.recommenders.pure.llm._parsing import extract_json_array + + # A reconsidered second block supersedes the first. + two_blocks = '```json\n[{"a": 1}]\n```\nRevise:\n```json\n[{"a": 2}]\n```' + assert extract_json_array(two_blocks) == '[{"a": 2}]' + # A stray bracket in trailing prose is not mistaken for the array. + assert extract_json_array('[{"a": 1}] note x[0]') == '[{"a": 1}]' + + +def test_parse_llm_response_uses_last_of_multiple_blocks(searchspace): + """When the model emits several JSON blocks, the last (revised) one is parsed.""" + first = _make_suggestions( + [{"temperature": 25.0, "pressure": 2.0, "n_cycles": 1, "catalyst": "A"}] + ) + second = _make_suggestions( + [{"temperature": 30.0, "pressure": 3.0, "n_cycles": 5, "catalyst": "C"}] + ) + response = f"```json\n{first}\n```\nWait, let me revise:\n```json\n{second}\n```" + df = parse_llm_response(response, searchspace) + assert len(df) == 1 + assert df["catalyst"].iloc[0] == "C" + + @patch("baybe._optional.llm.completion") def test_recommend_invalid_response_with_failed_recovery( mock_completion, recommender, searchspace From 7b725ab726f466621a323958f5643b126bcfa875 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Mon, 7 Sep 2026 08:52:19 +0200 Subject: [PATCH 61/96] Add type guard --- baybe/recommenders/pure/llm/_parsing.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/baybe/recommenders/pure/llm/_parsing.py b/baybe/recommenders/pure/llm/_parsing.py index ce850ac2d5..27f1990e8b 100644 --- a/baybe/recommenders/pure/llm/_parsing.py +++ b/baybe/recommenders/pure/llm/_parsing.py @@ -8,6 +8,7 @@ import pandas as pd +from baybe.constraints.base import DiscreteFilteringConstraint from baybe.exceptions import ( ConstraintViolationError, IneligiblePointsError, @@ -178,6 +179,10 @@ def parse_llm_response(response: str, /, searchspace: SearchSpace) -> pd.DataFra ) for constraint in searchspace.discrete.constraints: + # Only filtering constraints expose row-level validity; batch constraints are + # handled separately below. + if not isinstance(constraint, DiscreteFilteringConstraint): + continue invalid_idx = constraint.get_invalid(df) if not invalid_idx.empty: raise ConstraintViolationError( From e91dfad85e7416743d275debfa14d0b0226b52cc Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Mon, 7 Sep 2026 09:08:09 +0200 Subject: [PATCH 62/96] Add typed converter Without this, `mypyc` would not correctly recognize the type of the recommenders list. Instead of an `ignore` comment, I added this typed converter instead. --- baybe/recommenders/meta/sequential.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/baybe/recommenders/meta/sequential.py b/baybe/recommenders/meta/sequential.py index 8f581c74e0..365cdba57c 100644 --- a/baybe/recommenders/meta/sequential.py +++ b/baybe/recommenders/meta/sequential.py @@ -174,6 +174,13 @@ def recommend( return recommendation +def _to_recommender_list( + recommenders: Iterable[RecommenderProtocol], / +) -> list[RecommenderProtocol]: + """Convert an iterable of recommenders into a list.""" + return list(recommenders) + + @define class SequentialMetaRecommender(BaseSequentialMetaRecommender): """A meta recommender that uses a pre-defined sequence of recommenders. @@ -197,7 +204,8 @@ class SequentialMetaRecommender(BaseSequentialMetaRecommender): """ recommenders: list[RecommenderProtocol] = field( - converter=list, validator=deep_iterable(instance_of(RecommenderProtocol)) + converter=_to_recommender_list, + validator=deep_iterable(instance_of(RecommenderProtocol)), ) """A finite-length sequence of recommenders to be used. For infinite-length iterables, see From bfa4320c2030ec5fa3b35729ebc44936a55dc3fa Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Mon, 7 Sep 2026 09:09:01 +0200 Subject: [PATCH 63/96] Fix typing --- baybe/recommenders/pure/llm/llm.py | 20 +++++++++----------- 1 file changed, 9 insertions(+), 11 deletions(-) diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index d269482539..135d4ea9cd 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -320,17 +320,15 @@ def make_llm_alternating_recommender( from baybe.recommenders.meta.sequential import SequentialMetaRecommender from baybe.recommenders.pure.bayesian.botorch import BotorchRecommender - return SequentialMetaRecommender( - recommenders=[ - LLMRecommender( - model=model, - experiment_description=experiment_description, - litellm_args=litellm_args or {}, - ), - BotorchRecommender() if recommender is None else recommender, - ], - mode="cyclic", - ) + recommenders: list[RecommenderProtocol] = [ + LLMRecommender( + model=model, + experiment_description=experiment_description, + litellm_args=litellm_args or {}, + ), + BotorchRecommender() if recommender is None else recommender, + ] + return SequentialMetaRecommender(recommenders=recommenders, mode="cyclic") # Collect leftover original slotted classes processed by `attrs.define` From 2a18a4ebe56b282ff64b91d786e1b08e9da23f7c Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Mon, 7 Sep 2026 09:59:38 +0200 Subject: [PATCH 64/96] Use DiscreteSelectionConstraint instead of deprecated DiscreteExcludeConstraint in tests --- tests/test_llm_recommender.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/tests/test_llm_recommender.py b/tests/test_llm_recommender.py index c98ab1ebf8..5685ef1160 100644 --- a/tests/test_llm_recommender.py +++ b/tests/test_llm_recommender.py @@ -15,11 +15,11 @@ DiscreteConstraint, DiscreteCustomConstraint, DiscreteDependenciesConstraint, - DiscreteExcludeConstraint, DiscreteLinkedParametersConstraint, DiscreteNoLabelDuplicatesConstraint, DiscretePermutationInvarianceConstraint, DiscreteProductConstraint, + DiscreteSelectionConstraint, DiscreteSumConstraint, ) from baybe.exceptions import ( @@ -570,19 +570,20 @@ def test_parse_llm_response_numerical_out_of_tolerance_rejected(): # parameter bounds/values but violate the constraint as a combination or batch. # --------------------------------------------------------------------------- _ROW_CONSTRAINT_VIOLATION_CASES = { - DiscreteExcludeConstraint: ( + DiscreteSelectionConstraint: ( [ NumericalDiscreteParameter("x", values=[1, 2, 3]), CategoricalParameter("y", values=["a", "b", "c"]), ], [ - DiscreteExcludeConstraint( + DiscreteSelectionConstraint( parameters=["x", "y"], conditions=[ SubSelectionCondition(selection=[2]), SubSelectionCondition(selection=["b"]), ], combiner="AND", + exclude=True, ) ], [{"x": 2, "y": "b"}], From 2cbb669605bbdadd431fe43263c30fb6ed417e3e Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Mon, 7 Sep 2026 10:30:20 +0200 Subject: [PATCH 65/96] Add forbidden configurations into prompt Testing revealed that models often repeat recommendations, even if they should not do so. Since we cannot directly hand the corresponding flags easily down to the recommender, we attempt to include forbidden configurations directly in the prompt. --- baybe/recommenders/pure/llm/_prompts.py | 46 +++++++++++++++++++++++++ 1 file changed, 46 insertions(+) diff --git a/baybe/recommenders/pure/llm/_prompts.py b/baybe/recommenders/pure/llm/_prompts.py index 1c9bcf4a76..2a0937df0a 100644 --- a/baybe/recommenders/pure/llm/_prompts.py +++ b/baybe/recommenders/pure/llm/_prompts.py @@ -70,6 +70,16 @@ {{ pending_experiments }} {% endif %} +{% if forbidden_configurations is not none %} +FORBIDDEN CONFIGURATIONS: +The following configurations have ALREADY been tested and are NO LONGER selectable. You +MUST NOT recommend any of them again. Treat this list as the single source of truth +about what has been tested -- do not rely on your own memory of novelty. Before you +output each suggestion, compare it row-by-row against this list; if it matches a row, +discard it and choose a genuinely new, untested configuration instead: +{{ forbidden_configurations }} +{% endif %} + Please suggest {{ batch_size }} new experimental conditions that are likely to \ improve the optimization objective. For each suggestion, provide: @@ -96,6 +106,13 @@ Type: {{ param.kind }} {{ param.domain }} {% endfor %} +{% if forbidden_configurations is not none %} + +FORBIDDEN CONFIGURATIONS: these have already been tested and are NO LONGER selectable. +Do NOT recommend any of them. Check each corrected suggestion row-by-row against this +list and pick a genuinely new, untested configuration instead: +{{ forbidden_configurations }} +{% endif %} Please provide a corrected JSON response that follows the required format: {{ response_format }}\ @@ -126,6 +143,7 @@ class _PromptContext(TypedDict): parameters: tuple[_ParameterPromptInfo, ...] measurements: str | None pending_experiments: str | None + forbidden_configurations: str | None batch_size: int response_format: str @@ -134,6 +152,7 @@ class _RecoveryPromptContext(TypedDict): """Typed render context for the recovery prompt.""" parameters: tuple[_ParameterPromptInfo, ...] + forbidden_configurations: str | None recovery_instruction: str original_response: str response_format: str @@ -187,6 +206,31 @@ def _parameter_prompt_info(parameter: Parameter) -> _ParameterPromptInfo: } +def _forbidden_configurations(searchspace: SearchSpace) -> str | None: + """Render discrete configurations that are no longer eligible candidates. + + When the campaign forbids re-recommending already-used points (e.g. via + ``allow_recommending_already_recommended``), those points are dropped from the + eligible candidate set the recommender receives. Surfacing them lets the model avoid + proposing configurations that would be rejected as ineligible. + + Args: + searchspace: The (possibly filtered) search space to recommend for. + + Returns: + A rendered table of the forbidden discrete configurations, or ``None`` if the + search space has no discrete part or nothing has been filtered out. + """ + discrete = searchspace.discrete + if not discrete.parameters: + return None + eligible, _ = discrete.get_candidates() + forbidden = discrete.exp_rep[~discrete.exp_rep.index.isin(eligible.index)] + if forbidden.empty: + return None + return forbidden.to_string(index=False) + + def make_prompt( searchspace: SearchSpace, *, @@ -230,6 +274,7 @@ def make_prompt( "parameters": tuple(_parameter_prompt_info(p) for p in searchspace.parameters), "measurements": measurements_text, "pending_experiments": pending_text, + "forbidden_configurations": _forbidden_configurations(searchspace), "batch_size": batch_size, "response_format": _response_format(), } @@ -264,6 +309,7 @@ def make_recovery_prompt( context: _RecoveryPromptContext = { "parameters": tuple(_parameter_prompt_info(p) for p in searchspace.parameters), + "forbidden_configurations": _forbidden_configurations(searchspace), "recovery_instruction": error.recovery_instruction, "original_response": original_response, "response_format": _response_format(), From e22c929f721d3d7f4363dae52a1f9d72833aa807 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Mon, 7 Sep 2026 10:43:08 +0200 Subject: [PATCH 66/96] Align `make_prompt` signature with the canonical recommender argument order Reorder `make_prompt` to (batch_size, searchspace, objective, measurements, pending_experiments), matching `RecommenderProtocol.recommend`, and make the LLM-specific experiment_description keyword-only. --- baybe/recommenders/pure/llm/_prompts.py | 16 ++++++++++------ baybe/recommenders/pure/llm/llm.py | 8 ++++---- tests/test_llm_recommender.py | 5 +---- 3 files changed, 15 insertions(+), 14 deletions(-) diff --git a/baybe/recommenders/pure/llm/_prompts.py b/baybe/recommenders/pure/llm/_prompts.py index 2a0937df0a..cc7d0d20ac 100644 --- a/baybe/recommenders/pure/llm/_prompts.py +++ b/baybe/recommenders/pure/llm/_prompts.py @@ -232,26 +232,30 @@ def _forbidden_configurations(searchspace: SearchSpace) -> str | None: def make_prompt( + batch_size: int, searchspace: SearchSpace, + objective: Objective | None = None, + measurements: pd.DataFrame | None = None, + pending_experiments: pd.DataFrame | None = None, *, - batch_size: int, experiment_description: str, - objective: Objective | None, - measurements: pd.DataFrame | None, - pending_experiments: pd.DataFrame | None, ) -> str: """Construct the main prompt for the language model. + The recommendation-context arguments follow the canonical order used by + :meth:`baybe.recommenders.base.RecommenderProtocol.recommend`; the LLM-specific + ``experiment_description`` is keyword-only. + Args: - searchspace: The search space to generate recommendations for. batch_size: The number of recommendations to generate. - experiment_description: Textual description of the experiment. + searchspace: The search space to generate recommendations for. objective: Optional objective to include in the prompt. Set :attr:`baybe.objectives.base.Objective.metadata` to provide the language model with a description of what to optimize and per-target context such as units and descriptions. measurements: Optional measurements to include in the prompt. pending_experiments: Optional pending experiments to include in the prompt. + experiment_description: Textual description of the experiment. Returns: The constructed prompt. diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index 135d4ea9cd..422eebae72 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -212,12 +212,12 @@ def recommend( ) prompt = make_prompt( + batch_size, searchspace, - batch_size=batch_size, + objective, + measurements, + pending_experiments, experiment_description=self.experiment_description, - objective=objective, - measurements=measurements, - pending_experiments=pending_experiments, ) content = self._query_model(prompt) try: diff --git a/tests/test_llm_recommender.py b/tests/test_llm_recommender.py index 5685ef1160..631b0d492b 100644 --- a/tests/test_llm_recommender.py +++ b/tests/test_llm_recommender.py @@ -256,12 +256,9 @@ def test_response_contract_is_single_sourced(searchspace): assert _PARAMETERS_FIELD in response_format prompt = make_prompt( - searchspace, batch_size=1, + searchspace=searchspace, experiment_description="Test", - objective=None, - measurements=None, - pending_experiments=None, ) assert response_format in prompt From 29c2775027a31ee23834ee8ca30dd7800880127d Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Mon, 7 Sep 2026 11:03:29 +0200 Subject: [PATCH 67/96] Validate type of `litellm_args` keys --- baybe/recommenders/pure/llm/llm.py | 14 ++++++++++++-- tests/test_llm_recommender.py | 12 ++++++++++++ 2 files changed, 24 insertions(+), 2 deletions(-) diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index 422eebae72..1cf0057fa6 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -8,7 +8,7 @@ import pandas as pd from attrs import define, field -from attrs.validators import instance_of, min_len +from attrs.validators import deep_mapping, instance_of, min_len from typing_extensions import override from baybe.exceptions import BatchSizeError, LLMResponseError @@ -58,7 +58,17 @@ class LLMRecommender(PureRecommender, SerialMixin): description of what to optimize and per-target context such as units. """ - litellm_args: dict[str, Any] = field(factory=dict, converter=dict) + litellm_args: dict[str, Any] = field( + factory=dict, + converter=dict, + validator=deep_mapping( + key_validator=instance_of(str), + # Values are intentionally unconstrained: LiteLLM accepts heterogeneous + # argument types (str, int, float, bool, nested dicts, ...). + value_validator=lambda *_: None, + mapping_validator=instance_of(dict), + ), + ) """Additional arguments to pass to LiteLLM (e.g. ``temperature``, ``max_tokens``). API credentials must **not** be passed here — they would be stored in plain text diff --git a/tests/test_llm_recommender.py b/tests/test_llm_recommender.py index 631b0d492b..cdac0ba0b8 100644 --- a/tests/test_llm_recommender.py +++ b/tests/test_llm_recommender.py @@ -1017,6 +1017,18 @@ def test_construction_rejects_protected_keys(key, phrase): ) +def test_construction_rejects_non_string_keys(): + """Non-string keys in litellm_args raise during construction.""" + from baybe.recommenders.pure.llm.llm import LLMRecommender + + with pytest.raises(TypeError): + LLMRecommender( + model="m", + experiment_description="desc", + litellm_args={1: "value"}, + ) + + def test_str_representation(recommender): """String representation includes key information.""" s = str(recommender) From 37e98188302701b68e2ad289f6f1ef38ea0df569 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 10 Sep 2026 09:09:01 +0200 Subject: [PATCH 68/96] Enable handling of batch sizes >1 --- baybe/recommenders/pure/llm/_prompts.py | 8 +++++-- baybe/recommenders/pure/llm/_schema.py | 26 ++++++++++++-------- baybe/recommenders/pure/llm/llm.py | 5 +++- tests/test_llm_recommender.py | 32 +++++++++++++++++++++++-- 4 files changed, 56 insertions(+), 15 deletions(-) diff --git a/baybe/recommenders/pure/llm/_prompts.py b/baybe/recommenders/pure/llm/_prompts.py index cc7d0d20ac..cee9b83127 100644 --- a/baybe/recommenders/pure/llm/_prompts.py +++ b/baybe/recommenders/pure/llm/_prompts.py @@ -280,7 +280,7 @@ def make_prompt( "pending_experiments": pending_text, "forbidden_configurations": _forbidden_configurations(searchspace), "batch_size": batch_size, - "response_format": _response_format(), + "response_format": _response_format(batch_size), } template = Template( _PROMPT_TEMPLATE, @@ -294,6 +294,7 @@ def make_prompt( def make_recovery_prompt( searchspace: SearchSpace, *, + batch_size: int, error: LLMResponseError, original_response: str, ) -> str: @@ -301,6 +302,9 @@ def make_recovery_prompt( Args: searchspace: The search space to generate recommendations for. + batch_size: The number of recommendations required. Passed to + :func:`~baybe.recommenders.pure.llm._schema._response_format` so the + embedded format example matches the expected array length. error: The error that occurred while processing the previous response. Its :attr:`~baybe.exceptions.LLMResponseError.recovery_instruction` provides the error-specific guidance embedded in the prompt. @@ -316,7 +320,7 @@ def make_recovery_prompt( "forbidden_configurations": _forbidden_configurations(searchspace), "recovery_instruction": error.recovery_instruction, "original_response": original_response, - "response_format": _response_format(), + "response_format": _response_format(batch_size), } template = Template( _RECOVERY_PROMPT_TEMPLATE, diff --git a/baybe/recommenders/pure/llm/_schema.py b/baybe/recommenders/pure/llm/_schema.py index 925cb6e017..1a4fec2b41 100644 --- a/baybe/recommenders/pure/llm/_schema.py +++ b/baybe/recommenders/pure/llm/_schema.py @@ -12,19 +12,25 @@ """JSON field holding a suggestion's parameter assignments.""" -def _response_format() -> str: +def _response_format(batch_size: int = 1) -> str: """Render the response-format example embedded in the prompts. + Args: + batch_size: The number of suggestions to illustrate in the example. The + returned example contains exactly ``batch_size`` entries so the model + can see the expected array length at a glance. + Returns: A JSON example illustrating the expected response structure, using the field names defined in this module as the single source of truth. """ - return f"""\ -[ - {{ - "{_EXPLANATION_FIELD}": "Brief explanation of the suggestion", - "{_PARAMETERS_FIELD}": {{ - "": - }} - }} -]""" + entry = ( + f" {{\n" + f' "{_EXPLANATION_FIELD}": "Brief explanation of the suggestion",\n' + f' "{_PARAMETERS_FIELD}": {{\n' + f' "": \n' + f" }}\n" + f" }}" + ) + entries = ",\n".join(entry for _ in range(max(batch_size, 1))) + return f"[\n{entries}\n]" diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index 1cf0057fa6..9d387c90c1 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -236,7 +236,10 @@ def recommend( # The recommendation had an issue. Make a single, error-specific recovery # attempt, informing the model what went wrong. recovery_prompt = make_recovery_prompt( - searchspace, error=initial_error, original_response=content + searchspace, + batch_size=batch_size, + error=initial_error, + original_response=content, ) recovery_content = self._query_model(recovery_prompt) try: diff --git a/tests/test_llm_recommender.py b/tests/test_llm_recommender.py index cdac0ba0b8..1f8bbbc497 100644 --- a/tests/test_llm_recommender.py +++ b/tests/test_llm_recommender.py @@ -251,7 +251,7 @@ def test_recommend_with_objective( def test_response_contract_is_single_sourced(searchspace): """Both prompts and the parser share one definition of the response fields.""" - response_format = _response_format() + response_format = _response_format(batch_size=1) assert _EXPLANATION_FIELD in response_format assert _PARAMETERS_FIELD in response_format @@ -264,6 +264,7 @@ def test_response_contract_is_single_sourced(searchspace): recovery_prompt = make_recovery_prompt( searchspace, + batch_size=1, error=MalformedLLMResponseError("boom"), original_response="[]", ) @@ -276,6 +277,30 @@ def test_response_contract_is_single_sourced(searchspace): assert len(parse_llm_response(suggestions, searchspace)) == 1 +def test_response_format_batch_size(searchspace): + """Both prompts embed an example array with exactly batch_size entries.""" + for batch_size in (1, 2, 5): + fmt = _response_format(batch_size=batch_size) + # The format string must contain exactly batch_size copies of the field keys. + assert fmt.count(f'"{_EXPLANATION_FIELD}"') == batch_size + assert fmt.count(f'"{_PARAMETERS_FIELD}"') == batch_size + + prompt = make_prompt( + batch_size=batch_size, + searchspace=searchspace, + experiment_description="Test", + ) + assert fmt in prompt + + recovery_prompt = make_recovery_prompt( + searchspace, + batch_size=batch_size, + error=MalformedLLMResponseError("boom"), + original_response="[]", + ) + assert fmt in recovery_prompt + + def test_parameter_prompt_info(): """The typed prompt view maps each parameter type to a stable shape.""" from baybe.exceptions import IncompatibilityError @@ -825,7 +850,10 @@ def test_recommend_invalid_response_with_failed_recovery( def test_make_recovery_prompt_embeds_error_guidance(error, searchspace): """The recovery prompt embeds the error-specific recovery instruction.""" prompt = make_recovery_prompt( - searchspace, error=error, original_response="the original response" + searchspace, + batch_size=1, + error=error, + original_response="the original response", ) assert "WHAT WENT WRONG" in prompt assert error.recovery_instruction in prompt From c15c2ba50ebbeb1b7bf4961af29815df9b7305d3 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Tue, 6 Oct 2026 12:24:14 +0200 Subject: [PATCH 69/96] Move CHANGELOG entry to Unreleased section --- CHANGELOG.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 93e2c66ebb..ce969bfeb4 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -48,6 +48,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - `DiscreteLinearConstraint` for (optionally weighted) sum constraints on discrete parameters, supporting `coefficients` and mirroring `ContinuousLinearConstraint`'s `operator`/`rhs`/`coefficients` interface (replaces `DiscreteSumConstraint`) +- `LLMRecommender` for LLM-based experimental design suggestions via LiteLLM, + available through the optional `llm` dependency group ### Changed - `BOTORCH` GP preset now includes `BetaPrior(2.5, 1.5)` for the task covariance @@ -97,8 +99,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - `KernelFactory` now obeys the more general `GPComponentFactoryProtocol` ### Added -- `LLMRecommender` for LLM-based experimental design suggestions via LiteLLM, - available through the optional `llm` dependency group - Support for Python 3.14 - Support for pandas 3 - `Settings` class for unified and streamlined settings management From 824dec2bad381abbef75132594e228edc7ce1951 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Tue, 6 Oct 2026 12:49:43 +0200 Subject: [PATCH 70/96] Improve wording and handling of forbidden configurations --- baybe/recommenders/pure/llm/_prompts.py | 23 +++++++++++++++-------- 1 file changed, 15 insertions(+), 8 deletions(-) diff --git a/baybe/recommenders/pure/llm/_prompts.py b/baybe/recommenders/pure/llm/_prompts.py index cee9b83127..6bb9869994 100644 --- a/baybe/recommenders/pure/llm/_prompts.py +++ b/baybe/recommenders/pure/llm/_prompts.py @@ -16,6 +16,14 @@ from baybe.objectives.base import Objective +_FORBIDDEN_INSTRUCTIONS = """\ +The following configurations are currently NOT selectable. You MUST NOT recommend any +of them. Treat this list as the single source of truth about what not to recommend. +Do not rely on your own memory of novelty. Before you output each suggestion, compare it +row-by-row against this list; if it matches a row, discard it and choose a genuinely +new configuration instead:\ +""" + _PROMPT_TEMPLATE = """\ You are an expert experimental design assistant. Your task is to suggest new \ experimental conditions based on the following information: @@ -72,11 +80,7 @@ {% if forbidden_configurations is not none %} FORBIDDEN CONFIGURATIONS: -The following configurations have ALREADY been tested and are NO LONGER selectable. You -MUST NOT recommend any of them again. Treat this list as the single source of truth -about what has been tested -- do not rely on your own memory of novelty. Before you -output each suggestion, compare it row-by-row against this list; if it matches a row, -discard it and choose a genuinely new, untested configuration instead: +{{ forbidden_instructions }} {{ forbidden_configurations }} {% endif %} @@ -108,9 +112,8 @@ {% endfor %} {% if forbidden_configurations is not none %} -FORBIDDEN CONFIGURATIONS: these have already been tested and are NO LONGER selectable. -Do NOT recommend any of them. Check each corrected suggestion row-by-row against this -list and pick a genuinely new, untested configuration instead: +FORBIDDEN CONFIGURATIONS: +{{ forbidden_instructions }} {{ forbidden_configurations }} {% endif %} @@ -144,6 +147,7 @@ class _PromptContext(TypedDict): measurements: str | None pending_experiments: str | None forbidden_configurations: str | None + forbidden_instructions: str batch_size: int response_format: str @@ -153,6 +157,7 @@ class _RecoveryPromptContext(TypedDict): parameters: tuple[_ParameterPromptInfo, ...] forbidden_configurations: str | None + forbidden_instructions: str recovery_instruction: str original_response: str response_format: str @@ -279,6 +284,7 @@ def make_prompt( "measurements": measurements_text, "pending_experiments": pending_text, "forbidden_configurations": _forbidden_configurations(searchspace), + "forbidden_instructions": _FORBIDDEN_INSTRUCTIONS, "batch_size": batch_size, "response_format": _response_format(batch_size), } @@ -318,6 +324,7 @@ def make_recovery_prompt( context: _RecoveryPromptContext = { "parameters": tuple(_parameter_prompt_info(p) for p in searchspace.parameters), "forbidden_configurations": _forbidden_configurations(searchspace), + "forbidden_instructions": _FORBIDDEN_INSTRUCTIONS, "recovery_instruction": error.recovery_instruction, "original_response": original_response, "response_format": _response_format(batch_size), From 9814edf6c06c293f73ef9c8483cf8d790adfd382 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Tue, 6 Oct 2026 12:54:23 +0200 Subject: [PATCH 71/96] Improve docstrings --- baybe/recommenders/pure/llm/_prompts.py | 21 +++++++++------------ 1 file changed, 9 insertions(+), 12 deletions(-) diff --git a/baybe/recommenders/pure/llm/_prompts.py b/baybe/recommenders/pure/llm/_prompts.py index 6bb9869994..10566c1329 100644 --- a/baybe/recommenders/pure/llm/_prompts.py +++ b/baybe/recommenders/pure/llm/_prompts.py @@ -123,12 +123,7 @@ class _ParameterPromptInfo(TypedDict): - """Typed, presentation-only view of a parameter for prompt rendering. - - Gives the template a single stable shape (instead of the previous - dynamically-shaped ``SimpleNamespace``); the value domain is flattened into one - ``domain`` string so no field is conditionally present. - """ + """Typed, presentation-only view of a parameter for prompt rendering.""" name: str description: str | None @@ -212,15 +207,17 @@ def _parameter_prompt_info(parameter: Parameter) -> _ParameterPromptInfo: def _forbidden_configurations(searchspace: SearchSpace) -> str | None: - """Render discrete configurations that are no longer eligible candidates. + """Render discrete configurations that should not be recommended currently. - When the campaign forbids re-recommending already-used points (e.g. via - ``allow_recommending_already_recommended``), those points are dropped from the - eligible candidate set the recommender receives. Surfacing them lets the model avoid - proposing configurations that would be rejected as ineligible. + Some candidates should not be recommended in the current iteration, since it might + be forbidden to recommend points already recommended (e.g. via setting + ``allow_recommending_already_recommended``) or because the points are currently + pending. Hence, those points are dropped from the eligible candidate set the + recommender receives. Surfacing them lets the model avoid proposing configurations + that would be rejected as ineligible. Args: - searchspace: The (possibly filtered) search space to recommend for. + searchspace: The search space to recommend for. Returns: A rendered table of the forbidden discrete configurations, or ``None`` if the From d729230bcd779ee96bb464b9a5a47b9d4759afd2 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Wed, 7 Oct 2026 09:02:53 +0200 Subject: [PATCH 72/96] Change type of `recommenders` to `tuple` in `SequentialMetaRecommender` This is more in-line with our usage patterns. We use our `to_tuple` converter as we cannot use a plain `tuple` due to https://github.com/python/mypy/issues/8417. --- CHANGELOG.md | 2 ++ baybe/recommenders/meta/sequential.py | 14 ++++---------- 2 files changed, 6 insertions(+), 10 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index ce969bfeb4..c75de4688b 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -14,6 +14,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - `df_apply_permutation_augmentation` has a different interface and now expects permutation groups instead of column groups - `ParameterSelectorProtocol.__call__` now declares its input as positional-only +- `SequentialMetaRecommender.recommenders` is now a `tuple` instead of a `list`, making + the attribute immutable. Serialization is unaffected ### Fixed - `DiscretePermutationInvarianceConstraint` no longer erroneously removes points where diff --git a/baybe/recommenders/meta/sequential.py b/baybe/recommenders/meta/sequential.py index 365cdba57c..0c1d816dd7 100644 --- a/baybe/recommenders/meta/sequential.py +++ b/baybe/recommenders/meta/sequential.py @@ -26,6 +26,7 @@ block_serialization_hook, converter, ) +from baybe.utils.basic import to_tuple from baybe.utils.conversion import to_string _T = TypeVar("_T") @@ -174,13 +175,6 @@ def recommend( return recommendation -def _to_recommender_list( - recommenders: Iterable[RecommenderProtocol], / -) -> list[RecommenderProtocol]: - """Convert an iterable of recommenders into a list.""" - return list(recommenders) - - @define class SequentialMetaRecommender(BaseSequentialMetaRecommender): """A meta recommender that uses a pre-defined sequence of recommenders. @@ -192,7 +186,7 @@ class SequentialMetaRecommender(BaseSequentialMetaRecommender): Note: The provided sequence of recommenders will be internally pre-collected into a - list. If this is not acceptable, consider using + tuple. If this is not acceptable, consider using :class:`baybe.recommenders.meta.sequential.StreamingSequentialMetaRecommender` instead. @@ -203,8 +197,8 @@ class SequentialMetaRecommender(BaseSequentialMetaRecommender): recommenders available and ``mode="raise"``. """ - recommenders: list[RecommenderProtocol] = field( - converter=_to_recommender_list, + recommenders: tuple[RecommenderProtocol, ...] = field( + converter=to_tuple, # https://github.com/python/mypy/issues/8417 validator=deep_iterable(instance_of(RecommenderProtocol)), ) """A finite-length sequence of recommenders to be used. For infinite-length From 6c6d09c3e82da50086695ebeda631a9f72192301 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Wed, 7 Oct 2026 09:23:46 +0200 Subject: [PATCH 73/96] Improve and fix parameter representation in prompts Fixes two small issues: First, the prompt now only propagates `active_values` instead of all values. Second, it fixes a leak in `np` representation for numerical values. --- baybe/recommenders/pure/llm/_prompts.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/baybe/recommenders/pure/llm/_prompts.py b/baybe/recommenders/pure/llm/_prompts.py index 10566c1329..d73d5ce0dd 100644 --- a/baybe/recommenders/pure/llm/_prompts.py +++ b/baybe/recommenders/pure/llm/_prompts.py @@ -181,12 +181,17 @@ def _parameter_prompt_info(parameter: Parameter) -> _ParameterPromptInfo: # reason about structure, while still choosing by substance name. kind = "substance" substances = ", ".join( - f"{name} ({smiles})" for name, smiles in parameter.data.items() + f"{name} ({smiles})" + for name, smiles in parameter.data.items() + if name in parameter.active_values ) domain = f"Allowed values (choose by name; SMILES in parentheses): {substances}" elif isinstance(parameter, DiscreteParameter): kind = "discrete_numeric" if parameter.is_numerical else "categorical" - domain = f"Allowed values: {list(parameter.values)}" + values = parameter.active_values + if parameter.is_numerical: + values = tuple(value.item() for value in values) # avoid numpy-repr leak + domain = f"Allowed values: {list(values)}" else: raise IncompatibilityError( f"Parameter '{parameter.name}' has unsupported type " From a1fce3dfab55117aefc75c01543eda975dea9489 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Wed, 7 Oct 2026 16:59:37 +0200 Subject: [PATCH 74/96] Delete `LLMRecommender` tests There was a request to fully rewrite those, hence the current version was deleted. --- tests/hypothesis_strategies/recommenders.py | 38 - .../test_llm_recommender_serialization.py | 15 - tests/test_llm_recommender.py | 1064 ----------------- 3 files changed, 1117 deletions(-) delete mode 100644 tests/hypothesis_strategies/recommenders.py delete mode 100644 tests/serialization/test_llm_recommender_serialization.py delete mode 100644 tests/test_llm_recommender.py diff --git a/tests/hypothesis_strategies/recommenders.py b/tests/hypothesis_strategies/recommenders.py deleted file mode 100644 index fbae798b67..0000000000 --- a/tests/hypothesis_strategies/recommenders.py +++ /dev/null @@ -1,38 +0,0 @@ -"""Hypothesis strategies for recommenders.""" - -import hypothesis.strategies as st -from hypothesis import assume - -from baybe.recommenders.pure.llm.llm import ( - _CREDENTIAL_LITELLM_KEYS, - _RESERVED_LITELLM_KEYS, - LLMRecommender, -) - -_FORBIDDEN_LITELLM_KEYS = _RESERVED_LITELLM_KEYS | _CREDENTIAL_LITELLM_KEYS - -_litellm_values = st.one_of( - st.text(), - st.integers(), - st.floats(allow_nan=False, allow_infinity=False), - st.booleans(), -) -"""A strategy generating JSON-serializable values suitable for litellm args.""" - - -@st.composite -def _safe_litellm_args(draw: st.DrawFn) -> dict: - """Generate a litellm args dict containing no reserved or credential keys.""" - args = draw(st.dictionaries(st.text(min_size=1), _litellm_values, max_size=3)) - assume(not set(args).intersection(_FORBIDDEN_LITELLM_KEYS)) - return args - - -def llm_recommenders() -> st.SearchStrategy[LLMRecommender]: - """Strategy for :class:`~baybe.recommenders.pure.llm.llm.LLMRecommender`.""" - return st.builds( - LLMRecommender, - model=st.text(min_size=1), - experiment_description=st.text(min_size=1), - litellm_args=_safe_litellm_args(), - ) diff --git a/tests/serialization/test_llm_recommender_serialization.py b/tests/serialization/test_llm_recommender_serialization.py deleted file mode 100644 index 370cd5544e..0000000000 --- a/tests/serialization/test_llm_recommender_serialization.py +++ /dev/null @@ -1,15 +0,0 @@ -"""Serialization tests for the LLM recommender.""" - -from hypothesis import given - -from baybe.recommenders.pure.llm.llm import LLMRecommender -from tests.hypothesis_strategies.recommenders import llm_recommenders - - -@given(llm_recommenders()) -def test_llm_recommender_roundtrip(recommender: LLMRecommender): - """An LLMRecommender survives a serialization roundtrip across all configurations. - - Serialization does not touch LiteLLM, so no optional dependencies are required. - """ - assert LLMRecommender.from_json(recommender.to_json()) == recommender diff --git a/tests/test_llm_recommender.py b/tests/test_llm_recommender.py deleted file mode 100644 index 1f8bbbc497..0000000000 --- a/tests/test_llm_recommender.py +++ /dev/null @@ -1,1064 +0,0 @@ -"""Tests for the LLM-based recommender.""" - -import json -from types import SimpleNamespace -from unittest.mock import patch - -import pandas as pd -import pytest - -from baybe._optional.info import CHEM_INSTALLED, LLM_INSTALLED -from baybe.constraints.conditions import SubSelectionCondition, ThresholdCondition -from baybe.constraints.discrete import ( - DiscreteBatchConstraint, - DiscreteCardinalityConstraint, - DiscreteConstraint, - DiscreteCustomConstraint, - DiscreteDependenciesConstraint, - DiscreteLinkedParametersConstraint, - DiscreteNoLabelDuplicatesConstraint, - DiscretePermutationInvarianceConstraint, - DiscreteProductConstraint, - DiscreteSelectionConstraint, - DiscreteSumConstraint, -) -from baybe.exceptions import ( - ConstraintViolationError, - IneligiblePointsError, - InvalidParameterValueError, - LLMResponseError, - LLMResponseWarning, - MalformedLLMResponseError, - MissingParameterError, - NonNumericParameterError, - UnknownParameterError, -) -from baybe.parameters import ( - CategoricalParameter, - NumericalContinuousParameter, - NumericalDiscreteParameter, -) -from baybe.recommenders.pure.llm._parsing import parse_llm_response -from baybe.recommenders.pure.llm._prompts import make_prompt, make_recovery_prompt -from baybe.recommenders.pure.llm._schema import ( - _EXPLANATION_FIELD, - _PARAMETERS_FIELD, - _response_format, -) -from baybe.searchspace import SearchSpace -from baybe.utils.basic import get_subclasses - -pytestmark = pytest.mark.skipif( - not LLM_INSTALLED, reason="LLM dependencies not installed" -) - - -def _mock_response(content: str) -> SimpleNamespace: - """Create a mock LLM response.""" - return SimpleNamespace( - choices=[SimpleNamespace(message=SimpleNamespace(content=content))] - ) - - -def _make_suggestions(params_list: list[dict]) -> str: - """Create a JSON string of suggestions from a list of parameter dicts.""" - return json.dumps( - [ - {_EXPLANATION_FIELD: f"Suggestion {i}", _PARAMETERS_FIELD: p} - for i, p in enumerate(params_list) - ] - ) - - -def _filtered_discrete_space(exclude: dict): - """Build a 3x3 discrete space with one candidate filtered from the eligible set.""" - from attrs import evolve - - from baybe.searchspace._filtered import FilteredSubspaceDiscrete - - space = SearchSpace.from_product( - [ - NumericalDiscreteParameter("x", values=[1, 2, 3]), - NumericalDiscreteParameter("y", values=[1, 2, 3]), - ] - ) - exp_rep = space.discrete.exp_rep - mask_keep = ~( - (exp_rep["x"] == exclude["x"]) & (exp_rep["y"] == exclude["y"]) - ).to_numpy() - return evolve( - space, - discrete=FilteredSubspaceDiscrete.from_subspace(space.discrete, mask_keep), - ) - - -@pytest.fixture(name="searchspace") -def fixture_searchspace(): - """A search space with continuous, discrete numeric, and categorical parameters.""" - parameters = [ - NumericalContinuousParameter( - name="temperature", - bounds=(0.0, 100.0), - metadata={"description": "Reaction temperature", "unit": "°C"}, - ), - NumericalContinuousParameter( - name="pressure", - bounds=(0.0, 5.0), - metadata={"description": "Reaction pressure", "unit": "bar"}, - ), - NumericalDiscreteParameter( - name="n_cycles", - values=[1, 2, 3, 4, 5], - metadata={"description": "Number of reaction cycles"}, - ), - CategoricalParameter( - name="catalyst", - values=["A", "B", "C"], - metadata={"description": "Catalyst type"}, - ), - ] - return SearchSpace.from_product(parameters) - - -@pytest.fixture(name="recommender") -def fixture_recommender(): - """An LLMRecommender instance for testing.""" - from baybe.recommenders.pure.llm.llm import LLMRecommender - - return LLMRecommender( - model="gpt-5.4", - experiment_description="Test experiment", - ) - - -@pytest.fixture(name="valid_response") -def fixture_valid_response(): - """A valid mock LLM response with three suggestions.""" - return _mock_response( - _make_suggestions( - [ - {"temperature": 25.0, "pressure": 2.0, "n_cycles": 1, "catalyst": "A"}, - {"temperature": 30.0, "pressure": 1.5, "n_cycles": 3, "catalyst": "B"}, - {"temperature": 50.0, "pressure": 3.0, "n_cycles": 5, "catalyst": "C"}, - ] - ) - ) - - -@patch("baybe._optional.llm.completion") -def test_recommend_success(mock_completion, recommender, searchspace, valid_response): - """Successful recommendation returns a DataFrame with correct shape.""" - mock_completion.return_value = valid_response - - recommendations = recommender.recommend(batch_size=3, searchspace=searchspace) - - assert isinstance(recommendations, pd.DataFrame) - assert len(recommendations) == 3 - assert set(recommendations.columns) == { - "temperature", - "pressure", - "n_cycles", - "catalyst", - } - assert recommendations["temperature"].tolist() == [25.0, 30.0, 50.0] - assert recommendations["catalyst"].tolist() == ["A", "B", "C"] - assert recommendations["n_cycles"].tolist() == [1, 3, 5] - - -@patch("baybe._optional.llm.completion") -def test_recommend_with_measurements( - mock_completion, recommender, searchspace, valid_response -): - """Recommendations include previous measurements in prompt.""" - mock_completion.return_value = valid_response - - measurements = pd.DataFrame( - { - "temperature": [20.0, 25.0], - "pressure": [1.0, 2.0], - "n_cycles": [1, 2], - "catalyst": ["A", "B"], - "yield": [0.5, 0.7], - } - ) - - recommendations = recommender.recommend( - batch_size=3, searchspace=searchspace, measurements=measurements - ) - - assert isinstance(recommendations, pd.DataFrame) - assert len(recommendations) == 3 - prompt_content = mock_completion.call_args.kwargs.get( - "messages", mock_completion.call_args[1]["messages"] - )[0]["content"] - assert "PREVIOUS MEASUREMENTS" in prompt_content - - -@patch("baybe._optional.llm.completion") -def test_recommend_with_pending_experiments( - mock_completion, recommender, searchspace, valid_response -): - """Pending experiments are accepted and included in the prompt.""" - mock_completion.return_value = valid_response - - pending_experiments = pd.DataFrame( - { - "temperature": [10.0, 15.0], - "pressure": [1.0, 2.0], - "n_cycles": [2, 4], - "catalyst": ["A", "C"], - } - ) - - recommendations = recommender.recommend( - batch_size=3, - searchspace=searchspace, - pending_experiments=pending_experiments, - ) - - assert isinstance(recommendations, pd.DataFrame) - assert len(recommendations) == 3 - prompt_content = mock_completion.call_args.kwargs.get( - "messages", mock_completion.call_args[1]["messages"] - )[0]["content"] - assert "PENDING EXPERIMENTS" in prompt_content - - -@patch("baybe._optional.llm.completion") -def test_recommend_with_objective( - mock_completion, recommender, searchspace, valid_response -): - """A passed objective is rendered into the prompt.""" - from baybe.objectives.single import SingleTargetObjective - from baybe.targets.numerical import NumericalTarget - - mock_completion.return_value = valid_response - objective = SingleTargetObjective(NumericalTarget("yield", minimize=False)) - - with pytest.warns(UserWarning, match="objective has no metadata description"): - recommender.recommend( - batch_size=3, - searchspace=searchspace, - objective=objective, - ) - - prompt_content = mock_completion.call_args.kwargs.get( - "messages", mock_completion.call_args[1]["messages"] - )[0]["content"] - assert "OPTIMIZATION TARGETS" in prompt_content - assert "yield" in prompt_content - - -def test_response_contract_is_single_sourced(searchspace): - """Both prompts and the parser share one definition of the response fields.""" - response_format = _response_format(batch_size=1) - assert _EXPLANATION_FIELD in response_format - assert _PARAMETERS_FIELD in response_format - - prompt = make_prompt( - batch_size=1, - searchspace=searchspace, - experiment_description="Test", - ) - assert response_format in prompt - - recovery_prompt = make_recovery_prompt( - searchspace, - batch_size=1, - error=MalformedLLMResponseError("boom"), - original_response="[]", - ) - assert response_format in recovery_prompt - - # A response built from the shared field names parses successfully. - suggestions = _make_suggestions( - [{"temperature": 25.0, "pressure": 2.0, "n_cycles": 1, "catalyst": "A"}] - ) - assert len(parse_llm_response(suggestions, searchspace)) == 1 - - -def test_response_format_batch_size(searchspace): - """Both prompts embed an example array with exactly batch_size entries.""" - for batch_size in (1, 2, 5): - fmt = _response_format(batch_size=batch_size) - # The format string must contain exactly batch_size copies of the field keys. - assert fmt.count(f'"{_EXPLANATION_FIELD}"') == batch_size - assert fmt.count(f'"{_PARAMETERS_FIELD}"') == batch_size - - prompt = make_prompt( - batch_size=batch_size, - searchspace=searchspace, - experiment_description="Test", - ) - assert fmt in prompt - - recovery_prompt = make_recovery_prompt( - searchspace, - batch_size=batch_size, - error=MalformedLLMResponseError("boom"), - original_response="[]", - ) - assert fmt in recovery_prompt - - -def test_parameter_prompt_info(): - """The typed prompt view maps each parameter type to a stable shape.""" - from baybe.exceptions import IncompatibilityError - from baybe.recommenders.pure.llm._prompts import _parameter_prompt_info - - cont = _parameter_prompt_info( - NumericalContinuousParameter( - "x", bounds=(0.0, 1.0), metadata={"unit": "m", "description": "d"} - ) - ) - assert cont["kind"] == "continuous" - assert cont["domain"] == "Bounds: [0.0, 1.0]" - assert cont["unit"] == "m" - assert cont["description"] == "d" - assert cont["misc"] == () - - disc = _parameter_prompt_info(NumericalDiscreteParameter("n", values=[1, 2, 3])) - assert disc["kind"] == "discrete_numeric" - assert disc["domain"].startswith("Allowed values:") - assert disc["unit"] is None - - # Non-string misc values are normalized to strings at the prompt boundary. - cat = _parameter_prompt_info( - CategoricalParameter("c", values=["A", "B"], metadata={"note": 1}) - ) - assert cat["kind"] == "categorical" - assert cat["domain"] == "Allowed values: ['A', 'B']" - assert cat["misc"] == (("note", "1"),) - - with pytest.raises(IncompatibilityError, match="unsupported type"): - _parameter_prompt_info(SimpleNamespace(name="weird")) - - -@pytest.mark.skipif(not CHEM_INSTALLED, reason="Chemistry dependencies not installed") -def test_parameter_prompt_info_substance(): - """Substance parameters expose their SMILES alongside the substance names.""" - from baybe.parameters.substance import SubstanceParameter - from baybe.recommenders.pure.llm._prompts import _parameter_prompt_info - - info = _parameter_prompt_info( - SubstanceParameter("solvent", data={"water": "O", "ethanol": "CCO"}) - ) - assert info["kind"] == "substance" - assert "water (O)" in info["domain"] - assert "ethanol (CCO)" in info["domain"] - - -def test_make_llm_two_phase_recommender(): - """The two-phase factory wires an LLM warm-start into a TwoPhaseMetaRecommender.""" - from baybe.recommenders import ( - BotorchRecommender, - TwoPhaseMetaRecommender, - make_llm_two_phase_recommender, - ) - from baybe.recommenders.pure.llm.llm import LLMRecommender - - rec = make_llm_two_phase_recommender( - model="gpt-5.4", - experiment_description="Test", - switch_after=3, - litellm_args={"temperature": 0.1}, - ) - assert isinstance(rec, TwoPhaseMetaRecommender) - assert isinstance(rec.initial_recommender, LLMRecommender) - assert rec.initial_recommender.model == "gpt-5.4" - assert rec.initial_recommender.litellm_args == {"temperature": 0.1} - assert isinstance(rec.recommender, BotorchRecommender) - assert rec.switch_after == 3 - - # The returned recommender is a standard serializable type. - assert type(rec).from_json(rec.to_json()) == rec - - -def test_make_llm_alternating_recommender(): - """The alternating factory yields a cyclic LLM/Bayesian sequential recommender.""" - from baybe.recommenders import ( - RandomRecommender, - SequentialMetaRecommender, - make_llm_alternating_recommender, - ) - from baybe.recommenders.pure.llm.llm import LLMRecommender - - rec = make_llm_alternating_recommender( - model="gpt-5.4", - experiment_description="Test", - recommender=RandomRecommender(), - ) - assert isinstance(rec, SequentialMetaRecommender) - assert rec.mode == "cyclic" - assert len(rec.recommenders) == 2 - assert isinstance(rec.recommenders[0], LLMRecommender) - assert isinstance(rec.recommenders[1], RandomRecommender) - - -def test_prompt_templates_use_strict_undefined(): - """Undefined template variables fail fast instead of rendering empty.""" - from jinja2 import UndefinedError - - from baybe._optional.llm import StrictUndefined, Template - from baybe.recommenders.pure.llm._prompts import _PROMPT_TEMPLATE - - template = Template( - _PROMPT_TEMPLATE, - trim_blocks=True, - lstrip_blocks=True, - undefined=StrictUndefined, - ) - with pytest.raises(UndefinedError): - template.render() # empty context -> first undefined access raises - - -@pytest.mark.parametrize( - ("response_content", "error_type", "error_match"), - [ - pytest.param( - "Invalid JSON", - MalformedLLMResponseError, - "Error parsing JSON output", - id="invalid_json", - ), - pytest.param( - json.dumps( - [ - { - "explanation": "Test", - "parameters": { - "temperature": 150.0, - "pressure": 2.0, - "n_cycles": 1, - "catalyst": "A", - }, - } - ] - ), - InvalidParameterValueError, - "has invalid values in parameter", - id="out_of_bounds", - ), - pytest.param( - json.dumps( - [ - { - "explanation": "Test", - "parameters": { - "temperature": 25.0, - "pressure": 2.0, - "n_cycles": 1, - "catalyst": "D", - }, - } - ] - ), - InvalidParameterValueError, - "has invalid values in parameter", - id="invalid_categorical", - ), - pytest.param( - json.dumps( - [ - { - "explanation": "Test", - "parameters": {"temperature": 25.0, "catalyst": "A"}, - } - ] - ), - MissingParameterError, - "missing values for the following parameters", - id="missing_parameter", - ), - pytest.param( - json.dumps( - [ - { - "explanation": "Test", - "parameters": { - "temperature": 25.0, - "pressure": 2.0, - "n_cycles": 1, - "catalyst": "A", - "unknown": 1, - }, - } - ] - ), - UnknownParameterError, - "unknown parameter names", - id="unknown_parameter", - ), - pytest.param( - json.dumps({"explanation": "Test", "parameters": {}}), - MalformedLLMResponseError, - "Response must be a JSON array", - id="not_a_list", - ), - pytest.param( - json.dumps([]), - MalformedLLMResponseError, - "empty array with no suggestions", - id="empty_array", - ), - pytest.param( - json.dumps(["a string"]), - MalformedLLMResponseError, - "Each suggestion must be a JSON object", - id="suggestion_not_dict", - ), - pytest.param( - json.dumps([{"explanation": "Test", "parameters": [1, 2]}]), - MalformedLLMResponseError, - "Parameters must be a JSON object", - id="parameters_not_dict", - ), - pytest.param( - json.dumps( - [ - { - "parameters": { - "temperature": 25.0, - "pressure": 2.0, - "n_cycles": 1, - "catalyst": "A", - } - } - ] - ), - MalformedLLMResponseError, - "must contain an 'explanation' field", - id="missing_explanation", - ), - pytest.param( - json.dumps( - [ - { - "explanation": "Test", - "parameters": { - "temperature": "hot", - "pressure": 2.0, - "n_cycles": 1, - "catalyst": "A", - }, - } - ] - ), - NonNumericParameterError, - "has non-numeric entries", - id="non_numeric_continuous", - ), - ], -) -def test_parse_llm_response_errors( - response_content, error_type, error_match, searchspace -): - """Malformed responses raise the specific error subtype with a clear message.""" - with pytest.raises(error_type, match=error_match): - parse_llm_response(response_content, searchspace) - - -def test_parse_llm_response_numerical_tolerance_snaps_to_nearest(): - """A numerical-discrete value within tolerance is accepted and snapped. - - Matches how user measurement input is handled: ``validate_parameter_input`` - accepts values within the parameter tolerance and ``fuzzy_row_match`` snaps - them to the nearest allowed value. - """ - space = SearchSpace.from_product( - [NumericalDiscreteParameter("x", values=[1.0, 2.0, 3.0], tolerance=0.4)] - ) - # 1.3 is within tolerance 0.4 of 1.0 but not an exact allowed value. - result = parse_llm_response(_make_suggestions([{"x": 1.3}]), space) - assert result["x"].tolist() == [1.0] - - -def test_parse_llm_response_numerical_out_of_tolerance_rejected(): - """A numerical-discrete value outside tolerance is rejected.""" - space = SearchSpace.from_product( - [NumericalDiscreteParameter("x", values=[1.0, 2.0, 3.0], tolerance=0.1)] - ) - # 1.3 is outside tolerance 0.1 of every allowed value. - with pytest.raises(LLMResponseError, match="has invalid values in parameter"): - parse_llm_response(_make_suggestions([{"x": 1.3}]), space) - - -# --------------------------------------------------------------------------- -# Constraint violation test cases — one per concrete DiscreteConstraint class -# (except DiscreteBatchConstraint, which is tested separately below). -# -# Each entry maps a constraint class to (parameters, constraints, violations) -# where violations is a list of suggestion dicts that individually satisfy all -# parameter bounds/values but violate the constraint as a combination or batch. -# --------------------------------------------------------------------------- -_ROW_CONSTRAINT_VIOLATION_CASES = { - DiscreteSelectionConstraint: ( - [ - NumericalDiscreteParameter("x", values=[1, 2, 3]), - CategoricalParameter("y", values=["a", "b", "c"]), - ], - [ - DiscreteSelectionConstraint( - parameters=["x", "y"], - conditions=[ - SubSelectionCondition(selection=[2]), - SubSelectionCondition(selection=["b"]), - ], - combiner="AND", - exclude=True, - ) - ], - [{"x": 2, "y": "b"}], - ), - DiscreteSumConstraint: ( - [ - NumericalDiscreteParameter("a", values=[1, 2, 3]), - NumericalDiscreteParameter("b", values=[1, 2, 3]), - ], - [ - DiscreteSumConstraint( - parameters=["a", "b"], - condition=ThresholdCondition(threshold=6.0, operator="="), - ) - ], - [{"a": 1, "b": 1}], # sum=2, required sum=6 - ), - DiscreteProductConstraint: ( - [ - NumericalDiscreteParameter("a", values=[1, 2, 3]), - NumericalDiscreteParameter("b", values=[1, 2, 3]), - ], - [ - DiscreteProductConstraint( - parameters=["a", "b"], - condition=ThresholdCondition(threshold=6.0, operator=">="), - ) - ], - [{"a": 1, "b": 1}], # product=1, required product>=6 - ), - DiscreteNoLabelDuplicatesConstraint: ( - [ - CategoricalParameter("x", values=["A", "B", "C"]), - CategoricalParameter("y", values=["A", "B", "C"]), - ], - [DiscreteNoLabelDuplicatesConstraint(parameters=["x", "y"])], - [{"x": "A", "y": "A"}], # duplicate label across parameters - ), - DiscreteLinkedParametersConstraint: ( - [ - NumericalDiscreteParameter("x", values=[1, 2, 3]), - NumericalDiscreteParameter("y", values=[1, 2, 3]), - ], - [DiscreteLinkedParametersConstraint(parameters=["x", "y"])], - [{"x": 1, "y": 2}], # x and y must be equal - ), - DiscreteDependenciesConstraint: ( - [ - CategoricalParameter("switch", values=["on", "off"]), - CategoricalParameter("mode", values=["fast", "slow"]), - ], - [ - DiscreteDependenciesConstraint( - parameters=["switch"], - conditions=[SubSelectionCondition(selection=["on"])], - affected_parameters=[["mode"]], - ) - ], - # when switch="off" mode is irrelevant: two "off" rows are duplicates - [{"switch": "off", "mode": "fast"}, {"switch": "off", "mode": "slow"}], - ), - DiscretePermutationInvarianceConstraint: ( - [ - NumericalDiscreteParameter("a", values=[1, 2, 3]), - NumericalDiscreteParameter("b", values=[1, 2, 3]), - ], - [DiscretePermutationInvarianceConstraint(parameters=["a", "b"])], - # (1,2) and (2,1) are permutation-equivalent; the second is a duplicate - [{"a": 1, "b": 2}, {"a": 2, "b": 1}], - ), - DiscreteCustomConstraint: ( - [NumericalDiscreteParameter("x", values=[1, 2, 3])], - [DiscreteCustomConstraint(parameters=["x"], validator=lambda df: df["x"] != 2)], - [{"x": 2}], # custom validator rejects x=2 - ), - DiscreteCardinalityConstraint: ( - [ - NumericalDiscreteParameter("a", values=[0, 1, 2]), - NumericalDiscreteParameter("b", values=[0, 1, 2]), - ], - [DiscreteCardinalityConstraint(parameters=["a", "b"], max_cardinality=1)], - [{"a": 1, "b": 1}], # 2 nonzero values exceeds max_cardinality=1 - ), -} - -# Verify all concrete DiscreteConstraint subclasses (except DiscreteBatchConstraint) -# have a violation test case — fails at collection time if coverage lapses. -_ALL_ROW_CONSTRAINT_CLASSES = frozenset( - cls - for cls in get_subclasses(DiscreteConstraint) - if cls is not DiscreteBatchConstraint -) -assert frozenset(_ROW_CONSTRAINT_VIOLATION_CASES) == _ALL_ROW_CONSTRAINT_CLASSES, ( - "Missing constraint violation cases for: " - f"{_ALL_ROW_CONSTRAINT_CLASSES - frozenset(_ROW_CONSTRAINT_VIOLATION_CASES)}" -) - - -@pytest.mark.parametrize( - ("parameters", "constraints", "violation_suggestions"), - [ - pytest.param(*_ROW_CONSTRAINT_VIOLATION_CASES[cls], id=cls.__name__) - for cls in get_subclasses(DiscreteConstraint) - if cls is not DiscreteBatchConstraint - ], -) -def test_parse_llm_response_rejects_row_constraint_violations( - parameters, constraints, violation_suggestions -): - """Suggestions valid per-parameter but violating a discrete constraint raise.""" - space = SearchSpace.from_product(parameters=parameters, constraints=constraints) - response = _make_suggestions(violation_suggestions) - with pytest.raises(ConstraintViolationError, match="violate the.*constraint"): - parse_llm_response(response, space) - - -def test_parse_llm_response_rejects_batch_constraint_violation(): - """Batch suggestions with mixed values for a DiscreteBatchConstraint param raise.""" - parameters = [ - NumericalDiscreteParameter("x", values=[1, 2, 3]), - CategoricalParameter("y", values=["a", "b"]), - ] - space = SearchSpace.from_product( - parameters=parameters, - constraints=[DiscreteBatchConstraint(parameters=["x"])], - ) - # x values differ across suggestions — violates the batch constraint - response = _make_suggestions([{"x": 1, "y": "a"}, {"x": 2, "y": "b"}]) - with pytest.raises(ConstraintViolationError, match="DiscreteBatchConstraint"): - parse_llm_response(response, space) - - -def test_parse_llm_response_aligns_index_with_exp_rep(): - """Returned DataFrame index matches the exp_rep index of the search space.""" - parameters = [ - NumericalDiscreteParameter("x", values=[1, 2, 3]), - NumericalDiscreteParameter("y", values=[10, 20, 30]), - ] - space = SearchSpace.from_product(parameters=parameters) - # Suggest the last row of exp_rep — its index is not 0 - last_row = space.discrete.exp_rep.iloc[-1] - response = _make_suggestions([{"x": last_row["x"], "y": last_row["y"]}]) - result = parse_llm_response(response, space) - - assert list(result.index) == [space.discrete.exp_rep.index[-1]] - - -def test_parse_llm_response_warns_for_continuous_constraints(): - """A warning is issued when the search space has continuous constraints.""" - from baybe.constraints.continuous import ContinuousLinearConstraint - - parameters = [ - NumericalContinuousParameter("x", bounds=(0, 1)), - NumericalContinuousParameter("y", bounds=(0, 1)), - ] - space = SearchSpace.from_product( - parameters=parameters, - constraints=[ - ContinuousLinearConstraint( - parameters=["x", "y"], coefficients=[1, 1], rhs=1.5, operator="<=" - ) - ], - ) - response = _make_suggestions([{"x": 0.3, "y": 0.4}]) - with pytest.warns(LLMResponseWarning, match="continuous constraints"): - parse_llm_response(response, space) - - -@pytest.mark.parametrize( - "wrapper", - [ - "```json\n{payload}\n```", - "```\n{payload}\n```", - "Here are the suggestions:\n{payload}\nHope this helps!", - ], - ids=["json_fence", "bare_fence", "surrounding_prose"], -) -def test_parse_llm_response_strips_wrappers(wrapper, searchspace): - """Markdown fences and surrounding prose are stripped before JSON parsing.""" - payload = _make_suggestions( - [{"temperature": 25.0, "pressure": 2.0, "n_cycles": 1, "catalyst": "A"}] - ) - df = parse_llm_response(wrapper.format(payload=payload), searchspace) - assert len(df) == 1 - assert df["catalyst"].iloc[0] == "A" - - -def test_extract_json_array_handles_multiple_blocks_and_prose(): - """The last suggestion-like array wins; stray brackets in prose are ignored.""" - from baybe.recommenders.pure.llm._parsing import extract_json_array - - # A reconsidered second block supersedes the first. - two_blocks = '```json\n[{"a": 1}]\n```\nRevise:\n```json\n[{"a": 2}]\n```' - assert extract_json_array(two_blocks) == '[{"a": 2}]' - # A stray bracket in trailing prose is not mistaken for the array. - assert extract_json_array('[{"a": 1}] note x[0]') == '[{"a": 1}]' - - -def test_parse_llm_response_uses_last_of_multiple_blocks(searchspace): - """When the model emits several JSON blocks, the last (revised) one is parsed.""" - first = _make_suggestions( - [{"temperature": 25.0, "pressure": 2.0, "n_cycles": 1, "catalyst": "A"}] - ) - second = _make_suggestions( - [{"temperature": 30.0, "pressure": 3.0, "n_cycles": 5, "catalyst": "C"}] - ) - response = f"```json\n{first}\n```\nWait, let me revise:\n```json\n{second}\n```" - df = parse_llm_response(response, searchspace) - assert len(df) == 1 - assert df["catalyst"].iloc[0] == "C" - - -@patch("baybe._optional.llm.completion") -def test_recommend_invalid_response_with_failed_recovery( - mock_completion, recommender, searchspace -): - """An invalid response whose recovery also fails raises an informative error.""" - mock_completion.return_value = _mock_response("Invalid JSON") - - with pytest.raises(LLMResponseError, match="even after a recovery attempt"): - recommender.recommend(batch_size=3, searchspace=searchspace) - assert mock_completion.call_count == 2 - - -@pytest.mark.parametrize( - "error", - [ - MalformedLLMResponseError("bad"), - UnknownParameterError( - "bad", unknown_names={"foo"}, valid_names={"temperature"} - ), - MissingParameterError("bad", parameters={"pressure"}), - NonNumericParameterError("bad", detail="detail."), - InvalidParameterValueError("bad", detail="detail."), - ConstraintViolationError( - "bad", constraint_name="DiscreteSumConstraint", parameters=["a", "b"] - ), - IneligiblePointsError("bad", n_ineligible=2), - ], - ids=lambda e: type(e).__name__, -) -def test_make_recovery_prompt_embeds_error_guidance(error, searchspace): - """The recovery prompt embeds the error-specific recovery instruction.""" - prompt = make_recovery_prompt( - searchspace, - batch_size=1, - error=error, - original_response="the original response", - ) - assert "WHAT WENT WRONG" in prompt - assert error.recovery_instruction in prompt - assert "the original response" in prompt - - -@patch("baybe._optional.llm.completion") -def test_recovery_success(mock_completion, recommender, searchspace): - """Successful recovery from a malformed initial response.""" - invalid = _mock_response("Invalid JSON") - valid = _mock_response( - _make_suggestions( - [ - {"temperature": 50.0, "pressure": 3.0, "n_cycles": 2, "catalyst": "C"}, - ] - ) - ) - mock_completion.side_effect = [invalid, valid] - - recommendations = recommender.recommend(batch_size=1, searchspace=searchspace) - - assert len(recommendations) == 1 - assert mock_completion.call_count == 2 - - -@pytest.mark.parametrize( - "bad_parameters", - [ - pytest.param("not json at all", id="malformed"), - pytest.param( - { - "temperature": 25.0, - "pressure": 2.0, - "n_cycles": 1, - "catalyst": "A", - "unknown": 1, - }, - id="unknown_parameter", - ), - pytest.param({"temperature": 25.0, "catalyst": "A"}, id="missing_parameter"), - pytest.param( - {"temperature": "hot", "pressure": 2.0, "n_cycles": 1, "catalyst": "A"}, - id="non_numeric", - ), - pytest.param( - {"temperature": 150.0, "pressure": 2.0, "n_cycles": 1, "catalyst": "A"}, - id="invalid_value", - ), - ], -) -@patch("baybe._optional.llm.completion") -def test_recovery_per_error_category( - mock_completion, bad_parameters, recommender, searchspace -): - """Each response issue triggers exactly one recovery attempt that can succeed.""" - bad_content = ( - bad_parameters - if isinstance(bad_parameters, str) - else _make_suggestions([bad_parameters]) - ) - valid = _make_suggestions( - [{"temperature": 50.0, "pressure": 3.0, "n_cycles": 2, "catalyst": "C"}] - ) - mock_completion.side_effect = [_mock_response(bad_content), _mock_response(valid)] - - recommendations = recommender.recommend(batch_size=1, searchspace=searchspace) - - assert len(recommendations) == 1 - assert mock_completion.call_count == 2 - - -@patch("baybe._optional.llm.completion") -def test_batch_size_shortfall_triggers_recovery( - mock_completion, recommender, searchspace -): - """Too few valid suggestions trigger a recovery attempt that can succeed.""" - too_few = _mock_response( - _make_suggestions( - [{"temperature": 25.0, "pressure": 2.0, "n_cycles": 1, "catalyst": "A"}] - ) - ) - enough = _mock_response( - _make_suggestions( - [ - {"temperature": 25.0, "pressure": 2.0, "n_cycles": 1, "catalyst": "A"}, - {"temperature": 50.0, "pressure": 3.0, "n_cycles": 2, "catalyst": "B"}, - ] - ) - ) - mock_completion.side_effect = [too_few, enough] - - recommendations = recommender.recommend(batch_size=2, searchspace=searchspace) - - assert len(recommendations) == 2 - assert mock_completion.call_count == 2 - - -@patch("baybe._optional.llm.completion") -def test_batch_size_shortfall_failed_recovery( - mock_completion, recommender, searchspace -): - """A persistent batch-size shortfall raises after a single recovery attempt.""" - mock_completion.return_value = _mock_response( - _make_suggestions( - [{"temperature": 25.0, "pressure": 2.0, "n_cycles": 1, "catalyst": "A"}] - ) - ) - - with pytest.raises(LLMResponseError, match="even after a recovery attempt"): - recommender.recommend(batch_size=3, searchspace=searchspace) - assert mock_completion.call_count == 2 - - -@patch("baybe._optional.llm.completion") -def test_completion_failure_wrapped(mock_completion, recommender, searchspace): - """A failing completion call is wrapped in an LLMResponseError.""" - mock_completion.side_effect = RuntimeError("boom") - - with pytest.raises(LLMResponseError, match="call to the language model failed"): - recommender.recommend(batch_size=3, searchspace=searchspace) - - -def test_recommend_rejects_nonpositive_batch_size(recommender, searchspace): - """A batch size below 1 raises ValueError before any model call.""" - with pytest.raises(ValueError, match="at least request one recommendation"): - recommender.recommend(batch_size=0, searchspace=searchspace) - - -@patch("baybe._optional.llm.completion") -def test_recommend_allows_duplicate_configurations( - mock_completion, recommender, searchspace -): - """Duplicate configurations within a batch are permitted.""" - dup = {"temperature": 25.0, "pressure": 2.0, "n_cycles": 1, "catalyst": "A"} - mock_completion.return_value = _mock_response(_make_suggestions([dup, dup, dup])) - - recommendations = recommender.recommend(batch_size=3, searchspace=searchspace) - - assert len(recommendations) == 3 - assert recommendations["catalyst"].tolist() == ["A", "A", "A"] - - -def test_parse_llm_response_rejects_ineligible_points(): - """Suggestions matching filtered-out (ineligible) candidates raise.""" - filtered_space = _filtered_discrete_space(exclude={"x": 1, "y": 1}) - # The first suggestion targets the excluded candidate; the second is eligible. - response = _make_suggestions([{"x": 1, "y": 1}, {"x": 2, "y": 2}]) - with pytest.raises(IneligiblePointsError, match="do not correspond to eligible"): - parse_llm_response(response, filtered_space) - - -def test_initialization(recommender): - """LLMRecommender initializes with correct attributes.""" - assert recommender.model == "gpt-5.4" - assert recommender.experiment_description == "Test experiment" - - -def test_initialization_validation(): - """Empty required fields raise during construction.""" - from baybe.recommenders.pure.llm.llm import LLMRecommender - - with pytest.raises(ValueError, match="Length"): - LLMRecommender(model="", experiment_description="desc") - - with pytest.raises(ValueError, match="Length"): - LLMRecommender(model="m", experiment_description="") - - -@pytest.mark.parametrize( - ("key", "phrase"), - [ - ("api_key", "credential keys"), - ("api_base", "credential keys"), - ("api_version", "credential keys"), - ("model", "keys that are set"), - ("messages", "keys that are set"), - ], - ids=["api_key", "api_base", "api_version", "model", "messages"], -) -def test_construction_rejects_protected_keys(key, phrase): - """Credential or preset keys raise during construction.""" - from baybe.recommenders.pure.llm.llm import LLMRecommender - - with pytest.raises(ValueError, match=f"must not contain {phrase}"): - LLMRecommender( - model="m", - experiment_description="desc", - litellm_args={key: "secret"}, - ) - - -def test_construction_rejects_non_string_keys(): - """Non-string keys in litellm_args raise during construction.""" - from baybe.recommenders.pure.llm.llm import LLMRecommender - - with pytest.raises(TypeError): - LLMRecommender( - model="m", - experiment_description="desc", - litellm_args={1: "value"}, - ) - - -def test_str_representation(recommender): - """String representation includes key information.""" - s = str(recommender) - assert "LLMRecommender" in s - assert "gpt-5.4" in s From 61d258ee12c76f253b3c5c05da073063ac873ad6 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 8 Oct 2026 08:10:57 +0200 Subject: [PATCH 75/96] Bump `litellm` version to 1.97.0 Since `litellm` seems to be typed since this version this bump allows us to remove the package from previous exceptions regarding typing. --- baybe/recommenders/pure/llm/llm.py | 5 +- mypy.ini | 3 - pyproject.toml | 5 +- uv.lock | 151 ++++++++++++++++++----------- 4 files changed, 100 insertions(+), 64 deletions(-) diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index 9d387c90c1..1d30851064 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -121,8 +121,11 @@ def _query_model(self, prompt: str) -> str: f"identifier '{self.model}'." ) from e + # NOTE: `completion()` can also return a stream/coroutine (via `litellm_args`), + # whose types lack `.choices`; the `except` below handles those at runtime. try: - content = response.choices[0].message.content + choices = response.choices # pyrefly: ignore[missing-attribute] + content = choices[0].message.content except (AttributeError, IndexError, TypeError) as e: raise LLMResponseError( f"The language model returned an unexpected response structure: {e}." diff --git a/mypy.ini b/mypy.ini index 45bb8f27e1..184ba29f46 100644 --- a/mypy.ini +++ b/mypy.ini @@ -75,6 +75,3 @@ ignore_missing_imports = True [mypy-shap.*] ignore_missing_imports = True - -[mypy-litellm.*] -ignore_missing_imports = True diff --git a/pyproject.toml b/pyproject.toml index f4322b7ba3..d2a63fa564 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -106,7 +106,7 @@ insights = [ llm = [ "jinja2>=3.1.0", - "litellm>=1.83", + "litellm>=1.97.0", ] docs = [ @@ -210,8 +210,7 @@ project-excludes = [ "baybe/utils/dataframe.py", "baybe/utils/clustering_algorithms/third_party/kmedoids.py", ] -replace-imports-with-any = ["sklearn.*", "shap", "litellm"] -# Note: For litellm, this could be removed by bumping to a version >=1.97.0 +replace-imports-with-any = ["sklearn.*", "shap"] [tool.pyrefly.errors] unused-ignore = "error" diff --git a/uv.lock b/uv.lock index 0c8c6c35fc..e8d894a811 100644 --- a/uv.lock +++ b/uv.lock @@ -7,11 +7,11 @@ resolution-markers = [ "python_full_version >= '3.14' and platform_machine == 'x86_64' and sys_platform == 'darwin'", "(python_full_version >= '3.14' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version >= '3.14' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')", "python_full_version == '3.13.*' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and sys_platform == 'win32'", "python_full_version == '3.13.*' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and sys_platform == 'emscripten'", "python_full_version == '3.13.*' and platform_machine == 'x86_64' and sys_platform == 'darwin'", "(python_full_version == '3.13.*' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version == 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sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')", "python_full_version == '3.13.*' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and sys_platform == 'win32'", "python_full_version == '3.13.*' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and sys_platform == 'emscripten'", "python_full_version == '3.13.*' and platform_machine == 'x86_64' and sys_platform == 'darwin'", "(python_full_version == '3.13.*' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform == 'emscripten'", "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'darwin'", "(python_full_version == '3.12.*' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')", "python_full_version == '3.11.*' and sys_platform == 'win32'", @@ -7600,11 +7637,11 @@ resolution-markers = [ "python_full_version >= '3.14' and platform_machine == 'x86_64' and sys_platform == 'darwin'", "(python_full_version >= '3.14' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version >= '3.14' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')", "python_full_version == '3.13.*' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and sys_platform == 'win32'", "python_full_version == '3.13.*' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and sys_platform == 'emscripten'", "python_full_version == '3.13.*' and platform_machine == 'x86_64' and sys_platform == 'darwin'", "(python_full_version == '3.13.*' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform == 'emscripten'", "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'darwin'", "(python_full_version == '3.12.*' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')", ] @@ -7844,11 +7881,11 @@ resolution-markers = [ "python_full_version >= '3.14' and platform_machine == 'x86_64' and sys_platform == 'darwin'", "(python_full_version >= '3.14' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version >= '3.14' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')", "python_full_version == '3.13.*' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and sys_platform == 'win32'", "python_full_version == '3.13.*' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and sys_platform == 'emscripten'", "python_full_version == '3.13.*' and platform_machine == 'x86_64' and sys_platform == 'darwin'", "(python_full_version == '3.13.*' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform == 'emscripten'", "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'darwin'", "(python_full_version == '3.12.*' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')", ] @@ -8017,11 +8054,11 @@ resolution-markers = [ "python_full_version >= '3.14' and platform_machine == 'x86_64' and sys_platform == 'darwin'", "(python_full_version >= '3.14' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version >= '3.14' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')", "python_full_version == '3.13.*' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and sys_platform == 'win32'", "python_full_version == '3.13.*' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and sys_platform == 'emscripten'", "python_full_version == '3.13.*' and platform_machine == 'x86_64' and sys_platform == 'darwin'", "(python_full_version == '3.13.*' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform == 'emscripten'", "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'darwin'", "(python_full_version == '3.12.*' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')", "python_full_version == '3.11.*' and sys_platform == 'win32'", @@ -8078,11 +8115,11 @@ resolution-markers = [ "python_full_version >= '3.14' and platform_machine == 'x86_64' and sys_platform == 'darwin'", "(python_full_version >= '3.14' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version >= '3.14' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')", "python_full_version == '3.13.*' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and sys_platform == 'win32'", "python_full_version == '3.13.*' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and sys_platform == 'emscripten'", "python_full_version == '3.13.*' and platform_machine == 'x86_64' and sys_platform == 'darwin'", "(python_full_version == '3.13.*' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform == 'emscripten'", "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'darwin'", "(python_full_version == '3.12.*' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')", "python_full_version == '3.11.*' and sys_platform == 'win32'", @@ -8368,11 +8405,11 @@ resolution-markers = [ "python_full_version >= '3.14' and platform_machine == 'x86_64' and sys_platform == 'darwin'", "(python_full_version >= '3.14' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version >= '3.14' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')", "python_full_version == '3.13.*' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and sys_platform == 'win32'", "python_full_version == '3.13.*' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and sys_platform == 'emscripten'", "python_full_version == '3.13.*' and platform_machine == 'x86_64' and sys_platform == 'darwin'", "(python_full_version == '3.13.*' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform == 'emscripten'", "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'darwin'", "(python_full_version == '3.12.*' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')", ] @@ -9197,11 +9234,11 @@ resolution-markers = [ "python_full_version >= '3.14' and platform_machine == 'x86_64' and sys_platform == 'darwin'", "(python_full_version >= '3.14' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version >= '3.14' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')", "python_full_version == '3.13.*' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and sys_platform == 'win32'", "python_full_version == '3.13.*' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and sys_platform == 'emscripten'", "python_full_version == '3.13.*' and platform_machine == 'x86_64' and sys_platform == 'darwin'", "(python_full_version == '3.13.*' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform == 'emscripten'", "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'darwin'", "(python_full_version == '3.12.*' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')", "python_full_version == '3.11.*' and sys_platform == 'win32'", From c38fdb0c49ceb5821489afb08f92c5a1dc12bdee Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 8 Oct 2026 09:10:58 +0200 Subject: [PATCH 76/96] Rework and improve LLM-related exceptions and error handling All LLM-related exceptions now carry an `LLM` prefix in their class names. The hierarchy was extended by differentiating between general call errors (with authentication errors as one special subcase), and response errors. --- baybe/_optional/llm.py | 3 +- baybe/exceptions.py | 231 +++++++++++++----------- baybe/recommenders/pure/llm/_parsing.py | 60 +++--- baybe/recommenders/pure/llm/llm.py | 36 +++- 4 files changed, 184 insertions(+), 146 deletions(-) diff --git a/baybe/_optional/llm.py b/baybe/_optional/llm.py index 49e7612307..dfd0fa7eeb 100644 --- a/baybe/_optional/llm.py +++ b/baybe/_optional/llm.py @@ -8,11 +8,12 @@ raise OptionalImportError(name="jinja2", group="llm") from ex try: - from litellm import completion + from litellm import AuthenticationError, completion except ModuleNotFoundError as ex: raise OptionalImportError(name="litellm", group="llm") from ex __all__ = [ + "AuthenticationError", "StrictUndefined", "Template", "completion", diff --git a/baybe/exceptions.py b/baybe/exceptions.py index 096da7f47f..21db47c1fb 100644 --- a/baybe/exceptions.py +++ b/baybe/exceptions.py @@ -95,7 +95,123 @@ class InfeasibilityError(Exception): """An optimization problem has no feasible solution.""" -class LLMResponseError(Exception): +class NotEnoughPointsLeftError(Exception): + """ + More recommendations are requested than there are viable parameter configurations + left in the search space. + """ + + +class NoMCAcquisitionFunctionError(Exception): + """ + A Monte Carlo acquisition function is required but an analytical acquisition + function has been selected by the user. + """ + + +class EmptySearchSpaceError(Exception): + """The created search space contains no parameters.""" + + +class NoMeasurementsError(Exception): + """A context expected measurements but none were available.""" + + +class IncompleteMeasurementsError(Exception): + """A context expected complete measurements but none were available.""" + + +class NothingToSimulateError(Exception): + """There is nothing to simulate because there are no testable configurations.""" + + +class NothingToComputeError(Exception): + """There is nothing to compute because there are no inputs or existing data.""" + + +class NoRecommendersLeftError(Exception): + """A recommender is requested by a meta recommender but there are no recommenders + left. + """ + + +class NumericalUnderflowError(Exception): + """A computation would lead to numerical underflow.""" + + +class OptionalImportError(ImportError): + """An attempt was made to import an optional but uninstalled dependency.""" + + def __init__( + self, + *args: Any, + name: str | None = None, + path: str | None = None, + group: str | None = None, + ): + super().__init__(*args, name=name, path=path) + + # If no message has been explicitly set, create it from the context + if self.msg is None and name is not None: + group_str = f"`pip install 'baybe[{group}]'` or " if group else "" + self.msg = ( + f"The requested functionality requires the optional " # pyrefly: ignore[bad-assignment] + f"'{self.name}' package, which is currently not installed. " + f"Please install the dependency and try again. " + f"You can do so manually (e.g. `pip install {self.name}`) " + f"or using an appropriate optional dependency group " + f"(e.g. {group_str}`pip install 'baybe[extras]'`)." + ) + + +class DeprecationError(Exception): + """Signals the use of a deprecated mechanism to the user, interrupting execution.""" + + +class UnidentifiedSubclassError(Exception): + """A specified subclass cannot be found in the given class hierarchy.""" + + +class ModelNotTrainedError(Exception): + """A prediction/transformation is attempted before the model has been trained.""" + + +class UnmatchedAttributeError(Exception): + """An attribute cannot be matched against a certain callable signature.""" + + +class InvalidTargetValueError(Exception): + """A target value was entered that is not in the target space.""" + + +class NotAllowedError(Exception): + """An operation was attempted that is not allowed in the current context.""" + + +class UnsupportedEarlyFilteringError(Exception): + """A constraint does not support early filtering with the given parameters.""" + + +#### LLM exclusive exceptions #### + + +class LLMError(Exception): + """A language model interaction failed.""" + + +class LLMCallError(LLMError): + """The call to the language model failed before producing a response. + + Raised for failures such as network errors, rate limiting, timeouts, or an + unknown model identifier, where no response is returned to process. + """ + + +class LLMAuthenticationError(LLMCallError): + """Authentication with the language model provider failed.""" + + +class LLMResponseError(LLMError): """An error occurred while processing a language model response.""" @property @@ -108,7 +224,7 @@ def recovery_instruction(self) -> str: ) -class MalformedLLMResponseError(LLMResponseError): +class LLMMalformedResponseError(LLMResponseError): """A language model response could not be parsed into the expected structure.""" @property @@ -121,7 +237,7 @@ def recovery_instruction(self) -> str: ) -class UnknownParameterError(LLMResponseError): +class LLMUnknownParameterError(LLMResponseError): """A language model response referenced parameters outside the search space.""" def __init__( @@ -144,7 +260,7 @@ def recovery_instruction(self) -> str: ) -class MissingParameterError(LLMResponseError): +class LLMMissingParameterError(LLMResponseError): """A language model response omitted required search space parameters.""" def __init__(self, *args: Any, parameters: Collection[str]): @@ -161,7 +277,7 @@ def recovery_instruction(self) -> str: ) -class NonNumericParameterError(LLMResponseError): +class LLMNonNumericParameterError(LLMResponseError): """A language model response gave non-numeric values for a numerical parameter.""" def __init__(self, *args: Any, detail: str): @@ -178,7 +294,7 @@ def recovery_instruction(self) -> str: ) -class InvalidParameterValueError(LLMResponseError): +class LLMInvalidParameterValueError(LLMResponseError): """A language model response contained invalid parameter values.""" def __init__(self, *args: Any, detail: str): @@ -196,7 +312,7 @@ def recovery_instruction(self) -> str: ) -class ConstraintViolationError(LLMResponseError): +class LLMConstraintViolationError(LLMResponseError): """A language model response violated a search space constraint.""" def __init__( @@ -219,7 +335,7 @@ def recovery_instruction(self) -> str: ) -class IneligiblePointsError(LLMResponseError): +class LLMIneligiblePointsError(LLMResponseError): """A language model response proposed points outside the eligible candidate set.""" def __init__(self, *args: Any, n_ineligible: int): @@ -237,7 +353,7 @@ def recovery_instruction(self) -> str: ) -class BatchSizeError(LLMResponseError): +class LLMBatchSizeError(LLMResponseError): """A language model response contained the wrong number of recommendations.""" def __init__(self, *args: Any, requested: int, received: int): @@ -255,102 +371,5 @@ def recovery_instruction(self) -> str: ) -class NotEnoughPointsLeftError(Exception): - """ - More recommendations are requested than there are viable parameter configurations - left in the search space. - """ - - -class NoMCAcquisitionFunctionError(Exception): - """ - A Monte Carlo acquisition function is required but an analytical acquisition - function has been selected by the user. - """ - - -class EmptySearchSpaceError(Exception): - """The created search space contains no parameters.""" - - -class NoMeasurementsError(Exception): - """A context expected measurements but none were available.""" - - -class IncompleteMeasurementsError(Exception): - """A context expected complete measurements but none were available.""" - - -class NothingToSimulateError(Exception): - """There is nothing to simulate because there are no testable configurations.""" - - -class NothingToComputeError(Exception): - """There is nothing to compute because there are no inputs or existing data.""" - - -class NoRecommendersLeftError(Exception): - """A recommender is requested by a meta recommender but there are no recommenders - left. - """ - - -class NumericalUnderflowError(Exception): - """A computation would lead to numerical underflow.""" - - -class OptionalImportError(ImportError): - """An attempt was made to import an optional but uninstalled dependency.""" - - def __init__( - self, - *args: Any, - name: str | None = None, - path: str | None = None, - group: str | None = None, - ): - super().__init__(*args, name=name, path=path) - - # If no message has been explicitly set, create it from the context - if self.msg is None and name is not None: - group_str = f"`pip install 'baybe[{group}]'` or " if group else "" - self.msg = ( - f"The requested functionality requires the optional " # pyrefly: ignore[bad-assignment] - f"'{self.name}' package, which is currently not installed. " - f"Please install the dependency and try again. " - f"You can do so manually (e.g. `pip install {self.name}`) " - f"or using an appropriate optional dependency group " - f"(e.g. {group_str}`pip install 'baybe[extras]'`)." - ) - - -class DeprecationError(Exception): - """Signals the use of a deprecated mechanism to the user, interrupting execution.""" - - -class UnidentifiedSubclassError(Exception): - """A specified subclass cannot be found in the given class hierarchy.""" - - -class ModelNotTrainedError(Exception): - """A prediction/transformation is attempted before the model has been trained.""" - - -class UnmatchedAttributeError(Exception): - """An attribute cannot be matched against a certain callable signature.""" - - -class InvalidTargetValueError(Exception): - """A target value was entered that is not in the target space.""" - - -class NotAllowedError(Exception): - """An operation was attempted that is not allowed in the current context.""" - - -class UnsupportedEarlyFilteringError(Exception): - """A constraint does not support early filtering with the given parameters.""" - - # Collect leftover original slotted classes processed by `attrs.define` gc.collect() diff --git a/baybe/recommenders/pure/llm/_parsing.py b/baybe/recommenders/pure/llm/_parsing.py index 27f1990e8b..0eb01e6dd4 100644 --- a/baybe/recommenders/pure/llm/_parsing.py +++ b/baybe/recommenders/pure/llm/_parsing.py @@ -10,14 +10,14 @@ from baybe.constraints.base import DiscreteFilteringConstraint from baybe.exceptions import ( - ConstraintViolationError, - IneligiblePointsError, - InvalidParameterValueError, + LLMConstraintViolationError, + LLMIneligiblePointsError, + LLMInvalidParameterValueError, + LLMMalformedResponseError, + LLMMissingParameterError, + LLMNonNumericParameterError, LLMResponseWarning, - MalformedLLMResponseError, - MissingParameterError, - NonNumericParameterError, - UnknownParameterError, + LLMUnknownParameterError, ) from baybe.recommenders.pure.llm._schema import ( _EXPLANATION_FIELD, @@ -77,17 +77,19 @@ def parse_llm_response(response: str, /, searchspace: SearchSpace) -> pd.DataFra :meth:`baybe.searchspace.discrete.SubspaceDiscrete.get_candidates`). Raises: - MalformedLLMResponseError: If the response cannot be parsed into the expected + LLMMalformedResponseError: If the response cannot be parsed into the expected JSON structure. - UnknownParameterError: If a suggestion references parameters not in the search - space. - MissingParameterError: If a suggestion omits required search space parameters. - NonNumericParameterError: If a suggestion gives non-numeric values for a + LLMUnknownParameterError: If a suggestion references parameters not in the + search space. + LLMMissingParameterError: If a suggestion omits required search space + parameters. + LLMNonNumericParameterError: If a suggestion gives non-numeric values for a numerical parameter. - InvalidParameterValueError: If a suggestion contains invalid parameter values. - ConstraintViolationError: If a suggestion violates a discrete constraint + LLMInvalidParameterValueError: If a suggestion contains invalid parameter + values. + LLMConstraintViolationError: If a suggestion violates a discrete constraint (including batch constraints) present in the search space. - IneligiblePointsError: If a suggestion does not correspond to an eligible + LLMIneligiblePointsError: If a suggestion does not correspond to an eligible candidate of the search space. Warns: @@ -99,39 +101,39 @@ def parse_llm_response(response: str, /, searchspace: SearchSpace) -> pd.DataFra try: suggestions = json.loads(payload) except (JSONDecodeError, TypeError) as e: - raise MalformedLLMResponseError(f"Error parsing JSON output: {e}.") from e + raise LLMMalformedResponseError(f"Error parsing JSON output: {e}.") from e if not isinstance(suggestions, list): - raise MalformedLLMResponseError("Response must be a JSON array.") + raise LLMMalformedResponseError("Response must be a JSON array.") if not suggestions: - raise MalformedLLMResponseError( + raise LLMMalformedResponseError( "Response contains an empty array with no suggestions." ) recommendations = [] for suggestion in suggestions: if not isinstance(suggestion, dict): - raise MalformedLLMResponseError("Each suggestion must be a JSON object.") + raise LLMMalformedResponseError("Each suggestion must be a JSON object.") if _PARAMETERS_FIELD not in suggestion: - raise MalformedLLMResponseError( + raise LLMMalformedResponseError( f"Each suggestion must contain a '{_PARAMETERS_FIELD}' field." ) if _EXPLANATION_FIELD not in suggestion: - raise MalformedLLMResponseError( + raise LLMMalformedResponseError( f"Each suggestion must contain an '{_EXPLANATION_FIELD}' field." ) params = suggestion[_PARAMETERS_FIELD] if not isinstance(params, dict): - raise MalformedLLMResponseError("Parameters must be a JSON object.") + raise LLMMalformedResponseError("Parameters must be a JSON object.") param_names = {p.name for p in searchspace.parameters} unknown = set(params.keys()) - param_names if unknown: - raise UnknownParameterError( + raise LLMUnknownParameterError( f"Response contains unknown parameter names: {unknown}.", unknown_names=unknown, valid_names=param_names, @@ -144,7 +146,7 @@ def parse_llm_response(response: str, /, searchspace: SearchSpace) -> pd.DataFra # Detect missing columns up front so they surface as a distinct error. missing = {p.name for p in searchspace.parameters}.difference(df.columns) if missing: - raise MissingParameterError( + raise LLMMissingParameterError( f"Response is missing values for the following parameters: {missing}.", parameters=missing, ) @@ -159,9 +161,9 @@ def parse_llm_response(response: str, /, searchspace: SearchSpace) -> pd.DataFra numerical_measurements_must_be_within_tolerance=True, ) except TypeError as e: - raise NonNumericParameterError(str(e), detail=str(e)) from e + raise LLMNonNumericParameterError(str(e), detail=str(e)) from e except ValueError as e: - raise InvalidParameterValueError(str(e), detail=str(e)) from e + raise LLMInvalidParameterValueError(str(e), detail=str(e)) from e df = normalize_input_dtypes(df, searchspace.parameters) continuous_constraints = ( @@ -185,7 +187,7 @@ def parse_llm_response(response: str, /, searchspace: SearchSpace) -> pd.DataFra continue invalid_idx = constraint.get_invalid(df) if not invalid_idx.empty: - raise ConstraintViolationError( + raise LLMConstraintViolationError( f"{len(invalid_idx)} suggestion(s) violate the " f"'{type(constraint).__name__}' constraint on parameters " f"{constraint.parameters}.", @@ -197,7 +199,7 @@ def parse_llm_response(response: str, /, searchspace: SearchSpace) -> pd.DataFra param_name = constraint.parameters[0] unique_values = df[param_name].unique() if len(unique_values) > 1: - raise ConstraintViolationError( + raise LLMConstraintViolationError( f"Suggestions violate the '{type(constraint).__name__}' constraint on " f"parameter '{param_name}': all suggestions in a batch must share the " f"same value, but received {list(unique_values)}.", @@ -218,7 +220,7 @@ def parse_llm_response(response: str, /, searchspace: SearchSpace) -> pd.DataFra # detect the shortfall explicitly rather than let them vanish. n_ineligible = len(df) - len(aligned_index) if n_ineligible > 0: - raise IneligiblePointsError( + raise LLMIneligiblePointsError( f"{n_ineligible} suggestion(s) do not correspond to eligible " f"candidates of the search space.", n_ineligible=n_ineligible, diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index 1d30851064..b9087c0aa4 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -11,7 +11,12 @@ from attrs.validators import deep_mapping, instance_of, min_len from typing_extensions import override -from baybe.exceptions import BatchSizeError, LLMResponseError +from baybe.exceptions import ( + LLMAuthenticationError, + LLMBatchSizeError, + LLMCallError, + LLMResponseError, +) from baybe.objectives.base import Objective from baybe.recommenders.pure.base import PureRecommender from baybe.recommenders.pure.llm._parsing import parse_llm_response @@ -104,9 +109,12 @@ def _query_model(self, prompt: str) -> str: The raw text content of the model response. Raises: - LLMResponseError: If the model call fails or returns no usable content. + LLMAuthenticationError: If authentication with the provider fails. + LLMCallError: If the call fails for another reason (e.g. network, rate + limiting, timeout, unknown model) before a response is produced. + LLMResponseError: If a response is returned but contains no usable content. """ - from baybe._optional.llm import completion + from baybe._optional.llm import AuthenticationError, completion try: response = completion( @@ -114,11 +122,17 @@ def _query_model(self, prompt: str) -> str: messages=[{"role": "user", "content": prompt}], **self.litellm_args, ) + except AuthenticationError as e: + raise LLMAuthenticationError( + f"Authentication with the language model provider failed " + f"({type(e).__name__}): {e}. Check the API credentials for model " + f"'{self.model}' (e.g. the provider's API key environment variable)." + ) from e except Exception as e: - raise LLMResponseError( + raise LLMCallError( f"The call to the language model failed ({type(e).__name__}): {e}. " - f"Check your API credentials, network connection, and the model " - f"identifier '{self.model}'." + f"Check your network connection and the model identifier " + f"'{self.model}'." ) from e # NOTE: `completion()` can also return a stream/coroutine (via `litellm_args`), @@ -155,7 +169,7 @@ def _validate_response( """ output = parse_llm_response(content, searchspace) if len(output) < batch_size: - raise BatchSizeError( + raise LLMBatchSizeError( f"The language model returned {len(output)} valid recommendation(s) " f"instead of the requested {batch_size}.", requested=batch_size, @@ -186,9 +200,11 @@ def recommend( A DataFrame containing the recommendations as individual rows. Raises: - LLMResponseError: If the call to the language model fails, or if its - response cannot be turned into a valid recommendation batch even after - a recovery attempt. + LLMAuthenticationError: If authentication with the provider fails. + LLMCallError: If the call to the language model fails (e.g. network, rate + limiting, timeout) before a response is produced. + LLMResponseError: If the response cannot be turned into a valid + recommendation batch even after a recovery attempt. ValueError: If ``batch_size`` is smaller than 1. """ if batch_size < 1: From d96ac7a7cfe435779b0733ea162fb5f89b01f686 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 8 Oct 2026 12:53:12 +0200 Subject: [PATCH 77/96] Implement `_recommend_hybrid` instead of overriding `recommend` The `recommend` call now stashes necessary context for creating the prompts and delegates to `_recommend_hybrid`. As a consequence, validation and preprocessing of the `super` call is used and has been removed. --- baybe/recommenders/pure/llm/llm.py | 70 ++++++++++++++++++------------ 1 file changed, 42 insertions(+), 28 deletions(-) diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index b9087c0aa4..b4f36d550f 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -25,7 +25,6 @@ from baybe.searchspace.core import SearchSpaceType from baybe.serialization import SerialMixin from baybe.utils.conversion import to_string -from baybe.utils.validation import preprocess_dataframe, validate_object_names if TYPE_CHECKING: from baybe.recommenders.base import RecommenderProtocol @@ -52,6 +51,9 @@ class LLMRecommender(PureRecommender, SerialMixin): compatibility: ClassVar[SearchSpaceType] = SearchSpaceType.HYBRID # See base class. + supports_discrete_subset_generating_constraints: ClassVar[bool] = True + # See base class. + model: str = field(validator=(instance_of(str), min_len(1))) """The LiteLLM model identifier to use for recommendations.""" @@ -82,6 +84,15 @@ class LLMRecommender(PureRecommender, SerialMixin): (e.g. ``OPENAI_API_KEY``, ``ANTHROPIC_API_KEY``). """ + # Stashed context necessary for prompt building + _objective: Objective | None = field(default=None, init=False, eq=False) + + _measurements: pd.DataFrame | None = field(default=None, init=False, eq=False) + + _pending_experiments: pd.DataFrame | None = field( + default=None, init=False, eq=False + ) + @litellm_args.validator def _validate_litellm_args(self, attribute, value): # noqa: DOC101, DOC103 """Validate litellm_args does not contain reserved or credential keys.""" @@ -213,39 +224,42 @@ def recommend( f"provided {batch_size=}." ) - if objective is not None: - validate_object_names(searchspace.parameters + objective.targets) - if objective.metadata.is_empty: - warnings.warn( - "The objective has no metadata description. Without context on " - "what to optimize, the language model may produce suboptimal " - "suggestions. Set the 'description' field on the objective's " - "metadata to guide the LLM.", - UserWarning, - stacklevel=2, - ) - - if measurements is not None: - measurements = preprocess_dataframe( - measurements, - searchspace, - objective, - numerical_measurements_must_be_within_tolerance=False, + if objective is not None and objective.metadata.is_empty: + warnings.warn( + "The objective has no metadata description. Without context on " + "what to optimize, the language model may produce suboptimal " + "suggestions. Set the 'description' field on the objective's " + "metadata to guide the LLM.", + UserWarning, + stacklevel=2, ) - if pending_experiments is not None: - pending_experiments = preprocess_dataframe( - pending_experiments, - searchspace, - numerical_measurements_must_be_within_tolerance=False, - ) + # Stash context for `_recommend_hybrid`, then delegate to the base `recommend` + self._objective = objective + self._measurements = measurements + self._pending_experiments = pending_experiments + + return super().recommend( + batch_size=batch_size, + searchspace=searchspace, + objective=objective, + measurements=measurements, + pending_experiments=pending_experiments, + ) + @override + def _recommend_hybrid( + self, + searchspace: SearchSpace, + candidates_exp: pd.DataFrame, + batch_size: int, + ) -> pd.DataFrame: prompt = make_prompt( batch_size, searchspace, - objective, - measurements, - pending_experiments, + self._objective, + self._measurements, + self._pending_experiments, experiment_description=self.experiment_description, ) content = self._query_model(prompt) From c1fc103c14edaaf865c03647ad36648404041a60 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 8 Oct 2026 13:06:11 +0200 Subject: [PATCH 78/96] Remove examples and docs on LLMs It has been decided that those should be added in a follow-up PR in order to decrease the scope of this one --- docs/components/recommenders.md | 29 - examples/LLM/LLM_Header.md | 5 - examples/LLM/llm_recommender.py | 246 ---- examples/LLM/llm_recommender_dark.svg | 1718 ------------------------ examples/LLM/llm_recommender_light.svg | 1718 ------------------------ 5 files changed, 3716 deletions(-) delete mode 100644 examples/LLM/LLM_Header.md delete mode 100644 examples/LLM/llm_recommender.py delete mode 100644 examples/LLM/llm_recommender_dark.svg delete mode 100644 examples/LLM/llm_recommender_light.svg diff --git a/docs/components/recommenders.md b/docs/components/recommenders.md index 1afa7d7e68..d8e415f27e 100644 --- a/docs/components/recommenders.md +++ b/docs/components/recommenders.md @@ -88,35 +88,6 @@ BayBE provides two recommenders that recommend by sampling form the search space this recommender can be found [here](./../../examples/Custom_Surrogates/surrogate_params). -### LLM Recommenders - -The **[`LLMRecommender`](baybe.recommenders.pure.llm.llm.LLMRecommender)** queries a large -language model to propose experiments. This is particularly useful for warm-starting a -campaign, where a language model can inject prior knowledge to suggest more sensible -points than random sampling before enough data has been collected to train a surrogate -model. It builds a prompt from the search space and the provided ``experiment_description`` -and ``objective_description``, queries the model through -[LiteLLM](https://docs.litellm.ai/), and validates the returned suggestions against the -search space. The ``model`` field accepts any LiteLLM model identifier (e.g. -``"openai/gpt-4o"`` or ``"anthropic/claude-sonnet-4-6"``). - -This recommender requires the optional ``llm`` dependency group: - -~~~bash -pip install "baybe[llm]" -~~~ - -```{admonition} Credentials -:class: note -The recommender does not take an API key. LiteLLM reads the provider credentials from -environment variables (e.g. ``OPENAI_API_KEY``, ``ANTHROPIC_API_KEY``) selected by the -provider prefix of the ``model`` identifier. -``` - -A walk-through that warm-starts a campaign with the ``LLMRecommender`` before switching to -a Bayesian recommender can be found -[here](./../../examples/LLM/llm_recommender). - ## Meta Recommenders In analogy to meta studies, meta recommenders are wrappers that operate on a sequence diff --git a/examples/LLM/LLM_Header.md b/examples/LLM/LLM_Header.md deleted file mode 100644 index 57164044f8..0000000000 --- a/examples/LLM/LLM_Header.md +++ /dev/null @@ -1,5 +0,0 @@ -# LLM - -These examples demonstrate how to use BayBE's -{class}`~baybe.recommenders.pure.llm.llm.LLMRecommender` to let a large language model -propose experiments. diff --git a/examples/LLM/llm_recommender.py b/examples/LLM/llm_recommender.py deleted file mode 100644 index cee97555b2..0000000000 --- a/examples/LLM/llm_recommender.py +++ /dev/null @@ -1,246 +0,0 @@ -## Warm-Starting a Campaign with a Language Model - -# This example demonstrates the -# {class}`~baybe.recommenders.pure.llm.llm.LLMRecommender`, which queries a large -# language model (LLM) to propose experiments. We use it to guide the *first* iterations -# of a chemical reaction optimization, hand over to a regular Bayesian recommender once -# enough data has been collected, and compare this strategy against a random baseline and -# BayBE's default recommender. - -### How it works and why it helps early on - -# On each call, the recommender turns the search space, the experiment and objective -# descriptions, and any collected measurements into a prompt, sends it to an LLM via -# [LiteLLM](https://docs.litellm.ai/), and parses the returned JSON into validated -# experiments (retrying once if the response is malformed). - -# Bayesian optimization needs initial data before its surrogate becomes informative, so -# early experiments are usually random. An LLM can do better: needing no training data, it -# exploits literature and chemistry priors and understands the parameter semantics (names, -# units, allowed values) to propose sensible starting points. Once enough data exists, we -# switch to a {class}`~baybe.recommenders.pure.bayesian.botorch.core.BotorchRecommender` via a -# {class}`~baybe.recommenders.meta.sequential.TwoPhaseMetaRecommender`. - - -### Imports - -import json -import os -from contextlib import nullcontext -from types import SimpleNamespace -from unittest.mock import patch - -import pandas as pd -import seaborn as sns - -from baybe import Campaign, Settings -from baybe.objectives import SingleTargetObjective -from baybe.parameters import NumericalDiscreteParameter, SubstanceParameter -from baybe.recommenders import ( - BotorchRecommender, - LLMRecommender, - RandomRecommender, - TwoPhaseMetaRecommender, -) -from baybe.searchspace import SearchSpace -from baybe.simulation import simulate_scenarios -from baybe.targets import NumericalTarget -from examples.utils import create_example_plots - -### Settings - -# Let's define some general settings for our example: - -SMOKE_TEST = "SMOKE_TEST" in os.environ - -BATCH_SIZE = 1 -N_WARMSTART_EXPERIMENTS = 1 # experiments handled by the LLM before switching -N_DOE_ITERATIONS = 1 if SMOKE_TEST else 3 -# Kept low to bound the number of (potentially costly) LLM calls. -N_MC_ITERATIONS = 1 if SMOKE_TEST else 3 - -# LiteLLM reads the provider credentials from the environment. In CI (``SMOKE_TEST``) we -# fall back to a mocked LLM so the example runs offline; otherwise a real model is queried. - - -### Lookup Data - -# We optimize a direct arylation reaction, the same chemical example used in other BayBE -# examples. Since all parameter combinations have been measured, we can use a lookup table -# to obtain the reaction yield for any recommendation. The data set was obtained from -# [Shields, B.J., Stevens et al. Nature 590, 89-96 (2021)](https://doi.org/10.1038/s41586-021-03213-y). - -try: - lookup = pd.read_csv("benchmarks/data/direct_arylation/data.csv") -except FileNotFoundError: - lookup = pd.read_csv("../../../benchmarks/data/direct_arylation/data.csv") - - -### Defining the Optimization Problem - -# The reaction is described by three chemical substances (solvent, base, and ligand) and -# two numerical process parameters (temperature and concentration). The parameter names -# must match the columns of the lookup table. - -dict_solvent = { - "DMAc": r"CC(N(C)C)=O", - "Butyornitrile": r"CCCC#N", - "Butyl Ester": r"CCCCOC(C)=O", - "p-Xylene": r"CC1=CC=C(C)C=C1", -} - -dict_base = { - "Potassium acetate": r"O=C([O-])C.[K+]", - "Potassium pivalate": r"O=C([O-])C(C)(C)C.[K+]", - "Cesium acetate": r"O=C([O-])C.[Cs+]", - "Cesium pivalate": r"O=C([O-])C(C)(C)C.[Cs+]", -} - -dict_ligand = { - "BrettPhos": r"CC(C)C1=CC(C(C)C)=C(C(C(C)C)=C1)C2=C(P(C3CCCCC3)C4CCCCC4)C(OC)=" - "CC=C2OC", - "Di-tert-butylphenylphosphine": r"CC(C)(C)P(C1=CC=CC=C1)C(C)(C)C", - "(t-Bu)PhCPhos": r"CN(C)C1=CC=CC(N(C)C)=C1C2=CC=CC=C2P(C(C)(C)C)C3=CC=CC=C3", -} - -# We define the substance parameters using the dictionaries above and add descriptive -# metadata to the numerical parameters. The metadata (descriptions and units) is picked -# up automatically by the ``LLMRecommender`` and rendered into the prompt, giving the -# language model additional context. - -solvent = SubstanceParameter("Solvent", data=dict_solvent, encoding="MORDRED") -base = SubstanceParameter("Base", data=dict_base, encoding="MORDRED") -ligand = SubstanceParameter("Ligand", data=dict_ligand, encoding="MORDRED") - -temperature = NumericalDiscreteParameter( - "Temp_C", - values=[90, 105, 120], - tolerance=2, - metadata={"description": "Reaction temperature", "unit": "°C"}, -) -concentration = NumericalDiscreteParameter( - "Concentration", - values=[0.057, 0.1, 0.153], - tolerance=0.005, - metadata={"description": "Substrate concentration", "unit": "mol/L"}, -) - -searchspace = SearchSpace.from_product( - [solvent, base, ligand, temperature, concentration] -) - -# We maximize the reaction yield: - -objective = SingleTargetObjective(target=NumericalTarget(name="yield")) - - -### Setting Up the Recommenders - -# The ``LLMRecommender`` needs the model identifier plus textual descriptions of the -# experiment and objective, which convey the domain context to the language model. - -llm_recommender = LLMRecommender( - model="anthropic/claude-haiku-4-5", - experiment_description=( - "Optimization of a direct C-H arylation reaction. Each experiment selects a " - "solvent, a base, and a ligand (given by their names) together with a reaction " - "temperature and a substrate concentration." - ), -) - -# We compare three strategies: -# * **LLM + Bayesian**: the language model warm-starts the campaign and the meta -# recommender switches to the Bayesian recommender after ``N_WARMSTART_EXPERIMENTS``, -# * **Default**: BayBE's default recommender (random initialization, then Bayesian), and -# * **Random**: a pure random baseline. - -scenarios = { - "LLM + Bayesian": Campaign( - searchspace=searchspace, - objective=objective, - recommender=TwoPhaseMetaRecommender( - initial_recommender=llm_recommender, - recommender=BotorchRecommender(), - switch_after=N_WARMSTART_EXPERIMENTS, - ), - ), - "Default": Campaign(searchspace=searchspace, objective=objective), - "Random": Campaign( - searchspace=searchspace, - objective=objective, - recommender=RandomRecommender(), - ), -} - - -### Mocked Fallback - -# Without a credential, we patch the LiteLLM call to return random valid configurations, -# keeping the example runnable offline. With a real token, this is skipped. - - -def _make_mock_completion(space: SearchSpace): - """Create a fake LiteLLM ``completion`` returning random valid suggestions.""" - candidates = space.discrete.exp_rep - - def _mock_completion(*args, **kwargs): - rows = candidates.sample(min(len(candidates), 4 * BATCH_SIZE)) - suggestions = [ - {"explanation": "Mocked suggestion.", "parameters": row.to_dict()} - for _, row in rows.iterrows() - ] - content = json.dumps(suggestions, default=str) - message = SimpleNamespace(content=content) - return SimpleNamespace(choices=[SimpleNamespace(message=message)]) - - return _mock_completion - - -llm_context = ( - patch("baybe._optional.llm.completion", _make_mock_completion(searchspace)) - if SMOKE_TEST - else nullcontext() -) - -if SMOKE_TEST: - Settings(parallelize_simulation_runs=False).activate() - - -### Running the Comparison - -# We use `simulate_scenarios` to run a full optimization loop for each strategy, tracking -# the best yield found so far. The language model is queried through the (real or mocked) -# LiteLLM call for the duration of the simulation. - - -with llm_context: - results = simulate_scenarios( - scenarios, - lookup, - batch_size=BATCH_SIZE, - n_doe_iterations=N_DOE_ITERATIONS, - n_mc_iterations=N_MC_ITERATIONS, - ) - -print(results) - - -### Visualization - -# We plot the best yield found so far against the number of conducted experiments. The -# language model gives the "LLM + Bayesian" strategy a head start in the early -# iterations, before the Bayesian recommender takes over. - -results.rename(columns={"Scenario": "Strategy"}, inplace=True) -ax = sns.lineplot( - data=results, - marker="o", - markersize=10, - x="Num_Experiments", - y="yield_CumBest", - hue="Strategy", -) - -# Note that the displayed images were generated from a real run with 30 MC and 30 DoE -# iterations to demonstrate the effect of the LLM warm-start. -create_example_plots(ax=ax, base_name="llm_recommender") diff --git a/examples/LLM/llm_recommender_dark.svg b/examples/LLM/llm_recommender_dark.svg deleted file mode 100644 index 542707e9c3..0000000000 --- a/examples/LLM/llm_recommender_dark.svg +++ /dev/null @@ -1,1718 +0,0 @@ - - - - - - - - 2026-07-30T13:20:29.258180 - image/svg+xml - - - Matplotlib v3.10.9, https://matplotlib.org/ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/examples/LLM/llm_recommender_light.svg b/examples/LLM/llm_recommender_light.svg deleted file mode 100644 index 48f4758882..0000000000 --- a/examples/LLM/llm_recommender_light.svg +++ /dev/null @@ -1,1718 +0,0 @@ - - - - - - - - 2026-07-30T13:20:29.274463 - image/svg+xml - - - Matplotlib v3.10.9, https://matplotlib.org/ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - From 1e4efb5095f382be23080e9de77f931d2cc97faa Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 8 Oct 2026 14:48:19 +0200 Subject: [PATCH 79/96] Create convenience LLM-based meta recommenders --- CHANGELOG.md | 3 + baybe/recommenders/__init__.py | 14 +-- baybe/recommenders/meta/__init__.py | 6 + baybe/recommenders/meta/llm.py | 142 ++++++++++++++++++++++++ baybe/recommenders/pure/llm/__init__.py | 8 +- baybe/recommenders/pure/llm/llm.py | 88 +-------------- 6 files changed, 160 insertions(+), 101 deletions(-) create mode 100644 baybe/recommenders/meta/llm.py diff --git a/CHANGELOG.md b/CHANGELOG.md index c75de4688b..9cbc20c00b 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -52,6 +52,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 `operator`/`rhs`/`coefficients` interface (replaces `DiscreteSumConstraint`) - `LLMRecommender` for LLM-based experimental design suggestions via LiteLLM, available through the optional `llm` dependency group +- `LLMTwoPhaseRecommender` and `LLMAlternatingRecommender` convenience recommenders + built on top of `LLMRecommender`: the former warm-starts a campaign with the LLM and + then switches to another recommender, the latter alternates between the two ### Changed - `BOTORCH` GP preset now includes `BetaPrior(2.5, 1.5)` for the task covariance diff --git a/baybe/recommenders/__init__.py b/baybe/recommenders/__init__.py index c0446e2c15..c244cc6a32 100644 --- a/baybe/recommenders/__init__.py +++ b/baybe/recommenders/__init__.py @@ -1,5 +1,9 @@ """BayBE recommenders.""" +from baybe.recommenders.meta.llm import ( + LLMAlternatingRecommender, + LLMTwoPhaseRecommender, +) from baybe.recommenders.meta.sequential import ( SequentialMetaRecommender, StreamingSequentialMetaRecommender, @@ -7,11 +11,7 @@ ) from baybe.recommenders.naive import NaiveHybridSpaceRecommender from baybe.recommenders.pure.bayesian.botorch import BotorchRecommender -from baybe.recommenders.pure.llm import ( - LLMRecommender, - make_llm_alternating_recommender, - make_llm_two_phase_recommender, -) +from baybe.recommenders.pure.llm import LLMRecommender from baybe.recommenders.pure.nonpredictive.clustering import ( GaussianMixtureClusteringRecommender, KMeansClusteringRecommender, @@ -27,13 +27,13 @@ "FPSRecommender", "GaussianMixtureClusteringRecommender", "KMeansClusteringRecommender", + "LLMAlternatingRecommender", "LLMRecommender", + "LLMTwoPhaseRecommender", "PAMClusteringRecommender", "NaiveHybridSpaceRecommender", "RandomRecommender", "TwoPhaseMetaRecommender", "SequentialMetaRecommender", "StreamingSequentialMetaRecommender", - "make_llm_alternating_recommender", - "make_llm_two_phase_recommender", ] diff --git a/baybe/recommenders/meta/__init__.py b/baybe/recommenders/meta/__init__.py index fea13295cf..4f28ae68e7 100644 --- a/baybe/recommenders/meta/__init__.py +++ b/baybe/recommenders/meta/__init__.py @@ -5,6 +5,10 @@ query. """ +from baybe.recommenders.meta.llm import ( + LLMAlternatingRecommender, + LLMTwoPhaseRecommender, +) from baybe.recommenders.meta.sequential import ( SequentialMetaRecommender, StreamingSequentialMetaRecommender, @@ -12,6 +16,8 @@ ) __all__ = [ + "LLMAlternatingRecommender", + "LLMTwoPhaseRecommender", "TwoPhaseMetaRecommender", "SequentialMetaRecommender", "StreamingSequentialMetaRecommender", diff --git a/baybe/recommenders/meta/llm.py b/baybe/recommenders/meta/llm.py new file mode 100644 index 0000000000..ffde90a179 --- /dev/null +++ b/baybe/recommenders/meta/llm.py @@ -0,0 +1,142 @@ +"""Meta recommenders based on Large Language Models (LLMs).""" + +from __future__ import annotations + +import gc +from typing import Any + +from attrs import define, field +from attrs.validators import instance_of + +from baybe.recommenders.base import RecommenderProtocol +from baybe.recommenders.meta.sequential import ( + SequentialMetaRecommender, + TwoPhaseMetaRecommender, +) +from baybe.recommenders.pure.llm.llm import LLMRecommender + + +@define +class LLMTwoPhaseRecommender(TwoPhaseMetaRecommender): + """A two-phase recommender that warm-starts with an :class:`LLMRecommender`. + + A convenience specialization of + :class:`~baybe.recommenders.meta.sequential.TwoPhaseMetaRecommender` whose + :attr:`initial_recommender` must be an + :class:`~baybe.recommenders.pure.llm.llm.LLMRecommender`. The language model + proposes the first experiments, and the recommender switches to + :attr:`recommender` once :attr:`switch_after` measurements have been collected. + """ + + initial_recommender: RecommenderProtocol = field( + validator=instance_of(LLMRecommender), kw_only=True + ) + """The language-model recommender used before the switch.""" + + @classmethod + def from_model( + cls, + model: str, + experiment_description: str, + *, + litellm_args: dict[str, Any] | None = None, + switch_after: int = 1, + recommender: RecommenderProtocol | None = None, + ) -> LLMTwoPhaseRecommender: + """Create the recommender directly from a LiteLLM model identifier. + + Args: + model: The LiteLLM model identifier for the initial LLM recommender. + experiment_description: Textual description of the experiment for the LLM. + litellm_args: Optional additional arguments passed to LiteLLM. + switch_after: The number of collected experiments after which the + recommender switches from the LLM to ``recommender``. + recommender: The recommender used after the switch. Defaults to a + :class:`~baybe.recommenders.pure.bayesian.botorch.core.BotorchRecommender`. + + Returns: + The configured recommender. + """ + from baybe.recommenders.pure.bayesian.botorch import BotorchRecommender + + return cls( + initial_recommender=LLMRecommender( + model=model, + experiment_description=experiment_description, + litellm_args=litellm_args or {}, + ), + recommender=BotorchRecommender() if recommender is None else recommender, + switch_after=switch_after, + ) + + +@define +class LLMAlternatingRecommender(SequentialMetaRecommender): + """A recommender alternating between an :class:`LLMRecommender` and another one. + + A convenience specialization of + :class:`~baybe.recommenders.meta.sequential.SequentialMetaRecommender` that cycles + between a language-model recommender and another recommender, advancing whenever + new measurements become available. The sequence must consist of exactly two + recommenders, the first of which must be an + :class:`~baybe.recommenders.pure.llm.llm.LLMRecommender`. + """ + + def __attrs_post_init__(self) -> None: + """Validate the recommender sequence. + + Raises: + ValueError: If the sequence does not consist of exactly two recommenders. + ValueError: If the first recommender is not an + :class:`~baybe.recommenders.pure.llm.llm.LLMRecommender`. + """ + if len(self.recommenders) != 2: + raise ValueError( + f"'{self.__class__.__name__}' requires exactly two recommenders (an " + f"'{LLMRecommender.__name__}' alternating with another recommender), " + f"but {len(self.recommenders)} were provided." + ) + if not isinstance(self.recommenders[0], LLMRecommender): + raise ValueError( + f"'{self.__class__.__name__}' requires the first recommender in the " + f"sequence to be an '{LLMRecommender.__name__}'." + ) + + @classmethod + def from_model( + cls, + model: str, + experiment_description: str, + *, + litellm_args: dict[str, Any] | None = None, + recommender: RecommenderProtocol | None = None, + ) -> LLMAlternatingRecommender: + """Create the recommender directly from a LiteLLM model identifier. + + Args: + model: The LiteLLM model identifier for the LLM recommender. + experiment_description: Textual description of the experiment for the LLM. + litellm_args: Optional additional arguments passed to LiteLLM. + recommender: The recommender alternated with the LLM. Defaults to a + :class:`~baybe.recommenders.pure.bayesian.botorch.core.BotorchRecommender`. + + Returns: + The configured recommender, cycling between the LLM and ``recommender``. + """ + from baybe.recommenders.pure.bayesian.botorch import BotorchRecommender + + return cls( + recommenders=( + LLMRecommender( + model=model, + experiment_description=experiment_description, + litellm_args=litellm_args or {}, + ), + BotorchRecommender() if recommender is None else recommender, + ), + mode="cyclic", + ) + + +# Collect leftover original slotted classes processed by `attrs.define` +gc.collect() diff --git a/baybe/recommenders/pure/llm/__init__.py b/baybe/recommenders/pure/llm/__init__.py index 1a5db0936e..e4e3ca2ba9 100644 --- a/baybe/recommenders/pure/llm/__init__.py +++ b/baybe/recommenders/pure/llm/__init__.py @@ -1,13 +1,7 @@ """Recommender based on Large Language Models (LLMs).""" -from baybe.recommenders.pure.llm.llm import ( - LLMRecommender, - make_llm_alternating_recommender, - make_llm_two_phase_recommender, -) +from baybe.recommenders.pure.llm.llm import LLMRecommender __all__ = [ "LLMRecommender", - "make_llm_alternating_recommender", - "make_llm_two_phase_recommender", ] diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index b4f36d550f..acfaaa7bff 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -4,7 +4,7 @@ import gc import warnings -from typing import TYPE_CHECKING, Any, ClassVar +from typing import Any, ClassVar import pandas as pd from attrs import define, field @@ -26,13 +26,6 @@ from baybe.serialization import SerialMixin from baybe.utils.conversion import to_string -if TYPE_CHECKING: - from baybe.recommenders.base import RecommenderProtocol - from baybe.recommenders.meta.sequential import ( - SequentialMetaRecommender, - TwoPhaseMetaRecommender, - ) - # Keys that are wired in by the recommender itself and must not be overridden. _RESERVED_LITELLM_KEYS = frozenset({"model", "messages"}) @@ -298,84 +291,5 @@ def __str__(self) -> str: return to_string(self.__class__.__name__, *fields) -def make_llm_two_phase_recommender( - model: str, - experiment_description: str, - *, - switch_after: int = 1, - litellm_args: dict[str, Any] | None = None, - recommender: RecommenderProtocol | None = None, -) -> TwoPhaseMetaRecommender: - """Create a recommender that warm-starts with an LLM, then switches to Bayesian. - - The returned meta recommender uses an :class:`LLMRecommender` for the initial - experiments and switches to ``recommender`` once ``switch_after`` measurements have - been collected. - - Args: - model: The LiteLLM model identifier for the initial LLM recommender. - experiment_description: Textual description of the experiment for the LLM. - switch_after: The number of collected experiments after which the recommender - switches from the LLM to ``recommender``. - litellm_args: Optional additional arguments passed to LiteLLM. - recommender: The recommender used after the switch. Defaults to a - :class:`~baybe.recommenders.pure.bayesian.botorch.core.BotorchRecommender`. - - Returns: - A :class:`~baybe.recommenders.meta.sequential.TwoPhaseMetaRecommender` using the - LLM as its initial recommender. - """ - from baybe.recommenders.meta.sequential import TwoPhaseMetaRecommender - from baybe.recommenders.pure.bayesian.botorch import BotorchRecommender - - return TwoPhaseMetaRecommender( - initial_recommender=LLMRecommender( - model=model, - experiment_description=experiment_description, - litellm_args=litellm_args or {}, - ), - recommender=BotorchRecommender() if recommender is None else recommender, - switch_after=switch_after, - ) - - -def make_llm_alternating_recommender( - model: str, - experiment_description: str, - *, - litellm_args: dict[str, Any] | None = None, - recommender: RecommenderProtocol | None = None, -) -> SequentialMetaRecommender: - """Create a recommender that alternates each round between an LLM and Bayesian. - - The returned meta recommender cycles indefinitely between an - :class:`LLMRecommender` and ``recommender``, advancing to the next one whenever new - measurements become available. - - Args: - model: The LiteLLM model identifier for the LLM recommender. - experiment_description: Textual description of the experiment for the LLM. - litellm_args: Optional additional arguments passed to LiteLLM. - recommender: The recommender alternated with the LLM. Defaults to a - :class:`~baybe.recommenders.pure.bayesian.botorch.core.BotorchRecommender`. - - Returns: - A :class:`~baybe.recommenders.meta.sequential.SequentialMetaRecommender` in - cyclic mode, alternating between the LLM and ``recommender``. - """ - from baybe.recommenders.meta.sequential import SequentialMetaRecommender - from baybe.recommenders.pure.bayesian.botorch import BotorchRecommender - - recommenders: list[RecommenderProtocol] = [ - LLMRecommender( - model=model, - experiment_description=experiment_description, - litellm_args=litellm_args or {}, - ), - BotorchRecommender() if recommender is None else recommender, - ] - return SequentialMetaRecommender(recommenders=recommenders, mode="cyclic") - - # Collect leftover original slotted classes processed by `attrs.define` gc.collect() From 93d84a06e611eecade6d38c09f1e6d8e60ad9124 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 8 Oct 2026 15:11:30 +0200 Subject: [PATCH 80/96] Provide full context in recovery prompt Previously, some of the context and information was not used in the recovery prompt. To fix this, there is now a single general prompt that includes the full context, with an optional block containing recovery-related information if necessary. --- baybe/recommenders/pure/llm/_prompts.py | 118 ++++++++---------------- baybe/recommenders/pure/llm/llm.py | 13 ++- 2 files changed, 47 insertions(+), 84 deletions(-) diff --git a/baybe/recommenders/pure/llm/_prompts.py b/baybe/recommenders/pure/llm/_prompts.py index d73d5ce0dd..7cd0163bf2 100644 --- a/baybe/recommenders/pure/llm/_prompts.py +++ b/baybe/recommenders/pure/llm/_prompts.py @@ -84,18 +84,7 @@ {{ forbidden_configurations }} {% endif %} -Please suggest {{ batch_size }} new experimental conditions that are likely to \ -improve the optimization objective. -For each suggestion, provide: -1. A brief explanation of why you chose these values -2. The values for each parameter - -Format your response as a JSON array of objects with the following structure \ -(no backticks): -{{ response_format }} -""" - -_RECOVERY_PROMPT_TEMPLATE = """\ +{% if recovery_instruction is not none %} Your previous recommendation could not be used and needs to be corrected. WHAT WENT WRONG: @@ -104,21 +93,19 @@ ORIGINAL RESPONSE: {{ original_response }} -PARAMETERS: -{% for param in parameters %} -Parameter: {{ param.name }} -Type: {{ param.kind }} -{{ param.domain }} -{% endfor %} -{% if forbidden_configurations is not none %} - -FORBIDDEN CONFIGURATIONS: -{{ forbidden_instructions }} -{{ forbidden_configurations }} +Please provide a corrected set of {{ batch_size }} experimental conditions that \ +addresses the problem described above and improves the optimization objective. +{% else %} +Please suggest {{ batch_size }} new experimental conditions that are likely to \ +improve the optimization objective. {% endif %} +For each suggestion, provide: +1. A brief explanation of why you chose these values +2. The values for each parameter -Please provide a corrected JSON response that follows the required format: -{{ response_format }}\ +Format your response as a JSON array of objects with the following structure \ +(no backticks): +{{ response_format }} """ @@ -134,7 +121,7 @@ class _ParameterPromptInfo(TypedDict): class _PromptContext(TypedDict): - """Typed render context for the main prompt.""" + """Typed render context for the prompt.""" experiment_description: str objective: Objective | None @@ -145,17 +132,8 @@ class _PromptContext(TypedDict): forbidden_instructions: str batch_size: int response_format: str - - -class _RecoveryPromptContext(TypedDict): - """Typed render context for the recovery prompt.""" - - parameters: tuple[_ParameterPromptInfo, ...] - forbidden_configurations: str | None - forbidden_instructions: str - recovery_instruction: str - original_response: str - response_format: str + recovery_instruction: str | None + original_response: str | None def _parameter_prompt_info(parameter: Parameter) -> _ParameterPromptInfo: @@ -246,13 +224,21 @@ def make_prompt( pending_experiments: pd.DataFrame | None = None, *, experiment_description: str, + error: LLMResponseError | None = None, + original_response: str | None = None, ) -> str: - """Construct the main prompt for the language model. + """Construct the prompt for the language model. The recommendation-context arguments follow the canonical order used by :meth:`baybe.recommenders.base.RecommenderProtocol.recommend`; the LLM-specific ``experiment_description`` is keyword-only. + When ``error`` and ``original_response`` are provided, the prompt is rendered in + *recovery* mode: it carries the exact same context as the original query and appends + a correction section asking the model to fix its previous response. Because each + model call is stateless, carrying the full context here is what lets the recovery + attempt reason as well as the original one. + Args: batch_size: The number of recommendations to generate. searchspace: The search space to generate recommendations for. @@ -263,10 +249,21 @@ def make_prompt( measurements: Optional measurements to include in the prompt. pending_experiments: Optional pending experiments to include in the prompt. experiment_description: Textual description of the experiment. + error: If set, renders a recovery prompt using the error's + :attr:`~baybe.exceptions.LLMResponseError.recovery_instruction`. Must be + provided together with ``original_response``. + original_response: The previous, rejected response to be corrected. Must be + provided together with ``error``. + + Raises: + ValueError: If exactly one of ``error`` and ``original_response`` is provided. Returns: The constructed prompt. """ + if (error is None) != (original_response is None): + raise ValueError("'error' and 'original_response' must be provided together.") + from baybe._optional.llm import StrictUndefined, Template measurements_text = ( @@ -289,50 +286,13 @@ def make_prompt( "forbidden_instructions": _FORBIDDEN_INSTRUCTIONS, "batch_size": batch_size, "response_format": _response_format(batch_size), - } - template = Template( - _PROMPT_TEMPLATE, - trim_blocks=True, - lstrip_blocks=True, - undefined=StrictUndefined, - ) - return template.render(context) - - -def make_recovery_prompt( - searchspace: SearchSpace, - *, - batch_size: int, - error: LLMResponseError, - original_response: str, -) -> str: - """Construct the recovery prompt asking the model to correct its response. - - Args: - searchspace: The search space to generate recommendations for. - batch_size: The number of recommendations required. Passed to - :func:`~baybe.recommenders.pure.llm._schema._response_format` so the - embedded format example matches the expected array length. - error: The error that occurred while processing the previous response. Its - :attr:`~baybe.exceptions.LLMResponseError.recovery_instruction` provides the - error-specific guidance embedded in the prompt. - original_response: The original response that could not be used. - - Returns: - The constructed recovery prompt. - """ - from baybe._optional.llm import StrictUndefined, Template - - context: _RecoveryPromptContext = { - "parameters": tuple(_parameter_prompt_info(p) for p in searchspace.parameters), - "forbidden_configurations": _forbidden_configurations(searchspace), - "forbidden_instructions": _FORBIDDEN_INSTRUCTIONS, - "recovery_instruction": error.recovery_instruction, + "recovery_instruction": ( + error.recovery_instruction if error is not None else None + ), "original_response": original_response, - "response_format": _response_format(batch_size), } template = Template( - _RECOVERY_PROMPT_TEMPLATE, + _PROMPT_TEMPLATE, trim_blocks=True, lstrip_blocks=True, undefined=StrictUndefined, diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index acfaaa7bff..cb7c04cb3b 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -20,7 +20,7 @@ from baybe.objectives.base import Objective from baybe.recommenders.pure.base import PureRecommender from baybe.recommenders.pure.llm._parsing import parse_llm_response -from baybe.recommenders.pure.llm._prompts import make_prompt, make_recovery_prompt +from baybe.recommenders.pure.llm._prompts import make_prompt from baybe.searchspace import SearchSpace from baybe.searchspace.core import SearchSpaceType from baybe.serialization import SerialMixin @@ -259,11 +259,14 @@ def _recommend_hybrid( try: return self._validate_response(content, searchspace, batch_size) except LLMResponseError as initial_error: - # The recommendation had an issue. Make a single, error-specific recovery - # attempt, informing the model what went wrong. - recovery_prompt = make_recovery_prompt( + # The recommendation had an issue. Make a recovery attempt. + recovery_prompt = make_prompt( + batch_size, searchspace, - batch_size=batch_size, + self._objective, + self._measurements, + self._pending_experiments, + experiment_description=self.experiment_description, error=initial_error, original_response=content, ) From afdef5018eea53928937d978a4ebf81313937d13 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 8 Oct 2026 15:53:21 +0200 Subject: [PATCH 81/96] Improve handling of targets and objectives in prompt We did not carry all of the relevant information for targets and objectives in the prompt. This has now been fixed by including views of both targets and objectives in the prompt, similar to the way we do it for parameters. --- baybe/recommenders/pure/llm/_prompts.py | 135 ++++++++++++++++++++++-- 1 file changed, 125 insertions(+), 10 deletions(-) diff --git a/baybe/recommenders/pure/llm/_prompts.py b/baybe/recommenders/pure/llm/_prompts.py index 7cd0163bf2..a99466881a 100644 --- a/baybe/recommenders/pure/llm/_prompts.py +++ b/baybe/recommenders/pure/llm/_prompts.py @@ -5,16 +5,23 @@ from typing import TYPE_CHECKING, Literal, TypedDict from baybe.exceptions import IncompatibilityError, LLMResponseError +from baybe.objectives.desirability import DesirabilityObjective +from baybe.objectives.enum import Scalarizer +from baybe.objectives.pareto import ParetoObjective from baybe.parameters.base import DiscreteParameter, Parameter from baybe.parameters.numerical import NumericalContinuousParameter from baybe.parameters.substance import SubstanceParameter from baybe.recommenders.pure.llm._schema import _response_format from baybe.searchspace import SearchSpace +from baybe.targets.binary import BinaryTarget +from baybe.targets.numerical import NumericalTarget +from baybe.transformations import IdentityTransformation if TYPE_CHECKING: import pandas as pd from baybe.objectives.base import Objective + from baybe.targets.base import Target _FORBIDDEN_INSTRUCTIONS = """\ The following configurations are currently NOT selectable. You MUST NOT recommend any @@ -32,19 +39,26 @@ {{ experiment_description }} {% if objective is not none %} -{% if objective.metadata.description is not none %} OPTIMIZATION OBJECTIVE: -{{ objective.metadata.description }} - +{% if objective.description is not none %} +{{ objective.description }} +{% endif %} +{% if objective.combination is not none %} +{{ objective.combination }} {% endif %} -OPTIMIZATION TARGETS: + +TARGETS: {% for target in objective.targets %} Target: {{ target.name }} -{% if target.metadata.description is not none %} -Description: {{ target.metadata.description }} +Goal: {{ target.goal }} +{% if target.transformation is not none %} +Transformation applied before optimizing: {{ target.transformation }} {% endif %} -{% if target.metadata.unit is not none %} -Unit: {{ target.metadata.unit }} +{% if target.description is not none %} +Description: {{ target.description }} +{% endif %} +{% if target.unit is not none %} +Unit: {{ target.unit }} {% endif %} {% endfor %} @@ -120,11 +134,29 @@ class _ParameterPromptInfo(TypedDict): misc: tuple[tuple[str, str], ...] +class _TargetPromptInfo(TypedDict): + """Typed, presentation-only view of an optimization target.""" + + name: str + goal: str + transformation: str | None + description: str | None + unit: str | None + + +class _ObjectivePromptInfo(TypedDict): + """Typed, presentation-only view of the optimization objective.""" + + description: str | None + combination: str | None + targets: tuple[_TargetPromptInfo, ...] + + class _PromptContext(TypedDict): """Typed render context for the prompt.""" experiment_description: str - objective: Objective | None + objective: _ObjectivePromptInfo | None parameters: tuple[_ParameterPromptInfo, ...] measurements: str | None pending_experiments: str | None @@ -216,6 +248,87 @@ def _forbidden_configurations(searchspace: SearchSpace) -> str | None: return forbidden.to_string(index=False) +def _target_prompt_info(target: Target) -> _TargetPromptInfo: + """Build the prompt view of an optimization target. + + Args: + target: The target to describe. + + Returns: + A typed, presentation-only view of the target's optimization semantics. + + Raises: + IncompatibilityError: If the target type is not supported. + """ + transformation: str | None = None + if isinstance(target, NumericalTarget): + goal = "minimize" if target.minimize else "maximize" + transformation = ( + None + if isinstance(target.transformation, IdentityTransformation) + else str(target.transformation) + ) + elif isinstance(target, BinaryTarget): + goal = ( + f"achieve the success value '{target.success_value}' " + f"(as opposed to the failure value '{target.failure_value}')" + ) + else: + raise IncompatibilityError( + f"Target '{target.name}' has unsupported type " + f"'{type(target).__name__}'. Only '{NumericalTarget.__name__}' and " + f"'{BinaryTarget.__name__}' are supported." + ) + return { + "name": target.name, + "goal": goal, + "transformation": transformation, + "description": target.description, + "unit": target.unit, + } + + +def _objective_prompt_info(objective: Objective) -> _ObjectivePromptInfo: + """Build the prompt view of the optimization objective. + + Args: + objective: The objective to describe. + + Returns: + A typed, presentation-only view: the objective description, how its targets are + combined, and the per-target views. + """ + targets = tuple(_target_prompt_info(t) for t in objective.targets) + combination: str | None + if isinstance(objective, DesirabilityObjective): + aggregation = ( + "weighted geometric mean" + if objective.scalarizer is Scalarizer.GEOM_MEAN + else "weighted arithmetic mean" + ) + weights = ", ".join( + f"{t['name']}={w:.3g}" + for t, w in zip(targets, objective.normalized_weights) + ) + combination = ( + f"The targets are aggregated into a single desirability score via a " + f"{aggregation} (weights: {weights}), which is then maximized." + ) + elif isinstance(objective, ParetoObjective): + combination = ( + "The targets are optimized jointly as a multi-objective (Pareto) problem: " + "they are not combined into a single score; seek the best trade-offs, " + "optimizing each target in its stated direction." + ) + else: + combination = None + return { + "description": objective.metadata.description, + "combination": combination, + "targets": targets, + } + + def make_prompt( batch_size: int, searchspace: SearchSpace, @@ -278,7 +391,9 @@ def make_prompt( ) context: _PromptContext = { "experiment_description": experiment_description, - "objective": objective, + "objective": ( + _objective_prompt_info(objective) if objective is not None else None + ), "parameters": tuple(_parameter_prompt_info(p) for p in searchspace.parameters), "measurements": measurements_text, "pending_experiments": pending_text, From f71aa00107757956242f6ba49b04c77f9ef4a6bf Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Fri, 9 Oct 2026 09:56:57 +0200 Subject: [PATCH 82/96] Simplify the JSON array extraction --- baybe/recommenders/pure/llm/_parsing.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/baybe/recommenders/pure/llm/_parsing.py b/baybe/recommenders/pure/llm/_parsing.py index 0eb01e6dd4..24690760bf 100644 --- a/baybe/recommenders/pure/llm/_parsing.py +++ b/baybe/recommenders/pure/llm/_parsing.py @@ -28,22 +28,21 @@ from baybe.utils.validation import validate_parameter_input -def extract_json_array(response: str, /) -> str: +def extract_json_array(response: str, /) -> str | None: """Extract the JSON array payload from a raw language model response. Models often wrap the array in Markdown fences or prose, or emit several blocks (e.g. after reconsidering). Return the *last* array that is a list of objects (the - intended answer), ignoring prose and stray brackets like ``x[0]``. If none is found, - fall back to the last complete array, else the original text. + intended answer), ignoring prose and stray brackets like ``x[0]``. Args: response: The raw response text. Returns: - The substring spanning the extracted JSON array, or the original text if none. + The substring spanning the extracted JSON array, or ``None`` if no array of + objects is found. """ decoder = json.JSONDecoder() - last_array: str | None = None last_object_array: str | None = None search_start = 0 while (start := response.find("[", search_start)) != -1: @@ -52,16 +51,15 @@ def extract_json_array(response: str, /) -> str: except JSONDecodeError: search_start = start + 1 # not a valid array here; try the next "[" continue - last_array = response[start:end] is_object_list = ( isinstance(value, list) and bool(value) and all(isinstance(x, dict) for x in value) ) if is_object_list: - last_object_array = last_array + last_object_array = response[start:end] search_start = end # keep scanning; a later block wins - return last_object_array or last_array or response + return last_object_array def parse_llm_response(response: str, /, searchspace: SearchSpace) -> pd.DataFrame: @@ -98,9 +96,11 @@ def parse_llm_response(response: str, /, searchspace: SearchSpace) -> pd.DataFra of the LLM suggestions with such constraints is not guaranteed. """ payload = extract_json_array(response) + if payload is None: + raise LLMMalformedResponseError("Response contains no JSON array of objects.") try: suggestions = json.loads(payload) - except (JSONDecodeError, TypeError) as e: + except JSONDecodeError as e: raise LLMMalformedResponseError(f"Error parsing JSON output: {e}.") from e if not isinstance(suggestions, list): From 4e840c722fbc56e1a01ffa0baecffb6b0243423a Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Fri, 9 Oct 2026 10:00:34 +0200 Subject: [PATCH 83/96] Remove dead checks --- baybe/recommenders/pure/llm/_parsing.py | 11 ----------- 1 file changed, 11 deletions(-) diff --git a/baybe/recommenders/pure/llm/_parsing.py b/baybe/recommenders/pure/llm/_parsing.py index 24690760bf..8ccd408df4 100644 --- a/baybe/recommenders/pure/llm/_parsing.py +++ b/baybe/recommenders/pure/llm/_parsing.py @@ -103,19 +103,8 @@ def parse_llm_response(response: str, /, searchspace: SearchSpace) -> pd.DataFra except JSONDecodeError as e: raise LLMMalformedResponseError(f"Error parsing JSON output: {e}.") from e - if not isinstance(suggestions, list): - raise LLMMalformedResponseError("Response must be a JSON array.") - - if not suggestions: - raise LLMMalformedResponseError( - "Response contains an empty array with no suggestions." - ) - recommendations = [] for suggestion in suggestions: - if not isinstance(suggestion, dict): - raise LLMMalformedResponseError("Each suggestion must be a JSON object.") - if _PARAMETERS_FIELD not in suggestion: raise LLMMalformedResponseError( f"Each suggestion must contain a '{_PARAMETERS_FIELD}' field." From 685011d416629641225b698b028f989a679376e1 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Fri, 9 Oct 2026 11:08:30 +0200 Subject: [PATCH 84/96] Move model query inside of `try` block --- baybe/recommenders/pure/llm/llm.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index cb7c04cb3b..1f57b3a2cf 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -270,8 +270,8 @@ def _recommend_hybrid( error=initial_error, original_response=content, ) - recovery_content = self._query_model(recovery_prompt) try: + recovery_content = self._query_model(recovery_prompt) return self._validate_response( recovery_content, searchspace, batch_size ) From e9bf9d8f9696011ca23b579eb2b5eef2bc21183e Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Fri, 9 Oct 2026 11:49:10 +0200 Subject: [PATCH 85/96] Improve handling of point matching --- baybe/recommenders/pure/llm/_parsing.py | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/baybe/recommenders/pure/llm/_parsing.py b/baybe/recommenders/pure/llm/_parsing.py index 8ccd408df4..d8b4cb9f6b 100644 --- a/baybe/recommenders/pure/llm/_parsing.py +++ b/baybe/recommenders/pure/llm/_parsing.py @@ -70,9 +70,10 @@ def parse_llm_response(response: str, /, searchspace: SearchSpace) -> pd.DataFra searchspace: The search space to validate recommendations against. Returns: - A DataFrame containing the parsed recommendations, restricted to the eligible - candidate set of the search space (as returned by - :meth:`baybe.searchspace.discrete.SubspaceDiscrete.get_candidates`). + A DataFrame containing the parsed recommendations. All returned points are + eligible candidates of the search space (as returned by + :meth:`baybe.searchspace.discrete.SubspaceDiscrete.get_candidates`); suggestions + that are not are rejected. Raises: LLMMalformedResponseError: If the response cannot be parsed into the expected @@ -198,16 +199,14 @@ def parse_llm_response(response: str, /, searchspace: SearchSpace) -> pd.DataFra # Recover the exp_rep index (for campaign metadata tracking) via the same fuzzy # matching used for measurement input: exact for categorical, nearest numerical. - # Matching against get_candidates() (not exp_rep directly) ensures that suggestions - # snap to eligible points only, respecting the allow_recommending_* filters applied - # by the campaign via FilteredSubspaceDiscrete. discrete_params = searchspace.discrete.parameters if discrete_params: - exp_rep, _ = searchspace.discrete.get_candidates() + exp_rep = searchspace.discrete.exp_rep aligned_index = fuzzy_row_match(exp_rep, df, discrete_params) - # `fuzzy_row_match` silently drops suggestions with no eligible candidate, so - # detect the shortfall explicitly rather than let them vanish. - n_ineligible = len(df) - len(aligned_index) + n_unmatched = len(df) - len(aligned_index) + eligible_index = searchspace.discrete.get_candidates()[0].index + n_filtered_out = int((~aligned_index.isin(eligible_index)).sum()) + n_ineligible = n_unmatched + n_filtered_out if n_ineligible > 0: raise LLMIneligiblePointsError( f"{n_ineligible} suggestion(s) do not correspond to eligible " From 30a9f1cb7321559d0e07cfaedbf6c1203fd84ea8 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Fri, 9 Oct 2026 11:50:32 +0200 Subject: [PATCH 86/96] Force mode to be cyclic in LLMAlternatingRecommender --- baybe/recommenders/meta/llm.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/baybe/recommenders/meta/llm.py b/baybe/recommenders/meta/llm.py index ffde90a179..31e720d05b 100644 --- a/baybe/recommenders/meta/llm.py +++ b/baybe/recommenders/meta/llm.py @@ -101,6 +101,10 @@ def __attrs_post_init__(self) -> None: f"'{self.__class__.__name__}' requires the first recommender in the " f"sequence to be an '{LLMRecommender.__name__}'." ) + if not self.mode == "cyclic": + raise ValueError( + f"'{self.__class__.__name__}' requires the mode to be 'cyclic'." + ) @classmethod def from_model( From 0117694e490802fb23e2c3f94ae8edc9d0030c89 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Fri, 9 Oct 2026 15:23:49 +0200 Subject: [PATCH 87/96] Improve spacing in prompt --- baybe/recommenders/pure/llm/_prompts.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/baybe/recommenders/pure/llm/_prompts.py b/baybe/recommenders/pure/llm/_prompts.py index a99466881a..2f77e12fad 100644 --- a/baybe/recommenders/pure/llm/_prompts.py +++ b/baybe/recommenders/pure/llm/_prompts.py @@ -79,26 +79,26 @@ {% endfor %} {% endfor %} - {% if measurements is not none %} + PREVIOUS MEASUREMENTS: {{ measurements }} {% endif %} +{%- if pending_experiments is not none %} -{% if pending_experiments is not none %} PENDING EXPERIMENTS: The following experiments have already been proposed and are awaiting results. Do not recommend these again. {{ pending_experiments }} {% endif %} +{%- if forbidden_configurations is not none %} -{% if forbidden_configurations is not none %} FORBIDDEN CONFIGURATIONS: {{ forbidden_instructions }} {{ forbidden_configurations }} {% endif %} +{%- if recovery_instruction is not none %} -{% if recovery_instruction is not none %} Your previous recommendation could not be used and needs to be corrected. WHAT WENT WRONG: @@ -110,6 +110,7 @@ Please provide a corrected set of {{ batch_size }} experimental conditions that \ addresses the problem described above and improves the optimization objective. {% else %} + Please suggest {{ batch_size }} new experimental conditions that are likely to \ improve the optimization objective. {% endif %} From f9d88c164abd428770d53e207bb96e886ad3b4dc Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Fri, 9 Oct 2026 17:01:32 +0200 Subject: [PATCH 88/96] Add tests for LLM-related code parts --- tests/hypothesis_strategies/recommenders.py | 32 ++ tests/llm/__init__.py | 1 + tests/llm/_mock.py | 131 +++++ tests/llm/test_parsing.py | 412 ++++++++++++++ tests/llm/test_prompts.py | 512 ++++++++++++++++++ tests/llm/test_recommender.py | 287 ++++++++++ .../test_llm_recommender_serialization.py | 59 ++ .../test_meta_recommender_serialization.py | 12 + tests/test_imports.py | 6 + tests/test_meta_recommenders.py | 44 ++ .../test_llm_recommender_validation.py | 167 ++++++ 11 files changed, 1663 insertions(+) create mode 100644 tests/hypothesis_strategies/recommenders.py create mode 100644 tests/llm/__init__.py create mode 100644 tests/llm/_mock.py create mode 100644 tests/llm/test_parsing.py create mode 100644 tests/llm/test_prompts.py create mode 100644 tests/llm/test_recommender.py create mode 100644 tests/serialization/test_llm_recommender_serialization.py create mode 100644 tests/validation/test_llm_recommender_validation.py diff --git a/tests/hypothesis_strategies/recommenders.py b/tests/hypothesis_strategies/recommenders.py new file mode 100644 index 0000000000..cc76071e01 --- /dev/null +++ b/tests/hypothesis_strategies/recommenders.py @@ -0,0 +1,32 @@ +"""Hypothesis strategies for recommenders.""" + +import hypothesis.strategies as st + +from baybe.recommenders.pure.llm.llm import ( + _CREDENTIAL_LITELLM_KEYS, + _RESERVED_LITELLM_KEYS, + LLMRecommender, +) + +_FORBIDDEN_KEYS = _RESERVED_LITELLM_KEYS | _CREDENTIAL_LITELLM_KEYS + +_litellm_arg_keys = st.text(min_size=1).filter(lambda k: k not in _FORBIDDEN_KEYS) + +_litellm_arg_values = st.one_of( + st.integers(), + st.floats(allow_nan=False, allow_infinity=False), + st.booleans(), + st.text(), +) + + +@st.composite +def llm_recommenders(draw: st.DrawFn): + """Generate ``LLMRecommender`` instances with varied fields.""" + return LLMRecommender( + model=draw(st.text(min_size=1)), + experiment_description=draw(st.text(min_size=1)), + litellm_args=draw( + st.dictionaries(keys=_litellm_arg_keys, values=_litellm_arg_values) + ), + ) diff --git a/tests/llm/__init__.py b/tests/llm/__init__.py new file mode 100644 index 0000000000..bbd448d333 --- /dev/null +++ b/tests/llm/__init__.py @@ -0,0 +1 @@ +"""Tests for LLM-based recommenders.""" diff --git a/tests/llm/_mock.py b/tests/llm/_mock.py new file mode 100644 index 0000000000..7041cb5ce4 --- /dev/null +++ b/tests/llm/_mock.py @@ -0,0 +1,131 @@ +"""Shared mock helpers for LLM recommender tests.""" + +import json +from types import SimpleNamespace + +from baybe._optional.info import CHEM_INSTALLED +from baybe.objectives import SingleTargetObjective +from baybe.parameters import ( + CategoricalParameter, + NumericalContinuousParameter, + NumericalDiscreteParameter, +) +from baybe.searchspace import SearchSpace +from baybe.targets import NumericalTarget + +if CHEM_INSTALLED: + from baybe.parameters import SubstanceParameter + +PATCH_TARGET = "baybe._optional.llm.completion" +"""Patch target for mocking the LiteLLM ``completion`` function.""" + + +def mock_response(content): + """Build a fake LiteLLM response object from raw text content. + + Needs to use a SimpleNamespace to mimic the structure of a real LiteLLM response. + """ + return SimpleNamespace( + choices=[SimpleNamespace(message=SimpleNamespace(content=content))] + ) + + +def make_valid_json(searchspace, batch_size): + """Sample eligible candidates and return them as the expected JSON string.""" + params = searchspace.parameters + discrete_params = [p for p in params if p.is_discrete] + continuous_params = [p for p in params if not p.is_discrete] + + continuous_entry = { + cp.name: sum(cp.bounds.to_tuple()) / 2 for cp in continuous_params + } + + suggestions = [] + if discrete_params: + candidates = searchspace.discrete.get_candidates()[0] + for _, row in candidates.head(batch_size).iterrows(): + entry = {p.name: row[p.name] for p in discrete_params} + entry.update(continuous_entry) + suggestions.append({"explanation": "Mock suggestion.", "parameters": entry}) + else: + for _ in range(batch_size): + suggestions.append( + { + "explanation": "Mock suggestion.", + "parameters": dict(continuous_entry), + } + ) + + return json.dumps( + suggestions, + ) + + +def make_suggestion(searchspace, **overrides): + """Build a valid suggestion dict with optional parameter overrides. + + Overrides replace or add parameter values, e.g. ``Num="abc"`` to inject an + invalid value or ``Unknown="x"`` to add an extra parameter. + """ + params = {} + discrete_params = [p for p in searchspace.parameters if p.is_discrete] + continuous_params = [p for p in searchspace.parameters if not p.is_discrete] + if discrete_params: + row = searchspace.discrete.get_candidates()[0].iloc[0] + params.update({p.name: row[p.name] for p in discrete_params}) + for cp in continuous_params: + params[cp.name] = sum(cp.bounds.to_tuple()) / 2 + params.update(overrides) + return {"explanation": "test", "parameters": params} + + +def make_response_json(searchspace, **overrides): + """Build a JSON response string with one suggestion. + + Overrides are forwarded to :func:`make_suggestion`. + """ + return json.dumps( + [make_suggestion(searchspace, **overrides)], + ) + + +def make_discrete_searchspace(): + """A discrete search space with categorical, numerical, and substance params.""" + params: list = [ + CategoricalParameter("Cat", ("A", "B", "C")), + NumericalDiscreteParameter("Num", (1.0, 2.0, 3.0)), + ] + if CHEM_INSTALLED: + params.append( + SubstanceParameter( + "Solvent", + data={"Water": "O", "Ethanol": "CCO", "Methanol": "CO"}, + encoding="MORDRED", + ) + ) + return SearchSpace.from_product(params) + + +def make_continuous_searchspace(): + """A search space with a single continuous parameter.""" + return SearchSpace.from_product( + [NumericalContinuousParameter("Cont", bounds=(0.0, 10.0))] + ) + + +def make_hybrid_searchspace(): + """A search space with both discrete and continuous parameters.""" + return SearchSpace.from_product( + [ + CategoricalParameter("Cat", ("A", "B", "C")), + NumericalContinuousParameter("Cont", bounds=(0.0, 10.0)), + ] + ) + + +def make_objective(): + """An objective with metadata to avoid the missing-metadata warning.""" + return SingleTargetObjective( + target=NumericalTarget(name="yield"), + metadata={"description": "Maximize the reaction yield."}, + ) diff --git a/tests/llm/test_parsing.py b/tests/llm/test_parsing.py new file mode 100644 index 0000000000..245de3eb0f --- /dev/null +++ b/tests/llm/test_parsing.py @@ -0,0 +1,412 @@ +"""Tests for LLM response parsing and validation.""" + +import json + +import numpy as np +import pytest +from attrs import evolve + +from baybe.constraints.conditions import ThresholdCondition +from baybe.constraints.continuous import ContinuousLinearConstraint +from baybe.constraints.discrete import ( + DiscreteBatchConstraint, + DiscreteSelectionConstraint, +) +from baybe.exceptions import ( + LLMConstraintViolationError, + LLMIneligiblePointsError, + LLMInvalidParameterValueError, + LLMMalformedResponseError, + LLMMissingParameterError, + LLMNonNumericParameterError, + LLMResponseWarning, + LLMUnknownParameterError, +) +from baybe.parameters import ( + CategoricalParameter, + NumericalContinuousParameter, + NumericalDiscreteParameter, +) +from baybe.recommenders.pure.llm._parsing import extract_json_array, parse_llm_response +from baybe.recommenders.pure.llm._schema import ( + _EXPLANATION_FIELD, + _PARAMETERS_FIELD, + _response_format, +) +from baybe.searchspace import SearchSpace +from baybe.searchspace._filtered import FilteredSubspaceDiscrete +from tests.llm._mock import ( + make_discrete_searchspace, + make_response_json, + make_suggestion, +) + + +def _make_filtered_searchspace(params, *, forbidden_mask): + """Create a search space with certain candidates masked as ineligible.""" + ss = SearchSpace.from_product(params) + filtered = FilteredSubspaceDiscrete.from_subspace( + ss.discrete, ~np.array(forbidden_mask) + ) + return evolve(ss, discrete=filtered) + + +@pytest.fixture(name="discrete_searchspace", scope="module") +def fixture_discrete_searchspace(): + """A discrete search space for tests that do not depend on its contents.""" + return make_discrete_searchspace() + + +@pytest.mark.parametrize( + ("response", "expected"), + [ + pytest.param( + '[{"a": 1}, {"b": 2}]', + '[{"a": 1}, {"b": 2}]', + id="bare", + ), + pytest.param( + '```json\n[{"a": 1}]\n```', + '[{"a": 1}]', + id="markdown_fences", + ), + pytest.param( + 'Here are my suggestions: [{"a": 1}]', + '[{"a": 1}]', + id="prose_before", + ), + pytest.param( + '[{"first": 1}]\nActually, let me reconsider:\n[{"second": 2}]', + '[{"second": 2}]', + id="multi_block_last_wins", + ), + pytest.param( + '[{"a": 1}]\nSome text\n[1, 2, 3]', + '[{"a": 1}]', + id="object_array_before_scalar_array", + ), + pytest.param( + '[{"a": [1, 2]}, {"b": 3}]', + '[{"a": [1, 2]}, {"b": 3}]', + id="nested", + ), + ], +) +def test_extract_json_array(response, expected): + """The extractor finds the last JSON array of objects.""" + result = extract_json_array(response) + assert json.loads(result) == json.loads(expected) + + +@pytest.mark.parametrize( + "response", + [ + pytest.param("[]", id="empty_array"), + pytest.param("[1, 2, 3]", id="array_of_scalars"), + pytest.param("No JSON here at all.", id="no_array"), + pytest.param('[{"a": 1}, 2]', id="mixed_objects_and_scalars"), + pytest.param('[{"a": 1}, {"b": 2}', id="incomplete_array_opening"), + pytest.param('{"a": 1}, {"b": 2}]', id="incomplete_array_closing"), + ], +) +def test_extract_json_array_returns_none(response): + """The extractor returns None when no array of objects is found.""" + assert extract_json_array(response) is None + + +@pytest.mark.parametrize("batch_size", [1, 3], ids=["single", "batch"]) +def test_response_format(batch_size): + """The response format example contains exactly ``batch_size`` entries.""" + response_format = _response_format(batch_size) + assert response_format.count(f'"{_EXPLANATION_FIELD}":') == batch_size + assert response_format.count(f'"{_PARAMETERS_FIELD}":') == batch_size + + +@pytest.mark.parametrize( + ("response", "match"), + [ + pytest.param( + "Just some text without JSON.", + "Response contains no JSON array", + id="no_json", + ), + pytest.param( + '[{"explanation": "x"}]', + f"Each suggestion must contain a '{_PARAMETERS_FIELD}' field", + id="no_parameters", + ), + pytest.param( + '[{"parameters": {"any": 1}}]', + f"Each suggestion must contain an '{_EXPLANATION_FIELD}' field", + id="no_explanation", + ), + pytest.param( + '[{"explanation": "x", "parameters": [1, 2]}]', + "Parameters must be a JSON object", + id="params_not_dict", + ), + ], +) +def test_parse_llm_response_format_errors(discrete_searchspace, response, match): + """Malformed responses raise ``LLMMalformedResponseError``.""" + with pytest.raises(LLMMalformedResponseError, match=match): + parse_llm_response(response, discrete_searchspace) + + +@pytest.mark.parametrize( + ("searchspace", "overrides", "error_cls", "match"), + [ + pytest.param( + SearchSpace.from_product([CategoricalParameter("Cat", ("A", "B"))]), + {"Unknown": "x"}, + LLMUnknownParameterError, + "Response contains unknown parameter names", + id="unknown_param", + ), + pytest.param( + SearchSpace.from_product( + [NumericalDiscreteParameter("Num", (1.0, 2.0, 3.0))] + ), + {"Num": "abc"}, + LLMNonNumericParameterError, + "non-numeric entries in the provided dataframe", + id="non_numeric", + ), + pytest.param( + SearchSpace.from_product( + [NumericalDiscreteParameter("Num", (1.0, 2.0, 3.0))] + ), + {"Num": 999}, + LLMInvalidParameterValueError, + "invalid values in parameter", + id="out_of_bounds", + ), + pytest.param( + SearchSpace.from_product([CategoricalParameter("Cat", ("A", "B"))]), + {"Cat": "Z"}, + LLMInvalidParameterValueError, + "invalid values in parameter", + id="invalid_category", + ), + pytest.param( + SearchSpace.from_product( + [NumericalContinuousParameter("x", bounds=(0.0, 10.0))] + ), + {"x": 99.0}, + LLMInvalidParameterValueError, + "invalid values in parameter", + id="continuous_out_of_bounds", + ), + ], +) +def test_parse_llm_response_parameter_errors(searchspace, overrides, error_cls, match): + """Invalid parameter values raise the corresponding error.""" + response = make_response_json(searchspace, **overrides) + with pytest.raises(error_cls, match=match): + parse_llm_response(response, searchspace) + + +def test_parse_llm_response_missing_parameter(): + """Omitting a required parameter raises ``LLMMissingParameterError``.""" + searchspace = SearchSpace.from_product( + [ + CategoricalParameter("Cat", ("A", "B")), + NumericalDiscreteParameter("Num", (1.0, 2.0)), + ] + ) + suggestion = make_suggestion(searchspace) + del suggestion["parameters"]["Cat"] + response = json.dumps([suggestion]) + with pytest.raises( + LLMMissingParameterError, + match="Response is missing values for the following parameters: {'Cat'}", + ): + parse_llm_response(response, searchspace) + + +def test_parse_llm_response_selection_constraint_violation(): + """Violating a discrete selection constraint raises the correct error.""" + constraint = DiscreteSelectionConstraint( + parameters=["Num"], + conditions=[ThresholdCondition(threshold=1.5, operator=">=")], + ) + searchspace = SearchSpace.from_product( + [ + CategoricalParameter("Cat", ("A", "B")), + NumericalDiscreteParameter("Num", (1.0, 2.0, 3.0)), + ], + constraints=[constraint], + ) + response = make_response_json(searchspace, Num=1.0) + with pytest.raises( + LLMConstraintViolationError, match="DiscreteSelectionConstraint" + ): + parse_llm_response(response, searchspace) + + +def test_parse_llm_response_batch_constraint_violation(): + """Violating a batch constraint raises the correct error.""" + constraint = DiscreteBatchConstraint(parameters=["Cat"]) + searchspace = SearchSpace.from_product( + [ + CategoricalParameter("Cat", ("A", "B")), + NumericalDiscreteParameter("Num", (1.0, 2.0)), + ], + constraints=[constraint], + ) + suggestions = [ + {"explanation": "first", "parameters": {"Cat": "A", "Num": 1.0}}, + {"explanation": "second", "parameters": {"Cat": "B", "Num": 2.0}}, + ] + response = json.dumps(suggestions) + with pytest.raises(LLMConstraintViolationError, match="DiscreteBatchConstraint"): + parse_llm_response(response, searchspace) + + +def test_parse_llm_response_continuous_constraint_warning(): + """Continuous constraints trigger a warning since they cannot be validated.""" + searchspace = SearchSpace.from_product( + [ + NumericalContinuousParameter("x", bounds=(0.0, 10.0)), + NumericalContinuousParameter("y", bounds=(0.0, 10.0)), + ], + constraints=[ + ContinuousLinearConstraint( + parameters=["x", "y"], + coefficients=[1.0, 1.0], + rhs=10.0, + operator="<=", + ) + ], + ) + response = json.dumps([{"explanation": "test", "parameters": {"x": 3.0, "y": 4.0}}]) + with pytest.warns(LLMResponseWarning, match="cannot be validated"): + parse_llm_response(response, searchspace) + + +def test_parse_llm_response_happy_path(): + """A valid response is parsed into a DataFrame with correct shape and values.""" + searchspace = SearchSpace.from_product( + [ + CategoricalParameter("Cat", ("A", "B", "C")), + NumericalDiscreteParameter("Num", (1.0, 2.0, 3.0)), + ] + ) + suggestions = [ + {"explanation": "first", "parameters": {"Cat": "A", "Num": 1.0}}, + {"explanation": "second", "parameters": {"Cat": "B", "Num": 2.0}}, + ] + result = parse_llm_response(json.dumps(suggestions), searchspace) + assert set(result.columns) == {"Cat", "Num"} + assert list(result["Cat"]) == ["A", "B"] + assert list(result["Num"]) == [1.0, 2.0] + + +@pytest.mark.parametrize( + ("searchspace", "overrides"), + [ + pytest.param( + _make_filtered_searchspace( + [CategoricalParameter("Cat", ("A", "B", "C"))], + forbidden_mask=[False, True, False], # B is forbidden + ), + {"Cat": "B"}, + id="categorical_forbidden", + ), + pytest.param( + _make_filtered_searchspace( + [NumericalDiscreteParameter("Num", (1.0, 2.0, 3.0))], + forbidden_mask=[False, False, True], # 3.0 is forbidden + ), + {"Num": 3.0}, + id="numerical_forbidden", + ), + pytest.param( + _make_filtered_searchspace( + [NumericalDiscreteParameter("x", (1.0, 2.0, 3.0), tolerance=0.3)], + forbidden_mask=[True, False, False], # 1.0 is forbidden + ), + {"x": 1.2}, + id="tolerance_snap_forbidden", + ), + ], +) +def test_parse_llm_response_ineligible_points(searchspace, overrides): + """Proposing a forbidden candidate raises ``LLMIneligiblePointsError``.""" + response = make_response_json(searchspace, **overrides) + with pytest.raises(LLMIneligiblePointsError): + parse_llm_response(response, searchspace) + + +def test_parse_llm_response_tolerance_snap(): + """A value within tolerance snaps to the nearest allowed value.""" + searchspace = SearchSpace.from_product( + [NumericalDiscreteParameter("x", (1.0, 2.0, 3.0), tolerance=0.3)] + ) + response = json.dumps([{"explanation": "test", "parameters": {"x": 1.2}}]) + result = parse_llm_response(response, searchspace) + assert result["x"].iloc[0] == 1.0 + + +def test_parse_llm_response_hybrid_output(): + """Hybrid spaces produce output with aligned discrete and continuous parts.""" + searchspace = SearchSpace.from_product( + [ + CategoricalParameter("Cat", ("A", "B", "C")), + NumericalContinuousParameter("Cont", bounds=(0.0, 1.0)), + ] + ) + suggestions = [ + {"explanation": "first", "parameters": {"Cat": "A", "Cont": 0.3}}, + {"explanation": "second", "parameters": {"Cat": "A", "Cont": 0.7}}, + ] + result = parse_llm_response(json.dumps(suggestions), searchspace) + assert set(result.columns) == {"Cat", "Cont"} + assert list(result["Cat"]) == ["A", "A"] + assert list(result["Cont"]) == [0.3, 0.7] + + +def test_parse_llm_response_valid_then_invalid(): + """An error in a later suggestion still raises even if earlier ones are valid.""" + searchspace = SearchSpace.from_product([CategoricalParameter("Cat", ("A", "B"))]) + suggestions = [ + {"explanation": "valid", "parameters": {"Cat": "A"}}, + {"explanation": "invalid", "parameters": {"Cat": "Z"}}, + ] + with pytest.raises(LLMInvalidParameterValueError): + parse_llm_response(json.dumps(suggestions), searchspace) + + +def test_parse_llm_response_partial_missing_parameter(): + """A parameter present in one suggestion but absent in another is caught.""" + searchspace = SearchSpace.from_product( + [ + CategoricalParameter("Cat", ("A", "B")), + NumericalDiscreteParameter("Num", (1.0, 2.0)), + ] + ) + suggestions = [ + {"explanation": "first", "parameters": {"Cat": "A", "Num": 1.0}}, + {"explanation": "second", "parameters": {"Num": 2.0}}, + ] + with pytest.raises(LLMInvalidParameterValueError, match="missing values"): + parse_llm_response(json.dumps(suggestions), searchspace) + + +def test_parse_llm_response_continuous_only_happy_path(): + """A valid response for a continuous-only space is parsed correctly.""" + searchspace = SearchSpace.from_product( + [ + NumericalContinuousParameter("x", bounds=(0.0, 10.0)), + NumericalContinuousParameter("y", bounds=(0.0, 5.0)), + ] + ) + suggestions = [ + {"explanation": "first", "parameters": {"x": 3.0, "y": 2.0}}, + {"explanation": "second", "parameters": {"x": 7.0, "y": 4.0}}, + ] + result = parse_llm_response(json.dumps(suggestions), searchspace) + assert len(result) == 2 + assert set(result.columns) == {"x", "y"} + assert list(result["x"]) == [3.0, 7.0] + assert list(result["y"]) == [2.0, 4.0] diff --git a/tests/llm/test_prompts.py b/tests/llm/test_prompts.py new file mode 100644 index 0000000000..4de7d8dd01 --- /dev/null +++ b/tests/llm/test_prompts.py @@ -0,0 +1,512 @@ +"""Tests for LLM prompt construction.""" + +import pandas as pd +import pytest +from attrs import evolve +from pytest import param + +from baybe._optional.info import CHEM_INSTALLED, JINJA2_INSTALLED +from baybe.exceptions import LLMResponseError +from baybe.objectives import ( + DesirabilityObjective, + ParetoObjective, + SingleTargetObjective, +) +from baybe.objectives.enum import Scalarizer +from baybe.parameters import ( + CategoricalParameter, + NumericalContinuousParameter, + NumericalDiscreteParameter, +) +from baybe.recommenders.pure.llm._prompts import ( + _forbidden_configurations, + _objective_prompt_info, + _parameter_prompt_info, + _target_prompt_info, + make_prompt, +) +from baybe.searchspace import SearchSpace +from baybe.searchspace._filtered import FilteredSubspaceDiscrete +from baybe.targets import BinaryTarget, NumericalTarget +from baybe.transformations import AffineTransformation + +if not JINJA2_INSTALLED: + pytest.skip("Jinja2 not installed.", allow_module_level=True) + + +@pytest.mark.parametrize( + ("parameter", "expected_kind", "domain_contains", "domain_excludes"), + [ + param( + CategoricalParameter("Cat", ("A", "B", "C")), + "categorical", + ["A", "B", "C"], + [], + id="categorical", + ), + param( + CategoricalParameter("Cat", ("A", "B", "C"), active_values=("A", "B")), + "categorical", + ["A", "B"], + ["C"], + id="categorical_active_values", + ), + param( + NumericalDiscreteParameter("Num", (1.0, 2.0, 3.0)), + "discrete_numeric", + ["1.0", "2.0", "3.0"], + ["numpy", "np."], + id="numerical_discrete", + ), + param( + NumericalContinuousParameter("Cont", bounds=(0.0, 10.0)), + "continuous", + ["0.0", "10.0"], + [], + id="continuous", + ), + ], +) +def test_parameter_prompt_info( + parameter, expected_kind, domain_contains, domain_excludes +): + """The prompt view has the correct kind and domain for each parameter type.""" + info = _parameter_prompt_info(parameter) + assert info["name"] == parameter.name + assert info["kind"] == expected_kind + for substr in domain_contains: + assert substr in info["domain"] + for substr in domain_excludes: + assert substr not in info["domain"] + + +@pytest.mark.skipif(not CHEM_INSTALLED, reason="Chem dependencies not installed.") +def test_parameter_prompt_info_substance(): + """Substance parameters expose SMILES in the domain string.""" + from baybe.parameters import SubstanceParameter + + p = SubstanceParameter( + "Solvent", + data={"Water": "O", "Ethanol": "CCO"}, + encoding="MORDRED", + ) + info = _parameter_prompt_info(p) + assert info["kind"] == "substance" + assert "Water" in info["domain"] + assert "O" in info["domain"] + assert "Ethanol" in info["domain"] + assert "CCO" in info["domain"] + + +@pytest.mark.parametrize( + ("parameter", "expected_description", "expected_unit", "expected_misc"), + [ + param( + CategoricalParameter( + "Cat", + ("A", "B"), + metadata={ + "description": "A category", + "unit": "mg", + "purity": "high", + }, + ), + "A category", + "mg", + (("purity", "high"),), + id="with_metadata", + ), + param( + CategoricalParameter("Cat", ("A", "B")), + None, + None, + (), + id="no_metadata", + ), + ], +) +def test_parameter_prompt_info_metadata( + parameter, expected_description, expected_unit, expected_misc +): + """Metadata fields are forwarded when present and None/empty otherwise.""" + info = _parameter_prompt_info(parameter) + assert info["description"] == expected_description + assert info["unit"] == expected_unit + assert info["misc"] == expected_misc + + +@pytest.mark.parametrize( + ("target", "goal_contains", "exp_transformation", "exp_desc", "exp_unit"), + [ + param( + NumericalTarget("yield"), + ["maximize"], + None, + None, + None, + id="numerical_maximize", + ), + param( + NumericalTarget("yield", minimize=True), + ["minimize"], + None, + None, + None, + id="numerical_minimize", + ), + param( + NumericalTarget("yield", transformation=AffineTransformation(factor=2.0)), + ["maximize"], + "AffineTransformation(factor=2.0, shift=0.0)", + None, + None, + id="numerical_with_transformation", + ), + param( + BinaryTarget("success", success_value="yes", failure_value="no"), + ["yes", "no"], + None, + None, + None, + id="binary", + ), + param( + NumericalTarget( + "yield", + metadata={"description": "Reaction yield", "unit": "%"}, + ), + ["maximize"], + None, + "Reaction yield", + "%", + id="numerical_with_metadata", + ), + ], +) +def test_target_prompt_info( + target, goal_contains, exp_transformation, exp_desc, exp_unit +): + """The prompt view has the correct fields for each target type.""" + info = _target_prompt_info(target) + assert info["name"] == target.name + assert info["description"] == exp_desc + assert info["unit"] == exp_unit + assert info["transformation"] == exp_transformation + for substr in goal_contains: + assert substr in info["goal"] + + +_T1 = NumericalTarget("yield") +_T2 = NumericalTarget("cost", minimize=True) + + +@pytest.mark.parametrize( + ("objective", "combination_contains", "expected_description"), + [ + param( + SingleTargetObjective(target=_T1), + None, + None, + id="single_target", + ), + param( + DesirabilityObjective( + targets=[_T1, _T2], + scalarizer=Scalarizer.MEAN, + require_normalization=False, + ), + "weighted arithmetic mean", + None, + id="desirability_mean", + ), + param( + DesirabilityObjective( + targets=[ + NumericalTarget.normalized_sigmoid("yield", [[0, 0.1], [100, 0.9]]), + NumericalTarget.normalized_sigmoid("cost", [[0, 0.9], [100, 0.1]]), + ], + scalarizer=Scalarizer.GEOM_MEAN, + ), + "weighted geometric mean", + None, + id="desirability_geom_mean", + ), + param( + ParetoObjective(targets=[_T1, _T2]), + "Pareto", + None, + id="pareto", + ), + param( + SingleTargetObjective( + target=_T1, + metadata={"description": "Maximize yield"}, + ), + None, + "Maximize yield", + id="single_target_with_metadata", + ), + ], +) +def test_objective_prompt_info(objective, combination_contains, expected_description): + """The prompt view has the correct fields per objective type.""" + info = _objective_prompt_info(objective) + assert len(info["targets"]) == len(objective.targets) + assert info["description"] == expected_description + if combination_contains is None: + assert info["combination"] is None + else: + assert combination_contains in info["combination"] + + +_SEARCHSPACE = SearchSpace.from_product([CategoricalParameter("Cat", ("A", "B"))]) +_DESCRIPTION = "Test experiment." +_ALL_OPTIONAL_SECTIONS = { + "OPTIMIZATION OBJECTIVE", + "TARGETS", + "PREVIOUS MEASUREMENTS", + "PENDING EXPERIMENTS", + "WHAT WENT WRONG", + "ORIGINAL RESPONSE", +} + + +@pytest.mark.parametrize( + ("kwargs", "present_sections"), + [ + param( + {}, + set(), + id="no_optionals", + ), + param( + { + "objective": SingleTargetObjective( + target=_T1, + metadata={"description": "Maximize yield"}, + ), + }, + {"OPTIMIZATION OBJECTIVE", "TARGETS"}, + id="with_objective", + ), + param( + { + "measurements": pd.DataFrame({"Cat": ["A"], "yield": [42.0]}), + }, + {"PREVIOUS MEASUREMENTS"}, + id="with_measurements", + ), + param( + { + "pending_experiments": pd.DataFrame({"Cat": ["B"]}), + }, + {"PENDING EXPERIMENTS"}, + id="with_pending", + ), + param( + { + "error": LLMResponseError("test error"), + "original_response": "bad response", + }, + {"WHAT WENT WRONG", "ORIGINAL RESPONSE"}, + id="with_recovery", + ), + param( + { + "measurements": pd.DataFrame(columns=["Cat", "yield"]), + }, + set(), + id="empty_measurements_omitted", + ), + param( + { + "pending_experiments": pd.DataFrame(columns=["Cat"]), + }, + set(), + id="empty_pending_omitted", + ), + ], +) +def test_make_prompt_optional_sections(kwargs, present_sections): + """Optional prompt sections appear only when their arguments are provided.""" + prompt = make_prompt( + batch_size=1, + searchspace=_SEARCHSPACE, + experiment_description=_DESCRIPTION, + **kwargs, + ) + for section in present_sections: + assert section in prompt + for section in _ALL_OPTIONAL_SECTIONS - present_sections: + assert section not in prompt + + +@pytest.mark.parametrize( + ("error", "original_response"), + [ + param(LLMResponseError("err"), None, id="error_only"), + param(None, "response text", id="response_only"), + ], +) +def test_make_prompt_recovery_requires_both(error, original_response): + """Providing only one of ``error``/``original_response`` raises.""" + with pytest.raises(ValueError, match="must be provided together"): + make_prompt( + batch_size=1, + searchspace=_SEARCHSPACE, + experiment_description=_DESCRIPTION, + error=error, + original_response=original_response, + ) + + +def _filter_searchspace(searchspace, mask): + """Create a filtered copy of a search space.""" + return evolve( + searchspace, + discrete=FilteredSubspaceDiscrete.from_subspace( + searchspace.discrete, mask.to_numpy() + ), + ) + + +_DISCRETE_SS = SearchSpace.from_product( + [ + CategoricalParameter("Cat", ("A", "B", "C")), + NumericalDiscreteParameter("Num", (1.0, 2.0)), + ] +) +_HYBRID_SS = SearchSpace.from_product( + [ + CategoricalParameter("Cat", ("A", "B", "C")), + NumericalContinuousParameter("Cont", bounds=(0.0, 10.0)), + ] +) +_CONTINUOUS_SS = SearchSpace.from_product( + [NumericalContinuousParameter("x", bounds=(0.0, 10.0))] +) + + +@pytest.mark.parametrize( + "searchspace", + [ + param(_DISCRETE_SS, id="unfiltered_discrete"), + param(_CONTINUOUS_SS, id="continuous_only"), + ], +) +def test_forbidden_configurations_returns_none(searchspace): + """Without filtering or without a discrete part, the helper returns None.""" + assert _forbidden_configurations(searchspace) is None + + +@pytest.mark.parametrize( + ("searchspace", "expected"), + [ + param( + _filter_searchspace( + _DISCRETE_SS, + _DISCRETE_SS.discrete.exp_rep["Cat"] != "B", + ), + "Cat Num\n B 1.0\n B 2.0", + id="single_value_forbidden", + ), + param( + _filter_searchspace( + _DISCRETE_SS, + _DISCRETE_SS.discrete.exp_rep["Cat"].isin(["A"]), + ), + "Cat Num\n B 1.0\n B 2.0\n C 1.0\n C 2.0", + id="multiple_values_forbidden", + ), + param( + _filter_searchspace( + _DISCRETE_SS, + ~( + (_DISCRETE_SS.discrete.exp_rep["Cat"] == "A") + & (_DISCRETE_SS.discrete.exp_rep["Num"] == 1.0) + ), + ), + "Cat Num\n A 1.0", + id="single_row_forbidden", + ), + param( + _filter_searchspace( + _HYBRID_SS, + _HYBRID_SS.discrete.exp_rep["Cat"] != "B", + ), + "Cat\n B", + id="hybrid_forbidden", + ), + ], +) +def test_forbidden_configurations(searchspace, expected): + """The helper returns the correct forbidden rows.""" + assert _forbidden_configurations(searchspace) == expected + + +@pytest.mark.parametrize( + ("recovery_kwargs", "expected_present", "expected_absent"), + [ + param( + {}, + ["Please suggest 3 new experimental conditions"], + ["WHAT WENT WRONG", "ORIGINAL RESPONSE"], + id="without_recovery", + ), + param( + { + "error": LLMResponseError("test error"), + "original_response": "bad response", + }, + [ + "WHAT WENT WRONG", + "ORIGINAL RESPONSE", + "bad response", + "corrected set of 3", + ], + ["Please suggest"], + id="with_recovery", + ), + ], +) +def test_full_prompt(recovery_kwargs, expected_present, expected_absent): + """A fully populated prompt contains all sections and no template residue.""" + searchspace = _filter_searchspace( + _DISCRETE_SS, + _DISCRETE_SS.discrete.exp_rep["Cat"] != "B", + ) + prompt = make_prompt( + batch_size=3, + searchspace=searchspace, + objective=SingleTargetObjective( + target=NumericalTarget("yield"), + metadata={"description": "Maximize yield"}, + ), + measurements=pd.DataFrame({"Cat": ["A"], "Num": [1.0], "yield": [42.0]}), + pending_experiments=pd.DataFrame({"Cat": ["B"], "Num": [2.0]}), + experiment_description="A test experiment.", + **recovery_kwargs, + ) + + common_sections = [ + "EXPERIMENT DESCRIPTION", + "A test experiment.", + "OPTIMIZATION OBJECTIVE", + "Maximize yield", + "TARGETS", + "yield", + "PARAMETERS", + "Cat", + "Num", + "PREVIOUS MEASUREMENTS", + "PENDING EXPERIMENTS", + "FORBIDDEN CONFIGURATIONS", + "explanation", + "parameters", + ] + for section in common_sections + expected_present: + assert section in prompt + for section in expected_absent: + assert section not in prompt + for residue in ("{{", "}}", "{%", "%}"): + assert residue not in prompt diff --git a/tests/llm/test_recommender.py b/tests/llm/test_recommender.py new file mode 100644 index 0000000000..6c7003b882 --- /dev/null +++ b/tests/llm/test_recommender.py @@ -0,0 +1,287 @@ +"""Tests for the LLM recommender integration.""" + +import warnings +from unittest.mock import patch + +import pandas as pd +import pytest +from pytest import param + +from baybe import Campaign +from baybe._optional.info import LLM_INSTALLED +from baybe._optional.llm import AuthenticationError +from baybe.exceptions import ( + LLMAuthenticationError, + LLMCallError, + LLMMalformedResponseError, + LLMResponseError, +) +from baybe.objectives import SingleTargetObjective +from baybe.recommenders.pure.llm._prompts import make_prompt +from baybe.recommenders.pure.llm.llm import LLMRecommender +from baybe.targets import NumericalTarget +from tests.llm._mock import ( + PATCH_TARGET, + make_continuous_searchspace, + make_discrete_searchspace, + make_hybrid_searchspace, + make_objective, + make_valid_json, + mock_response, +) + +if not LLM_INSTALLED: + pytest.skip("LLM dependencies not installed.", allow_module_level=True) + +_RECOMMENDER = LLMRecommender( + model="test/model", experiment_description="Test experiment." +) +_OBJECTIVE = make_objective() + + +@pytest.mark.parametrize( + "searchspace_factory", + [ + param(make_discrete_searchspace, id="discrete"), + param(make_continuous_searchspace, id="continuous"), + param(make_hybrid_searchspace, id="hybrid"), + ], +) +@pytest.mark.parametrize("batch_size", [1, 3], ids=["single", "batch"]) +def test_recommend_happy_path(searchspace_factory, batch_size): + """The recommend flow returns a DataFrame with correct shape. + + Value correctness is covered by the parsing tests; this test verifies + the end-to-end flow (prompt building, model call, parsing). + """ + searchspace = searchspace_factory() + with patch(PATCH_TARGET) as mock: + mock.return_value = mock_response(make_valid_json(searchspace, batch_size)) + result = _RECOMMENDER.recommend(batch_size, searchspace, _OBJECTIVE) + assert len(result) == batch_size + assert set(result.columns) == {p.name for p in searchspace.parameters} + + +def test_completion_arguments_forwarded(): + """The model, prompt, and litellm_args are forwarded to the completion call.""" + searchspace = make_discrete_searchspace() + recommender = LLMRecommender( + model="test/my-model", + experiment_description="Test.", + litellm_args={"temperature": 0.5, "max_tokens": 100}, + ) + with patch(PATCH_TARGET) as mock: + mock.return_value = mock_response(make_valid_json(searchspace, 1)) + recommender.recommend(1, searchspace, _OBJECTIVE) + + mock.assert_called_once() + kwargs = mock.call_args.kwargs + assert kwargs["model"] == "test/my-model" + expected_prompt = make_prompt( + 1, searchspace, _OBJECTIVE, experiment_description="Test." + ) + assert kwargs["messages"] == [{"role": "user", "content": expected_prompt}] + assert kwargs["temperature"] == 0.5 + assert kwargs["max_tokens"] == 100 + + +_SEARCHSPACE = make_discrete_searchspace() +_AUTH_ERROR = AuthenticationError( + message="bad key", llm_provider="test", model="test/model" +) + + +@pytest.mark.parametrize( + ("exception", "expected_error"), + [ + param( + _AUTH_ERROR, + LLMAuthenticationError, + id="auth", + ), + param( + ConnectionError("timeout"), + LLMCallError, + id="provider", + ), + ], +) +def test_call_errors(exception, expected_error): + """Provider-level failures are wrapped and preserve the original cause.""" + with patch(PATCH_TARGET, side_effect=exception) as mock: + with pytest.raises(expected_error): + _RECOMMENDER.recommend(1, _SEARCHSPACE, _OBJECTIVE) + assert mock.call_count == 1 + + +def test_empty_response(): + """A response with ``None`` content raises without a recovery attempt.""" + with patch(PATCH_TARGET, return_value=mock_response(None)) as mock: + with pytest.raises(LLMResponseError): + _RECOMMENDER.recommend(1, _SEARCHSPACE, _OBJECTIVE) + assert mock.call_count == 1 + + +@pytest.mark.parametrize( + ("batch_size", "recovery_response", "expected_error"), + [ + param( + 1, + mock_response(make_valid_json(_SEARCHSPACE, 1)), + None, + id="success_single", + ), + param( + 3, + mock_response(make_valid_json(_SEARCHSPACE, 3)), + None, + id="success_batch", + ), + param( + 1, + mock_response("not valid json"), + LLMResponseError, + id="bad_again", + ), + param( + 1, + _AUTH_ERROR, + LLMAuthenticationError, + id="auth_on_recovery", + ), + param( + 1, + ConnectionError("timeout"), + LLMCallError, + id="provider_on_recovery", + ), + ], +) +def test_recovery(batch_size, recovery_response, expected_error): + """After a bad first response, a recovery attempt is made exactly once.""" + side_effect = [mock_response("not valid json"), recovery_response] + with patch(PATCH_TARGET, side_effect=side_effect) as mock: + if expected_error is None: + _RECOMMENDER.recommend(batch_size, _SEARCHSPACE, _OBJECTIVE) + else: + with pytest.raises(expected_error): + _RECOMMENDER.recommend(batch_size, _SEARCHSPACE, _OBJECTIVE) + assert mock.call_count == 2 + + +def test_recovery_prompt_content(): + """The recovery prompt carries the full context and the error details.""" + bad_content = "not valid json" + valid_content = make_valid_json(_SEARCHSPACE, 1) + side_effect = [mock_response(bad_content), mock_response(valid_content)] + with patch(PATCH_TARGET, side_effect=side_effect) as mock: + _RECOMMENDER.recommend(1, _SEARCHSPACE, _OBJECTIVE) + + prompt_kwargs = dict( + batch_size=1, + searchspace=_SEARCHSPACE, + objective=_OBJECTIVE, + experiment_description=_RECOMMENDER.experiment_description, + ) + expected_first = make_prompt(**prompt_kwargs) + expected_recovery = make_prompt( + **prompt_kwargs, + error=LLMMalformedResponseError("Response contains no JSON array of objects."), + original_response=bad_content, + ) + + actual_first = mock.call_args_list[0].kwargs["messages"][0]["content"] + actual_recovery = mock.call_args_list[1].kwargs["messages"][0]["content"] + assert actual_first == expected_first + assert actual_recovery == expected_recovery + + +@pytest.mark.parametrize( + ("objective", "warns"), + [ + param( + SingleTargetObjective(target=NumericalTarget("yield")), + True, + id="no_metadata", + ), + param( + _OBJECTIVE, + False, + id="with_metadata", + ), + ], +) +def test_objective_metadata_warning(objective, warns): + """A warning is raised when the objective has no metadata description.""" + searchspace = make_discrete_searchspace() + with patch(PATCH_TARGET) as mock: + mock.return_value = mock_response(make_valid_json(searchspace, 1)) + if warns: + with pytest.warns(UserWarning, match="no metadata description"): + _RECOMMENDER.recommend(1, searchspace, objective) + else: + with warnings.catch_warnings(): + warnings.simplefilter("error") + _RECOMMENDER.recommend(1, searchspace, objective) + + +def test_context_stash_replacement(): + """The stashed context reflects the current call, not a previous one.""" + searchspace = make_discrete_searchspace() + first_measurements = pd.DataFrame( + {"Cat": ["A"], "Num": [1.0], "Solvent": ["Water"], "yield": [10.0]} + ) + second_measurements = pd.DataFrame( + {"Cat": ["B"], "Num": [2.0], "Solvent": ["Ethanol"], "yield": [20.0]} + ) + + with patch(PATCH_TARGET) as mock: + mock.return_value = mock_response(make_valid_json(searchspace, 1)) + _RECOMMENDER.recommend( + 1, searchspace, _OBJECTIVE, measurements=first_measurements + ) + _RECOMMENDER.recommend( + 1, searchspace, _OBJECTIVE, measurements=second_measurements + ) + + first_prompt = mock.call_args_list[0].kwargs["messages"][0]["content"] + second_prompt = mock.call_args_list[1].kwargs["messages"][0]["content"] + + expected_first = make_prompt( + 1, + searchspace, + _OBJECTIVE, + measurements=first_measurements, + experiment_description=_RECOMMENDER.experiment_description, + ) + expected_second = make_prompt( + 1, + searchspace, + _OBJECTIVE, + measurements=second_measurements, + experiment_description=_RECOMMENDER.experiment_description, + ) + assert first_prompt == expected_first + assert second_prompt == expected_second + + +def test_recommend_with_campaign(): + """Stateful recommendation through a Campaign works across iterations.""" + searchspace = make_discrete_searchspace() + campaign = Campaign( + searchspace=searchspace, + objective=_OBJECTIVE, + recommender=LLMRecommender(model="test/model", experiment_description="Test."), + allow_recommending_already_recommended=True, + ) + + with patch(PATCH_TARGET) as mock: + mock.return_value = mock_response(make_valid_json(searchspace, 1)) + rec1 = campaign.recommend(batch_size=1) + + rec1["yield"] = 42.0 + campaign.add_measurements(rec1) + + with patch(PATCH_TARGET) as mock: + mock.return_value = mock_response(make_valid_json(searchspace, 1)) + campaign.recommend(batch_size=1) diff --git a/tests/serialization/test_llm_recommender_serialization.py b/tests/serialization/test_llm_recommender_serialization.py new file mode 100644 index 0000000000..086d905118 --- /dev/null +++ b/tests/serialization/test_llm_recommender_serialization.py @@ -0,0 +1,59 @@ +"""LLM recommender serialization tests.""" + +import hypothesis.strategies as st +from hypothesis import given + +from baybe.recommenders import RandomRecommender +from baybe.recommenders.meta.llm import LLMTwoPhaseRecommender +from baybe.recommenders.pure.llm.llm import LLMRecommender +from tests.conftest import select_recommender +from tests.hypothesis_strategies.recommenders import llm_recommenders +from tests.serialization.utils import roundtrip + + +@given(data=st.data()) +def test_llm_recommender_roundtrip(data: st.DataObject): + """A serialization roundtrip yields an equivalent object.""" + recommender = data.draw(llm_recommenders()) + roundtripped = LLMRecommender.from_json(recommender.to_json()) + assert roundtripped == recommender + + +def test_stashed_context_not_serialized(): + """Private stashed fields do not appear in the serialized form.""" + recommender = LLMRecommender(model="test/model", experiment_description="Test.") + recommender._objective = "should not be serialized" + recommender._measurements = "should not be serialized" + recommender._pending_experiments = "should not be serialized" + + serialized = recommender.to_dict() + assert "_objective" not in serialized + assert "_measurements" not in serialized + assert "_pending_experiments" not in serialized + + roundtripped = LLMRecommender.from_dict(serialized) + assert roundtripped._objective is None + assert roundtripped._measurements is None + assert roundtripped._pending_experiments is None + + +_LLM_KWARGS = {"model": "test/model", "experiment_description": "Test."} + + +def test_llm_two_phase_state_serialization(): + """The LLM two-phase recommender preserves its switching state.""" + llm_rec = LLMRecommender(**_LLM_KWARGS) + secondary = RandomRecommender() + recommender = LLMTwoPhaseRecommender( + initial_recommender=llm_rec, + recommender=secondary, + switch_after=1, + ) + + assert select_recommender(recommender, 0) is llm_rec + assert select_recommender(recommender, 1) is secondary + + recommender2 = roundtrip(recommender) + assert recommender2 == recommender + rec = select_recommender(recommender2, 1) + assert rec == secondary diff --git a/tests/serialization/test_meta_recommender_serialization.py b/tests/serialization/test_meta_recommender_serialization.py index b594afd5ac..223a7debde 100644 --- a/tests/serialization/test_meta_recommender_serialization.py +++ b/tests/serialization/test_meta_recommender_serialization.py @@ -10,6 +10,10 @@ TwoPhaseMetaRecommender, ) from baybe.recommenders.meta.base import MetaRecommender +from baybe.recommenders.meta.llm import ( + LLMAlternatingRecommender, + LLMTwoPhaseRecommender, +) from tests.conftest import select_recommender from tests.serialization.utils import assert_roundtrip_consistency, roundtrip @@ -17,12 +21,20 @@ RECOMMENDERS = [RandomRecommender(), FPSRecommender()] assert len(RECOMMENDERS) == len({rec.__class__.__name__ for rec in RECOMMENDERS}) +_LLM_KWARGS = {"model": "test/model", "experiment_description": "Test."} + @pytest.mark.parametrize( "recommender", [ TwoPhaseMetaRecommender(), SequentialMetaRecommender(recommenders=[RandomRecommender()]), + LLMTwoPhaseRecommender.from_model( + **_LLM_KWARGS, recommender=RandomRecommender() + ), + LLMAlternatingRecommender.from_model( + **_LLM_KWARGS, recommender=RandomRecommender() + ), ], ) def test_roundtrip(recommender: MetaRecommender): diff --git a/tests/test_imports.py b/tests/test_imports.py index 69e0bd23aa..4818c80a0f 100644 --- a/tests/test_imports.py +++ b/tests/test_imports.py @@ -104,6 +104,12 @@ def test_imports(module: str): "baybe.utils.clustering_algorithms.third_party", "baybe.utils.clustering_algorithms.third_party.kmedoids", ], + "litellm": [ + "baybe._optional.llm", + ], + "jinja2": [ + "baybe._optional.llm", + ], } """Modules (dict values) for which certain imports (dict keys) are permitted.""" diff --git a/tests/test_meta_recommenders.py b/tests/test_meta_recommenders.py index 895be5b184..2e981050ef 100644 --- a/tests/test_meta_recommenders.py +++ b/tests/test_meta_recommenders.py @@ -10,7 +10,12 @@ SequentialMetaRecommender, TwoPhaseMetaRecommender, ) +from baybe.recommenders.meta.llm import ( + LLMAlternatingRecommender, + LLMTwoPhaseRecommender, +) from baybe.recommenders.meta.sequential import StreamingSequentialMetaRecommender +from baybe.recommenders.pure.llm.llm import LLMRecommender from tests.conftest import select_recommender RECOMMENDERS = [RandomRecommender(), FPSRecommender(), BotorchRecommender()] @@ -126,3 +131,42 @@ def test_sequential_meta_recommender(cls, mode): # Pretend the recommender was used meta_recommender._was_used = True + + +_LLM_KWARGS = {"model": "test/model", "experiment_description": "Test."} + + +def test_llm_two_phase_meta_recommender(): + """The LLM two-phase recommender switches at the configured point.""" + llm_rec = LLMRecommender(**_LLM_KWARGS) + secondary = RandomRecommender() + recommender = LLMTwoPhaseRecommender( + initial_recommender=llm_rec, + recommender=secondary, + switch_after=2, + ) + + assert select_recommender(recommender, 0) is llm_rec + assert select_recommender(recommender, 1) is llm_rec + assert select_recommender(recommender, 2) is secondary + assert select_recommender(recommender, 3) is secondary + + +def test_llm_alternating_recommender(): + """The LLM alternating recommender cycles between LLM and secondary.""" + llm_rec = LLMRecommender(**_LLM_KWARGS) + secondary = RandomRecommender() + recommender = LLMAlternatingRecommender( + recommenders=(llm_rec, secondary), mode="cyclic" + ) + + rec = select_recommender(recommender, 1) + assert rec is llm_rec + recommender._was_used = True + + rec = select_recommender(recommender, 2) + assert rec is secondary + recommender._was_used = True + + rec = select_recommender(recommender, 3) + assert rec is llm_rec diff --git a/tests/validation/test_llm_recommender_validation.py b/tests/validation/test_llm_recommender_validation.py new file mode 100644 index 0000000000..129006d613 --- /dev/null +++ b/tests/validation/test_llm_recommender_validation.py @@ -0,0 +1,167 @@ +"""Validation tests for LLM recommenders.""" + +import pytest +from pytest import param + +from baybe.recommenders import RandomRecommender +from baybe.recommenders.meta.llm import ( + LLMAlternatingRecommender, + LLMTwoPhaseRecommender, +) +from baybe.recommenders.pure.llm.llm import LLMRecommender + +# "Valid" at construction as we do not check those before making any call +_VALID_KWARGS = {"model": "test/model", "experiment_description": "Test experiment."} + + +@pytest.mark.parametrize( + ("overrides", "error_cls", "match"), + [ + param( + {"model": ""}, + ValueError, + "Length of 'model' must be >= 1", + id="empty_model", + ), + param( + {"experiment_description": ""}, + ValueError, + "Length of 'experiment_description' must be >= 1", + id="empty_description", + ), + param( + {"litellm_args": {"model": "x"}}, + ValueError, + "'litellm_args' must not contain keys that are set explicitly", + id="reserved_key_model", + ), + param( + {"litellm_args": {"messages": []}}, + ValueError, + "'litellm_args' must not contain keys that are set explicitly", + id="reserved_key_messages", + ), + param( + {"litellm_args": {"api_key": "x"}}, + ValueError, + "'litellm_args' must not contain credential keys", + id="credential_key_api_key", + ), + param( + {"litellm_args": {"api_base": "x"}}, + ValueError, + "'litellm_args' must not contain credential keys", + id="credential_key_api_base", + ), + ], +) +def test_llm_recommender_invalid_construction(overrides, error_cls, match): + """Invalid arguments to ``LLMRecommender`` raise at construction time.""" + kwargs = {**_VALID_KWARGS, **overrides} + with pytest.raises(error_cls, match=match): + LLMRecommender(**kwargs) + + +@pytest.mark.parametrize( + ("cls", "kwargs", "error_cls", "match"), + [ + param( + LLMTwoPhaseRecommender, + { + "initial_recommender": RandomRecommender(), + "recommender": RandomRecommender(), + }, + TypeError, + "instance_of", + id="twophase_initial_not_llm", + ), + param( + LLMAlternatingRecommender, + { + "recommenders": ( + LLMRecommender(**_VALID_KWARGS), + RandomRecommender(), + RandomRecommender(), + ), + "mode": "cyclic", + }, + ValueError, + "exactly two", + id="alternating_wrong_length", + ), + param( + LLMAlternatingRecommender, + { + "recommenders": (RandomRecommender(), RandomRecommender()), + "mode": "cyclic", + }, + ValueError, + "first recommender", + id="alternating_first_not_llm", + ), + param( + LLMAlternatingRecommender, + { + "recommenders": ( + LLMRecommender(**_VALID_KWARGS), + RandomRecommender(), + ), + "mode": "raise", + }, + ValueError, + "mode", + id="alternating_wrong_mode", + ), + ], +) +def test_llm_meta_recommender_invalid_construction(cls, kwargs, error_cls, match): + """Invalid arguments to LLM meta recommenders raise at construction time.""" + with pytest.raises(error_cls, match=match): + cls(**kwargs) + + +@pytest.mark.parametrize( + ("cls", "from_model_kwargs", "expected_switch_after"), + [ + param( + LLMTwoPhaseRecommender, + {}, + 1, + id="twophase_defaults", + ), + param( + LLMTwoPhaseRecommender, + {"switch_after": 5}, + 5, + id="twophase_custom_switch", + ), + param( + LLMAlternatingRecommender, + {}, + None, + id="alternating_defaults", + ), + ], +) +def test_from_model_constructors(cls, from_model_kwargs, expected_switch_after): + """The ``from_model`` classmethods wire up the internal structure correctly.""" + secondary = RandomRecommender() + rec = cls.from_model( + **_VALID_KWARGS, + litellm_args={"temperature": 0.5}, + recommender=secondary, + **from_model_kwargs, + ) + + if cls is LLMTwoPhaseRecommender: + llm_rec = rec.initial_recommender + assert rec.recommender is secondary + assert rec.switch_after == expected_switch_after + else: + llm_rec = rec.recommenders[0] + assert rec.recommenders[1] is secondary + assert rec.mode == "cyclic" + + assert llm_rec.model == "test/model" + assert llm_rec.experiment_description == "Test experiment." + assert llm_rec.litellm_args == {"temperature": 0.5} From 1392ac330120a749c8eb4f36b48fc5a3e2f5108c Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Mon, 12 Oct 2026 08:26:37 +0200 Subject: [PATCH 89/96] Fix typing issues in `get_`- and `find-subclass` methods It seems like newer versions of `mypy` require more detailed type information that the simple `type` annotation we had in `find_subclass` --- baybe/utils/basic.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/baybe/utils/basic.py b/baybe/utils/basic.py index 9cfd73479f..9e1df8657a 100644 --- a/baybe/utils/basic.py +++ b/baybe/utils/basic.py @@ -19,7 +19,6 @@ if TYPE_CHECKING: from _typeshed import ConvertibleToFloat -_C = TypeVar("_C", bound=type) _T = TypeVar("_T") _U = TypeVar("_U") @@ -50,7 +49,9 @@ def is_all_instance( return all(isinstance(y, t) for y in x) -def get_subclasses(cls: _C, recursive: bool = True, abstract: bool = False) -> list[_C]: +def get_subclasses( + cls: type[_T], recursive: bool = True, abstract: bool = False +) -> list[type[_T]]: """Return a list of subclasses for the given class. Args: @@ -226,7 +227,7 @@ def refers_to(cls: type, name_or_abbr: str, /) -> bool: ) -def find_subclass(base: type, name_or_abbr: str, /): +def find_subclass(base: type[_T], name_or_abbr: str, /) -> type[_T]: """Retrieve a specific subclass of a base class via its name or abbreviation.""" try: return next(cl for cl in get_subclasses(base) if refers_to(cl, name_or_abbr)) From b6184149152812b38c3b038db282f0de7b6893c6 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Tue, 13 Oct 2026 17:22:27 +0200 Subject: [PATCH 90/96] Move optional import after check if dependency is available --- tests/llm/test_recommender.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tests/llm/test_recommender.py b/tests/llm/test_recommender.py index 6c7003b882..4eeed29090 100644 --- a/tests/llm/test_recommender.py +++ b/tests/llm/test_recommender.py @@ -9,7 +9,6 @@ from baybe import Campaign from baybe._optional.info import LLM_INSTALLED -from baybe._optional.llm import AuthenticationError from baybe.exceptions import ( LLMAuthenticationError, LLMCallError, @@ -33,6 +32,9 @@ if not LLM_INSTALLED: pytest.skip("LLM dependencies not installed.", allow_module_level=True) + +from baybe._optional.llm import AuthenticationError + _RECOMMENDER = LLMRecommender( model="test/model", experiment_description="Test experiment." ) From 7f113975aff6c91a9788d8efea4a5849fccac82f Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Tue, 13 Oct 2026 17:28:06 +0200 Subject: [PATCH 91/96] Remove broken links --- baybe/recommenders/meta/llm.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/baybe/recommenders/meta/llm.py b/baybe/recommenders/meta/llm.py index 31e720d05b..06cbe28bb5 100644 --- a/baybe/recommenders/meta/llm.py +++ b/baybe/recommenders/meta/llm.py @@ -18,7 +18,7 @@ @define class LLMTwoPhaseRecommender(TwoPhaseMetaRecommender): - """A two-phase recommender that warm-starts with an :class:`LLMRecommender`. + """A two-phase recommender that warm-starts with an LLM recommender. A convenience specialization of :class:`~baybe.recommenders.meta.sequential.TwoPhaseMetaRecommender` whose @@ -72,7 +72,7 @@ def from_model( @define class LLMAlternatingRecommender(SequentialMetaRecommender): - """A recommender alternating between an :class:`LLMRecommender` and another one. + """A recommender alternating between an LLM recommender and another one. A convenience specialization of :class:`~baybe.recommenders.meta.sequential.SequentialMetaRecommender` that cycles From 67f1e39fa4ba17f478d183e79352a84c9d5cdd9e Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Wed, 14 Oct 2026 16:42:38 +0200 Subject: [PATCH 92/96] Add missing pytest skip --- tests/llm/test_prompts.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tests/llm/test_prompts.py b/tests/llm/test_prompts.py index 4de7d8dd01..90a36a04aa 100644 --- a/tests/llm/test_prompts.py +++ b/tests/llm/test_prompts.py @@ -5,7 +5,7 @@ from attrs import evolve from pytest import param -from baybe._optional.info import CHEM_INSTALLED, JINJA2_INSTALLED +from baybe._optional.info import CHEM_INSTALLED, JINJA2_INSTALLED, LLM_INSTALLED from baybe.exceptions import LLMResponseError from baybe.objectives import ( DesirabilityObjective, @@ -271,6 +271,7 @@ def test_objective_prompt_info(objective, combination_contains, expected_descrip } +@pytest.mark.skipif(not LLM_INSTALLED, reason="LLM dependencies not installed.") @pytest.mark.parametrize( ("kwargs", "present_sections"), [ @@ -444,6 +445,7 @@ def test_forbidden_configurations(searchspace, expected): assert _forbidden_configurations(searchspace) == expected +@pytest.mark.skipif(not LLM_INSTALLED, reason="LLM dependencies not installed.") @pytest.mark.parametrize( ("recovery_kwargs", "expected_present", "expected_absent"), [ From aa394dec36d9b5090768e1f636f07d77aa7c1b84 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 15 Oct 2026 09:42:18 +0200 Subject: [PATCH 93/96] Improve recovery behavior for unusable responses Move the model query into the recovery block and raise `LLMMalformedResponseError` for responses with an unexpected structure or without content. These now trigger the same single recovery attempt as invalid responses. Call and authentication errors still propagate without recovery. The recovery prompt now omits the original response if there is none. --- baybe/recommenders/pure/llm/_prompts.py | 21 ++++++++------- baybe/recommenders/pure/llm/llm.py | 14 +++++++--- tests/llm/test_prompts.py | 21 +++++++-------- tests/llm/test_recommender.py | 36 +++++++++++++++---------- 4 files changed, 52 insertions(+), 40 deletions(-) diff --git a/baybe/recommenders/pure/llm/_prompts.py b/baybe/recommenders/pure/llm/_prompts.py index 2f77e12fad..6741b661fb 100644 --- a/baybe/recommenders/pure/llm/_prompts.py +++ b/baybe/recommenders/pure/llm/_prompts.py @@ -103,9 +103,11 @@ WHAT WENT WRONG: {{ recovery_instruction }} +{% if original_response is not none %} ORIGINAL RESPONSE: {{ original_response }} +{% endif %} Please provide a corrected set of {{ batch_size }} experimental conditions that \ addresses the problem described above and improves the optimization objective. @@ -347,9 +349,9 @@ def make_prompt( :meth:`baybe.recommenders.base.RecommenderProtocol.recommend`; the LLM-specific ``experiment_description`` is keyword-only. - When ``error`` and ``original_response`` are provided, the prompt is rendered in - *recovery* mode: it carries the exact same context as the original query and appends - a correction section asking the model to fix its previous response. Because each + When ``error`` is provided, the prompt is rendered in *recovery* mode: it carries + the exact same context as the original query and appends a correction section + asking the model to fix its previous response. Because each model call is stateless, carrying the full context here is what lets the recovery attempt reason as well as the original one. @@ -364,19 +366,18 @@ def make_prompt( pending_experiments: Optional pending experiments to include in the prompt. experiment_description: Textual description of the experiment. error: If set, renders a recovery prompt using the error's - :attr:`~baybe.exceptions.LLMResponseError.recovery_instruction`. Must be - provided together with ``original_response``. - original_response: The previous, rejected response to be corrected. Must be - provided together with ``error``. + :attr:`~baybe.exceptions.LLMResponseError.recovery_instruction`. + original_response: The previous, rejected response to be corrected, if the + model returned any content. Requires ``error``. Raises: - ValueError: If exactly one of ``error`` and ``original_response`` is provided. + ValueError: If ``original_response`` is provided without ``error``. Returns: The constructed prompt. """ - if (error is None) != (original_response is None): - raise ValueError("'error' and 'original_response' must be provided together.") + if original_response is not None and error is None: + raise ValueError("'original_response' can only be provided with 'error'.") from baybe._optional.llm import StrictUndefined, Template diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index 1f57b3a2cf..8458289f28 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -15,6 +15,7 @@ LLMAuthenticationError, LLMBatchSizeError, LLMCallError, + LLMMalformedResponseError, LLMResponseError, ) from baybe.objectives.base import Objective @@ -116,7 +117,8 @@ def _query_model(self, prompt: str) -> str: LLMAuthenticationError: If authentication with the provider fails. LLMCallError: If the call fails for another reason (e.g. network, rate limiting, timeout, unknown model) before a response is produced. - LLMResponseError: If a response is returned but contains no usable content. + LLMMalformedResponseError: If a response is returned but has an unexpected + structure or no content. """ from baybe._optional.llm import AuthenticationError, completion @@ -145,12 +147,14 @@ def _query_model(self, prompt: str) -> str: choices = response.choices # pyrefly: ignore[missing-attribute] content = choices[0].message.content except (AttributeError, IndexError, TypeError) as e: - raise LLMResponseError( + raise LLMMalformedResponseError( f"The language model returned an unexpected response structure: {e}." ) from e if content is None: - raise LLMResponseError("The language model returned empty content (None).") + raise LLMMalformedResponseError( + "The language model returned empty content (None)." + ) return content @@ -255,8 +259,10 @@ def _recommend_hybrid( self._pending_experiments, experiment_description=self.experiment_description, ) - content = self._query_model(prompt) + # Stays `None` if the model returns no usable content + content: str | None = None try: + content = self._query_model(prompt) return self._validate_response(content, searchspace, batch_size) except LLMResponseError as initial_error: # The recommendation had an issue. Make a recovery attempt. diff --git a/tests/llm/test_prompts.py b/tests/llm/test_prompts.py index 90a36a04aa..a52ac91f01 100644 --- a/tests/llm/test_prompts.py +++ b/tests/llm/test_prompts.py @@ -312,6 +312,11 @@ def test_objective_prompt_info(objective, combination_contains, expected_descrip {"WHAT WENT WRONG", "ORIGINAL RESPONSE"}, id="with_recovery", ), + param( + {"error": LLMResponseError("test error")}, + {"WHAT WENT WRONG"}, + id="with_recovery_without_response", + ), param( { "measurements": pd.DataFrame(columns=["Cat", "yield"]), @@ -342,22 +347,14 @@ def test_make_prompt_optional_sections(kwargs, present_sections): assert section not in prompt -@pytest.mark.parametrize( - ("error", "original_response"), - [ - param(LLMResponseError("err"), None, id="error_only"), - param(None, "response text", id="response_only"), - ], -) -def test_make_prompt_recovery_requires_both(error, original_response): - """Providing only one of ``error``/``original_response`` raises.""" - with pytest.raises(ValueError, match="must be provided together"): +def test_make_prompt_original_response_requires_error(): + """Providing ``original_response`` without ``error`` raises.""" + with pytest.raises(ValueError, match="can only be provided with 'error'"): make_prompt( batch_size=1, searchspace=_SEARCHSPACE, experiment_description=_DESCRIPTION, - error=error, - original_response=original_response, + original_response="response text", ) diff --git a/tests/llm/test_recommender.py b/tests/llm/test_recommender.py index 4eeed29090..9754cf04aa 100644 --- a/tests/llm/test_recommender.py +++ b/tests/llm/test_recommender.py @@ -1,6 +1,7 @@ """Tests for the LLM recommender integration.""" import warnings +from types import SimpleNamespace from unittest.mock import patch import pandas as pd @@ -116,14 +117,14 @@ def test_call_errors(exception, expected_error): assert mock.call_count == 1 -def test_empty_response(): - """A response with ``None`` content raises without a recovery attempt.""" - with patch(PATCH_TARGET, return_value=mock_response(None)) as mock: - with pytest.raises(LLMResponseError): - _RECOMMENDER.recommend(1, _SEARCHSPACE, _OBJECTIVE) - assert mock.call_count == 1 - - +@pytest.mark.parametrize( + "first_response", + [ + param(mock_response("not valid json"), id="invalid_json"), + param(mock_response(None), id="empty_content"), + param(SimpleNamespace(), id="unexpected_structure"), + ], +) @pytest.mark.parametrize( ("batch_size", "recovery_response", "expected_error"), [ @@ -159,9 +160,9 @@ def test_empty_response(): ), ], ) -def test_recovery(batch_size, recovery_response, expected_error): - """After a bad first response, a recovery attempt is made exactly once.""" - side_effect = [mock_response("not valid json"), recovery_response] +def test_recovery(first_response, batch_size, recovery_response, expected_error): + """After an unusable first response, a recovery attempt is made exactly once.""" + side_effect = [first_response, recovery_response] with patch(PATCH_TARGET, side_effect=side_effect) as mock: if expected_error is None: _RECOMMENDER.recommend(batch_size, _SEARCHSPACE, _OBJECTIVE) @@ -171,9 +172,15 @@ def test_recovery(batch_size, recovery_response, expected_error): assert mock.call_count == 2 -def test_recovery_prompt_content(): +@pytest.mark.parametrize( + "bad_content", + [ + param("not valid json", id="invalid_json"), + param(None, id="empty_content"), + ], +) +def test_recovery_prompt_content(bad_content): """The recovery prompt carries the full context and the error details.""" - bad_content = "not valid json" valid_content = make_valid_json(_SEARCHSPACE, 1) side_effect = [mock_response(bad_content), mock_response(valid_content)] with patch(PATCH_TARGET, side_effect=side_effect) as mock: @@ -186,9 +193,10 @@ def test_recovery_prompt_content(): experiment_description=_RECOMMENDER.experiment_description, ) expected_first = make_prompt(**prompt_kwargs) + # Only the recovery instruction of the error type enters the prompt expected_recovery = make_prompt( **prompt_kwargs, - error=LLMMalformedResponseError("Response contains no JSON array of objects."), + error=LLMMalformedResponseError("Unused message."), original_response=bad_content, ) From 74570a9eb6298c30b172757787b7530ade165b77 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 15 Oct 2026 09:45:28 +0200 Subject: [PATCH 94/96] Prevent users from passing `stream` key We always require the full response before parsing it, and streaming would break this. --- baybe/recommenders/pure/llm/llm.py | 15 +++++++++------ .../validation/test_llm_recommender_validation.py | 10 ++++++++-- 2 files changed, 17 insertions(+), 8 deletions(-) diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index 8458289f28..e66effae92 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -27,8 +27,10 @@ from baybe.serialization import SerialMixin from baybe.utils.conversion import to_string -# Keys that are wired in by the recommender itself and must not be overridden. -_RESERVED_LITELLM_KEYS = frozenset({"model", "messages"}) +# Keys that are controlled by the recommender itself and must not be overridden: the +# model and messages are set explicitly, and the response must not be streamed since +# it is parsed as a whole. +_RESERVED_LITELLM_KEYS = frozenset({"model", "messages", "stream"}) # Credential keys that LiteLLM accepts inline but must be supplied via environment # variables instead (e.g. OPENAI_API_KEY, ANTHROPIC_API_KEY). Blocking them at @@ -93,8 +95,8 @@ def _validate_litellm_args(self, attribute, value): # noqa: DOC101, DOC103 conflicts = _RESERVED_LITELLM_KEYS & set(value.keys()) if conflicts: raise ValueError( - f"'{attribute.name}' must not contain keys that are set explicitly: " - f"{conflicts}. Use the dedicated class attributes instead." + f"'{attribute.name}' must not contain keys that are controlled by the " + f"recommender itself: {sorted(conflicts)}." ) cred_conflicts = _CREDENTIAL_LITELLM_KEYS & set(value.keys()) if cred_conflicts: @@ -141,8 +143,9 @@ def _query_model(self, prompt: str) -> str: f"'{self.model}'." ) from e - # NOTE: `completion()` can also return a stream/coroutine (via `litellm_args`), - # whose types lack `.choices`; the `except` below handles those at runtime. + # NOTE: `completion()` is typed to also return a stream, which lacks `.choices`. + # Streaming is excluded via `_RESERVED_LITELLM_KEYS`, and the `except` below + # guards against any other unexpected response type at runtime. try: choices = response.choices # pyrefly: ignore[missing-attribute] content = choices[0].message.content diff --git a/tests/validation/test_llm_recommender_validation.py b/tests/validation/test_llm_recommender_validation.py index 129006d613..3a5ed78896 100644 --- a/tests/validation/test_llm_recommender_validation.py +++ b/tests/validation/test_llm_recommender_validation.py @@ -32,15 +32,21 @@ param( {"litellm_args": {"model": "x"}}, ValueError, - "'litellm_args' must not contain keys that are set explicitly", + "'litellm_args' must not contain keys that are controlled by", id="reserved_key_model", ), param( {"litellm_args": {"messages": []}}, ValueError, - "'litellm_args' must not contain keys that are set explicitly", + "'litellm_args' must not contain keys that are controlled by", id="reserved_key_messages", ), + param( + {"litellm_args": {"stream": True}}, + ValueError, + "'litellm_args' must not contain keys that are controlled by", + id="reserved_key_stream", + ), param( {"litellm_args": {"api_key": "x"}}, ValueError, From d7ff0bb46bd07ba14227ff91d1f8b5e591a83809 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 15 Oct 2026 10:46:01 +0200 Subject: [PATCH 95/96] Build LLM prompt from serialized objects Describe objective, parameters and constraints via their serialized form. This change now includes constraints in the prompt (these were missing earlier) and also improves handling of match targets and transformations in general. Some design decision that have been made (i) custom constraints and transformations are rejected, since their logic cannot be described to the language model, (ii) only include active labels for label-like parameters. (iii) drop the data of custom parameters. In addition, this also restricts measurement tables to the parameter and target columns, and no longer explicitly forbids pending experiments in the prompt text. --- baybe/recommenders/pure/llm/_prompts.py | 360 +++++++++--------------- baybe/recommenders/pure/llm/llm.py | 44 +++ baybe/transformations/utils.py | 19 ++ tests/llm/test_prompts.py | 340 ++++++++++------------ tests/llm/test_recommender.py | 68 +++++ tests/test_transformations.py | 22 ++ 6 files changed, 439 insertions(+), 414 deletions(-) diff --git a/baybe/recommenders/pure/llm/_prompts.py b/baybe/recommenders/pure/llm/_prompts.py index 6741b661fb..2eb961f9e7 100644 --- a/baybe/recommenders/pure/llm/_prompts.py +++ b/baybe/recommenders/pure/llm/_prompts.py @@ -2,26 +2,26 @@ from __future__ import annotations -from typing import TYPE_CHECKING, Literal, TypedDict - -from baybe.exceptions import IncompatibilityError, LLMResponseError -from baybe.objectives.desirability import DesirabilityObjective -from baybe.objectives.enum import Scalarizer -from baybe.objectives.pareto import ParetoObjective -from baybe.parameters.base import DiscreteParameter, Parameter -from baybe.parameters.numerical import NumericalContinuousParameter +import json +from collections.abc import Iterable +from typing import TYPE_CHECKING, Any, TypedDict + +from attrs import fields + +from baybe.exceptions import LLMResponseError +from baybe.parameters.base import _DiscreteLabelLikeParameter +from baybe.parameters.categorical import CategoricalParameter +from baybe.parameters.custom import CustomDiscreteParameter from baybe.parameters.substance import SubstanceParameter from baybe.recommenders.pure.llm._schema import _response_format from baybe.searchspace import SearchSpace -from baybe.targets.binary import BinaryTarget -from baybe.targets.numerical import NumericalTarget -from baybe.transformations import IdentityTransformation if TYPE_CHECKING: import pandas as pd + from baybe.constraints.base import Constraint from baybe.objectives.base import Objective - from baybe.targets.base import Target + from baybe.parameters.base import Parameter _FORBIDDEN_INSTRUCTIONS = """\ The following configurations are currently NOT selectable. You MUST NOT recommend any @@ -33,52 +33,46 @@ _PROMPT_TEMPLATE = """\ You are an expert experimental design assistant. Your task is to suggest new \ -experimental conditions based on the following information: +experimental conditions based on the following information. Objects are given in \ +JSON format, one object per line. EXPERIMENT DESCRIPTION: {{ experiment_description }} {% if objective is not none %} OPTIMIZATION OBJECTIVE: -{% if objective.description is not none %} -{{ objective.description }} -{% endif %} -{% if objective.combination is not none %} -{{ objective.combination }} -{% endif %} +Each target is optimized by maximizing its transformed value (or minimizing it if \ +"minimize" is true); "constructor_info", if present, shows how the target was defined, \ +e.g. the "match_value" of a match target. +{{ objective }} + +Transformations map a measured value x to the value being optimized: +- IdentityTransformation: x +- AffineTransformation: factor * x + shift +- ClampingTransformation: x limited to the range [cutoffs.lower, cutoffs.upper] +- AbsoluteTransformation: |x| +- LogarithmicTransformation: ln(x) +- ExponentialTransformation: exp(x) +- PowerTransformation: x ** exponent +- SigmoidTransformation: 1 / (1 + exp(-steepness * (center - x))) +- TwoSidedAffineTransformation: slope_left * (x - midpoint) if x < midpoint, \ +else slope_right * (x - midpoint) +- BellTransformation: exp(-((x - center) / sigma) ** 2 / 2), i.e. highest at x = center +- TriangularTransformation: rising linearly from 0 at cutoffs.lower to 1 at peak, \ +falling linearly back to 0 at cutoffs.upper, and 0 outside the cutoffs +- ChainedTransformation: the listed transformations applied in order, first one first +- AdditiveTransformation / MultiplicativeTransformation: sum / product of the listed \ +transformations, each applied to x -TARGETS: -{% for target in objective.targets %} -Target: {{ target.name }} -Goal: {{ target.goal }} -{% if target.transformation is not none %} -Transformation applied before optimizing: {{ target.transformation }} -{% endif %} -{% if target.description is not none %} -Description: {{ target.description }} -{% endif %} -{% if target.unit is not none %} -Unit: {{ target.unit }} -{% endif %} - -{% endfor %} {% endif %} PARAMETERS: -{% for param in parameters %} -Parameter: {{ param.name }} -{% if param.description is not none %} -Description: {{ param.description }} -{% endif %} -Type: {{ param.kind }} -{{ param.domain }} -{% if param.unit is not none %} -Unit: {{ param.unit }} -{% endif %} -{% for key, value in param.misc %} -{{ key }}: {{ value }} -{% endfor %} +{{ parameters }} +{% if constraints is not none %} -{% endfor %} +CONSTRAINTS: +All suggestions must satisfy the following constraints. +{{ constraints }} +{% endif %} {% if measurements is not none %} PREVIOUS MEASUREMENTS: @@ -88,7 +82,7 @@ PENDING EXPERIMENTS: The following experiments have already been proposed and are awaiting results. -Do not recommend these again. +Take them into account when generating your recommendations. {{ pending_experiments }} {% endif %} {%- if forbidden_configurations is not none %} @@ -126,41 +120,13 @@ """ -class _ParameterPromptInfo(TypedDict): - """Typed, presentation-only view of a parameter for prompt rendering.""" - - name: str - description: str | None - kind: Literal["continuous", "discrete_numeric", "categorical", "substance"] - domain: str - unit: str | None - misc: tuple[tuple[str, str], ...] - - -class _TargetPromptInfo(TypedDict): - """Typed, presentation-only view of an optimization target.""" - - name: str - goal: str - transformation: str | None - description: str | None - unit: str | None - - -class _ObjectivePromptInfo(TypedDict): - """Typed, presentation-only view of the optimization objective.""" - - description: str | None - combination: str | None - targets: tuple[_TargetPromptInfo, ...] - - class _PromptContext(TypedDict): """Typed render context for the prompt.""" experiment_description: str - objective: _ObjectivePromptInfo | None - parameters: tuple[_ParameterPromptInfo, ...] + objective: str | None + parameters: str + constraints: str | None measurements: str | None pending_experiments: str | None forbidden_configurations: str | None @@ -171,57 +137,80 @@ class _PromptContext(TypedDict): original_response: str | None -def _parameter_prompt_info(parameter: Parameter) -> _ParameterPromptInfo: - """Build the prompt view of a parameter. +def _drop_empty(obj: Any, /) -> Any: + """Recursively drop ``None`` values and empty dictionaries from dictionaries. Args: - parameter: The parameter to describe. + obj: The serialized object to clean up. Returns: - A typed, presentation-only view consumed by the prompt template. + The cleaned-up object. + """ + if isinstance(obj, dict): + cleaned = {key: _drop_empty(value) for key, value in obj.items()} + return {k: v for k, v in cleaned.items() if v is not None and v != {}} + if isinstance(obj, list): + return [_drop_empty(value) for value in obj] + return obj - Raises: - IncompatibilityError: If the parameter type is not supported. + +def _to_prompt_dict(obj: Parameter | Constraint | Objective, /) -> dict[str, Any]: + """Serialize an object into the representation shown to the language model. + + ``None`` values and empty dictionaries are dropped since they carry no information + for the language model. For label-like parameters, ``values`` holds the active + labels only. Substance parameters keep the SMILES of their active labels, while + the descriptor table of custom parameters is omitted since it is only relevant for + the surrogate model. + + Args: + obj: The object to serialize. + + Returns: + The serialized object. """ - if isinstance(parameter, NumericalContinuousParameter): - lower, upper = parameter.bounds.to_tuple() - kind: Literal["continuous", "discrete_numeric", "categorical", "substance"] = ( - "continuous" - ) - domain = f"Bounds: [{lower}, {upper}]" - elif isinstance(parameter, SubstanceParameter): - # Substances are chemical compounds: expose their SMILES so the model can - # reason about structure, while still choosing by substance name. - kind = "substance" - substances = ", ".join( - f"{name} ({smiles})" - for name, smiles in parameter.data.items() - if name in parameter.active_values - ) - domain = f"Allowed values (choose by name; SMILES in parentheses): {substances}" - elif isinstance(parameter, DiscreteParameter): - kind = "discrete_numeric" if parameter.is_numerical else "categorical" - values = parameter.active_values - if parameter.is_numerical: - values = tuple(value.item() for value in values) # avoid numpy-repr leak - domain = f"Allowed values: {list(values)}" - else: - raise IncompatibilityError( - f"Parameter '{parameter.name}' has unsupported type " - f"'{type(parameter).__name__}'. Only " - f"'{NumericalContinuousParameter.__name__}' and " - f"'{DiscreteParameter.__name__}' subclasses are supported." - ) - return { - "name": parameter.name, - "description": parameter.description, - "kind": kind, - "domain": domain, - "unit": parameter.unit, - "misc": tuple( - (key, str(value)) for key, value in parameter.metadata.misc.items() - ), - } + serialized = obj.to_dict() + if isinstance(obj, _DiscreteLabelLikeParameter): + active = list(obj.active_values) + serialized.pop(fields(_DiscreteLabelLikeParameter)._active_values.alias) + serialized[fields(CategoricalParameter)._values.alias] = active + if isinstance(obj, SubstanceParameter): + serialized[fields(SubstanceParameter).data.alias] = { + label: smiles for label, smiles in obj.data.items() if label in active + } + elif isinstance(obj, CustomDiscreteParameter): + del serialized[fields(CustomDiscreteParameter).data.alias] + return _drop_empty(serialized) + + +def _to_json_lines(serialized_objects: Iterable[dict[str, Any]], /) -> str: + """Render serialized objects as JSON, one object per line. + + Args: + serialized_objects: The serialized objects to render. + + Returns: + The rendered objects. + """ + return "\n".join(json.dumps(obj, ensure_ascii=False) for obj in serialized_objects) + + +def _table_text(df: pd.DataFrame | None, columns: list[str], /) -> str | None: + """Render the given columns of a dataframe as a table. + + Restricting the columns excludes non-experimental columns, such as the metadata + columns added by :class:`~baybe.campaign.Campaign`. + + Args: + df: The optional dataframe to render. + columns: The columns to include. + + Returns: + The rendered table, or ``None`` if no or an empty dataframe is provided. + """ + if df is None or df.empty: + return None + return df[columns].to_string(index=False) def _forbidden_configurations(searchspace: SearchSpace) -> str | None: @@ -251,87 +240,6 @@ def _forbidden_configurations(searchspace: SearchSpace) -> str | None: return forbidden.to_string(index=False) -def _target_prompt_info(target: Target) -> _TargetPromptInfo: - """Build the prompt view of an optimization target. - - Args: - target: The target to describe. - - Returns: - A typed, presentation-only view of the target's optimization semantics. - - Raises: - IncompatibilityError: If the target type is not supported. - """ - transformation: str | None = None - if isinstance(target, NumericalTarget): - goal = "minimize" if target.minimize else "maximize" - transformation = ( - None - if isinstance(target.transformation, IdentityTransformation) - else str(target.transformation) - ) - elif isinstance(target, BinaryTarget): - goal = ( - f"achieve the success value '{target.success_value}' " - f"(as opposed to the failure value '{target.failure_value}')" - ) - else: - raise IncompatibilityError( - f"Target '{target.name}' has unsupported type " - f"'{type(target).__name__}'. Only '{NumericalTarget.__name__}' and " - f"'{BinaryTarget.__name__}' are supported." - ) - return { - "name": target.name, - "goal": goal, - "transformation": transformation, - "description": target.description, - "unit": target.unit, - } - - -def _objective_prompt_info(objective: Objective) -> _ObjectivePromptInfo: - """Build the prompt view of the optimization objective. - - Args: - objective: The objective to describe. - - Returns: - A typed, presentation-only view: the objective description, how its targets are - combined, and the per-target views. - """ - targets = tuple(_target_prompt_info(t) for t in objective.targets) - combination: str | None - if isinstance(objective, DesirabilityObjective): - aggregation = ( - "weighted geometric mean" - if objective.scalarizer is Scalarizer.GEOM_MEAN - else "weighted arithmetic mean" - ) - weights = ", ".join( - f"{t['name']}={w:.3g}" - for t, w in zip(targets, objective.normalized_weights) - ) - combination = ( - f"The targets are aggregated into a single desirability score via a " - f"{aggregation} (weights: {weights}), which is then maximized." - ) - elif isinstance(objective, ParetoObjective): - combination = ( - "The targets are optimized jointly as a multi-objective (Pareto) problem: " - "they are not combined into a single score; seek the best trade-offs, " - "optimizing each target in its stated direction." - ) - else: - combination = None - return { - "description": objective.metadata.description, - "combination": combination, - "targets": targets, - } - - def make_prompt( batch_size: int, searchspace: SearchSpace, @@ -349,11 +257,15 @@ def make_prompt( :meth:`baybe.recommenders.base.RecommenderProtocol.recommend`; the LLM-specific ``experiment_description`` is keyword-only. + The objective, parameters and constraints are included in their serialized form. + Measurements and pending experiments are restricted to their parameter and target + columns. + When ``error`` is provided, the prompt is rendered in *recovery* mode: it carries the exact same context as the original query and appends a correction section - asking the model to fix its previous response. Because each - model call is stateless, carrying the full context here is what lets the recovery - attempt reason as well as the original one. + asking the model to fix its previous response. Because each model call is + stateless, carrying the full context here is what lets the recovery attempt reason + as well as the original one. Args: batch_size: The number of recommendations to generate. @@ -381,24 +293,26 @@ def make_prompt( from baybe._optional.llm import StrictUndefined, Template - measurements_text = ( - measurements.to_string(index=False) - if measurements is not None and not measurements.empty - else None - ) - pending_text = ( - pending_experiments.to_string(index=False) - if pending_experiments is not None and not pending_experiments.empty - else None - ) + parameter_names = [p.name for p in searchspace.parameters] + target_names = [t.name for t in objective.targets] if objective is not None else [] + constraints = searchspace.constraints context: _PromptContext = { "experiment_description": experiment_description, "objective": ( - _objective_prompt_info(objective) if objective is not None else None + _to_json_lines([_to_prompt_dict(objective)]) + if objective is not None + else None + ), + "parameters": _to_json_lines( + _to_prompt_dict(p) for p in searchspace.parameters + ), + "constraints": ( + _to_json_lines(_to_prompt_dict(c) for c in constraints) + if constraints + else None ), - "parameters": tuple(_parameter_prompt_info(p) for p in searchspace.parameters), - "measurements": measurements_text, - "pending_experiments": pending_text, + "measurements": _table_text(measurements, parameter_names + target_names), + "pending_experiments": _table_text(pending_experiments, parameter_names), "forbidden_configurations": _forbidden_configurations(searchspace), "forbidden_instructions": _FORBIDDEN_INSTRUCTIONS, "batch_size": batch_size, diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index e66effae92..7275b1b224 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -11,7 +11,9 @@ from attrs.validators import deep_mapping, instance_of, min_len from typing_extensions import override +from baybe.constraints.discrete import DiscreteCustomConstraint from baybe.exceptions import ( + IncompatibilityError, LLMAuthenticationError, LLMBatchSizeError, LLMCallError, @@ -25,6 +27,9 @@ from baybe.searchspace import SearchSpace from baybe.searchspace.core import SearchSpaceType from baybe.serialization import SerialMixin +from baybe.targets.numerical import NumericalTarget +from baybe.transformations.basic import CustomTransformation +from baybe.transformations.utils import _iterate_transformations from baybe.utils.conversion import to_string # Keys that are controlled by the recommender itself and must not be overridden: the @@ -189,6 +194,41 @@ def _validate_response( # NOTE: Duplicate configurations within a batch are permitted. return output.head(batch_size) + def _validate_compatibility( + self, searchspace: SearchSpace, objective: Objective | None + ) -> None: + """Validate that the recommendation context can be described to the model. + + Custom constraints and transformations are defined by arbitrary callables, + whose logic cannot be communicated to the language model. + + Args: + searchspace: The search space to generate recommendations for. + objective: The optional objective of the recommendation context. + + Raises: + IncompatibilityError: If the search space contains a custom constraint. + IncompatibilityError: If a target uses a custom transformation. + """ + for constraint in searchspace.discrete.constraints: + if isinstance(constraint, DiscreteCustomConstraint): + raise IncompatibilityError( + f"'{self.__class__.__name__}' does not support " + f"'{DiscreteCustomConstraint.__name__}' since its logic cannot be " + f"described to the language model." + ) + for target in objective.targets if objective is not None else (): + if isinstance(target, NumericalTarget) and any( + isinstance(t, CustomTransformation) + for t in _iterate_transformations(target.transformation) + ): + raise IncompatibilityError( + f"'{self.__class__.__name__}' does not support " + f"'{CustomTransformation.__name__}' (used by target " + f"'{target.name}') since its logic cannot be described to the " + f"language model." + ) + @override def recommend( self, @@ -211,6 +251,8 @@ def recommend( A DataFrame containing the recommendations as individual rows. Raises: + IncompatibilityError: If the search space or objective contains custom + components that cannot be described to the language model. LLMAuthenticationError: If authentication with the provider fails. LLMCallError: If the call to the language model fails (e.g. network, rate limiting, timeout) before a response is produced. @@ -224,6 +266,8 @@ def recommend( f"provided {batch_size=}." ) + self._validate_compatibility(searchspace, objective) + if objective is not None and objective.metadata.is_empty: warnings.warn( "The objective has no metadata description. Without context on " diff --git a/baybe/transformations/utils.py b/baybe/transformations/utils.py index 71e1ff8ef6..dfa510e21d 100644 --- a/baybe/transformations/utils.py +++ b/baybe/transformations/utils.py @@ -56,6 +56,25 @@ def _flatten_transformations( yield t +def _iterate_transformations( + transformation: Transformation, / +) -> Iterable[Transformation]: + """Recursively iterate over a transformation and all its nested components.""" + from baybe.transformations.composite import ( + AdditiveTransformation, + ChainedTransformation, + MultiplicativeTransformation, + ) + + yield transformation + if isinstance( + transformation, + (ChainedTransformation, AdditiveTransformation, MultiplicativeTransformation), + ): + for t in transformation.transformations: + yield from _iterate_transformations(t) + + def compress_transformations( transformations: Iterable[Transformation], / ) -> tuple[Transformation, ...]: diff --git a/tests/llm/test_prompts.py b/tests/llm/test_prompts.py index a52ac91f01..e2b58bd441 100644 --- a/tests/llm/test_prompts.py +++ b/tests/llm/test_prompts.py @@ -6,264 +6,178 @@ from pytest import param from baybe._optional.info import CHEM_INSTALLED, JINJA2_INSTALLED, LLM_INSTALLED +from baybe.constraints import ( + ContinuousLinearConstraint, + DiscreteSelectionConstraint, + SubSelectionCondition, +) from baybe.exceptions import LLMResponseError from baybe.objectives import ( DesirabilityObjective, ParetoObjective, SingleTargetObjective, ) -from baybe.objectives.enum import Scalarizer from baybe.parameters import ( CategoricalParameter, + CustomDiscreteParameter, NumericalContinuousParameter, NumericalDiscreteParameter, + TaskParameter, ) from baybe.recommenders.pure.llm._prompts import ( + _drop_empty, _forbidden_configurations, - _objective_prompt_info, - _parameter_prompt_info, - _target_prompt_info, + _to_prompt_dict, make_prompt, ) from baybe.searchspace import SearchSpace from baybe.searchspace._filtered import FilteredSubspaceDiscrete from baybe.targets import BinaryTarget, NumericalTarget -from baybe.transformations import AffineTransformation if not JINJA2_INSTALLED: pytest.skip("Jinja2 not installed.", allow_module_level=True) @pytest.mark.parametrize( - ("parameter", "expected_kind", "domain_contains", "domain_excludes"), + ("parameter", "expected_values"), [ param( CategoricalParameter("Cat", ("A", "B", "C")), - "categorical", ["A", "B", "C"], - [], id="categorical", ), param( CategoricalParameter("Cat", ("A", "B", "C"), active_values=("A", "B")), - "categorical", ["A", "B"], - ["C"], id="categorical_active_values", ), param( - NumericalDiscreteParameter("Num", (1.0, 2.0, 3.0)), - "discrete_numeric", - ["1.0", "2.0", "3.0"], - ["numpy", "np."], - id="numerical_discrete", + CategoricalParameter("Cat", ("A", "B", "C"), active_values=("A",)), + ["A"], + id="categorical_single_active_value", ), param( - NumericalContinuousParameter("Cont", bounds=(0.0, 10.0)), - "continuous", - ["0.0", "10.0"], - [], - id="continuous", + TaskParameter("Task", ("T1", "T2", "T3"), active_values=("T1",)), + ["T1"], + id="task_active_values", ), - ], -) -def test_parameter_prompt_info( - parameter, expected_kind, domain_contains, domain_excludes -): - """The prompt view has the correct kind and domain for each parameter type.""" - info = _parameter_prompt_info(parameter) - assert info["name"] == parameter.name - assert info["kind"] == expected_kind - for substr in domain_contains: - assert substr in info["domain"] - for substr in domain_excludes: - assert substr not in info["domain"] - - -@pytest.mark.skipif(not CHEM_INSTALLED, reason="Chem dependencies not installed.") -def test_parameter_prompt_info_substance(): - """Substance parameters expose SMILES in the domain string.""" - from baybe.parameters import SubstanceParameter - - p = SubstanceParameter( - "Solvent", - data={"Water": "O", "Ethanol": "CCO"}, - encoding="MORDRED", - ) - info = _parameter_prompt_info(p) - assert info["kind"] == "substance" - assert "Water" in info["domain"] - assert "O" in info["domain"] - assert "Ethanol" in info["domain"] - assert "CCO" in info["domain"] - - -@pytest.mark.parametrize( - ("parameter", "expected_description", "expected_unit", "expected_misc"), - [ param( - CategoricalParameter( - "Cat", - ("A", "B"), - metadata={ - "description": "A category", - "unit": "mg", - "purity": "high", - }, + CustomDiscreteParameter( + "Custom", + pd.DataFrame({"d1": [1.0, 2.0, 3.0]}, index=["L1", "L2", "L3"]), + active_values=("L1", "L2"), ), - "A category", - "mg", - (("purity", "high"),), - id="with_metadata", + ["L1", "L2"], + id="custom_active_values", ), param( - CategoricalParameter("Cat", ("A", "B")), - None, - None, - (), - id="no_metadata", + NumericalDiscreteParameter("Num", (1.0, 2.0, 3.0)), + [1.0, 2.0, 3.0], + id="numerical_discrete", ), ], ) -def test_parameter_prompt_info_metadata( - parameter, expected_description, expected_unit, expected_misc -): - """Metadata fields are forwarded when present and None/empty otherwise.""" - info = _parameter_prompt_info(parameter) - assert info["description"] == expected_description - assert info["unit"] == expected_unit - assert info["misc"] == expected_misc +def test_to_prompt_dict(parameter, expected_values): + """Only the active values of a parameter are shown, without encoding data.""" + serialized = _to_prompt_dict(parameter) + assert serialized["name"] == parameter.name + assert serialized["values"] == expected_values + assert "active_values" not in serialized + assert "data" not in serialized -@pytest.mark.parametrize( - ("target", "goal_contains", "exp_transformation", "exp_desc", "exp_unit"), - [ - param( - NumericalTarget("yield"), - ["maximize"], - None, - None, - None, - id="numerical_maximize", - ), - param( - NumericalTarget("yield", minimize=True), - ["minimize"], - None, - None, - None, - id="numerical_minimize", - ), - param( - NumericalTarget("yield", transformation=AffineTransformation(factor=2.0)), - ["maximize"], - "AffineTransformation(factor=2.0, shift=0.0)", - None, - None, - id="numerical_with_transformation", - ), - param( - BinaryTarget("success", success_value="yes", failure_value="no"), - ["yes", "no"], - None, - None, - None, - id="binary", - ), - param( - NumericalTarget( - "yield", - metadata={"description": "Reaction yield", "unit": "%"}, - ), - ["maximize"], - None, - "Reaction yield", - "%", - id="numerical_with_metadata", - ), - ], -) -def test_target_prompt_info( - target, goal_contains, exp_transformation, exp_desc, exp_unit -): - """The prompt view has the correct fields for each target type.""" - info = _target_prompt_info(target) - assert info["name"] == target.name - assert info["description"] == exp_desc - assert info["unit"] == exp_unit - assert info["transformation"] == exp_transformation - for substr in goal_contains: - assert substr in info["goal"] +@pytest.mark.skipif(not CHEM_INSTALLED, reason="Chem dependencies not installed.") +def test_to_prompt_dict_substance(): + """Substance parameters show the SMILES of their active values only.""" + from baybe.parameters import SubstanceParameter + parameter = SubstanceParameter( + "Solvent", + data={"Water": "O", "Ethanol": "CCO", "Benzene": "c1ccccc1"}, + active_values=("Water", "Ethanol"), + ) + serialized = _to_prompt_dict(parameter) + assert serialized["values"] == ["Water", "Ethanol"] + assert serialized["data"] == {"Water": "O", "Ethanol": "CCO"} + assert "active_values" not in serialized + + +def test_drop_empty(): + """None values and (resulting) empty dictionaries are dropped recursively.""" + serialized = { + "none": None, + "empty": {}, + "emptied": {"none": None}, + "nested": [{"none": None, "kept": 1}], + "falsy": 0, + "empty_list": [], + } + assert _drop_empty(serialized) == { + "nested": [{"kept": 1}], + "falsy": 0, + "empty_list": [], + } + +_SEARCHSPACE = SearchSpace.from_product([CategoricalParameter("Cat", ("A", "B"))]) +_DESCRIPTION = "Test experiment." _T1 = NumericalTarget("yield") _T2 = NumericalTarget("cost", minimize=True) +@pytest.mark.skipif(not LLM_INSTALLED, reason="LLM dependencies not installed.") @pytest.mark.parametrize( - ("objective", "combination_contains", "expected_description"), + ("objective", "expected_content"), [ + param(SingleTargetObjective(_T2), '"minimize": true', id="direction"), param( - SingleTargetObjective(target=_T1), - None, - None, - id="single_target", - ), - param( - DesirabilityObjective( - targets=[_T1, _T2], - scalarizer=Scalarizer.MEAN, - require_normalization=False, + SingleTargetObjective( + NumericalTarget.match_bell("pH", match_value=7.0, sigma=0.5) ), - "weighted arithmetic mean", - None, - id="desirability_mean", + '"match_value": 7.0', + id="match_target", ), param( DesirabilityObjective( - targets=[ - NumericalTarget.normalized_sigmoid("yield", [[0, 0.1], [100, 0.9]]), - NumericalTarget.normalized_sigmoid("cost", [[0, 0.9], [100, 0.1]]), + [ + NumericalTarget.normalized_ramp("yield", (0, 100)), + NumericalTarget.normalized_ramp("cost", (0, 10), descending=True), ], - scalarizer=Scalarizer.GEOM_MEAN, + weights=[2, 1], ), - "weighted geometric mean", - None, - id="desirability_geom_mean", - ), - param( - ParetoObjective(targets=[_T1, _T2]), - "Pareto", - None, - id="pareto", + '"weights": [2.0, 1.0]', + id="desirability", ), + param(ParetoObjective([_T1, _T2]), '"type": "ParetoObjective"', id="pareto"), param( SingleTargetObjective( - target=_T1, - metadata={"description": "Maximize yield"}, + BinaryTarget("success", success_value="yes", failure_value="no") ), - None, - "Maximize yield", - id="single_target_with_metadata", + '"success_value": "yes"', + id="binary", + ), + param( + SingleTargetObjective(_T1, metadata={"description": "Maximize yield"}), + '"description": "Maximize yield"', + id="metadata", ), ], ) -def test_objective_prompt_info(objective, combination_contains, expected_description): - """The prompt view has the correct fields per objective type.""" - info = _objective_prompt_info(objective) - assert len(info["targets"]) == len(objective.targets) - assert info["description"] == expected_description - if combination_contains is None: - assert info["combination"] is None - else: - assert combination_contains in info["combination"] +def test_make_prompt_objective(objective, expected_content): + """The objective is included with all information relevant for optimizing.""" + prompt = make_prompt( + batch_size=1, + searchspace=_SEARCHSPACE, + objective=objective, + experiment_description=_DESCRIPTION, + ) + print(prompt) + assert expected_content in prompt -_SEARCHSPACE = SearchSpace.from_product([CategoricalParameter("Cat", ("A", "B"))]) -_DESCRIPTION = "Test experiment." _ALL_OPTIONAL_SECTIONS = { "OPTIMIZATION OBJECTIVE", - "TARGETS", + "CONSTRAINTS", "PREVIOUS MEASUREMENTS", "PENDING EXPERIMENTS", "WHAT WENT WRONG", @@ -287,9 +201,33 @@ def test_objective_prompt_info(objective, combination_contains, expected_descrip metadata={"description": "Maximize yield"}, ), }, - {"OPTIMIZATION OBJECTIVE", "TARGETS"}, + {"OPTIMIZATION OBJECTIVE"}, id="with_objective", ), + param( + { + "searchspace": SearchSpace.from_product( + [CategoricalParameter("Cat", ("A", "B", "C"))], + [ + DiscreteSelectionConstraint( + ["Cat"], [SubSelectionCondition(["C"])], exclude=True + ) + ], + ), + }, + {"CONSTRAINTS"}, + id="with_discrete_constraint", + ), + param( + { + "searchspace": SearchSpace.from_product( + [NumericalContinuousParameter("x", (0.0, 1.0))], + [ContinuousLinearConstraint(["x"], "<=", [1.0], 0.5)], + ), + }, + {"CONSTRAINTS"}, + id="with_continuous_constraint", + ), param( { "measurements": pd.DataFrame({"Cat": ["A"], "yield": [42.0]}), @@ -336,10 +274,12 @@ def test_objective_prompt_info(objective, combination_contains, expected_descrip def test_make_prompt_optional_sections(kwargs, present_sections): """Optional prompt sections appear only when their arguments are provided.""" prompt = make_prompt( - batch_size=1, - searchspace=_SEARCHSPACE, - experiment_description=_DESCRIPTION, - **kwargs, + **{ + "batch_size": 1, + "searchspace": _SEARCHSPACE, + "experiment_description": _DESCRIPTION, + **kwargs, + } ) for section in present_sections: assert section in prompt @@ -358,6 +298,24 @@ def test_make_prompt_original_response_requires_error(): ) +@pytest.mark.skipif(not LLM_INSTALLED, reason="LLM dependencies not installed.") +def test_make_prompt_excludes_metadata_columns(): + """Only parameter and target columns of the dataframes appear in the prompt.""" + prompt = make_prompt( + batch_size=1, + searchspace=_SEARCHSPACE, + objective=SingleTargetObjective(_T1), + measurements=pd.DataFrame( + {"Cat": ["A"], "yield": [42.0], "BatchNr": [1], "FitNr": [1.0]} + ), + pending_experiments=pd.DataFrame({"Cat": ["B"], "BatchNr": [2]}), + experiment_description=_DESCRIPTION, + ) + assert "42.0" in prompt + assert "BatchNr" not in prompt + assert "FitNr" not in prompt + + def _filter_searchspace(searchspace, mask): """Create a filtered copy of a search space.""" return evolve( @@ -492,7 +450,6 @@ def test_full_prompt(recovery_kwargs, expected_present, expected_absent): "A test experiment.", "OPTIMIZATION OBJECTIVE", "Maximize yield", - "TARGETS", "yield", "PARAMETERS", "Cat", @@ -507,5 +464,6 @@ def test_full_prompt(recovery_kwargs, expected_present, expected_absent): assert section in prompt for section in expected_absent: assert section not in prompt - for residue in ("{{", "}}", "{%", "%}"): + # Jinja expressions are written with inner spaces, unlike nested JSON objects + for residue in ("{{ ", " }}", "{%", "%}"): assert residue not in prompt diff --git a/tests/llm/test_recommender.py b/tests/llm/test_recommender.py index 9754cf04aa..6ec0c1cd7a 100644 --- a/tests/llm/test_recommender.py +++ b/tests/llm/test_recommender.py @@ -10,7 +10,9 @@ from baybe import Campaign from baybe._optional.info import LLM_INSTALLED +from baybe.constraints import DiscreteCustomConstraint from baybe.exceptions import ( + IncompatibilityError, LLMAuthenticationError, LLMCallError, LLMMalformedResponseError, @@ -19,7 +21,14 @@ from baybe.objectives import SingleTargetObjective from baybe.recommenders.pure.llm._prompts import make_prompt from baybe.recommenders.pure.llm.llm import LLMRecommender +from baybe.searchspace import SearchSpace from baybe.targets import NumericalTarget +from baybe.transformations import ( + AdditiveTransformation, + AffineTransformation, + ChainedTransformation, + CustomTransformation, +) from tests.llm._mock import ( PATCH_TARGET, make_continuous_searchspace, @@ -117,6 +126,65 @@ def test_call_errors(exception, expected_error): assert mock.call_count == 1 +def _custom_function(x): + """A custom torch callable.""" + return x + + +def _custom_validator(df): + """A custom constraint validator.""" + return df["Num"] > 1 + + +@pytest.mark.parametrize( + ("searchspace", "transformation", "match"), + [ + param( + SearchSpace.from_product( + _SEARCHSPACE.parameters, + [DiscreteCustomConstraint(["Num"], _custom_validator)], + ), + None, + "DiscreteCustomConstraint", + id="custom_constraint", + ), + param( + _SEARCHSPACE, + CustomTransformation(_custom_function), + "CustomTransformation", + id="custom_transformation", + ), + param( + _SEARCHSPACE, + ChainedTransformation( + [CustomTransformation(_custom_function), AffineTransformation(2.0)] + ), + "CustomTransformation", + id="chained_custom_transformation", + ), + param( + _SEARCHSPACE, + AdditiveTransformation( + [CustomTransformation(_custom_function), AffineTransformation(2.0)] + ), + "CustomTransformation", + id="additive_custom_transformation", + ), + ], +) +def test_custom_components_rejected(searchspace, transformation, match): + """Custom components are rejected before any call to the language model.""" + objective = ( + _OBJECTIVE + if transformation is None + else SingleTargetObjective(NumericalTarget("y", transformation=transformation)) + ) + with patch(PATCH_TARGET) as mock: + with pytest.raises(IncompatibilityError, match=match): + _RECOMMENDER.recommend(1, searchspace, objective) + assert mock.call_count == 0 + + @pytest.mark.parametrize( "first_response", [ diff --git a/tests/test_transformations.py b/tests/test_transformations.py index ddbfdd0633..7510390a3f 100644 --- a/tests/test_transformations.py +++ b/tests/test_transformations.py @@ -26,6 +26,7 @@ AdditiveTransformation, MultiplicativeTransformation, ) +from baybe.transformations.utils import _iterate_transformations from baybe.utils.dataframe import to_tensor from baybe.utils.interval import Interval @@ -82,6 +83,27 @@ def test_unnesting_chained_transformations(): assert c == ChainedTransformation([t(2), t(3), t(4), t(5), t(6), t(7), t(8), t(9)]) +def test_transformation_iteration(): + """Iterating a transformation yields it and all its nested components.""" + t1 = AffineTransformation(factor=2.0) + t2 = AbsoluteTransformation() + t3 = BellTransformation(center=1.0) + additive = AdditiveTransformation([t2, t3]) + multiplicative = MultiplicativeTransformation([t1, additive]) + chained = ChainedTransformation([multiplicative, t3]) + + assert list(_iterate_transformations(t1)) == [t1] + assert list(_iterate_transformations(chained)) == [ + chained, + multiplicative, + t1, + additive, + t2, + t3, + t3, + ] + + def test_affine_transformation_compression(): """Compression of affine transformations works as expected.""" t = IdentityTransformation() From 2d627eb60eb03957fc5ea3a3f0c9f8e2d6516570 Mon Sep 17 00:00:00 2001 From: "Alexander V. Hopp" Date: Thu, 15 Oct 2026 11:09:58 +0200 Subject: [PATCH 96/96] Improve handling of batches and their sizes Check the number of suggestions before validating them and validate only the first `batch_size` ones, so that invalid surplus suggestions don't cause the whole response to be rejected. Also reject batches containing duplicate configurations. --- baybe/exceptions.py | 19 +++++- baybe/recommenders/pure/llm/_parsing.py | 51 +++++++++++--- baybe/recommenders/pure/llm/_prompts.py | 8 +-- baybe/recommenders/pure/llm/llm.py | 35 +--------- tests/llm/_mock.py | 21 +++--- tests/llm/test_parsing.py | 89 +++++++++++++++++++++---- tests/llm/test_prompts.py | 2 +- 7 files changed, 155 insertions(+), 70 deletions(-) diff --git a/baybe/exceptions.py b/baybe/exceptions.py index 21db47c1fb..1ac4702d39 100644 --- a/baybe/exceptions.py +++ b/baybe/exceptions.py @@ -353,6 +353,23 @@ def recovery_instruction(self) -> str: ) +class LLMDuplicateRecommendationsError(LLMResponseError): + """A language model response contained identical recommendations.""" + + def __init__(self, *args: Any, n_duplicates: int): + super().__init__(*args) + self.n_duplicates = n_duplicates + + @property + @override + def recovery_instruction(self) -> str: + return ( + f"{self.n_duplicates} of your proposed configurations duplicate another " + f"configuration of the same batch. All recommendations within a batch must " + f"be distinct." + ) + + class LLMBatchSizeError(LLMResponseError): """A language model response contained the wrong number of recommendations.""" @@ -365,7 +382,7 @@ def __init__(self, *args: Any, requested: int, received: int): @override def recovery_instruction(self) -> str: return ( - f"Your previous response provided {self.received} valid recommendation(s), " + f"Your previous response provided {self.received} recommendation(s), " f"but exactly {self.requested} are required. Provide {self.requested} " f"distinct, valid recommendations." ) diff --git a/baybe/recommenders/pure/llm/_parsing.py b/baybe/recommenders/pure/llm/_parsing.py index d8b4cb9f6b..36d72d8e38 100644 --- a/baybe/recommenders/pure/llm/_parsing.py +++ b/baybe/recommenders/pure/llm/_parsing.py @@ -10,7 +10,9 @@ from baybe.constraints.base import DiscreteFilteringConstraint from baybe.exceptions import ( + LLMBatchSizeError, LLMConstraintViolationError, + LLMDuplicateRecommendationsError, LLMIneligiblePointsError, LLMInvalidParameterValueError, LLMMalformedResponseError, @@ -62,22 +64,30 @@ def extract_json_array(response: str, /) -> str | None: return last_object_array -def parse_llm_response(response: str, /, searchspace: SearchSpace) -> pd.DataFrame: +def parse_llm_response( + response: str, /, searchspace: SearchSpace, batch_size: int +) -> pd.DataFrame: """Parse a language model response into a DataFrame of recommendations. + Only the first ``batch_size`` suggestions are validated and returned, surplus + suggestions are discarded. + Args: response: The response from the language model. searchspace: The search space to validate recommendations against. + batch_size: The number of recommendations to return. Returns: - A DataFrame containing the parsed recommendations. All returned points are - eligible candidates of the search space (as returned by - :meth:`baybe.searchspace.discrete.SubspaceDiscrete.get_candidates`); suggestions - that are not are rejected. + A DataFrame containing exactly ``batch_size`` distinct recommendations. All + returned points are eligible candidates of the search space (as returned by + :meth:`baybe.searchspace.discrete.SubspaceDiscrete.get_candidates`); + suggestions that are not are rejected. Raises: LLMMalformedResponseError: If the response cannot be parsed into the expected JSON structure. + LLMBatchSizeError: If the response contains fewer than ``batch_size`` + suggestions. LLMUnknownParameterError: If a suggestion references parameters not in the search space. LLMMissingParameterError: If a suggestion omits required search space @@ -90,6 +100,7 @@ def parse_llm_response(response: str, /, searchspace: SearchSpace) -> pd.DataFra (including batch constraints) present in the search space. LLMIneligiblePointsError: If a suggestion does not correspond to an eligible candidate of the search space. + LLMDuplicateRecommendationsError: If the recommendations are not distinct. Warns: LLMResponseWarning: If the search space contains continuous constraints. @@ -104,6 +115,21 @@ def parse_llm_response(response: str, /, searchspace: SearchSpace) -> pd.DataFra except JSONDecodeError as e: raise LLMMalformedResponseError(f"Error parsing JSON output: {e}.") from e + # Check the number of suggestions before validating any of them + if len(suggestions) < batch_size: + raise LLMBatchSizeError( + f"The language model returned {len(suggestions)} recommendation(s) " + f"instead of the requested {batch_size}.", + requested=batch_size, + received=len(suggestions), + ) + # IMPROVE: Instead of discarding surplus suggestions, all suggestions could be + # validated individually and the first valid ones selected, saving a recovery + # attempt when only some of them are invalid. This requires suggestion-wise + # validation, a selection that respects batch constraints, and a decision on + # which error to report if too few valid suggestions remain. + suggestions = suggestions[:batch_size] + recommendations = [] for suggestion in suggestions: if _PARAMETERS_FIELD not in suggestion: @@ -217,8 +243,17 @@ def parse_llm_response(response: str, /, searchspace: SearchSpace) -> pd.DataFra if continuous_param_names: rec_disc = exp_rep.loc[aligned_index] rec_cont = df[continuous_param_names].set_axis(aligned_index) - return pd.concat([rec_disc, rec_cont], axis=1) + recommendations_df = pd.concat([rec_disc, rec_cont], axis=1) else: - return exp_rep.loc[aligned_index] + recommendations_df = exp_rep.loc[aligned_index] + else: + recommendations_df = df - return df + # Checked on the final configurations so that values snapped to the same allowed + # value (within tolerance) are also detected + if (n_duplicates := int(recommendations_df.duplicated().sum())) > 0: + raise LLMDuplicateRecommendationsError( + f"{n_duplicates} suggestion(s) duplicate another suggestion of the batch.", + n_duplicates=n_duplicates, + ) + return recommendations_df diff --git a/baybe/recommenders/pure/llm/_prompts.py b/baybe/recommenders/pure/llm/_prompts.py index 2eb961f9e7..4c8de1d980 100644 --- a/baybe/recommenders/pure/llm/_prompts.py +++ b/baybe/recommenders/pure/llm/_prompts.py @@ -103,12 +103,12 @@ {{ original_response }} {% endif %} -Please provide a corrected set of {{ batch_size }} experimental conditions that \ -addresses the problem described above and improves the optimization objective. +Please provide a corrected set of {{ batch_size }} distinct experimental conditions \ +that addresses the problem described above and improves the optimization objective. {% else %} -Please suggest {{ batch_size }} new experimental conditions that are likely to \ -improve the optimization objective. +Please suggest {{ batch_size }} distinct new experimental conditions that are likely \ +to improve the optimization objective. {% endif %} For each suggestion, provide: 1. A brief explanation of why you chose these values diff --git a/baybe/recommenders/pure/llm/llm.py b/baybe/recommenders/pure/llm/llm.py index 7275b1b224..2aafd49d93 100644 --- a/baybe/recommenders/pure/llm/llm.py +++ b/baybe/recommenders/pure/llm/llm.py @@ -15,7 +15,6 @@ from baybe.exceptions import ( IncompatibilityError, LLMAuthenticationError, - LLMBatchSizeError, LLMCallError, LLMMalformedResponseError, LLMResponseError, @@ -166,34 +165,6 @@ def _query_model(self, prompt: str) -> str: return content - def _validate_response( - self, content: str, searchspace: SearchSpace, batch_size: int - ) -> pd.DataFrame: - """Parse and validate a raw response into a recommendation batch. - - Args: - content: The raw text content of the model response. - searchspace: The search space to validate recommendations against. - batch_size: The number of recommendations to generate. - - Returns: - A DataFrame with exactly ``batch_size`` recommendations. - - Raises: - LLMResponseError: If the response cannot be parsed/validated, or if it - contains fewer than ``batch_size`` recommendations. - """ - output = parse_llm_response(content, searchspace) - if len(output) < batch_size: - raise LLMBatchSizeError( - f"The language model returned {len(output)} valid recommendation(s) " - f"instead of the requested {batch_size}.", - requested=batch_size, - received=len(output), - ) - # NOTE: Duplicate configurations within a batch are permitted. - return output.head(batch_size) - def _validate_compatibility( self, searchspace: SearchSpace, objective: Objective | None ) -> None: @@ -310,7 +281,7 @@ def _recommend_hybrid( content: str | None = None try: content = self._query_model(prompt) - return self._validate_response(content, searchspace, batch_size) + return parse_llm_response(content, searchspace, batch_size) except LLMResponseError as initial_error: # The recommendation had an issue. Make a recovery attempt. recovery_prompt = make_prompt( @@ -325,9 +296,7 @@ def _recommend_hybrid( ) try: recovery_content = self._query_model(recovery_prompt) - return self._validate_response( - recovery_content, searchspace, batch_size - ) + return parse_llm_response(recovery_content, searchspace, batch_size) except LLMResponseError as recovery_error: raise LLMResponseError( f"The language model failed to produce a valid recommendation, " diff --git a/tests/llm/_mock.py b/tests/llm/_mock.py index 7041cb5ce4..fc796de76d 100644 --- a/tests/llm/_mock.py +++ b/tests/llm/_mock.py @@ -36,24 +36,25 @@ def make_valid_json(searchspace, batch_size): discrete_params = [p for p in params if p.is_discrete] continuous_params = [p for p in params if not p.is_discrete] - continuous_entry = { - cp.name: sum(cp.bounds.to_tuple()) / 2 for cp in continuous_params - } + def continuous_entry(i): + """Distinct interior points of the continuous bounds.""" + fraction = (i + 1) / (batch_size + 1) + return { + cp.name: cp.bounds.lower + fraction * (cp.bounds.upper - cp.bounds.lower) + for cp in continuous_params + } suggestions = [] if discrete_params: candidates = searchspace.discrete.get_candidates()[0] - for _, row in candidates.head(batch_size).iterrows(): + for i, (_, row) in enumerate(candidates.head(batch_size).iterrows()): entry = {p.name: row[p.name] for p in discrete_params} - entry.update(continuous_entry) + entry.update(continuous_entry(i)) suggestions.append({"explanation": "Mock suggestion.", "parameters": entry}) else: - for _ in range(batch_size): + for i in range(batch_size): suggestions.append( - { - "explanation": "Mock suggestion.", - "parameters": dict(continuous_entry), - } + {"explanation": "Mock suggestion.", "parameters": continuous_entry(i)} ) return json.dumps( diff --git a/tests/llm/test_parsing.py b/tests/llm/test_parsing.py index 245de3eb0f..4cd598015f 100644 --- a/tests/llm/test_parsing.py +++ b/tests/llm/test_parsing.py @@ -13,7 +13,9 @@ DiscreteSelectionConstraint, ) from baybe.exceptions import ( + LLMBatchSizeError, LLMConstraintViolationError, + LLMDuplicateRecommendationsError, LLMIneligiblePointsError, LLMInvalidParameterValueError, LLMMalformedResponseError, @@ -150,7 +152,7 @@ def test_response_format(batch_size): def test_parse_llm_response_format_errors(discrete_searchspace, response, match): """Malformed responses raise ``LLMMalformedResponseError``.""" with pytest.raises(LLMMalformedResponseError, match=match): - parse_llm_response(response, discrete_searchspace) + parse_llm_response(response, discrete_searchspace, batch_size=1) @pytest.mark.parametrize( @@ -203,7 +205,7 @@ def test_parse_llm_response_parameter_errors(searchspace, overrides, error_cls, """Invalid parameter values raise the corresponding error.""" response = make_response_json(searchspace, **overrides) with pytest.raises(error_cls, match=match): - parse_llm_response(response, searchspace) + parse_llm_response(response, searchspace, batch_size=1) def test_parse_llm_response_missing_parameter(): @@ -221,7 +223,7 @@ def test_parse_llm_response_missing_parameter(): LLMMissingParameterError, match="Response is missing values for the following parameters: {'Cat'}", ): - parse_llm_response(response, searchspace) + parse_llm_response(response, searchspace, batch_size=1) def test_parse_llm_response_selection_constraint_violation(): @@ -241,7 +243,7 @@ def test_parse_llm_response_selection_constraint_violation(): with pytest.raises( LLMConstraintViolationError, match="DiscreteSelectionConstraint" ): - parse_llm_response(response, searchspace) + parse_llm_response(response, searchspace, batch_size=1) def test_parse_llm_response_batch_constraint_violation(): @@ -260,7 +262,7 @@ def test_parse_llm_response_batch_constraint_violation(): ] response = json.dumps(suggestions) with pytest.raises(LLMConstraintViolationError, match="DiscreteBatchConstraint"): - parse_llm_response(response, searchspace) + parse_llm_response(response, searchspace, batch_size=2) def test_parse_llm_response_continuous_constraint_warning(): @@ -281,7 +283,7 @@ def test_parse_llm_response_continuous_constraint_warning(): ) response = json.dumps([{"explanation": "test", "parameters": {"x": 3.0, "y": 4.0}}]) with pytest.warns(LLMResponseWarning, match="cannot be validated"): - parse_llm_response(response, searchspace) + parse_llm_response(response, searchspace, batch_size=1) def test_parse_llm_response_happy_path(): @@ -296,7 +298,7 @@ def test_parse_llm_response_happy_path(): {"explanation": "first", "parameters": {"Cat": "A", "Num": 1.0}}, {"explanation": "second", "parameters": {"Cat": "B", "Num": 2.0}}, ] - result = parse_llm_response(json.dumps(suggestions), searchspace) + result = parse_llm_response(json.dumps(suggestions), searchspace, batch_size=2) assert set(result.columns) == {"Cat", "Num"} assert list(result["Cat"]) == ["A", "B"] assert list(result["Num"]) == [1.0, 2.0] @@ -335,7 +337,7 @@ def test_parse_llm_response_ineligible_points(searchspace, overrides): """Proposing a forbidden candidate raises ``LLMIneligiblePointsError``.""" response = make_response_json(searchspace, **overrides) with pytest.raises(LLMIneligiblePointsError): - parse_llm_response(response, searchspace) + parse_llm_response(response, searchspace, batch_size=1) def test_parse_llm_response_tolerance_snap(): @@ -344,7 +346,7 @@ def test_parse_llm_response_tolerance_snap(): [NumericalDiscreteParameter("x", (1.0, 2.0, 3.0), tolerance=0.3)] ) response = json.dumps([{"explanation": "test", "parameters": {"x": 1.2}}]) - result = parse_llm_response(response, searchspace) + result = parse_llm_response(response, searchspace, batch_size=1) assert result["x"].iloc[0] == 1.0 @@ -360,7 +362,7 @@ def test_parse_llm_response_hybrid_output(): {"explanation": "first", "parameters": {"Cat": "A", "Cont": 0.3}}, {"explanation": "second", "parameters": {"Cat": "A", "Cont": 0.7}}, ] - result = parse_llm_response(json.dumps(suggestions), searchspace) + result = parse_llm_response(json.dumps(suggestions), searchspace, batch_size=2) assert set(result.columns) == {"Cat", "Cont"} assert list(result["Cat"]) == ["A", "A"] assert list(result["Cont"]) == [0.3, 0.7] @@ -374,7 +376,7 @@ def test_parse_llm_response_valid_then_invalid(): {"explanation": "invalid", "parameters": {"Cat": "Z"}}, ] with pytest.raises(LLMInvalidParameterValueError): - parse_llm_response(json.dumps(suggestions), searchspace) + parse_llm_response(json.dumps(suggestions), searchspace, batch_size=2) def test_parse_llm_response_partial_missing_parameter(): @@ -390,7 +392,7 @@ def test_parse_llm_response_partial_missing_parameter(): {"explanation": "second", "parameters": {"Num": 2.0}}, ] with pytest.raises(LLMInvalidParameterValueError, match="missing values"): - parse_llm_response(json.dumps(suggestions), searchspace) + parse_llm_response(json.dumps(suggestions), searchspace, batch_size=2) def test_parse_llm_response_continuous_only_happy_path(): @@ -405,8 +407,69 @@ def test_parse_llm_response_continuous_only_happy_path(): {"explanation": "first", "parameters": {"x": 3.0, "y": 2.0}}, {"explanation": "second", "parameters": {"x": 7.0, "y": 4.0}}, ] - result = parse_llm_response(json.dumps(suggestions), searchspace) + result = parse_llm_response(json.dumps(suggestions), searchspace, batch_size=2) assert len(result) == 2 assert set(result.columns) == {"x", "y"} assert list(result["x"]) == [3.0, 7.0] assert list(result["y"]) == [2.0, 4.0] + + +_CAT_NUM_PARAMETERS = [ + CategoricalParameter("Cat", ("A", "B")), + NumericalDiscreteParameter("Num", (1.0, 2.0)), +] + + +@pytest.mark.parametrize( + ("constraints", "surplus"), + [ + pytest.param([], {"Cat": "Z", "Num": 1.0}, id="invalid_surplus"), + pytest.param( + [DiscreteBatchConstraint(parameters=["Cat"])], + {"Cat": "B", "Num": 2.0}, + id="batch_constraint_violated_by_surplus", + ), + ], +) +def test_parse_llm_response_surplus_discarded(constraints, surplus): + """Surplus suggestions are discarded without being validated.""" + searchspace = SearchSpace.from_product(_CAT_NUM_PARAMETERS, constraints) + suggestions = [ + {"explanation": "requested", "parameters": {"Cat": "A", "Num": 1.0}}, + {"explanation": "surplus", "parameters": surplus}, + ] + result = parse_llm_response(json.dumps(suggestions), searchspace, batch_size=1) + assert list(result["Cat"]) == ["A"] + assert list(result["Num"]) == [1.0] + + +def test_parse_llm_response_too_few_suggestions(): + """Too few suggestions raise before any suggestion is validated.""" + searchspace = SearchSpace.from_product(_CAT_NUM_PARAMETERS) + suggestions = [{"explanation": "invalid", "parameters": {"Cat": "Z", "Num": 1.0}}] + with pytest.raises(LLMBatchSizeError, match="returned 1 recommendation"): + parse_llm_response(json.dumps(suggestions), searchspace, batch_size=2) + + +@pytest.mark.parametrize( + ("parameter", "values"), + [ + pytest.param(CategoricalParameter("p", ("A", "B")), ["A", "A"], id="identical"), + pytest.param( + NumericalDiscreteParameter("p", (1.0, 2.0), tolerance=0.3), + [1.0, 1.2], + id="snapped_to_same_value", + ), + pytest.param( + NumericalContinuousParameter("p", (0.0, 1.0)), + [0.5, 0.5], + id="continuous", + ), + ], +) +def test_parse_llm_response_duplicates(parameter, values): + """Duplicate configurations within a batch raise an error.""" + searchspace = SearchSpace.from_product([parameter]) + suggestions = [{"explanation": "x", "parameters": {"p": v}} for v in values] + with pytest.raises(LLMDuplicateRecommendationsError, match="1 suggestion"): + parse_llm_response(json.dumps(suggestions), searchspace, batch_size=2) diff --git a/tests/llm/test_prompts.py b/tests/llm/test_prompts.py index e2b58bd441..28b9625bbb 100644 --- a/tests/llm/test_prompts.py +++ b/tests/llm/test_prompts.py @@ -406,7 +406,7 @@ def test_forbidden_configurations(searchspace, expected): [ param( {}, - ["Please suggest 3 new experimental conditions"], + ["Please suggest 3 distinct new experimental conditions"], ["WHAT WENT WRONG", "ORIGINAL RESPONSE"], id="without_recovery", ),