From 07f864edd9956e1b82405e15bf0089d685938e4c Mon Sep 17 00:00:00 2001 From: Sebastian Lobentanzer Date: Tue, 3 Dec 2024 17:26:04 +0100 Subject: [PATCH] Ruff (#219) * adjust pre-commit config and poetry settings for switching to ruff * add target version of ruff * run ruff pre-commit and fix some tests * another round of running ruff Bumping patch to keep track of the extensive reformat in case some problems occur later --- .pre-commit-config.yaml | 74 +- RAG_api_tool.txt | 51 - benchmark/benchmark_utils.py | 139 +- benchmark/conftest.py | 81 +- benchmark/load_dataset.py | 185 +- benchmark/test_api_calling.py | 17 +- benchmark/test_biocypher_query_generation.py | 145 +- benchmark/test_rag_interpretation.py | 39 +- benchmark/test_text_extraction.py | 17 +- benchmark/test_text_image_multimodality.py | 30 +- benchmark/test_user_interaction.py | 34 +- benchmark/test_vectorstore_semantic_search.py | 9 +- biochatter/_image.py | 65 +- biochatter/_misc.py | 50 +- biochatter/_stats.py | 8 +- biochatter/api_agent/__init__.py | 12 +- biochatter/api_agent/abc.py | 49 +- biochatter/api_agent/api_agent.py | 43 +- biochatter/api_agent/bio_tools.py | 367 +- biochatter/api_agent/blast.py | 98 +- biochatter/api_agent/oncokb.py | 100 +- biochatter/database_agent.py | 55 +- biochatter/kg_langgraph_agent.py | 87 +- biochatter/langgraph_agent_base.py | 141 +- biochatter/llm_connect.py | 399 +- biochatter/podcast.py | 68 +- biochatter/prompts.py | 217 +- biochatter/query_interaction.py | 20 +- biochatter/rag_agent.py | 52 +- biochatter/selector_agent.py | 76 +- biochatter/vectorstore.py | 181 +- biochatter/vectorstore_agent.py | 298 +- docs/scripts/hooks.py | 395 +- poetry.lock | 5528 +++++++++-------- pyproject.toml | 53 +- scripts/add_version_column.py | 9 +- scripts/ollama_conversation.py | 3 +- scripts/podcast_single_document.py | 12 +- scripts/update_benchmark_hashes.py | 29 +- scripts/xinference_conversation.py | 3 +- test/mock_langchain.py | 28 +- test/mock_pymilvus.py | 33 +- test/no_podcast.py | 11 +- test/test_agent_selector.py | 64 +- test/test_api_agent.py | 89 +- ...st_benchmark_biocypher_query_generation.py | 54 +- test/test_benchmark_vectorstore.py | 31 +- test/test_database_agent.py | 28 +- test/test_kg_langgraph_agent.py | 58 +- test/test_llm_connect.py | 114 +- test/test_prompts.py | 101 +- test/test_query_interaction.py | 45 +- test/test_rag_agent.py | 44 +- test/test_vectorstore.py | 67 +- test/test_vectorstore_agent.py | 27 +- 55 files changed, 4886 insertions(+), 5147 deletions(-) delete mode 100644 RAG_api_tool.txt diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index ba4a62d4..17585155 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -4,47 +4,37 @@ fail_fast: false default_language_version: python: python3 default_stages: - - commit - - push + - commit + - push minimum_pre_commit_version: 2.7.1 repos: - - repo: https://github.com/ambv/black - rev: 23.7.0 - hooks: - - id: black - - repo: https://github.com/timothycrosley/isort - rev: 5.12.0 - hooks: - - id: isort - additional_dependencies: [toml] - - repo: https://github.com/snok/pep585-upgrade - rev: v1.0 - hooks: - - id: upgrade-type-hints - - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.4.0 - hooks: - - id: check-docstring-first - - id: end-of-file-fixer - - id: check-added-large-files - - id: mixed-line-ending - - id: trailing-whitespace - exclude: ^.bumpversion.cfg$ - - id: check-merge-conflict - - id: check-case-conflict - - id: check-symlinks - - id: check-yaml - args: [--unsafe] - - id: check-ast - - id: fix-encoding-pragma - args: [--remove] # for Python3 codebase, it's not necessary - - id: requirements-txt-fixer - - repo: https://github.com/pre-commit/pygrep-hooks - rev: v1.10.0 - hooks: - - id: python-no-eval - - id: python-use-type-annotations - - id: python-check-blanket-noqa - - id: rst-backticks - - id: rst-directive-colons - - id: rst-inline-touching-normal + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.3.3 + hooks: + - id: ruff + args: [--fix] + - id: ruff-format + - repo: https://github.com/snok/pep585-upgrade + rev: v1.0 + hooks: + - id: upgrade-type-hints + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.4.0 + hooks: + - id: check-docstring-first + - id: check-added-large-files + - id: check-merge-conflict + - id: check-case-conflict + - id: check-symlinks + - id: check-yaml + args: [--unsafe] + - id: requirements-txt-fixer + - repo: https://github.com/pre-commit/pygrep-hooks + rev: v1.10.0 + hooks: + - id: python-no-eval + - id: python-use-type-annotations + - id: python-check-blanket-noqa + - id: rst-backticks + - id: rst-directive-colons + - id: rst-inline-touching-normal diff --git a/RAG_api_tool.txt b/RAG_api_tool.txt deleted file mode 100644 index aba8d133..00000000 --- a/RAG_api_tool.txt +++ /dev/null @@ -1,51 +0,0 @@ -General: - -The API agent for the RAG agent will: - -Answer questions by create API calls for specific db: integrate logic from baio into biochatter. - -How: -RagAgent - -new mode: - API: api_agent - -api_agent.py - FIRST version: - static_dependencies: - Create a new dir in root 'api_agent_docs' to handle all the below except if indicated - otherwise with '->' - - -prompts -> new class in prompts.py - -vectorised API documentation of specific db - -file system to handle responses (use baio logic) - -file system to track calls - -define clear test cases - - -Documents for the api_agent ---------------------------- --docs - | - --api_agent - | - --BLAST_tool - | - persistent_files - | - --vectorstore - | | - | --BLAST_db_faiss_index # 2^nd step - | - --api_documentation - | - --ncbi - | - BLAST.txt -> adapted from jin et al. 2023 - --BLAST_response - | - --response_files -> BLAST response files - - --results - | - results.json -> tracks all api calls, responses and answers found - -For now we will only use a one shot example for BLASTn and BLASTp. diff --git a/benchmark/benchmark_utils.py b/benchmark/benchmark_utils.py index 6b745b99..0cbd7ac1 100644 --- a/benchmark/benchmark_utils.py +++ b/benchmark/benchmark_utils.py @@ -1,11 +1,10 @@ -from datetime import datetime import re +from datetime import datetime -from nltk.corpus import wordnet -import pytest import importlib_metadata - import pandas as pd +import pytest +from nltk.corpus import wordnet def benchmark_already_executed( @@ -13,12 +12,11 @@ def benchmark_already_executed( task: str, md5_hash: str, ) -> bool: - """ - - Checks if the benchmark task and subtask test case for the model_name have + """Checks if the benchmark task and subtask test case for the model_name have already been executed. Args: + ---- model_name (str): The model name, e.g. "gpt-3.5-turbo" task (str): The benchmark task, e.g. "biocypher_query_generation" @@ -28,9 +26,10 @@ def benchmark_already_executed( dictionary representation of the test case. Returns: - + ------- bool: True if the benchmark case for the model_name has already been run, False otherwise + """ task_results = return_or_create_result_file(task) @@ -43,13 +42,7 @@ def benchmark_already_executed( if task_results.empty: return False - run = ( - task_results[ - (task_results["model_name"] == model_name) - & (task_results["md5_hash"] == md5_hash) - ].shape[0] - > 0 - ) + run = task_results[(task_results["model_name"] == model_name) & (task_results["md5_hash"] == md5_hash)].shape[0] > 0 return run @@ -62,6 +55,7 @@ def skip_if_already_run( """Helper function to check if the test case is already executed. Args: + ---- model_name (str): The model name, e.g. "gpt-3.5-turbo" task (str): The benchmark task, e.g. "biocypher_query_generation" @@ -69,24 +63,27 @@ def skip_if_already_run( md5_hash (str): The md5 hash of the test case, e.g., "72434e7a340a3f6dd047b944988491b7". It is created from the dictionary representation of the test case. + """ if benchmark_already_executed(model_name, task, md5_hash): pytest.skip( - f"Benchmark for {task} with hash {md5_hash} with {model_name} already executed" + f"Benchmark for {task} with hash {md5_hash} with {model_name} already executed", ) def return_or_create_result_file( task: str, ): - """ - Returns the result file for the task or creates it if it does not exist. + """Returns the result file for the task or creates it if it does not exist. Args: + ---- task (str): The benchmark task, e.g. "biocypher_query_generation" Returns: + ------- pd.DataFrame: The result file for the task + """ file_path = get_result_file_path(task) try: @@ -101,22 +98,24 @@ def return_or_create_result_file( "md5_hash", "datetime", "biochatter_version", - ] + ], ) results.to_csv(file_path, index=False) return results def return_or_create_failure_mode_file(task: str): - """ - Returns the failure mode file for the task or creates it if it does not + """Returns the failure mode file for the task or creates it if it does not exist. Args: + ---- task (str): The benchmark task, e.g. "biocypher_query_generation" Returns: + ------- pd.DataFrame: The failure mode recording file for the task + """ file_path = get_failure_mode_file_path(task) try: @@ -131,22 +130,24 @@ def return_or_create_failure_mode_file(task: str): "failure_modes", "md5_hash", "datetime", - ] + ], ) results.to_csv(file_path, index=False) return results def return_or_create_confidence_file(task: str): - """ - Returns the confidence file for the task or creates it if it does not + """Returns the confidence file for the task or creates it if it does not exist. Args: + ---- task (str): The benchmark task, e.g. "biocypher_query_generation" Returns: + ------- pd.DataFrame: The confidence recording file for the task + """ file_path = get_confidence_file_path(task) try: @@ -160,36 +161,38 @@ def return_or_create_confidence_file(task: str): "incorrect_confidence", "md5_hash", "datetime", - ] + ], ) results.to_csv(file_path, index=False) return results def get_confidence_file_path(task: str) -> str: - """ - - Returns the path to the confidence recording file. + """Returns the path to the confidence recording file. Args: + ---- task (str): The benchmark task, e.g. "biocypher_query_generation" Returns: + ------- str: The path to the confidence file + """ return f"benchmark/results/{task}_confidence.csv" def get_failure_mode_file_path(task: str) -> str: - """ - - Returns the path to the failure mode recording file. + """Returns the path to the failure mode recording file. Args: + ---- task (str): The benchmark task, e.g. "biocypher_query_generation" Returns: + ------- str: The path to the failure mode file + """ return f"benchmark/results/{task}_failure_modes.csv" @@ -205,12 +208,14 @@ def write_results_to_file( """Writes the benchmark results for the subtask to the result file. Args: + ---- model_name (str): The model name, e.g. "gpt-3.5-turbo" subtask (str): The benchmark subtask test case, e.g. "entities" score (str): The benchmark score, e.g. "5" iterations (str): The number of iterations, e.g. "7" md5_hash (str): The md5 hash of the test case file_path (str): The path to the result file + """ results = pd.read_csv(file_path, header=0) now = datetime.now().strftime("%Y-%m-%d %H:%M:%S") @@ -220,7 +225,7 @@ def write_results_to_file( columns=results.columns, ) results = pd.concat([results, new_row], ignore_index=True).sort_values( - by=["model_name", "subtask"] + by=["model_name", "subtask"], ) results.to_csv(file_path, index=False) @@ -233,12 +238,11 @@ def write_confidence_to_file( md5_hash: str, file_path: str, ): - """ - - Writes the confidence scores for a given response to a subtask to the given + """Writes the confidence scores for a given response to a subtask to the given file path. Args: + ---- model_name (str): The model name, e.g. "gpt-3.5-turbo" subtask (str): The benchmark subtask test case, e.g. "multimodal_answer" @@ -250,6 +254,7 @@ def write_confidence_to_file( md5_hash (str): The md5 hash of the test case file_path (str): The path to the result file + """ results = pd.read_csv(file_path, header=0) now = datetime.now().strftime("%Y-%m-%d %H:%M:%S") @@ -262,12 +267,12 @@ def write_confidence_to_file( incorrect_confidence, md5_hash, now, - ] + ], ], columns=results.columns, ) results = pd.concat([results, new_row], ignore_index=True).sort_values( - by=["model_name", "subtask"] + by=["model_name", "subtask"], ) results.to_csv(file_path, index=False) @@ -281,12 +286,11 @@ def write_failure_modes_to_file( md5_hash: str, file_path: str, ): - """ - - Writes the failure modes identified for a given response to a subtask to + """Writes the failure modes identified for a given response to a subtask to the given file path. Args: + ---- model_name (str): The model name, e.g. "gpt-3.5-turbo" subtask (str): The benchmark subtask test case, e.g. "entities" @@ -301,6 +305,7 @@ def write_failure_modes_to_file( md5_hash (str): The md5 hash of the test case file_path (str): The path to the result file + """ results = pd.read_csv(file_path, header=0) now = datetime.now().strftime("%Y-%m-%d %H:%M:%S") @@ -314,23 +319,25 @@ def write_failure_modes_to_file( failure_modes, md5_hash, now, - ] + ], ], columns=results.columns, ) results = pd.concat([results, new_row], ignore_index=True).sort_values( - by=["model_name", "subtask"] + by=["model_name", "subtask"], ) results.to_csv(file_path, index=False) def categorize_failure_modes( - actual_answer, expected_answer, regex=False + actual_answer, + expected_answer, + regex=False, ) -> str: - """ - Categorises the mode of failure for a given response to a subtask. + """Categorises the mode of failure for a given response to a subtask. Args: + ---- actual_answer (str): The actual response given to the subtask question expected_answer (str): The expected response for the subtask @@ -338,9 +345,11 @@ def categorize_failure_modes( regex (bool): Whether the expected answer is a regex expression Returns: + ------- str: The mode of failure, e.g. "Case Sensitivity", "Partial Match", "Format Error", "Synonym", "Words Missing", "Entire Answer Incorrect", "Other" + """ if not regex: # Check if the answer is right, but the case sensitivity was wrong (e.g. a / A) @@ -352,14 +361,14 @@ def categorize_failure_modes( return "Format Error" # Check if some of the answer is partially right, but only if it's more than one letter - elif len(expected_answer) > 1 and ( - actual_answer in expected_answer or expected_answer in actual_answer - ): + elif len(expected_answer) > 1 and (actual_answer in expected_answer or expected_answer in actual_answer): return "Partial Match" # Check if the format of the answer is wrong, but the answer otherwise is right (e.g. "a b" instead of "ab") elif re.sub(r"\s+", "", actual_answer.lower()) == re.sub( - r"\s+", "", expected_answer.lower() + r"\s+", + "", + expected_answer.lower(), ): return "Format Error" @@ -371,39 +380,32 @@ def categorize_failure_modes( elif ( re.search(r"\w+", actual_answer) and re.search(r"\w+", expected_answer) - and any(char.isdigit() for char in actual_answer) - != any(char.isdigit() for char in expected_answer) + and any(char.isdigit() for char in actual_answer) != any(char.isdigit() for char in expected_answer) ): return "Format Error" # Check if partial match with case sensitivity - elif ( - actual_answer.lower() in expected_answer.lower() - or expected_answer.lower() in actual_answer.lower() - ): + elif actual_answer.lower() in expected_answer.lower() or expected_answer.lower() in actual_answer.lower(): return "Partial Match / case Sensitivity" # Else the answer may be completely wrong else: return "Other" - else: - # Check if all the words in actual_answer are expected but some of the expected are missing - if all(word in expected_answer for word in actual_answer.split()): - return "Words Missing" + elif all(word in expected_answer for word in actual_answer.split()): + return "Words Missing" - # Check if some words in actual_answer are incorrect (present in actual_answer but not in expected_answer) - # elif any(word not in expected_answer for word in actual_answer.split()): - # return "Incorrect Words" + # Check if some words in actual_answer are incorrect (present in actual_answer but not in expected_answer) + # elif any(word not in expected_answer for word in actual_answer.split()): + # return "Incorrect Words" - # Check if the entire actual_answer is completely different from the expected_answer - else: - return "Entire Answer Incorrect" + # Check if the entire actual_answer is completely different from the expected_answer + else: + return "Entire Answer Incorrect" def is_synonym(word1, word2): - """ - Tests if the input arguments word1 and word2 are synonyms of each other. + """Tests if the input arguments word1 and word2 are synonyms of each other. If yes, the function returns True, False otherwise. """ if word2.lower() in ["yes", "no", "ja", "nein"]: @@ -424,9 +426,12 @@ def get_result_file_path(file_name: str) -> str: """Returns the path to the result file. Args: + ---- file_name (str): The name of the result file Returns: + ------- str: The path to the result file + """ return f"benchmark/results/{file_name}.csv" diff --git a/benchmark/conftest.py b/benchmark/conftest.py index a2af2127..0514de0a 100644 --- a/benchmark/conftest.py +++ b/benchmark/conftest.py @@ -1,20 +1,20 @@ import os -from xinference.client import Client -import pytest -import requests - import numpy as np import pandas as pd +import pytest +import requests +from xinference.client import Client -from biochatter.prompts import BioCypherPromptEngine from biochatter.llm_connect import ( - GptConversation, AnthropicConversation, + GptConversation, XinferenceConversation, ) -from .load_dataset import get_benchmark_dataset +from biochatter.prompts import BioCypherPromptEngine + from .benchmark_utils import benchmark_already_executed +from .load_dataset import get_benchmark_dataset # how often should each benchmark be run? N_ITERATIONS = 1 @@ -265,15 +265,13 @@ # names, model sizes and quantizations XINFERENCE_MODEL_NAMES = [ f"{model_name}:{model_size}:{model_format}:{quantization}" - for model_name in XINFERENCE_MODELS.keys() + for model_name in XINFERENCE_MODELS for model_size in XINFERENCE_MODELS[model_name]["model_size_in_billions"] for model_format in [XINFERENCE_MODELS[model_name]["model_format"]] for quantization in XINFERENCE_MODELS[model_name]["quantization"] ] -BENCHMARKED_MODELS = ( - OPENAI_MODEL_NAMES + ANTHROPIC_MODEL_NAMES + XINFERENCE_MODEL_NAMES -) +BENCHMARKED_MODELS = OPENAI_MODEL_NAMES + ANTHROPIC_MODEL_NAMES + XINFERENCE_MODEL_NAMES BENCHMARKED_MODELS.sort() # Xinference IP and port @@ -290,18 +288,14 @@ def client(): @pytest.fixture(scope="session", autouse=True) def register_model(client): - """ - Register custom (non-builtin) models with the Xinference server. Should only + """Register custom (non-builtin) models with the Xinference server. Should only happen once per session. """ - if client is None: return # ignore if server is not running registrations = client.list_model_registrations(model_type="LLM") - registered_models = [ - registration["model_name"] for registration in registrations - ] + registered_models = [registration["model_name"] for registration in registrations] if "openbiollm-llama3-8b" not in registered_models: with open("benchmark/models/openbiollm-llama3-8b.json") as fd: @@ -315,17 +309,15 @@ def register_model(client): def pytest_collection_modifyitems(items): - """ - Pytest hook function to modify the collected test items. + """Pytest hook function to modify the collected test items. Called once after collection has been performed. Used here to order items by their `callspec.id` (which starts with the model name and configuration) to ensure running all tests for one model before moving to the next model. """ - items.sort( - key=lambda item: (item.callspec.id if hasattr(item, "callspec") else "") + key=lambda item: (item.callspec.id if hasattr(item, "callspec") else ""), ) # can we skip here the tests (model x hash) that have already been executed? @@ -337,7 +329,7 @@ def model_name(request): return request.param -@pytest.fixture +@pytest.fixture() def multiple_testing(request): def run_multiple_times(test_func, *args, **kwargs): scores = [] @@ -356,10 +348,9 @@ def calculate_bool_vector_score(vector: list[bool]) -> tuple[int, int]: return (score, max) -@pytest.fixture +@pytest.fixture() def conversation(request, model_name, client): - """ - Decides whether to run the test or skip due to the test having been run + """Decides whether to run the test or skip due to the test having been run before. If not skipped, will create a conversation object for interfacing with the model. """ @@ -378,7 +369,8 @@ def conversation(request, model_name, client): correct=False, ) conversation.set_api_key( - os.getenv("OPENAI_API_KEY"), user="benchmark_user" + os.getenv("OPENAI_API_KEY"), + user="benchmark_user", ) elif model_name in ANTHROPIC_MODEL_NAMES: @@ -388,7 +380,8 @@ def conversation(request, model_name, client): correct=False, ) conversation.set_api_key( - os.getenv("ANTHROPIC_API_KEY"), user="benchmark_user" + os.getenv("ANTHROPIC_API_KEY"), + user="benchmark_user", ) elif model_name in XINFERENCE_MODEL_NAMES: @@ -398,7 +391,7 @@ def conversation(request, model_name, client): _model_format, _model_quantization, ) = model_name.split(":") - if not "_" in _model_size: + if "_" not in _model_size: _model_size = int(_model_size) # if exact model already running, return conversation @@ -407,10 +400,8 @@ def conversation(request, model_name, client): for running_model in running_models: if ( running_models[running_model]["model_name"] == _model_name - and running_models[running_model]["model_size_in_billions"] - == _model_size - and running_models[running_model]["quantization"] - == _model_quantization + and running_models[running_model]["model_size_in_billions"] == _model_size + and running_models[running_model]["quantization"] == _model_quantization ): conversation = XinferenceConversation( base_url=BENCHMARK_URL, @@ -448,11 +439,9 @@ def conversation(request, model_name, client): return conversation -@pytest.fixture +@pytest.fixture() def prompt_engine(request, model_name, conversation): - """ - Generates a constructor for the prompt engine for the current model name. - """ + """Generates a constructor for the prompt engine for the current model name.""" def conversation_factory(): return conversation @@ -467,7 +456,7 @@ def setup_prompt_engine(kg_schema_dict): return setup_prompt_engine -@pytest.fixture +@pytest.fixture() def evaluation_conversation(): conversation = GptConversation( model_name="gpt-3.5-turbo", @@ -489,15 +478,12 @@ def pytest_addoption(parser): @pytest.fixture(autouse=True, scope="session") def delete_results_csv_file_content(request): - """ - If --run-all is set, the former benchmark data are deleted and all + """If --run-all is set, the former benchmark data are deleted and all benchmarks are executed again. """ if request.config.getoption("--run-all"): RESULT_FILES = [ - f"benchmark/results/{file}" - for file in os.listdir("benchmark/results") - if file.endswith(".csv") + f"benchmark/results/{file}" for file in os.listdir("benchmark/results") if file.endswith(".csv") ] for f in RESULT_FILES: if os.path.exists(f): @@ -508,11 +494,7 @@ def delete_results_csv_file_content(request): @pytest.fixture(scope="session") def result_files(): - RESULT_FILES = [ - f"benchmark/results/{file}" - for file in os.listdir("benchmark/results") - if file.endswith(".csv") - ] + RESULT_FILES = [f"benchmark/results/{file}" for file in os.listdir("benchmark/results") if file.endswith(".csv")] result_files = {} result_columns = [ "model_name", @@ -542,8 +524,7 @@ def result_files(): def pytest_generate_tests(metafunc): - """ - Pytest hook function to generate test cases. + """Pytest hook function to generate test cases. Called once for each test case in the benchmark test collection. If fixture is part of test declaration, the test is parametrized. """ @@ -578,7 +559,7 @@ def pytest_generate_tests(metafunc): ) -@pytest.fixture +@pytest.fixture() def kg_schemas(): data = BENCHMARK_DATASET return data["kg_schemas"] diff --git a/benchmark/load_dataset.py b/benchmark/load_dataset.py index 8590ef6f..b9e36d42 100644 --- a/benchmark/load_dataset.py +++ b/benchmark/load_dataset.py @@ -1,31 +1,35 @@ -from ast import literal_eval -from base64 import b64decode -import os +"""Module for loading and managing benchmark datasets. + +Including both public test data from the repository and encrypted hold-out test +data. +""" + import copy -import json import hashlib import itertools -from typing import Dict +import json +import os +from base64 import b64decode +from pathlib import Path -from cryptography.fernet import Fernet +import pandas as pd import rsa import yaml +from cryptography.fernet import Fernet -import pandas as pd - - -def get_benchmark_dataset() -> Dict[str, pd.DataFrame | Dict[str, str]]: - """ - Get benchmark dataset: +def get_benchmark_dataset() -> dict[str, pd.DataFrame | dict[str, str]]: + """Get benchmark dataset. - if the env variable HOLD_OUT_TEST_DATA_PRIVATE_KEY is set, the hold out test set is used - otherwise, the public test set from this repository is used - Returns: + Returns + ------- dict: keys are filenames and values are test data. + """ if os.environ.get("HOLD_OUT_TEST_DATA_PRIVATE_KEY"): test_data = _load_hold_out_test_dataset() @@ -35,40 +39,43 @@ def get_benchmark_dataset() -> Dict[str, pd.DataFrame | Dict[str, str]]: return test_data -def _load_hold_out_test_dataset() -> Dict[str, pd.DataFrame | Dict[str, str]]: +def _load_hold_out_test_dataset() -> dict[str, pd.DataFrame | dict[str, str]]: """Load hold out test dataset. - Returns: + Returns + ------- dict: keys are filenames and values are test data. + """ print("Use hold out test data for benchmarking") private_key = _get_private_key_from_env_variable() encrypted_test_data = _get_encrypted_test_data() - decrypted_test_data = _decrypt_data(encrypted_test_data, private_key) - return decrypted_test_data + return _decrypt_data(encrypted_test_data, private_key) -def _get_all_benchmark_files(directory_path): +def _get_all_benchmark_files(directory_path: str) -> list[str]: """Get all files in the directory ending with _data.yaml. Args: + ---- directory_path (str): Path to the directory. Returns: + ------- list: List of file paths ending with _data.yaml. + """ - return [ - os.path.join(directory_path, f) - for f in os.listdir(directory_path) - if f.endswith("_data.yaml") - ] + directory = Path(directory_path) + return [str(directory / f) for f in os.listdir(directory) if f.endswith("_data.yaml")] -def _load_test_data_from_this_repository(): +def _load_test_data_from_this_repository() -> dict[str, pd.DataFrame | dict[str, str]]: """Load and combine test data from this repository. - Returns: + Returns + ------- dict: Combined test data from all YAML files. + """ print("Using public test data from this repository for benchmarking.") directory_path = "./benchmark/data" @@ -76,13 +83,12 @@ def _load_test_data_from_this_repository(): combined_data = {} for file_path in benchmark_files: - with open(file_path, "r", encoding="utf-8", errors="ignore") as stream: + with Path(file_path).open(encoding="utf-8", errors="ignore") as stream: try: yaml_data = yaml.safe_load(stream) if not isinstance(yaml_data, dict): - raise ValueError( - f"Expected a dictionary but got {type(yaml_data)} from file {file_path}" - ) + msg = f"Expected dict, got {type(yaml_data).__name__}" + raise TypeError(msg) yaml_data = _get_yaml_data(yaml_data) combined_data.update(yaml_data) except yaml.YAMLError as exc: @@ -91,7 +97,7 @@ def _load_test_data_from_this_repository(): return combined_data -def _get_yaml_data(yaml_data): +def _get_yaml_data(yaml_data: dict) -> dict: # expand multi-instruction tests yaml_data = _expand_multi_instruction(yaml_data) # generate hash for each case @@ -102,29 +108,27 @@ def _get_yaml_data(yaml_data): def _delete_outdated_benchmark_results(data_dict: dict) -> None: - """ - Delete outdated benchmark results. + """Delete outdated benchmark results. + Opens the corresponding result file for each test and deletes the results that have hashes that are not in the current test data. Args: + ---- data_dict (dict): The yaml data. - """ + """ # get all current hashes for comparison - current_hashes = [] - for key in data_dict.keys(): - if isinstance(data_dict[key], list): - for i in range(len(data_dict[key])): - if isinstance(data_dict[key][i], dict): - current_hashes.append(data_dict[key][i]["hash"]) + current_hashes = [ + data_dict[key][i]["hash"] + for key in data_dict + if isinstance(data_dict[key], list) + for i in range(len(data_dict[key])) + if isinstance(data_dict[key][i], dict) + ] # get all result files - result_files = [ - f"benchmark/results/{file}" - for file in os.listdir("benchmark/results") - if file.endswith(".csv") - ] + result_files = [f"benchmark/results/{file}" for file in os.listdir("benchmark/results") if file.endswith(".csv")] # delete outdated results for file in result_files: @@ -142,46 +146,50 @@ def _delete_outdated_benchmark_results(data_dict: dict) -> None: def _hash_each_case(data_dict: dict) -> dict: - """ - Create a hash for each case in the test data to identify tests that have - been run or modified. + """Create a hash for each test case. + + Create a hash in the test data to identify tests that have been run or + modified. Args: + ---- data_dict (dict): The yaml data. Returns: + ------- dict: The yaml data with a hash for each case. + """ - for key in data_dict.keys(): + for key in data_dict: if isinstance(data_dict[key], list): for i in range(len(data_dict[key])): if isinstance(data_dict[key][i], dict): - data_dict[key][i]["hash"] = hashlib.md5( - json.dumps(data_dict[key][i]).encode("utf-8") + data_dict[key][i]["hash"] = hashlib.md5( # noqa: S324 + json.dumps(data_dict[key][i]).encode("utf-8"), ).hexdigest() return data_dict def _expand_multi_instruction(data_dict: dict) -> dict: - """ - Expands tests with input dictionaries that contain dictionaries. + """Expand tests with input dictionaries that contain dictionaries. Args: + ---- data_dict (dict): The yaml data. Returns: + ------- dict: The expanded yaml data. + """ - for module_key in data_dict.keys(): + for module_key in data_dict: if "kg_schemas" not in module_key: test_list = data_dict[module_key] expanded_test_list = [] for test in test_list: dicts = { - key: value - for key, value in test["input"].items() - if isinstance(value, dict) and key != "format" + key: value for key, value in test["input"].items() if isinstance(value, dict) and key != "format" } if not dicts: expanded_test_list.append(test) @@ -191,21 +199,17 @@ def _expand_multi_instruction(data_dict: dict) -> dict: query_type = None new_case = copy.deepcopy(test) new_case["case"] = ":".join( - [test["case"]] + [key for key in list(combination)] + [test["case"], *combination], ) - dict_keys = [key for key in dicts.keys()] - key_value_combinations = zip(dict_keys, list(combination)) + dict_keys = list(dicts) + key_value_combinations = zip(dict_keys, list(combination), strict=True) for key, value in key_value_combinations: new_case["input"][key] = dicts[key][value] if key == "query": - new_case["input"]["format"] = test["input"][ - "format" - ][value] + new_case["input"]["format"] = test["input"]["format"][value] query_type = value elif key == "caption": - new_case["expected"]["answer"] = test["expected"][ - "answer" - ][value][query_type] + new_case["expected"]["answer"] = test["expected"]["answer"][value][query_type] expanded_test_list.append(new_case) data_dict[module_key] = expanded_test_list @@ -216,47 +220,54 @@ def _expand_multi_instruction(data_dict: dict) -> dict: def _get_private_key_from_env_variable() -> rsa.PrivateKey: """Get the private key from an environment variable. - Returns: + Returns + ------- rsa.PrivateKey: The private key. + """ private_key_base64 = os.environ.get("HOLD_OUT_TEST_DATA_PRIVATE_KEY") private_key_str = b64decode(private_key_base64).decode("utf-8") - private_key = rsa.PrivateKey.load_pkcs1(private_key_str.encode("utf-8")) - return private_key + return rsa.PrivateKey.load_pkcs1(private_key_str.encode("utf-8")) -def _get_encrypted_test_data() -> Dict[str, dict[str, str]]: +def _get_encrypted_test_data() -> dict[str, dict[str, str]]: """Get encrypted test data. - currently from manually copied file benchmark/encrypted_llm_test_data.json + + Currently from manually copied file benchmark/encrypted_llm_test_data.json TODO: automatically load test dataset (from github releases)? - Returns: + Returns + ------- dict: keys are filenames and values are encrypted test data. + """ json_file_path = "./benchmark/encrypted_llm_test_data.json" - with open(json_file_path, "r") as json_file: - encrypted_test_data = json.load(json_file) - return encrypted_test_data + with Path(json_file_path).open() as json_file: + return json.load(json_file) def _decrypt_data( - encrypted_test_data: dict, private_key: rsa.PrivateKey + encrypted_test_data: dict, + private_key: rsa.PrivateKey, ) -> dict: """Decrypt the test data. Args: + ---- encrypted_test_data (dict): keys are filenames and values are encrypted test data. private_key (rsa.PrivateKey): The private key. Returns: + ------- dict: keys are filenames and values are decrypted test data. Todo: genericise to eval automatically + """ decrypted_test_data = {} - for key in encrypted_test_data.keys(): + for key in encrypted_test_data: decrypted = _decrypt(encrypted_test_data[key], private_key) if key.endswith(".yaml"): @@ -276,34 +287,20 @@ def _decrypt(payload: dict[str, str], private_key: rsa.PrivateKey) -> str: """Decrypt a payload. Args: + ---- payload (Dict[str, str]): Payload with key and data to decrypt. private_key (rsa.PrivateKey): Private key to decrypt the payload. Returns: + ------- str: Decrypted data. + """ enc_symmetric_key = b64decode(payload["key"]) enc_data = b64decode(payload["data"]) symmetric_key = rsa.decrypt(enc_symmetric_key, private_key) f = Fernet(symmetric_key) - data = f.decrypt(enc_data).decode("utf-8") - return data - - -def _apply_literal_eval(df: pd.DataFrame, columns: list[str]): - """Apply literal_eval to columns in a dataframe - In the csv file lists and dicts are stored as strings. - By calling literal_eval they are transformed to lists and dicts again. - - Args: - df (pd.DataFrame): Dataframe. - columns (List[str]): Columns to apply literal_eval to. - """ - for col_name in columns: - if col_name in df.columns: - df[col_name] = df[col_name].apply( - lambda x: literal_eval(x) if pd.notna(x) else {} - ) + return f.decrypt(enc_data).decode("utf-8") if __name__ == "__main__": diff --git a/benchmark/test_api_calling.py b/benchmark/test_api_calling.py index c198ebd1..67c51789 100644 --- a/benchmark/test_api_calling.py +++ b/benchmark/test_api_calling.py @@ -1,17 +1,18 @@ -from urllib.parse import urlencode import inspect import re +from urllib.parse import urlencode import pytest from biochatter._misc import ensure_iterable -from biochatter.api_agent import OncoKBQueryBuilder, BioToolsQueryBuilder -from .conftest import calculate_bool_vector_score +from biochatter.api_agent import BioToolsQueryBuilder, OncoKBQueryBuilder + from .benchmark_utils import ( - skip_if_already_run, get_result_file_path, + skip_if_already_run, write_results_to_file, ) +from .conftest import calculate_bool_vector_score def test_api_calling( @@ -23,11 +24,13 @@ def test_api_calling( yaml_data = test_data_api_calling task = f"{inspect.currentframe().f_code.co_name.replace('test_', '')}" skip_if_already_run( - model_name=model_name, task=task, md5_hash=yaml_data["hash"] + model_name=model_name, + task=task, + md5_hash=yaml_data["hash"], ) if "gpt-" not in model_name: pytest.skip( - f"model {model_name} does not support API calling for {task} benchmark" + f"model {model_name} does not support API calling for {task} benchmark", ) def run_test(): @@ -50,7 +53,7 @@ def run_test(): score = [] for expected_part in ensure_iterable( - yaml_data["expected"]["parts_of_query"] + yaml_data["expected"]["parts_of_query"], ): if re.search(expected_part, api_query): score.append(True) diff --git a/benchmark/test_biocypher_query_generation.py b/benchmark/test_biocypher_query_generation.py index 9c5fa0d2..253cf80d 100644 --- a/benchmark/test_biocypher_query_generation.py +++ b/benchmark/test_biocypher_query_generation.py @@ -1,16 +1,17 @@ -import re -import json import inspect +import json +import re import pytest from biochatter.prompts import BioCypherPromptEngine -from .conftest import calculate_bool_vector_score + from .benchmark_utils import ( - skip_if_already_run, get_result_file_path, + skip_if_already_run, write_results_to_file, ) +from .conftest import calculate_bool_vector_score def test_naive_query_generation_using_schema( @@ -23,7 +24,9 @@ def test_naive_query_generation_using_schema( yaml_data = test_data_biocypher_query_generation task = f"{inspect.currentframe().f_code.co_name.replace('test_', '')}" skip_if_already_run( - model_name=model_name, task=task, md5_hash=yaml_data["hash"] + model_name=model_name, + task=task, + md5_hash=yaml_data["hash"], ) schema = kg_schemas[yaml_data["input"]["kg_schema"]] @@ -33,11 +36,11 @@ def run_test(): conversation.append_system_message( "You are a database expert. Please write a Cypher query to " "retrieve information for the user. The schema of the graph is " - "defined as follows: " + "defined as follows: ", ) conversation.append_system_message(json.dumps(schema, indent=2)) conversation.append_system_message( - "Only return the query, nothing else." + "Only return the query, nothing else.", ) query, _, _ = conversation.query(yaml_data["input"]["prompt"]) @@ -46,12 +49,11 @@ def run_test(): for expected_part_of_query in yaml_data["expected"]["parts_of_query"]: if isinstance(expected_part_of_query, tuple): score.append( - expected_part_of_query[0] in query - or expected_part_of_query[1] in query + expected_part_of_query[0] in query or expected_part_of_query[1] in query, ) else: score.append( - (re.search(expected_part_of_query, query) is not None) + re.search(expected_part_of_query, query) is not None, ) return calculate_bool_vector_score(score) @@ -74,11 +76,14 @@ def get_prompt_engine( """Helper function to create the prompt engine for the test. Args: + ---- kg_schema_dict (dict): The KG schema create_prompt_engine: The function to create the BioCypherPromptEngine Returns: + ------- BioCypherPromptEngine: The prompt engine for the test + """ return create_prompt_engine(kg_schema_dict=kg_schema_dict) @@ -94,10 +99,13 @@ def test_entity_selection( yaml_data = test_data_biocypher_query_generation task = f"{inspect.currentframe().f_code.co_name.replace('test_', '')}" skip_if_already_run( - model_name=model_name, task=task, md5_hash=yaml_data["hash"] + model_name=model_name, + task=task, + md5_hash=yaml_data["hash"], ) prompt_engine = get_prompt_engine( - kg_schemas[yaml_data["input"]["kg_schema"]], prompt_engine + kg_schemas[yaml_data["input"]["kg_schema"]], + prompt_engine, ) def run_test(): @@ -138,10 +146,13 @@ def test_relationship_selection( if not yaml_data["expected"]["relationships"]: pytest.skip("No relationships to test") skip_if_already_run( - model_name=model_name, task=task, md5_hash=yaml_data["hash"] + model_name=model_name, + task=task, + md5_hash=yaml_data["hash"], ) prompt_engine = get_prompt_engine( - kg_schemas[yaml_data["input"]["kg_schema"]], prompt_engine + kg_schemas[yaml_data["input"]["kg_schema"]], + prompt_engine, ) prompt_engine.question = yaml_data["input"]["prompt"] @@ -155,23 +166,18 @@ def run_test(): assert success score = [] - for expected_relationship_label_key in yaml_data["expected"][ - "relationship_labels" - ].keys(): + for expected_relationship_label_key in yaml_data["expected"]["relationship_labels"].keys(): score.append( - expected_relationship_label_key - in prompt_engine.selected_relationship_labels.keys() + expected_relationship_label_key in prompt_engine.selected_relationship_labels.keys(), ) - for expected_relationship_label_value in yaml_data["expected"][ - "relationship_labels" - ][expected_relationship_label_key]: + for expected_relationship_label_value in yaml_data["expected"]["relationship_labels"][ + expected_relationship_label_key + ]: try: score.append( expected_relationship_label_value - in prompt_engine.selected_relationship_labels[ - expected_relationship_label_key - ] + in prompt_engine.selected_relationship_labels[expected_relationship_label_key], ) except KeyError: score.append(False) @@ -200,17 +206,18 @@ def test_property_selection( yaml_data = test_data_biocypher_query_generation task = f"{inspect.currentframe().f_code.co_name.replace('test_', '')}" skip_if_already_run( - model_name=model_name, task=task, md5_hash=yaml_data["hash"] + model_name=model_name, + task=task, + md5_hash=yaml_data["hash"], ) prompt_engine = get_prompt_engine( - kg_schemas[yaml_data["input"]["kg_schema"]], prompt_engine + kg_schemas[yaml_data["input"]["kg_schema"]], + prompt_engine, ) prompt_engine.question = yaml_data["input"]["prompt"] prompt_engine.selected_entities = yaml_data["expected"]["entities"] - prompt_engine.selected_relationships = yaml_data["expected"][ - "relationships" - ] + prompt_engine.selected_relationships = yaml_data["expected"]["relationships"] def run_test(): conversation.reset() # needs to be reset for each test @@ -218,35 +225,25 @@ def run_test(): if success: score = [] - for expected_property_key in yaml_data["expected"][ - "properties" - ].keys(): + for expected_property_key in yaml_data["expected"]["properties"].keys(): try: score.append( - expected_property_key - in prompt_engine.selected_properties.keys() + expected_property_key in prompt_engine.selected_properties.keys(), ) except KeyError: score.append(False) - for expected_property_value in yaml_data["expected"][ - "properties" - ][expected_property_key]: + for expected_property_value in yaml_data["expected"]["properties"][expected_property_key]: try: score.append( - expected_property_value - in prompt_engine.selected_properties[ - expected_property_key - ] + expected_property_value in prompt_engine.selected_properties[expected_property_key], ) except KeyError: score.append(False) else: total_properties = len( - yaml_data["expected"]["properties"].keys() - ) + sum( - len(v) for v in yaml_data["expected"]["properties"].values() - ) + yaml_data["expected"]["properties"].keys(), + ) + sum(len(v) for v in yaml_data["expected"]["properties"].values()) score = [False] * total_properties return calculate_bool_vector_score(score) @@ -274,10 +271,13 @@ def test_query_generation( yaml_data = test_data_biocypher_query_generation task = f"{inspect.currentframe().f_code.co_name.replace('test_', '')}" skip_if_already_run( - model_name=model_name, task=task, md5_hash=yaml_data["hash"] + model_name=model_name, + task=task, + md5_hash=yaml_data["hash"], ) prompt_engine = get_prompt_engine( - kg_schemas[yaml_data["input"]["kg_schema"]], prompt_engine + kg_schemas[yaml_data["input"]["kg_schema"]], + prompt_engine, ) def run_test(): @@ -295,12 +295,11 @@ def run_test(): for expected_part_of_query in yaml_data["expected"]["parts_of_query"]: if isinstance(expected_part_of_query, tuple): score.append( - expected_part_of_query[0] in query - or expected_part_of_query[1] in query + expected_part_of_query[0] in query or expected_part_of_query[1] in query, ) else: score.append( - (re.search(expected_part_of_query, query) is not None) + re.search(expected_part_of_query, query) is not None, ) return calculate_bool_vector_score(score) @@ -327,10 +326,13 @@ def test_end_to_end_query_generation( yaml_data = test_data_biocypher_query_generation task = f"{inspect.currentframe().f_code.co_name.replace('test_', '')}" skip_if_already_run( - model_name=model_name, task=task, md5_hash=yaml_data["hash"] + model_name=model_name, + task=task, + md5_hash=yaml_data["hash"], ) prompt_engine = get_prompt_engine( - kg_schemas[yaml_data["input"]["kg_schema"]], prompt_engine + kg_schemas[yaml_data["input"]["kg_schema"]], + prompt_engine, ) def run_test(): @@ -341,19 +343,16 @@ def run_test(): query_language="Cypher", ) score = [] - for expected_part_of_query in yaml_data["expected"][ - "parts_of_query" - ]: + for expected_part_of_query in yaml_data["expected"]["parts_of_query"]: if isinstance(expected_part_of_query, tuple): score.append( - expected_part_of_query[0] in query - or expected_part_of_query[1] in query + expected_part_of_query[0] in query or expected_part_of_query[1] in query, ) else: score.append( - (re.search(expected_part_of_query, query) is not None) + re.search(expected_part_of_query, query) is not None, ) - except ValueError as e: + except ValueError: score = [False for _ in yaml_data["expected"]["parts_of_query"]] return calculate_bool_vector_score(score) @@ -464,10 +463,13 @@ def test_property_exists( yaml_data = test_data_biocypher_query_generation task = f"{inspect.currentframe().f_code.co_name.replace('test_', '')}" skip_if_already_run( - model_name=model_name, task=task, md5_hash=yaml_data["hash"] + model_name=model_name, + task=task, + md5_hash=yaml_data["hash"], ) prompt_engine = get_prompt_engine( - kg_schemas[yaml_data["input"]["kg_schema"]], prompt_engine + kg_schemas[yaml_data["input"]["kg_schema"]], + prompt_engine, ) def run_test(): @@ -490,22 +492,16 @@ def run_test(): ) = get_used_property_from_query(query) for entity, property in used_entity_property.items(): - if ( - entity in prompt_engine.entities.keys() - and "properties" in prompt_engine.entities[entity] - ): + if entity in prompt_engine.entities.keys() and "properties" in prompt_engine.entities[entity]: # check property used is in available properties for entities avail_property_entity = list( - prompt_engine.entities[entity]["properties"].keys() + prompt_engine.entities[entity]["properties"].keys(), ) score.append(property in avail_property_entity) - elif ( - entity in prompt_engine.relationships.keys() - and "properties" in prompt_engine.relationships[entity] - ): + elif entity in prompt_engine.relationships.keys() and "properties" in prompt_engine.relationships[entity]: # check property used is in available properties for relationships avail_property_entity = list( - prompt_engine.relationships[entity]["properties"].keys() + prompt_engine.relationships[entity]["properties"].keys(), ) score.append(property in avail_property_entity) else: @@ -537,10 +533,9 @@ def test_regex(test_data_biocypher_query_generation): for expected_part_of_query in yaml_data["expected"]["parts_of_query"]: if isinstance(expected_part_of_query, tuple): score.append( - expected_part_of_query[0] in query - or expected_part_of_query[1] in query + expected_part_of_query[0] in query or expected_part_of_query[1] in query, ) else: - score.append((re.search(expected_part_of_query, query) is not None)) + score.append(re.search(expected_part_of_query, query) is not None) assert True diff --git a/benchmark/test_rag_interpretation.py b/benchmark/test_rag_interpretation.py index 7f26488c..6ea2474e 100644 --- a/benchmark/test_rag_interpretation.py +++ b/benchmark/test_rag_interpretation.py @@ -2,13 +2,12 @@ import pytest -from biochatter._misc import ensure_iterable -from .conftest import calculate_bool_vector_score from .benchmark_utils import ( - skip_if_already_run, get_result_file_path, + skip_if_already_run, write_results_to_file, ) +from .conftest import calculate_bool_vector_score def test_explicit_relevance_of_single_fragments( @@ -20,25 +19,22 @@ def test_explicit_relevance_of_single_fragments( yaml_data = test_data_rag_interpretation task = f"{inspect.currentframe().f_code.co_name.replace('test_', '')}" skip_if_already_run( - model_name=model_name, task=task, md5_hash=yaml_data["hash"] + model_name=model_name, + task=task, + md5_hash=yaml_data["hash"], ) if "explicit" not in yaml_data["case"]: pytest.skip( - f"test case {yaml_data['case']} not supported for {task} benchmark" + f"test case {yaml_data['case']} not supported for {task} benchmark", ) def run_test(): conversation.reset() # needs to be reset for each test - [ - conversation.append_system_message(m) - for m in yaml_data["input"]["system_messages"] - ] + [conversation.append_system_message(m) for m in yaml_data["input"]["system_messages"]] response, _, _ = conversation.query(yaml_data["input"]["prompt"]) # lower case, remove punctuation - response = ( - response.lower().replace(".", "").replace("?", "").replace("!", "") - ).strip() + response = (response.lower().replace(".", "").replace("?", "").replace("!", "")).strip() score = [] @@ -68,19 +64,18 @@ def test_implicit_relevance_of_multiple_fragments( yaml_data = test_data_rag_interpretation task = f"{inspect.currentframe().f_code.co_name.replace('test_', '')}" skip_if_already_run( - model_name=model_name, task=task, md5_hash=yaml_data["hash"] + model_name=model_name, + task=task, + md5_hash=yaml_data["hash"], ) if "implicit" not in yaml_data["case"]: pytest.skip( - f"test case {yaml_data['case']} not supported for {task} benchmark" + f"test case {yaml_data['case']} not supported for {task} benchmark", ) def run_test(): conversation.reset() # needs to be reset for each test - [ - conversation.append_system_message(m) - for m in yaml_data["input"]["system_messages"] - ] + [conversation.append_system_message(m) for m in yaml_data["input"]["system_messages"]] response, _, _ = conversation.query(yaml_data["input"]["prompt"]) msg = ( @@ -97,13 +92,9 @@ def run_test(): eval, _, _ = evaluation_conversation.query(response) # lower case, remove punctuation - eval = ( - eval.lower().replace(".", "").replace("?", "").replace("!", "") - ).strip() + eval = (eval.lower().replace(".", "").replace("?", "").replace("!", "")).strip() - score = ( - [True] if eval == yaml_data["expected"]["behaviour"] else [False] - ) + score = [True] if eval == yaml_data["expected"]["behaviour"] else [False] return calculate_bool_vector_score(score) diff --git a/benchmark/test_text_extraction.py b/benchmark/test_text_extraction.py index 17e81ad2..40bedaeb 100644 --- a/benchmark/test_text_extraction.py +++ b/benchmark/test_text_extraction.py @@ -3,8 +3,8 @@ import evaluate from .benchmark_utils import ( - skip_if_already_run, get_result_file_path, + skip_if_already_run, write_results_to_file, ) @@ -25,7 +25,9 @@ def test_sourcedata_info_extraction( yaml_data = test_data_text_extraction task = f"{inspect.currentframe().f_code.co_name.replace('test_', '')}" skip_if_already_run( - model_name=model_name, task=task, md5_hash=yaml_data["hash"] + model_name=model_name, + task=task, + md5_hash=yaml_data["hash"], ) def run_test(): @@ -33,8 +35,8 @@ def run_test(): # Define the system prompt [ conversation.append_system_message( - yaml_data["input"]["system_messages"] - ) + yaml_data["input"]["system_messages"], + ), ] user_query = ( @@ -45,7 +47,8 @@ def run_test(): response, _, _ = conversation.query(user_query) rouge_score = calculate_rouge_score( - response, yaml_data["expected"]["answer"] + response, + yaml_data["expected"]["answer"], ) return (rouge_score, 1) @@ -66,6 +69,4 @@ def calculate_rouge_score(response, expected): """Application of the ROUGE metric to evaluate the response of the model.""" rouge = evaluate.load("rouge") - return rouge.compute(predictions=[response], references=[expected])[ - "rouge1" - ] + return rouge.compute(predictions=[response], references=[expected])["rouge1"] diff --git a/benchmark/test_text_image_multimodality.py b/benchmark/test_text_image_multimodality.py index 14715dba..a0cbe3f0 100644 --- a/benchmark/test_text_image_multimodality.py +++ b/benchmark/test_text_image_multimodality.py @@ -5,26 +5,26 @@ # The expected answer is whether the figure caption actually belongs to the figure panel # Load the data -import os import hashlib import inspect - -import pytest +import os import numpy as np +import pytest from benchmark.conftest import calculate_bool_vector_score + from .benchmark_utils import ( - skip_if_already_run, - get_result_file_path, - write_results_to_file, get_confidence_file_path, + get_result_file_path, + skip_if_already_run, write_confidence_to_file, + write_results_to_file, ) # Load the data -@pytest.fixture +@pytest.fixture() def data_list(): data_path = "benchmark/data/source_data_clip" return [ @@ -36,10 +36,12 @@ def data_list(): # Run benchmark def test_multimodal_answer( - data_list, model_name, conversation, multiple_testing + data_list, + model_name, + conversation, + multiple_testing, ): - """ - Select randomly from the list of folders in data_list: + """Select randomly from the list of folders in data_list: - n examples with true positives (figure and caption match) - n examples with true negatives (figure and caption do not match) @@ -66,9 +68,7 @@ def test_multimodal_answer( def run_test(): # True positives: list of tuples containing the same file name twice - true_positives = [ - (f, f) for f in np.random.choice(data_list, n, replace=False) - ] + true_positives = [(f, f) for f in np.random.choice(data_list, n, replace=False)] # True negatives: list of tuples containing different file names # Check that the randomly selected names are different true_negatives = [] @@ -83,7 +83,7 @@ def run_test(): for f1, f2 in true_positives + true_negatives: conversation.reset() # Load the image and the caption - with open(os.path.join(f1, f1.split("/")[-1] + ".txt"), "r") as f: + with open(os.path.join(f1, f1.split("/")[-1] + ".txt")) as f: caption = f.read() image_path = os.path.join(f2, f2.split("/")[-1] + ".jpg") @@ -111,7 +111,7 @@ def run_test(): "file": f1.split("/")[-1], "answer": answer, "confidence": confidence, - } + }, ) # Consume results and write to file diff --git a/benchmark/test_user_interaction.py b/benchmark/test_user_interaction.py index 751708b1..2c1b08e7 100644 --- a/benchmark/test_user_interaction.py +++ b/benchmark/test_user_interaction.py @@ -1,19 +1,15 @@ -import re import inspect +import re -import nltk -import pytest - -from biochatter._misc import ensure_iterable -from .conftest import calculate_bool_vector_score from .benchmark_utils import ( - skip_if_already_run, - get_result_file_path, - write_results_to_file, categorize_failure_modes, get_failure_mode_file_path, + get_result_file_path, + skip_if_already_run, write_failure_modes_to_file, + write_results_to_file, ) +from .conftest import calculate_bool_vector_score def test_medical_exam( @@ -38,7 +34,9 @@ def test_medical_exam( task = f"{inspect.currentframe().f_code.co_name.replace('test_', '')}" skip_if_already_run( - model_name=model_name, task=task, md5_hash=yaml_data["hash"] + model_name=model_name, + task=task, + md5_hash=yaml_data["hash"], ) failure_mode = "other" actual_answer = "" @@ -50,17 +48,12 @@ def run_test(): nonlocal failure_mode conversation.reset() # needs to be reset for each test # Define the system prompt - [ - conversation.append_system_message(m) - for m in yaml_data["input"]["system_messages"] - ] + [conversation.append_system_message(m) for m in yaml_data["input"]["system_messages"]] # Define the user prompt response, _, _ = conversation.query(yaml_data["input"]["prompt"]) # Set response to lower case and remove punctuation - response = ( - response.lower().replace(".", "").replace("?", "").replace("!", "") - ).strip() + response = (response.lower().replace(".", "").replace("?", "").replace("!", "")).strip() print(yaml_data["case"]) print(response) @@ -78,7 +71,8 @@ def run_test(): if not is_correct: actual_answer = response failure_mode = categorize_failure_modes( - actual_answer, expected_answer + actual_answer, + expected_answer, ) # calculate for answers with regex @@ -94,7 +88,9 @@ def run_test(): score.append(False) actual_answer = actual_answer + response failure_mode = categorize_failure_modes( - actual_answer, expected_answer, True + actual_answer, + expected_answer, + True, ) return calculate_bool_vector_score(score) diff --git a/benchmark/test_vectorstore_semantic_search.py b/benchmark/test_vectorstore_semantic_search.py index caa02036..b430f927 100644 --- a/benchmark/test_vectorstore_semantic_search.py +++ b/benchmark/test_vectorstore_semantic_search.py @@ -1,11 +1,12 @@ -import os import inspect +import os import pytest -from biochatter.vectorstore import DocumentReader, DocumentEmbedder -from .conftest import calculate_bool_vector_score +from biochatter.vectorstore import DocumentEmbedder, DocumentReader + from .benchmark_utils import get_result_file_path +from .conftest import calculate_bool_vector_score # TODO: make vectorstore / retriever a part of the matrix @@ -49,5 +50,5 @@ def test_retrieval_augmented_generation(model, chunk_size): # record sum in CSV file with open(get_result_file_path(task), "a") as f: f.write( - f"{model},{chunk_size},{calculate_bool_vector_score(correct)}\n" + f"{model},{chunk_size},{calculate_bool_vector_score(correct)}\n", ) diff --git a/biochatter/_image.py b/biochatter/_image.py index 03982870..174c2931 100644 --- a/biochatter/_image.py +++ b/biochatter/_image.py @@ -1,24 +1,27 @@ +import base64 import io import os -import base64 -import tempfile # needed for test import subprocess +import tempfile -from PIL import Image import pdf2image +from PIL import Image def convert_and_resize_image(image: Image, max_size: int = 1024) -> Image: - """ - Convert the image to RGB format if needed and resize it to have a maximum - dimension of max_size. + """Convert the image to RGB format. - Parameters: + Do so if needed and resize it to have a maximum dimension of max_size. + + Args: + ---- image (PIL.Image): The input image. max_size (int): The maximum size for the image's width or height. Returns: + ------- PIL.Image: The converted and resized PIL image. + """ if image.mode != "RGB": image = image.convert("RGB") @@ -27,14 +30,16 @@ def convert_and_resize_image(image: Image, max_size: int = 1024) -> Image: def convert_to_png(image: Image) -> bytes: - """ - Convert a PIL image to PNG format. + """Convert a PIL image to PNG format. - Parameters: + Args: + ---- image (PIL.Image): The input image. Returns: + ------- bytes: The PNG image data. + """ with io.BytesIO() as output: image.save(output, format="PNG") @@ -42,15 +47,17 @@ def convert_to_png(image: Image) -> bytes: def convert_to_pil_image(file_path: str, dpi: int = 300) -> Image: - """ - Convert various image formats (PDF, EPS, TIFF, JPG, PNG) to a PIL image. + """Convert various image formats (PDF, EPS, TIFF, JPG, PNG) to a PIL image. - Parameters: + Args: + ---- file_path (str): The path to the image file. dpi (int): Dots per inch for high-resolution EPS conversion. Returns: + ------- PIL.Image: The converted PIL image. + """ file_path = os.path.abspath(file_path) file_ext = os.path.splitext(file_path)[1].lower() @@ -84,16 +91,18 @@ def convert_to_pil_image(file_path: str, dpi: int = 300) -> Image: def process_image(path: str, max_size: int) -> str: - """ - Process an image, converting it to PNG and resizing if necessary, then + """Process an image, converting it to PNG and resizing if necessary, then encode to base64. - Parameters: + Args: + ---- path (str): The path to the image file. max_size (int): The maximum size for the image's width or height. Returns: + ------- str: The base64 encoded image data. + """ image = convert_to_pil_image(path) png_image = convert_to_png(image) @@ -101,14 +110,16 @@ def process_image(path: str, max_size: int) -> str: def encode_image(image_path): - """ - Encode an image file to a base64 string, converting formats if necessary. + """Encode an image file to a base64 string, converting formats if necessary. - Parameters: + Args: + ---- image_path (str): The path to the image file. Returns: + ------- str: The base64 encoded image data. + """ supported_formats = (".webp", ".jpg", ".jpeg", ".gif", ".png") file_ext = os.path.splitext(image_path)[1].lower() @@ -121,31 +132,31 @@ def encode_image(image_path): def encode_image_from_url(url: str) -> str: - """ - Download an image from a URL, convert to base64, and return the base64 + """Download an image from a URL, convert to base64, and return the base64 string. - Parameters: + Args: + ---- url (str): The URL of the image. Returns: + ------- str: The base64 encoded image data. + """ from urllib.request import urlopen - import tempfile # Get the file extension from the content type with urlopen(url) as response: content_type = response.info().get_content_type() extension = content_type.split("/")[-1] - extension = ( - "jpg" if extension == "jpeg" else extension - ) # normalize extension + extension = "jpg" if extension == "jpeg" else extension # normalize extension with ( urlopen(url) as response, tempfile.NamedTemporaryFile( - suffix=f".{extension}", delete=False + suffix=f".{extension}", + delete=False, ) as tmp_file, ): tmp_file.write(response.read()) diff --git a/biochatter/_misc.py b/biochatter/_misc.py index 31ca13a1..63773f0c 100644 --- a/biochatter/_misc.py +++ b/biochatter/_misc.py @@ -1,17 +1,11 @@ # straight copy from BioCypher repo # TODO: have a utils package for both repos +import re +from collections.abc import Generator, ItemsView, Iterable, KeysView, Mapping, ValuesView from typing import ( Any, - Union, - Mapping, - KeysView, - Generator, - ItemsView, - ValuesView, ) -from collections.abc import Iterable -import re import stringcase @@ -39,10 +33,7 @@ def to_list(value: Any) -> list: - """ - Ensures that ``value`` is a list. - """ - + """Ensures that ``value`` is a list.""" if isinstance(value, LIST_LIKE): value = list(value) @@ -53,10 +44,7 @@ def to_list(value: Any) -> list: def ensure_iterable(value: Any) -> Iterable: - """ - Returns iterables, except strings, wraps simple types into tuple. - """ - + """Returns iterables, except strings, wraps simple types into tuple.""" return value if isinstance(value, LIST_LIKE) else (value,) @@ -75,67 +63,77 @@ def from_pascal(s: str, sep: str = " ") -> str: def pascalcase_to_sentencecase(s: str) -> str: - """ - Convert PascalCase to sentence case. + """Convert PascalCase to sentence case. Args: + ---- s: Input string in PascalCase Returns: + ------- string in sentence case form + """ return from_pascal(s, sep=" ") def snakecase_to_sentencecase(s: str) -> str: - """ - Convert snake_case to sentence case. + """Convert snake_case to sentence case. Args: + ---- s: Input string in snake_case Returns: + ------- string in sentence case form + """ return stringcase.sentencecase(s).lower() def sentencecase_to_snakecase(s: str) -> str: - """ - Convert sentence case to snake_case. + """Convert sentence case to snake_case. Args: + ---- s: Input string in sentence case Returns: + ------- string in snake_case form + """ return stringcase.snakecase(s).lower() def sentencecase_to_pascalcase(s: str) -> str: - """ - Convert sentence case to PascalCase. + """Convert sentence case to PascalCase. Args: + ---- s: Input string in sentence case Returns: + ------- string in PascalCase form + """ return re.sub(r"(?:^| )([a-zA-Z])", lambda match: match.group(1).upper(), s) def to_lower_sentence_case(s: str) -> str: - """ - Convert any string to lower sentence case. Works with snake_case, + """Convert any string to lower sentence case. Works with snake_case, PascalCase, and sentence case. Args: + ---- s: Input string Returns: + ------- string in lower sentence case form + """ if "_" in s: return snakecase_to_sentencecase(s) diff --git a/biochatter/_stats.py b/biochatter/_stats.py index 9cfb6b0b..4294bea2 100644 --- a/biochatter/_stats.py +++ b/biochatter/_stats.py @@ -1,11 +1,11 @@ # BioChatter usage statistics (for community key usage) # keep persistent statistics about community key usage in redis # adapted from https://github.com/mobarski/ask-my-pdf -from time import strftime import os +from time import strftime -from retry import retry import redis +from retry import retry DEFAULT_USER = "community" @@ -68,9 +68,7 @@ def get_community_usage_cost(): usage_stats = get_stats(user=DEFAULT_USER) data = usage_stats.get(f"usage:[date]:{DEFAULT_USER}") used = 0.0 - used += ( - 0.04 * data.get("total_tokens:gpt-4", 0) / 1000 - ) # prompt_price=0.03 but output_price=0.06 + used += 0.04 * data.get("total_tokens:gpt-4", 0) / 1000 # prompt_price=0.03 but output_price=0.06 used += 0.02 * data.get("total_tokens:text-davinci-003", 0) / 1000 used += 0.002 * data.get("total_tokens:text-curie-001", 0) / 1000 used += 0.002 * data.get("total_tokens:gpt-3.5-turbo", 0) / 1000 diff --git a/biochatter/api_agent/__init__.py b/biochatter/api_agent/__init__.py index 4d9084ab..21f0fca9 100644 --- a/biochatter/api_agent/__init__.py +++ b/biochatter/api_agent/__init__.py @@ -1,4 +1,10 @@ from .abc import BaseFetcher, BaseInterpreter, BaseQueryBuilder +from .api_agent import APIAgent +from .bio_tools import ( + BioToolsFetcher, + BioToolsInterpreter, + BioToolsQueryBuilder, +) from .blast import ( BlastFetcher, BlastInterpreter, @@ -6,12 +12,6 @@ BlastQueryParameters, ) from .oncokb import OncoKBFetcher, OncoKBInterpreter, OncoKBQueryBuilder -from .bio_tools import ( - BioToolsFetcher, - BioToolsInterpreter, - BioToolsQueryBuilder, -) -from .api_agent import APIAgent __all__ = [ "BaseFetcher", diff --git a/biochatter/api_agent/abc.py b/biochatter/api_agent/abc.py index dcca8194..f94297c0 100644 --- a/biochatter/api_agent/abc.py +++ b/biochatter/api_agent/abc.py @@ -1,22 +1,18 @@ from abc import ABC, abstractmethod -from typing import Any, Optional from collections.abc import Callable -from pydantic import BaseModel from langchain_core.prompts import ChatPromptTemplate +from pydantic import BaseModel from biochatter.llm_connect import Conversation class BaseQueryBuilder(ABC): - """ - An abstract base class for query builders. - """ + """An abstract base class for query builders.""" @property def structured_output_prompt(self) -> ChatPromptTemplate: - """ - Defines a structured output prompt template. This provides a default + """Defines a structured output prompt template. This provides a default implementation for an API agent that can be overridden by subclasses to return a ChatPromptTemplate-compatible object. """ @@ -31,7 +27,7 @@ def structured_output_prompt(self) -> ChatPromptTemplate: "Use the given format to extract information from the following input: {input}", ), ("human", "Tip: Make sure to answer in the correct format"), - ] + ], ) @abstractmethod @@ -40,21 +36,22 @@ def create_runnable( query_parameters: "BaseModel", conversation: "Conversation", ) -> Callable: - """ - Creates a runnable object for executing queries. Must be implemented by + """Creates a runnable object for executing queries. Must be implemented by subclasses. Should use the LangChain `create_structured_output_runnable` method to generate the Callable. Args: + ---- query_parameters: A Pydantic data model that specifies the fields of the API that should be queried. conversation: A BioChatter conversation object. Returns: + ------- A Callable object that can execute the query. + """ - pass @abstractmethod def parameterise_query( @@ -62,27 +59,26 @@ def parameterise_query( question: str, conversation: "Conversation", ) -> BaseModel: - """ - - Parameterises a query object (a Pydantic model with the fields of the + """Parameterises a query object (a Pydantic model with the fields of the API) based on the given question using a BioChatter conversation instance. Must be implemented by subclasses. Args: + ---- question (str): The question to be answered. conversation: The BioChatter conversation object containing the LLM that should parameterise the query. Returns: + ------- A parameterised instance of the query object (Pydantic BaseModel) + """ - pass class BaseFetcher(ABC): - """ - Abstract base class for fetchers. A fetcher is responsible for submitting + """Abstract base class for fetchers. A fetcher is responsible for submitting queries (in systems where submission and fetching are separate) and fetching and saving results of queries. It has to implement a `fetch_results()` method, which can wrap a multi-step procedure to submit and retrieve. Should @@ -94,22 +90,21 @@ class BaseFetcher(ABC): def fetch_results( self, query_model: BaseModel, - retries: Optional[int] = 3, + retries: int | None = 3, ): - """ - Fetches results by submitting a query. Can implement a multi-step + """Fetches results by submitting a query. Can implement a multi-step procedure if submitting and fetching are distinct processes (e.g., in the case of long processing times as in the case of BLAST). Args: + ---- query_model: the Pydantic model describing the parameterised query + """ - pass class BaseInterpreter(ABC): - """ - Abstract base class for result interpreters. The interpreter is aware of the + """Abstract base class for result interpreters. The interpreter is aware of the nature and structure of the results and can extract and summarise information from them. """ @@ -121,10 +116,10 @@ def summarise_results( conversation_factory: Callable, response_text: str, ) -> str: - """ - Summarises an answer based on the given parameters. + """Summarises an answer based on the given parameters. Args: + ---- question (str): The question that was asked. conversation_factory (Callable): A function that creates a @@ -133,11 +128,13 @@ def summarise_results( response_text (str): The response.text returned from the request. Returns: + ------- A summary of the answer. Todo: + ---- Genericise (remove file path and n_lines parameters, and use a generic way to get the results). The child classes should manage the specifics of the results. + """ - pass diff --git a/biochatter/api_agent/api_agent.py b/biochatter/api_agent/api_agent.py index 40bec6fe..21c8e9aa 100644 --- a/biochatter/api_agent/api_agent.py +++ b/biochatter/api_agent/api_agent.py @@ -1,4 +1,3 @@ -from typing import Optional from collections.abc import Callable from pydantic import BaseModel @@ -29,15 +28,14 @@ def __init__( fetcher: "BaseFetcher", interpreter: "BaseInterpreter", ): - """ - - API agent class to interact with a tool's API for querying and fetching + """API agent class to interact with a tool's API for querying and fetching results. The query fields have to be defined in a Pydantic model (`BaseModel`) and used (i.e., parameterised by the LLM) in the query builder. Specific API agents are defined in submodules of this directory (`api_agent`). The agent's logic is implemented in the `execute` method. - Attributes: + Attributes + ---------- conversation_factory (Callable): A function used to create a BioChatter conversation, providing LLM access. @@ -49,6 +47,7 @@ def __init__( result_interpreter (BaseInterpreter): An instance of a child of the BaseInterpreter class. + """ self.conversation_factory = conversation_factory self.query_builder = query_builder @@ -56,9 +55,8 @@ def __init__( self.interpreter = interpreter self.final_answer = None - def parameterise_query(self, question: str) -> Optional[BaseModel]: - """ - Use LLM to parameterise a query (a Pydantic model) based on the given + def parameterise_query(self, question: str) -> BaseModel | None: + """Use LLM to parameterise a query (a Pydantic model) based on the given question using a BioChatter conversation instance. """ try: @@ -68,13 +66,14 @@ def parameterise_query(self, question: str) -> Optional[BaseModel]: print(f"Error generating query: {e}") return None - def fetch_results(self, query_model: str) -> Optional[str]: - """ - Fetch the results of the query using the individual API's implementation + def fetch_results(self, query_model: str) -> str | None: + """Fetch the results of the query using the individual API's implementation (either single-step or submit-retrieve). Args: + ---- query_model: the parameterised query Pydantic model + """ try: return self.fetcher.fetch_results(query_model, 100) @@ -83,11 +82,11 @@ def fetch_results(self, query_model: str) -> Optional[str]: return None def summarise_results( - self, question: str, response_text: str - ) -> Optional[str]: - """ - Summarise the retrieved results to extract the answer to the question. - """ + self, + question: str, + response_text: str, + ) -> str | None: + """Summarise the retrieved results to extract the answer to the question.""" try: return self.interpreter.summarise_results( question=question, @@ -98,15 +97,16 @@ def summarise_results( print(f"Error extracting answer: {e}") return None - def execute(self, question: str) -> Optional[str]: - """ - Wrapper that uses class methods to execute the API agent logic. Consists + def execute(self, question: str) -> str | None: + """Wrapper that uses class methods to execute the API agent logic. Consists of 1) query generation, 2) query submission, 3) results fetching, and 4) answer extraction. The final answer is stored in the final_answer attribute. Args: + ---- question (str): The question to be answered. + """ # Generate query try: @@ -138,7 +138,4 @@ def execute(self, question: str) -> Optional[str]: return final_answer def get_description(self, tool_name: str, tool_desc: str): - return ( - f"This API agent interacts with {tool_name}'s API for querying and " - f"fetching results. {tool_desc}" - ) + return f"This API agent interacts with {tool_name}'s API for querying and fetching results. {tool_desc}" diff --git a/biochatter/api_agent/bio_tools.py b/biochatter/api_agent/bio_tools.py index 6745acc2..9e45330b 100644 --- a/biochatter/api_agent/bio_tools.py +++ b/biochatter/api_agent/bio_tools.py @@ -1,21 +1,27 @@ -from typing import Optional -from collections.abc import Callable +"""Module for interacting with the bio.tools API.""" + import uuid +from collections.abc import Callable +from typing import TYPE_CHECKING -from langchain_core.prompts import ChatPromptTemplate -from langchain_core.pydantic_v1 import Field, BaseModel -from langchain_core.output_parsers import StrOutputParser -from langchain.chains.openai_functions import create_structured_output_runnable import requests +from langchain.chains.openai_functions import create_structured_output_runnable +from langchain_core.output_parsers import StrOutputParser +from langchain_core.prompts import ChatPromptTemplate +from langchain_core.pydantic_v1 import BaseModel, Field + +if TYPE_CHECKING: + from biochatter.llm_connect import Conversation -from biochatter.llm_connect import Conversation from .abc import BaseFetcher, BaseInterpreter, BaseQueryBuilder BIOTOOLS_QUERY_PROMPT = """ -You are a world class algorithm for creating queries in structured formats. Your task is to use the web API of bio.tools to answer questions about bioinformatics tools and their properties. +You are a world class algorithm for creating queries in structured formats. Your task is to use the web API of bio.tools +to answer questions about bioinformatics tools and their properties. You have to extract the appropriate information out of the examples: -1. To list information about the tools, use the endpoint https://bio.tools/api/t/ with parameters like name, description, homepage, etc. +1. To list information about the tools, use the endpoint https://bio.tools/api/t/ with parameters like name, +description, homepage, etc. Use these formats to generate queries based on the question provided. Below is more information about the bio.tools API: @@ -30,7 +36,7 @@ GET /t/ ================== ============================================================================================ -Parameter Search behaviour +Parameter Search behaviour ================== ============================================================================================ biotoolsID Search for bio.tools tool ID (usually quoted - to get exact match) @@ -38,165 +44,165 @@ name Search for tool name (quoted as needed) - `name=signalp `_ + `name=signalp `_ homepage Exact search for tool homepage URL (**must** be quoted) - `homepage="http://cbs.dtu.dk/services/SignalP/" `_ + `homepage="http://cbs.dtu.dk/services/SignalP/" `_ description Search over tool description (quoted as needed) - `description="peptide cleavage" `_ + `description="peptide cleavage" `_ version Exact search for tool version (**must** be quoted) - `version="4.1" `_ + `version="4.1" `_ topic Search for EDAM Topic (term) (quoted as needed) - `topic="Proteomics" `_ + `topic="Proteomics" `_ -topicID Exact search for EDAM Topic (URI): **must** be quoted +topicID Exact search for EDAM Topic (URI): **must** be quoted - `topicID="topic_3510" `_ -function Fuzzy search over function (input, operation, output, note and command) + `topicID="topic_3510" `_ +function Fuzzy search over function (input, operation, output, note and command) - `function="Sequence analysis" `_ -operation Fuzzy search for EDAM Operation (term) (quoted as needed) + `function="Sequence analysis" `_ +operation Fuzzy search for EDAM Operation (term) (quoted as needed) - `operation="Sequence analysis" `_ + `operation="Sequence analysis" `_ operationID Exact search for EDAM Operation (ID) (**must** be quoted) - `operationID="operation_2403" `_ -dataType Fuzzy search over input and output for EDAM Data (term) (quoted as needed) + `operationID="operation_2403" `_ +dataType Fuzzy search over input and output for EDAM Data (term) (quoted as needed) - `dataType="Protein sequence" `_ -dataTypeID Exact search over input and output for EDAM Data (ID) (**must** be quoted) + `dataType="Protein sequence" `_ +dataTypeID Exact search over input and output for EDAM Data (ID) (**must** be quoted) - `dataTypeID="data_2976" `_ -dataFormat Fuzzy search over input and output for EDAM Format (term) (quoted as needed) + `dataTypeID="data_2976" `_ +dataFormat Fuzzy search over input and output for EDAM Format (term) (quoted as needed) - `dataFormat="FASTA" `_ + `dataFormat="FASTA" `_ dataFormatID Exact search over input and output for EDAM Format (ID) (**must** be quoted) - `dataFormatID="format_1929" `_ + `dataFormatID="format_1929" `_ input Fuzzy search over input for EDAM Data and Format (term) (quoted as needed) - `input="Protein sequence" `_ -inputID Exact search over input for EDAM Data and Format (ID) (**must** be quoted) + `input="Protein sequence" `_ +inputID Exact search over input for EDAM Data and Format (ID) (**must** be quoted) - `inputID="data_2976" `_ -inputDataType Fuzzy search over input for EDAM Data (term) (quoted as needed) + `inputID="data_2976" `_ +inputDataType Fuzzy search over input for EDAM Data (term) (quoted as needed) - `inputDataType="Protein sequence" `_ + `inputDataType="Protein sequence" `_ inputDataTypeID Exact search over input for EDAM Data (ID) (**must** be quoted) - `inputDataTypeID="data_2976" `_ -inputDataFormat Fuzzy search over input for EDAM Format (term) (quoted as needed) + `inputDataTypeID="data_2976" `_ +inputDataFormat Fuzzy search over input for EDAM Format (term) (quoted as needed) - `inputDataFormat="FASTA" `_ + `inputDataFormat="FASTA" `_ inputDataFormatID Exact search over input for EDAM Format (ID) (**must** be quoted) - `inputDataFormatID="format_1929" `_ + `inputDataFormatID="format_1929" `_ output Fuzzy search over output for EDAM Data and Format (term) (quoted as needed) - `output="Sequence alignment" `_ + `output="Sequence alignment" `_ outputID Exact search over output for EDAM Data and Format (ID) (**must** be quoted) - `outputID="data_0863" `_ + `outputID="data_0863" `_ outputDataType Fuzzy search over output for EDAM Data (term) (quoted as needed) - `outputDataType="Sequence alignment" `_ + `outputDataType="Sequence alignment" `_ outputDataTypeID Exact search over output for EDAM Data (ID) (**must** be quoted) - `outputDataTypeID="data_0863" `_ -outputDataFormat Fuzzy search over output for EDAM Format (term) (quoted as needed) + `outputDataTypeID="data_0863" `_ +outputDataFormat Fuzzy search over output for EDAM Format (term) (quoted as needed) - `outputDataFormat="ClustalW format" `_ + `outputDataFormat="ClustalW format" `_ outputDataFormatID Exact search over output for EDAM Format (ID) (**must** be quoted) - `outputDataFormatID="format_1982" `_ + `outputDataFormatID="format_1982" `_ toolType Exact search for tool type - `toolType="Command-line tool" `_ + `toolType="Command-line tool" `_ collectionID Exact search for tool collection (normally quoted) - `collectionID="Rare Disease" `_ + `collectionID="Rare Disease" `_ maturity Exact search for tool maturity - `maturity=Mature `_ -operatingSystem Exact search for tool operating system + `maturity=Mature `_ +operatingSystem Exact search for tool operating system - `operatingSystem=Linux `_ + `operatingSystem=Linux `_ language Exact search for programming language - `language=Java `_ -cost Exact search for cost + `language=Java `_ +cost Exact search for cost - `cost="Free of charge" `_ + `cost="Free of charge" `_ license Exact search for software or data usage license (quoted as needed) - `license="GPL-3.0" `_ -accessibility Exact search for tool accessibility + `license="GPL-3.0" `_ +accessibility Exact search for tool accessibility - `accessibility="Open access" `_ -credit Fuzzy search over credit (name, email, URL, ORCID iD, type of entity, type of role and note) + `accessibility="Open access" `_ +credit Fuzzy search over credit (name, email, URL, ORCID iD, type of entity, type of role and note) - `credit="Henrik Nielsen" `_ -creditName Exact search for name of credited entity + `credit="Henrik Nielsen" `_ +creditName Exact search for name of credited entity - `creditName="Henrik Nielsen" `_ -creditTypeRole Exact search for role of credited entity + `creditName="Henrik Nielsen" `_ +creditTypeRole Exact search for role of credited entity - `creditTypeRole=Developer `_ -creditTypeEntity Exact search for type of credited entity + `creditTypeRole=Developer `_ +creditTypeEntity Exact search for type of credited entity - `creditTypeEntity="Funding agency" `_ + `creditTypeEntity="Funding agency" `_ creditOrcidID Exact search for ORCID iD of credited entity (**must** be quoted) - `creditOrcidID="0000-0001-5121-2036" `_ -publication Fuzzy search over publication (DOI, PMID, PMCID, publication type and tool version) (quoted as needed) + `creditOrcidID="0000-0001-5121-2036" `_ +publication Fuzzy search over publication (DOI, PMID, PMCID, publication type and tool version) (quoted as needed) - `publication=10.12688/f1000research.12974.1 `_ + `publication=10.12688/f1000research.12974.1 `_ publicationID Exact search for publication ID (DOI, PMID or PMCID) (**must** be quoted) - `publicationID="10.12688/f1000research.12974.1" `_ + `publicationID="10.12688/f1000research.12974.1" `_ publicationType Exact search for publication type - `publicationType=Primary `_ + `publicationType=Primary `_ publicationVersion Exact search for tool version associated with a publication (**must** be quoted) - `publicationVersion="1.0" `_ + `publicationVersion="1.0" `_ link Fuzzy search over general link (URL, type and note) (quote as needed) - `link="Issue tracker" `_ + `link="Issue tracker" `_ linkType Exact search for type of information found at a link `linkType="Issue tracker" `_ -documentation Fuzzy search over documentation link (URL, type and note) (quote as needed) +documentation Fuzzy search over documentation link (URL, type and note) (quote as needed) - `documentation=Manual `_ + `documentation=Manual `_ documentationType Exact search for type of documentation - `documentationType=Manual `_ + `documentationType=Manual `_ download Fuzzy search over download link (URL, type, version and note) (quote as needed) - `download=Binaries `_ + `download=Binaries `_ downloadType Exact search for type of download - `downloadType=Binaries `_ + `downloadType=Binaries `_ downloadVersion Exact search for tool version associated with a download (**must** be quoted) - `downloadVersion="1.0" `_ -otherID Fuzzy search over alternate tool IDs (ID value, type of ID and version) + `downloadVersion="1.0" `_ +otherID Fuzzy search over alternate tool IDs (ID value, type of ID and version) - `otherID="rrid:SCR_015644" `_ + `otherID="rrid:SCR_015644" `_ otherIDValue Exact search for value of alternate tool ID (**must** be quoted) - `otherIDValue="rrid:SCR_015644" `_ -otherIDType Exact search for type of alternate tool ID + `otherIDValue="rrid:SCR_015644" `_ +otherIDType Exact search for type of alternate tool ID - `otherIDType=RRID `_ + `otherIDType=RRID `_ otherIDVersion Exact search for tool version associated with an alternate ID (**must** be quoted) - `otherIDVersion="1.0" `_ + `otherIDVersion="1.0" `_ ================== ============================================================================================ @@ -224,8 +230,10 @@ e.g. https://bio.tools/api/tool?topicID=”topic_3510” Values of other parameters can be quoted or unquoted: -Unquoted values invoke a fuzzy word search: it will search for fuzzy matches of words in the search phrase, to the target field -Quoted values invoke an exact phrase search; it will search for an exact match of the full-length of the search phrase, to the target field (matches to target substrings are allowed) +Unquoted values invoke a fuzzy word search: it will search for fuzzy matches of words in the search phrase, to the +target field +Quoted values invoke an exact phrase search; it will search for an exact match of the full-length of the search phrase, +to the target field (matches to target substrings are allowed) e.g. https://bio.tools/api/tool?biotoolsID=”blast” returns the tool with biotoolsID of “blast” (the “canonical” blast) https://bio.tools/api/tool?biotoolsID=blast returns all tools with “blast” in their biotoolsID (all blast flavours) @@ -235,13 +243,16 @@ BIOTOOLS_SUMMARY_PROMPT = """ You have to answer this question in a clear and concise manner: {question} Be factual!\n\ You are a world leading bioinformatician who knows everything about bio.tools packages.\n\ -Do not make up information, only use the provided information and mention how relevant the found information is based on your knowledge about bio.tools.\n\ +Do not make up information, only use the provided information and mention how relevant the found information is based on +your knowledge about bio.tools.\n\ Here is the information relevant to the question found on the bio.tools web API:\n\ {context} """ class BioToolsQueryParameters(BaseModel): + """Parameters for querying the bio.tools API.""" + base_url: str = Field( default="https://bio.tools/api/", description="Base URL for the BioTools API.", @@ -250,223 +261,225 @@ class BioToolsQueryParameters(BaseModel): ..., description="Specific API endpoint to hit. Example: 't/' for listing tools.", ) - biotoolsID: Optional[str] = Field( + biotoolsID: str | None = Field( # noqa: N815 None, description="Search for bio.tools tool ID (usually quoted - to get exact match)", ) - name: Optional[str] = Field( + name: str | None = Field( None, description="Search for tool name (quoted as needed: quoted for exact match, unquoted for fuzzy search)", ) - homepage: Optional[str] = Field( + homepage: str | None = Field( None, description="Exact search for tool homepage URL (**must** be quoted)", ) - description: Optional[str] = Field( + description: str | None = Field( None, description="Search over tool description (quoted as needed)", ) - version: Optional[str] = Field( + version: str | None = Field( None, description="Exact search for tool version (**must** be quoted)", ) - topic: Optional[str] = Field( + topic: str | None = Field( None, description="Search for EDAM Topic (term) (quoted as needed)", ) - topicID: Optional[str] = Field( + topicID: str | None = Field( # noqa: N815 None, description="Exact search for EDAM Topic (URI): **must** be quoted", ) - function: Optional[str] = Field( + function: str | None = Field( None, description="Fuzzy search over function (input, operation, output, note and command)", ) - operation: Optional[str] = Field( + operation: str | None = Field( None, description="Fuzzy search for EDAM Operation (term) (quoted as needed)", ) - operationID: Optional[str] = Field( + operationID: str | None = Field( # noqa: N815 None, description="Exact search for EDAM Operation (ID) (**must** be quoted)", ) - dataType: Optional[str] = Field( + dataType: str | None = Field( # noqa: N815 None, description="Fuzzy search over input and output for EDAM Data (term) (quoted as needed)", ) - dataTypeID: Optional[str] = Field( + dataTypeID: str | None = Field( # noqa: N815 None, description="Exact search over input and output for EDAM Data (ID) (**must** be quoted)", ) - dataFormat: Optional[str] = Field( + dataFormat: str | None = Field( # noqa: N815 None, description="Fuzzy search over input and output for EDAM Format (term) (quoted as needed)", ) - dataFormatID: Optional[str] = Field( + dataFormatID: str | None = Field( # noqa: N815 None, description="Exact search over input and output for EDAM Format (ID) (**must** be quoted)", ) - input: Optional[str] = Field( + input: str | None = Field( None, description="Fuzzy search over input for EDAM Data and Format (term) (quoted as needed)", ) - inputID: Optional[str] = Field( + inputID: str | None = Field( # noqa: N815 None, description="Exact search over input for EDAM Data and Format (ID) (**must** be quoted)", ) - inputDataType: Optional[str] = Field( + inputDataType: str | None = Field( # noqa: N815 None, description="Fuzzy search over input for EDAM Data (term) (quoted as needed)", ) - inputDataTypeID: Optional[str] = Field( + inputDataTypeID: str | None = Field( # noqa: N815 None, description="Exact search over input for EDAM Data (ID) (**must** be quoted)", ) - inputDataFormat: Optional[str] = Field( + inputDataFormat: str | None = Field( # noqa: N815 None, description="Fuzzy search over input for EDAM Format (term) (quoted as needed)", ) - inputDataFormatID: Optional[str] = Field( + inputDataFormatID: str | None = Field( # noqa: N815 None, description="Exact search over input for EDAM Format (ID) (**must** be quoted)", ) - output: Optional[str] = Field( + output: str | None = Field( None, description="Fuzzy search over output for EDAM Data and Format (term) (quoted as needed)", ) - outputID: Optional[str] = Field( + outputID: str | None = Field( # noqa: N815 None, description="Exact search over output for EDAM Data and Format (ID) (**must** be quoted)", ) - outputDataType: Optional[str] = Field( + outputDataType: str | None = Field( # noqa: N815 None, description="Fuzzy search over output for EDAM Data (term) (quoted as needed)", ) - outputDataTypeID: Optional[str] = Field( + outputDataTypeID: str | None = Field( # noqa: N815 None, description="Exact search over output for EDAM Data (ID) (**must** be quoted)", ) - outputDataFormat: Optional[str] = Field( + outputDataFormat: str | None = Field( # noqa: N815 None, description="Fuzzy search over output for EDAM Format (term) (quoted as needed)", ) - outputDataFormatID: Optional[str] = Field( + outputDataFormatID: str | None = Field( # noqa: N815 None, description="Exact search over output for EDAM Format (ID) (**must** be quoted)", ) - toolType: Optional[str] = Field( + toolType: str | None = Field( # noqa: N815 None, description="Exact search for tool type", ) - collectionID: Optional[str] = Field( + collectionID: str | None = Field( # noqa: N815 None, description="Exact search for tool collection (normally quoted)", ) - maturity: Optional[str] = Field( + maturity: str | None = Field( None, description="Exact search for tool maturity", ) - operatingSystem: Optional[str] = Field( + operatingSystem: str | None = Field( # noqa: N815 None, description="Exact search for tool operating system", ) - language: Optional[str] = Field( + language: str | None = Field( None, description="Exact search for programming language", ) - cost: Optional[str] = Field( + cost: str | None = Field( None, description="Exact search for cost", ) - license: Optional[str] = Field( + license: str | None = Field( None, description="Exact search for software or data usage license (quoted as needed)", ) - accessibility: Optional[str] = Field( + accessibility: str | None = Field( None, description="Exact search for tool accessibility", ) - credit: Optional[str] = Field( + credit: str | None = Field( None, description="Fuzzy search over credit (name, email, URL, ORCID iD, type of entity, type of role and note)", ) - creditName: Optional[str] = Field( + creditName: str | None = Field( # noqa: N815 None, description="Exact search for name of credited entity", ) - creditTypeRole: Optional[str] = Field( + creditTypeRole: str | None = Field( # noqa: N815 None, description="Exact search for role of credited entity", ) - creditTypeEntity: Optional[str] = Field( + creditTypeEntity: str | None = Field( # noqa: N815 None, description="Exact search for type of credited entity", ) - creditOrcidID: Optional[str] = Field( + creditOrcidID: str | None = Field( # noqa: N815 None, description="Exact search for ORCID iD of credited entity (**must** be quoted)", ) - publication: Optional[str] = Field( + publication: str | None = Field( None, - description="Fuzzy search over publication (DOI, PMID, PMCID, publication type and tool version) (quoted as needed)", + description=( + "Fuzzy search over publication (DOI, PMID, PMCID, publication type and tool version) (quoted as needed)" + ), ) - publicationID: Optional[str] = Field( + publicationID: str | None = Field( # noqa: N815 None, description="Exact search for publication ID (DOI, PMID or PMCID) (**must** be quoted)", ) - publicationType: Optional[str] = Field( + publicationType: str | None = Field( # noqa: N815 None, description="Exact search for publication type", ) - publicationVersion: Optional[str] = Field( + publicationVersion: str | None = Field( # noqa: N815 None, description="Exact search for tool version associated with a publication (**must** be quoted)", ) - link: Optional[str] = Field( + link: str | None = Field( None, description="Fuzzy search over general link (URL, type and note) (quote as needed)", ) - linkType: Optional[str] = Field( + linkType: str | None = Field( # noqa: N815 None, description="Exact search for type of information found at a link", ) - documentation: Optional[str] = Field( + documentation: str | None = Field( None, description="Fuzzy search over documentation link (URL, type and note) (quote as needed)", ) - documentationType: Optional[str] = Field( + documentationType: str | None = Field( # noqa: N815 None, description="Exact search for type of documentation", ) - download: Optional[str] = Field( + download: str | None = Field( None, description="Fuzzy search over download link (URL, type, version and note) (quote as needed)", ) - downloadType: Optional[str] = Field( + downloadType: str | None = Field( # noqa: N815 None, description="Exact search for type of download", ) - downloadVersion: Optional[str] = Field( + downloadVersion: str | None = Field( # noqa: N815 None, description="Exact search for tool version associated with a download (**must** be quoted)", ) - otherID: Optional[str] = Field( + otherID: str | None = Field( # noqa: N815 None, description="Fuzzy search over alternate tool IDs (ID value, type of ID and version)", ) - otherIDValue: Optional[str] = Field( + otherIDValue: str | None = Field( # noqa: N815 None, description="Exact search for value of alternate tool ID (**must** be quoted)", ) - otherIDType: Optional[str] = Field( + otherIDType: str | None = Field( # noqa: N815 None, description="Exact search for type of alternate tool ID", ) - otherIDVersion: Optional[str] = Field( + otherIDVersion: str | None = Field( # noqa: N815 None, description="Exact search for tool version associated with an alternate ID (**must** be quoted)", ) - question_uuid: Optional[str] = Field( + question_uuid: str | None = Field( default_factory=lambda: str(uuid.uuid4()), description="Unique identifier for the question.", ) @@ -480,18 +493,22 @@ def create_runnable( query_parameters: "BioToolsQueryParameters", conversation: "Conversation", ) -> Callable: - """ - Creates a runnable object for executing queries using the LangChain - `create_structured_output_runnable` method. + """Create a runnable object for executing queries. + + Create runnable using the LangChain `create_structured_output_runnable` + method. Args: + ---- query_parameters: A Pydantic data model that specifies the fields of the API that should be queried. conversation: A BioChatter conversation object. Returns: + ------- A Callable object that can execute the query. + """ return create_structured_output_runnable( output_schema=query_parameters, @@ -504,21 +521,25 @@ def parameterise_query( question: str, conversation: "Conversation", ) -> BioToolsQueryParameters: - """ + """Generate an BioToolsQuery object. - Generates an BioToolsQuery object based on the given question, prompt, + Generate a BioToolsQuery object based on the given question, prompt, and BioChatter conversation. Uses a Pydantic model to define the API fields. Creates a runnable that can be invoked on LLMs that are qualified to parameterise functions. Args: + ---- question (str): The question to be answered. conversation: The conversation object used for parameterising the BioToolsQuery. Returns: - BioToolsQueryParameters: the parameterised query object (Pydantic model) + ------- + BioToolsQueryParameters: the parameterised query object (Pydantic + model) + """ runnable = self.create_runnable( query_parameters=BioToolsQueryParameters, @@ -526,20 +547,27 @@ def parameterise_query( ) oncokb_call_obj = runnable.invoke( { - "input": f"Answer:\n{question} based on:\n {BIOTOOLS_QUERY_PROMPT}" - } + "input": f"Answer:\n{question} based on:\n {BIOTOOLS_QUERY_PROMPT}", + }, ) oncokb_call_obj.question_uuid = str(uuid.uuid4()) return oncokb_call_obj class BioToolsFetcher(BaseFetcher): + """A class for retrieving API results from BioTools. + + Retrieves API results given a parameterized BioToolsQuery. """ - A class for retrieving API results from BioTools given a parameterized - BioToolsQuery. - """ - def __init__(self, api_token="demo"): + def __init__(self, api_token: str = "demo") -> None: # noqa: S107 + """Initialise the BioToolsFetcher. + + Args: + ---- + api_token: The API token for the BioTools API. + + """ self.headers = { "Authorization": f"Bearer {api_token}", "Accept": "application/json", @@ -547,50 +575,61 @@ def __init__(self, api_token="demo"): self.base_url = "https://bio.tools/api" def fetch_results( - self, request_data: BioToolsQueryParameters, retries: Optional[int] = 3 + self, + request_data: BioToolsQueryParameters, + retries: int | None = 3, # noqa: ARG002 ) -> str: - """Function to submit the BioTools query and fetch the results directly. + """Submit the BioTools query and fetch the results directly. + No multi-step procedure, thus no wrapping of submission and retrieval in this case. Args: + ---- request_data: BioToolsQuery object (Pydantic model) containing the BioTools query parameters. + retries: The number of retries to fetch the results. + Returns: + ------- str: The results of the BioTools query. + """ # Submit the query and get the URL params = request_data.dict(exclude_unset=True) endpoint = params.pop("endpoint") params.pop("question_uuid") full_url = f"{self.base_url}/{endpoint}" - response = requests.get(full_url, headers=self.headers, params=params) + response = requests.get(full_url, headers=self.headers, params=params, timeout=30) response.raise_for_status() # Fetch the results from the URL - results_response = requests.get(response.url, headers=self.headers) + results_response = requests.get(response.url, headers=self.headers, timeout=30) results_response.raise_for_status() return results_response.text class BioToolsInterpreter(BaseInterpreter): + """A class for interpreting BioTools results.""" + def summarise_results( self, question: str, conversation_factory: Callable, response_text: str, ) -> str: - """ - Function to extract the answer from the BLAST results. + """Extract the answer from the BLAST results. Args: + ---- question (str): The question to be answered. conversation_factory: A BioChatter conversation object. response_text (str): The response.text returned by bio.tools. Returns: + ------- str: The extracted answer from the BLAST results. """ @@ -605,13 +644,13 @@ def summarise_results( "them for the user.", ), ("user", "{input}"), - ] + ], ) summary_prompt = BIOTOOLS_SUMMARY_PROMPT.format( - question=question, context=response_text + question=question, + context=response_text, ) output_parser = StrOutputParser() conversation = conversation_factory() chain = prompt | conversation.chat | output_parser - answer = chain.invoke({"input": {summary_prompt}}) - return answer + return chain.invoke({"input": {summary_prompt}}) diff --git a/biochatter/api_agent/blast.py b/biochatter/api_agent/blast.py index 6e5d011e..1eb592b7 100644 --- a/biochatter/api_agent/blast.py +++ b/biochatter/api_agent/blast.py @@ -1,18 +1,18 @@ -from typing import Optional -from urllib.parse import urlencode -from collections.abc import Callable import re import time import uuid +from collections.abc import Callable +from urllib.parse import urlencode -from pydantic import Field, BaseModel -from langchain_core.prompts import ChatPromptTemplate -from langchain_core.pydantic_v1 import Field, BaseModel -from langchain_core.output_parsers import StrOutputParser -from langchain.chains.openai_functions import create_structured_output_runnable import requests +from langchain.chains.openai_functions import create_structured_output_runnable +from langchain_core.output_parsers import StrOutputParser +from langchain_core.prompts import ChatPromptTemplate +from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from biochatter.llm_connect import Conversation + from .abc import BaseFetcher, BaseInterpreter, BaseQueryBuilder BLAST_QUERY_PROMPT = """ @@ -43,61 +43,61 @@ class BlastQueryParameters(BaseModel): - """ - - BlastQuery is a Pydantic model for the parameters of a BLAST query request, + """BlastQuery is a Pydantic model for the parameters of a BLAST query request, used for configuring and sending a request to the NCBI BLAST query API. The fields are dynamically configured by the LLM based on the user's question. """ - url: Optional[str] = Field( + url: str | None = Field( default="https://blast.ncbi.nlm.nih.gov/Blast.cgi?", description="ALWAYS USE DEFAULT, DO NOT CHANGE", ) - cmd: Optional[str] = Field( + cmd: str | None = Field( default="Put", description="Command to execute, 'Put' for submitting query, 'Get' for retrieving results.", ) - program: Optional[str] = Field( + program: str | None = Field( default="blastn", description="BLAST program to use, e.g., 'blastn' for nucleotide-nucleotide BLAST, 'blastp' for protein-protein BLAST.", ) - database: Optional[str] = Field( + database: str | None = Field( default="nt", description="Database to search, e.g., 'nt' for nucleotide database, 'nr' for non redundant protein database, pdb the Protein Data Bank database, which is used specifically for protein structures, 'refseq_rna' and 'refseq_genomic': specialized databases for RNA sequences and genomic sequences", ) - query: Optional[str] = Field( + query: str | None = Field( None, description="Nucleotide or protein sequence for the BLAST or blat query, make sure to always keep the entire sequence given.", ) - format_type: Optional[str] = Field( + format_type: str | None = Field( default="Text", description="Format of the BLAST results, e.g., 'Text', 'XML'.", ) - rid: Optional[str] = Field( - None, description="Request ID for retrieving BLAST results." + rid: str | None = Field( + None, + description="Request ID for retrieving BLAST results.", ) - other_params: Optional[dict] = Field( + other_params: dict | None = Field( default={"email": "user@example.com"}, description="Other optional BLAST parameters, including user email.", ) - max_hits: Optional[int] = Field( + max_hits: int | None = Field( default=15, description="Maximum number of hits to return in the BLAST results.", ) - sort_by: Optional[str] = Field( + sort_by: str | None = Field( default="score", description="Criterion to sort BLAST results by, e.g., 'score', 'evalue'.", ) - megablast: Optional[str] = Field( - default="on", description="Set to 'on' for human genome alignemnts" + megablast: str | None = Field( + default="on", + description="Set to 'on' for human genome alignemnts", ) - question_uuid: Optional[str] = Field( + question_uuid: str | None = Field( default_factory=lambda: str(uuid.uuid4()), description="Unique identifier for the question.", ) - full_url: Optional[str] = Field( + full_url: str | None = Field( default="TBF", description="Full URL to be used to submit the BLAST query", ) @@ -111,18 +111,20 @@ def create_runnable( query_parameters: "BlastQueryParameters", conversation: "Conversation", ) -> Callable: - """ - Creates a runnable object for executing queries using the LangChain + """Creates a runnable object for executing queries using the LangChain `create_structured_output_runnable` method. Args: + ---- query_parameters: A Pydantic data model that specifies the fields of the API that should be queried. conversation: A BioChatter conversation object. Returns: + ------- A Callable object that can execute the query. + """ return create_structured_output_runnable( output_schema=query_parameters, @@ -135,35 +137,36 @@ def parameterise_query( question: str, conversation: "Conversation", ) -> BlastQueryParameters: - """ - Generates a BlastQuery object based on the given question, prompt, and + """Generates a BlastQuery object based on the given question, prompt, and BioChatter conversation. Uses a Pydantic model to define the API fields. Creates a runnable that can be invoked on LLMs that are qualified to parameterise functions. Args: + ---- question (str): The question to be answered. conversation: The conversation object used for parameterising the BlastQuery. Returns: + ------- BlastQuery: the parameterised query object (Pydantic model) + """ runnable = self.create_runnable( query_parameters=BlastQueryParameters, conversation=conversation, ) blast_call_obj = runnable.invoke( - {"input": f"Answer:\n{question} based on:\n {BLAST_QUERY_PROMPT}"} + {"input": f"Answer:\n{question} based on:\n {BLAST_QUERY_PROMPT}"}, ) blast_call_obj.question_uuid = str(uuid.uuid4()) return blast_call_obj class BlastFetcher(BaseFetcher): - """ - A class for retrieving API results from BLAST given a parameterised + """A class for retrieving API results from BLAST given a parameterised BlastQuery. TODO add a limit of characters to be returned from the response.text? @@ -174,10 +177,14 @@ def _submit_query(self, request_data: BlastQueryParameters) -> str: It submits the structured BlastQuery obj and return the RID. Args: + ---- request_data: BlastQuery object containing the BLAST query parameters. + Returns: + ------- str: The Request ID (RID) for the submitted BLAST query. + """ data = { "CMD": request_data.cmd, @@ -250,7 +257,8 @@ def _fetch_results( if "ThereAreHits=yes" in status_text: print(f"{question_uuid} results are ready, retrieving.") results_response = requests.get( - base_url, params=get_results_params + base_url, + params=get_results_params, ) results_response.raise_for_status() # Save the results to a file @@ -259,23 +267,27 @@ def _fetch_results( return "No hits found" if attempt == retries - 1: raise TimeoutError( - "Maximum attempts reached. Results may not be ready." + "Maximum attempts reached. Results may not be ready.", ) def fetch_results( - self, query_model: BlastQueryParameters, retries: int = 20 + self, + query_model: BlastQueryParameters, + retries: int = 20, ) -> str: - """ - Submit request and fetch results from BLAST API. Wraps individual + """Submit request and fetch results from BLAST API. Wraps individual submission and retrieval of results. Args: + ---- query_model: the Pydantic model of the query retries: the number of maximum retries Returns: + ------- str: the result from the BLAST API + """ rid = self._submit_query(request_data=query_model) return self._fetch_results( @@ -292,15 +304,16 @@ def summarise_results( conversation_factory: Callable, response_text: str, ) -> str: - """ - Function to extract the answer from the BLAST results. + """Function to extract the answer from the BLAST results. Args: + ---- question (str): The question to be answered. conversation_factory: A BioChatter conversation object. response_text (str): The response.text returned by NCBI. Returns: + ------- str: The extracted answer from the BLAST results. """ @@ -311,10 +324,11 @@ def summarise_results( "You are a world class molecular biologist who knows everything about NCBI and BLAST results.", ), ("user", "{input}"), - ] + ], ) summary_prompt = BLAST_SUMMARY_PROMPT.format( - question=question, context=response_text + question=question, + context=response_text, ) output_parser = StrOutputParser() conversation = conversation_factory() diff --git a/biochatter/api_agent/oncokb.py b/biochatter/api_agent/oncokb.py index 2d2646f6..0c6043c4 100644 --- a/biochatter/api_agent/oncokb.py +++ b/biochatter/api_agent/oncokb.py @@ -1,17 +1,14 @@ -from typing import Optional -from urllib.parse import urlencode -from collections.abc import Callable -import re -import time import uuid +from collections.abc import Callable -from langchain_core.prompts import ChatPromptTemplate -from langchain_core.pydantic_v1 import Field, BaseModel -from langchain_core.output_parsers import StrOutputParser -from langchain.chains.openai_functions import create_structured_output_runnable import requests +from langchain.chains.openai_functions import create_structured_output_runnable +from langchain_core.output_parsers import StrOutputParser +from langchain_core.prompts import ChatPromptTemplate +from langchain_core.pydantic_v1 import BaseModel, Field from biochatter.llm_connect import Conversation + from .abc import BaseFetcher, BaseInterpreter, BaseQueryBuilder ONCOKB_QUERY_PROMPT = """ @@ -118,70 +115,75 @@ class OncoKBQueryParameters(BaseModel): ..., description="Specific API endpoint to hit. Example: 'annotate/mutations/byProteinChange'.", ) - referenceGenome: Optional[str] = Field( + referenceGenome: str | None = Field( default="GRCh37", description="Reference genome, either GRCh37 or GRCh38. The default is GRCh37.", ) - hugoSymbol: Optional[str] = Field( + hugoSymbol: str | None = Field( None, description="The gene symbol used in Human Genome Organisation. Example: BRAF.", ) - entrezGeneId: Optional[int] = Field( + entrezGeneId: int | None = Field( None, description="The entrez gene ID. Higher priority than hugoSymbol. Example: 673.", ) - tumorType: Optional[str] = Field( + tumorType: str | None = Field( None, description="OncoTree(http://oncotree.info) tumor type name. The field supports OncoTree Code, OncoTree Name and OncoTree Main type. Example: Melanoma.", ) - alteration: Optional[str] = Field( - None, description="Protein Change. Example: V600E." + alteration: str | None = Field( + None, + description="Protein Change. Example: V600E.", ) - consequence: Optional[str] = Field( - None, description="Consequence. Example: missense_variant." + consequence: str | None = Field( + None, + description="Consequence. Example: missense_variant.", ) - proteinStart: Optional[int] = Field( - None, description="Protein Start. Example: 600." + proteinStart: int | None = Field( + None, + description="Protein Start. Example: 600.", ) - proteinEnd: Optional[int] = Field( - None, description="Protein End. Example: 600." + proteinEnd: int | None = Field( + None, + description="Protein End. Example: 600.", ) - copyNameAlterationType: Optional[str] = Field( + copyNameAlterationType: str | None = Field( None, description="Copy number alteration type. Available types: AMPLIFICATION, DELETION, GAIN, LOSS.", ) - structuralVariantType: Optional[str] = Field( + structuralVariantType: str | None = Field( None, description="Structural variant type. Available values: DELETION, TRANSLOCATION, DUPLICATION, INSERTION, INVERSION, FUSION, UNKNOWN.", ) - isFunctionalFusion: Optional[bool] = Field( + isFunctionalFusion: bool | None = Field( default=False, description="Whether it is a functional fusion. Default value: false.", ) - hugoSymbolA: Optional[str] = Field( + hugoSymbolA: str | None = Field( None, description="The gene symbol A used in Human Genome Organisation. Example: ABL1.", ) - entrezGeneIdA: Optional[int] = Field( + entrezGeneIdA: int | None = Field( None, description="The entrez gene ID A. Higher priority than hugoSymbolA. Example: 25.", ) - hugoSymbolB: Optional[str] = Field( + hugoSymbolB: str | None = Field( None, description="The gene symbol B used in Human Genome Organisation. Example: BCR.", ) - entrezGeneIdB: Optional[int] = Field( + entrezGeneIdB: int | None = Field( None, description="The entrez gene ID B. Higher priority than hugoSymbolB. Example: 613.", ) - genomicLocation: Optional[str] = Field( + genomicLocation: str | None = Field( None, description="Genomic location. Example: 7,140453136,140453136,A,T.", ) - hgvsg: Optional[str] = Field( - None, description="HGVS genomic format. Example: 7:g.140453136A>T." + hgvsg: str | None = Field( + None, + description="HGVS genomic format. Example: 7:g.140453136A>T.", ) - question_uuid: Optional[str] = Field( + question_uuid: str | None = Field( default_factory=lambda: str(uuid.uuid4()), description="Unique identifier for the question.", ) @@ -195,18 +197,20 @@ def create_runnable( query_parameters: "OncoKBQueryParameters", conversation: "Conversation", ) -> Callable: - """ - Creates a runnable object for executing queries using the LangChain + """Creates a runnable object for executing queries using the LangChain `create_structured_output_runnable` method. Args: + ---- query_parameters: A Pydantic data model that specifies the fields of the API that should be queried. conversation: A BioChatter conversation object. Returns: + ------- A Callable object that can execute the query. + """ return create_structured_output_runnable( output_schema=query_parameters, @@ -219,35 +223,36 @@ def parameterise_query( question: str, conversation: "Conversation", ) -> OncoKBQueryParameters: - """ - Generates an OncoKBQuery object based on the given question, prompt, and + """Generates an OncoKBQuery object based on the given question, prompt, and BioChatter conversation. Uses a Pydantic model to define the API fields. Creates a runnable that can be invoked on LLMs that are qualified to parameterise functions. Args: + ---- question (str): The question to be answered. conversation: The conversation object used for parameterising the OncoKBQuery. Returns: + ------- OncoKBQueryParameters: the parameterised query object (Pydantic model) + """ runnable = self.create_runnable( query_parameters=OncoKBQueryParameters, conversation=conversation, ) oncokb_call_obj = runnable.invoke( - {"input": f"Answer:\n{question} based on:\n {ONCOKB_QUERY_PROMPT}"} + {"input": f"Answer:\n{question} based on:\n {ONCOKB_QUERY_PROMPT}"}, ) oncokb_call_obj.question_uuid = str(uuid.uuid4()) return oncokb_call_obj class OncoKBFetcher(BaseFetcher): - """ - A class for retrieving API results from OncoKB given a parameterized + """A class for retrieving API results from OncoKB given a parameterized OncoKBQuery. """ @@ -259,18 +264,23 @@ def __init__(self, api_token="demo"): self.base_url = "https://demo.oncokb.org/api/v1" def fetch_results( - self, request_data: OncoKBQueryParameters, retries: Optional[int] = 3 + self, + request_data: OncoKBQueryParameters, + retries: int | None = 3, ) -> str: """Function to submit the OncoKB query and fetch the results directly. No multi-step procedure, thus no wrapping of submission and retrieval in this case. Args: + ---- request_data: OncoKBQuery object (Pydantic model) containing the OncoKB query parameters. Returns: + ------- str: The results of the OncoKB query. + """ # Submit the query and get the URL params = request_data.dict(exclude_unset=True) @@ -294,15 +304,16 @@ def summarise_results( conversation_factory: Callable, response_text: str, ) -> str: - """ - Function to extract the answer from the BLAST results. + """Function to extract the answer from the BLAST results. Args: + ---- question (str): The question to be answered. conversation_factory: A BioChatter conversation object. response_text (str): The response.text returned by OncoKB. Returns: + ------- str: The extracted answer from the BLAST results. """ @@ -316,10 +327,11 @@ def summarise_results( "them for the user.", ), ("user", "{input}"), - ] + ], ) summary_prompt = ONCOKB_SUMMARY_PROMPT.format( - question=question, context=response_text + question=question, + context=response_text, ) output_parser = StrOutputParser() conversation = conversation_factory() diff --git a/biochatter/database_agent.py b/biochatter/database_agent.py index 3f79a8d8..e0f689a5 100644 --- a/biochatter/database_agent.py +++ b/biochatter/database_agent.py @@ -1,13 +1,12 @@ -from collections.abc import Callable import json -from typing import Dict, List, Optional +from collections.abc import Callable -from langchain.schema import Document import neo4j_utils as nu +from langchain.schema import Document -from .prompts import BioCypherPromptEngine from .constants import MAX_AGENT_DESC_LENGTH from .kg_langgraph_agent import KGQueryReflexionAgent +from .prompts import BioCypherPromptEngine class DatabaseAgent: @@ -19,12 +18,12 @@ def __init__( conversation_factory: Callable, use_reflexion: bool, ) -> None: - """ - Create a DatabaseAgent analogous to the VectorDatabaseAgentMilvus class, + """Create a DatabaseAgent analogous to the VectorDatabaseAgentMilvus class, which can return results from a database using a query engine. Currently limited to Neo4j for development. Args: + ---- connection_args (dict): A dictionary of arguments to connect to the database. Contains database name, URI, user, and password. @@ -33,6 +32,7 @@ def __init__( use_reflexion (bool): Whether to use the ReflexionAgent to generate the query. + """ self.conversation_factory = conversation_factory self.prompt_engine = BioCypherPromptEngine( @@ -45,9 +45,7 @@ def __init__( self.use_reflexion = use_reflexion def connect(self) -> None: - """ - Connect to the database and authenticate. - """ + """Connect to the database and authenticate.""" db_name = self.connection_args.get("db_name") uri = f"{self.connection_args.get('host')}:{self.connection_args.get('port')}" uri = uri if uri.startswith("bolt://") else "bolt://" + uri @@ -61,7 +59,7 @@ def connect(self) -> None: ) def is_connected(self) -> bool: - return not self.driver is None + return self.driver is not None def _generate_query(self, query: str): if self.use_reflexion: @@ -71,11 +69,7 @@ def _generate_query(self, query: str): ) query_prompt = self.prompt_engine.generate_query_prompt(query) agent_result = agent.execute(query, query_prompt) - tool_result = ( - [agent_result.tool_result] - if agent_result.tool_result is not None - else None - ) + tool_result = [agent_result.tool_result] if agent_result.tool_result is not None else None return agent_result.answer, tool_result else: query = self.prompt_engine.generate_query(query) @@ -84,10 +78,10 @@ def _generate_query(self, query: str): def _build_response( self, - results: List[Dict], + results: list[dict], cypher_query: str, - results_num: Optional[int] = 3, - ) -> List[Document]: + results_num: int | None = 3, + ) -> list[Document]: if len(results) == 0: return [ Document( @@ -100,7 +94,7 @@ def _build_response( "their question effectively." ), metadata={"cypher_query": cypher_query}, - ) + ), ] clipped_results = results[:results_num] if results_num > 0 else results @@ -117,28 +111,30 @@ def _build_response( "their question effectively." ), metadata={"cypher_query": cypher_query}, - ) + ), ] def get_query_results(self, query: str, k: int = 3) -> list[Document]: - """ - Generate a query using the prompt engine and return the results. + """Generate a query using the prompt engine and return the results. Replicates vector database similarity search API. Results are returned as a list of Document objects to align with the vector database agent. Args: + ---- query (str): A query string. k (int): The number of results to return. Returns: + ------- List[Document]: A list of Document objects. The page content values are the literal dictionaries returned by the query, the metadata values are the cypher query used to generate the results, for now. + """ (cypher_query, tool_result) = self._generate_query( - query + query, ) # self.prompt_engine.generate_query(query) # TODO some logic if it fails? if tool_result is not None: @@ -154,7 +150,9 @@ def get_query_results(self, query: str, k: int = 3) -> list[Document]: if results is None or len(results) == 0 or results[0] is None: return [] return self._build_response( - results=results[0], cypher_query=cypher_query, results_num=k + results=results[0], + cypher_query=cypher_query, + results_num=k, ) def get_description(self): @@ -162,13 +160,8 @@ def get_description(self): if result[0]: schema_info_node = result[0][0]["n"] - schema_dict_content = schema_info_node["schema_info"][ - :MAX_AGENT_DESC_LENGTH - ] # limit to 1000 characters - return ( - f"the graph database contains the following nodes and edges: \n\n" - f"{schema_dict_content}" - ) + schema_dict_content = schema_info_node["schema_info"][:MAX_AGENT_DESC_LENGTH] # limit to 1000 characters + return f"the graph database contains the following nodes and edges: \n\n{schema_dict_content}" # schema_info is not found in database nodes_query = "MATCH (n) RETURN DISTINCT labels(n) LIMIT 300" diff --git a/biochatter/kg_langgraph_agent.py b/biochatter/kg_langgraph_agent.py index 23936809..3af9efb3 100644 --- a/biochatter/kg_langgraph_agent.py +++ b/biochatter/kg_langgraph_agent.py @@ -1,22 +1,20 @@ -from typing import Any, Optional -from datetime import datetime -from collections.abc import Callable import json import logging +from collections.abc import Callable +from datetime import datetime -from langgraph.graph import END -from langchain_openai import ChatOpenAI -from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder -from langchain_core.messages import AIMessage, BaseMessage, ToolMessage -from langchain_core.pydantic_v1 import Field, BaseModel +import neo4j_utils as nu from langchain.output_parsers.openai_tools import ( - PydanticToolsParser, JsonOutputToolsParser, + PydanticToolsParser, ) -import neo4j_utils as nu +from langchain_core.messages import AIMessage, BaseMessage, ToolMessage +from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder +from langchain_core.pydantic_v1 import BaseModel, Field +from langchain_openai import ChatOpenAI +from langgraph.graph import END from biochatter.langgraph_agent_base import ( - END_NODE, EXECUTE_TOOL_NODE, ReflexionAgent, ReflexionAgentLogger, @@ -37,7 +35,7 @@ def log_step_message(self, step: int, node_name: str, output: BaseMessage): self._log_message(f"## {step}, {node_name}") self._log_message(f'Answer: {parsed_output[0]["args"]["answer"]}') self._log_message( - f'Reflection | Improving: {parsed_output[0]["args"]["reflection"]}' + f'Reflection | Improving: {parsed_output[0]["args"]["reflection"]}', ) self._log_message("Reflection | Search Queries:") for i, sq in enumerate(parsed_output[0]["args"][SEARCH_QUERIES]): @@ -46,14 +44,14 @@ def log_step_message(self, step: int, node_name: str, output: BaseMessage): self._log_message("Reflection | Revised Query:") self._log_message(parsed_output[0]["args"][REVISED_QUERY]) self._log_message( - "-------------------------------- Node Output --------------------------------" + "-------------------------------- Node Output --------------------------------", ) - except Exception as e: + except Exception: self._log_message(str(output)[:100] + " ...", "error") def log_final_result(self, final_result: ReflexionAgentResult) -> None: self._log_message( - "\n\n-------------------------------- Final Generated Response --------------------------------" + "\n\n-------------------------------- Final Generated Response --------------------------------", ) obj = vars(final_result) self._log_message(json.dumps(obj)) @@ -75,10 +73,10 @@ class GenerateQuery(BaseModel): """Generate the query.""" answer: str = Field( - description="Cypher query for graph database according to user's question." + description="Cypher query for graph database according to user's question.", ) reflection: str = Field( - description="Your reflection on the initial answer, critique of what to improve" + description="Your reflection on the initial answer, critique of what to improve", ) search_queries: list[str] = Field(description=SEARCH_QUERIES_DESCRIPTION) @@ -95,11 +93,10 @@ def __init__( self, conversation_factory: Callable, connection_args: dict[str, str], - query_lang: Optional[str] = "Cypher", - max_steps: Optional[int] = 20, + query_lang: str | None = "Cypher", + max_steps: int | None = 20, ): - """ - LLM agent reflexion framework: + r"""LLM agent reflexion framework: start -> draft -> execute tool -> revise -> evaluation -> end /|\ | @@ -110,6 +107,7 @@ def __init__( query implementation. Args: + ---- conversation_factory: function to return the Conversation to use for the LLM connection @@ -143,7 +141,7 @@ def __init__( "2. Please limit the results to a maximum of 30 items" ), ), - ] + ], ).partial(time=lambda: datetime.now().isoformat()) self.parser = JsonOutputToolsParser(return_id=True) self.connection_args = connection_args @@ -153,12 +151,7 @@ def _connect_db(self): if self.neodriver is not None: return try: - db_uri = ( - "bolt://" - + self.connection_args.get("host") - + ":" - + self.connection_args.get("port") - ) + db_uri = "bolt://" + self.connection_args.get("host") + ":" + self.connection_args.get("port") self.neodriver = nu.Driver( db_name=self.connection_args.get("db_name") or "neo4j", db_uri=db_uri, @@ -167,10 +160,10 @@ def _connect_db(self): logger.error(e) def _query_graph_database(self, query: str): - """ - Try to execute the query in Neo4j and return the result. + """Try to execute the query in Neo4j and return the result. Args: + ---- query: the query string """ @@ -182,11 +175,12 @@ def _query_graph_database(self, query: str): return [] # empty result def _create_initial_responder( - self, prompt: Optional[str] = None + self, + prompt: str | None = None, ) -> ResponderWithRetries: llm: ChatOpenAI = self.conversation.chat initial_chain = self.actor_prompt_template.partial( - instruction=prompt if prompt is not None else "" + instruction=prompt if prompt is not None else "", ) | llm.bind_tools( tools=[GenerateQuery], tool_choice="GenerateQuery", @@ -195,7 +189,8 @@ def _create_initial_responder( return ResponderWithRetries(runnable=initial_chain, validator=validator) def _create_revise_responder( - self, prompt: str | None = None + self, + prompt: str | None = None, ) -> ResponderWithRetries: revision_instruction = """ Revise your previous query using the query result and follow the guidelines: @@ -205,14 +200,15 @@ def _create_revise_responder( """ llm: ChatOpenAI = self.conversation.chat revision_chain = self.actor_prompt_template.partial( - instruction=revision_instruction + instruction=revision_instruction, ) | llm.bind_tools( tools=[ReviseQuery], tool_choice="ReviseQuery", ) validator = PydanticToolsParser(tools=[ReviseQuery]) return ResponderWithRetries( - runnable=revision_chain, validator=validator + runnable=revision_chain, + validator=validator, ) def _tool_function(self, state: list[BaseMessage]): @@ -225,11 +221,7 @@ def _tool_function(self, state: list[BaseMessage]): query = ( parsed_args[REVISED_QUERY] if REVISED_QUERY in parsed_args - else ( - parsed_args[REVISED_QUERY_DESCRIPTION] - if REVISED_QUERY_DESCRIPTION in parsed_args - else None - ) + else (parsed_args[REVISED_QUERY_DESCRIPTION] if REVISED_QUERY_DESCRIPTION in parsed_args else None) ) if query is not None: result = self._query_graph_database(query) @@ -247,10 +239,10 @@ def _tool_function(self, state: list[BaseMessage]): { "query": query, "result": result[0] if len(result) > 0 else [], - } + }, ) except Exception as e: - logger.error(f"Error occurred: {str(e)}") + logger.error(f"Error occurred: {e!s}") content = None if len(results) > 1: @@ -286,9 +278,7 @@ def _get_last_tool_results_num(state: list[BaseMessage]): for k in res.keys(): if res[k] is None: continue - if isinstance(res[k], str) and ( - res[k] == "None" or res[k] == "null" - ): + if isinstance(res[k], str) and (res[k] == "None" or res[k] == "null"): continue empty = False break @@ -316,15 +306,16 @@ def _should_continue(self, state: list[BaseMessage]): if res == END: return res score = self._get_last_score(state) - if not score is None and score >= 7: + if score is not None and score >= 7: return END query_results_num = KGQueryReflexionAgent._get_last_tool_results_num( - state + state, ) return END if query_results_num > 0 else EXECUTE_TOOL_NODE def _parse_final_result( - self, messages: list[BaseMessage] + self, + messages: list[BaseMessage], ) -> ReflexionAgentResult: output = messages[-1] result = self.parser.invoke(output)[0]["args"] diff --git a/biochatter/langgraph_agent_base.py b/biochatter/langgraph_agent_base.py index 6f140eaf..3ebabb4f 100644 --- a/biochatter/langgraph_agent_base.py +++ b/biochatter/langgraph_agent_base.py @@ -1,20 +1,20 @@ -from abc import ABC, abstractmethod -from typing import Any, List, Union, Literal, Optional -from datetime import datetime -from collections.abc import Callable import json import logging +from abc import ABC, abstractmethod +from collections.abc import Callable +from datetime import datetime +from typing import Any, Literal -from langsmith import traceable -from langgraph.graph import END, MessageGraph -from langgraph.graph.graph import CompiledGraph from langchain_core.messages import ( AIMessage, BaseMessage, - ToolMessage, HumanMessage, + ToolMessage, ) from langchain_core.pydantic_v1 import ValidationError +from langgraph.graph import END, MessageGraph +from langgraph.graph.graph import CompiledGraph +from langsmith import traceable logger = logging.getLogger(__name__) @@ -29,46 +29,35 @@ def log_step_message( node_name: str, output: BaseMessage, ): - """ - log step message + """Log step message Args: step int: step index output BaseMessage: step message """ - pass def log_final_result(self, final_result: dict[str, Any]) -> None: - """ - log final result + """Log final result Args: output BaseMessage: last step message """ - pass def _log_message( self, msg: str = "", - level: Optional[Literal["info", "error", "warn"]] = "info", + level: Literal["info", "error", "warn"] | None = "info", ): - """ - Save log message + """Save log message Args: + ---- msg: the message to be logged level: the log level to write + """ - logger_func = ( - logger.info - if level == "info" - else (logger.error if level == "error" else logger.warning) - ) + logger_func = logger.info if level == "info" else (logger.error if level == "error" else logger.warning) logger_func(msg) - self._logs = ( - self._logs - + f"[{level}]" - + f"{datetime.now().isoformat()} - {msg}\n" - ) + self._logs = self._logs + f"[{level}]" + f"{datetime.now().isoformat()} - {msg}\n" @property def logs(self): @@ -76,23 +65,21 @@ def logs(self): class ResponderWithRetries: - """ - Raise request to LLM with 3 retries - """ + """Raise request to LLM with 3 retries""" def __init__(self, runnable, validator): - """ - Args: + """Args: + ---- runnable: LLM agent validator: used to validate response + """ self.runnable = runnable self.validator = validator @traceable def respond(self, state: list[BaseMessage]): - """ - Invoke LLM agent, this function will be called by LangGraph + """Invoke LLM agent, this function will be called by LangGraph Args: state List[BaseMessage]: message history """ @@ -114,14 +101,13 @@ def respond(self, state: list[BaseMessage]): class ReflexionAgentResult: - def __init__(self, answer: str | None, tool_result: List[Any] | None): + def __init__(self, answer: str | None, tool_result: list[Any] | None): self.answer = answer self.tool_result = tool_result class ReflexionAgent(ABC): - """ - LLM agent reflexion framework: + r"""LLM agent reflexion framework: start -> draft -> execute tool -> revise -> evaluation -> end /|\ | @@ -133,13 +119,14 @@ class ReflexionAgent(ABC): def __init__( self, conversation_factory: Callable, - max_steps: Optional[int] = 20, - agent_logger: Optional[ReflexionAgentLogger] = ReflexionAgentLogger(), + max_steps: int | None = 20, + agent_logger: ReflexionAgentLogger | None = ReflexionAgentLogger(), ): - """ - Args: + """Args: + ---- conversation_factory Callable: the callback to create Conversation max_steps int: max steps for reflextion loop + """ if max_steps <= 0: max_steps = ReflexionAgent.RECURSION_LIMIT @@ -154,8 +141,7 @@ def __init__( self.agent_logger = agent_logger def _should_continue(self, state: list[BaseMessage]): - """ - Determine if we need to continue reflexion + """Determine if we need to continue reflexion Args: state List[BaseMessage]: message history """ @@ -166,62 +152,58 @@ def _should_continue(self, state: list[BaseMessage]): @abstractmethod def _tool_function(self, state: list[BaseMessage]) -> ToolMessage: - """ - tool function, execute tool based on initial draft or revised answer + """Tool function, execute tool based on initial draft or revised answer Args: state List[BaseMessage]: message history Returns: ToolMessage """ - pass @abstractmethod def _create_initial_responder( - self, prompt: Optional[str] = None + self, + prompt: str | None = None, ) -> ResponderWithRetries: - """ - draft responder, draft initial answer + """Draft responder, draft initial answer Args: prompt str: prompt for LLM to draft initial answer """ - pass @abstractmethod def _create_revise_responder( - self, prompt: Optional[str] = None + self, + prompt: str | None = None, ) -> ResponderWithRetries: - """ - revise responder, revise answer according to tool function result + """Revise responder, revise answer according to tool function result Args: prompt str: prompt for LLM to draft initial answer """ - pass @abstractmethod def _parse_final_result( - self, messages: list[BaseMessage] + self, + messages: list[BaseMessage], ) -> ReflexionAgentResult: - """ - parse the result of the last step + """Parse the result of the last step Args: output BaseMessage: last step message Returns: ReflexionAgentResult: the parsed reuslt of the last step """ - pass def get_logs(self): return self.agent_logger.logs @staticmethod def _get_num_iterations(state: list[BaseMessage]): - """ - Calculate iteration number + """Calculate iteration number Args: state List[BaseMessage]: message history - Returns: + Returns + ------- int: the iterations number + """ i = 0 for m in state[::-1]: @@ -232,9 +214,7 @@ def _get_num_iterations(state: list[BaseMessage]): @staticmethod def _get_user_question(state: list[BaseMessage]): - """ - get user's question from messages array - """ + """Get user's question from messages array""" for m in state: if not isinstance(m, HumanMessage): continue @@ -243,9 +223,7 @@ def _get_user_question(state: list[BaseMessage]): @staticmethod def _get_last_tool_result(messages: list[BaseMessage]): - """ - get result of the last tool node - """ + """Get result of the last tool node""" for m in messages[::-1]: if not isinstance(m, ToolMessage): continue @@ -253,15 +231,17 @@ def _get_last_tool_result(messages: list[BaseMessage]): return content["result"] return None - def _build_graph(self, prompt: Optional[str] = None): - """ - Build Langgraph graph for execution of chained LLM processes. + def _build_graph(self, prompt: str | None = None): + """Build Langgraph graph for execution of chained LLM processes. Args: + ---- prompt str: prompt for LLM Returns: + ------- CompiledGraph | None: a Langgraph graph or None in case of errors + """ try: self.initial_responder = self._create_initial_responder(prompt) @@ -283,17 +263,18 @@ def _build_graph(self, prompt: Optional[str] = None): def _execute_graph( self, - graph: Optional[CompiledGraph] = None, - question: Optional[str] = "", + graph: CompiledGraph | None = None, + question: str | None = "", ) -> ReflexionAgentResult: - """ - execute Langgraph graph + """Execute Langgraph graph Args: graph CompiledGraph: Langgraph graph question str: user question - Returns: + Returns + ------- answer str | None: string answer parsed from Langgraph graph execution + """ if graph is None: return None @@ -320,19 +301,23 @@ def _execute_graph( return final_result def execute( - self, question: str, prompt: Optional[str] = None + self, + question: str, + prompt: str | None = None, ) -> ReflexionAgentResult: - """ - Execute ReflexionAgent. Wrapper for building a graph and executing it, + """Execute ReflexionAgent. Wrapper for building a graph and executing it, returning the final answer. Args: + ---- question str: user question prompt str: user prompt Returns: + ------- answer str | None: If it executes successfully, an answer to the question will be returned, otherwise, it returns None + """ if len(question) == 0: return None diff --git a/biochatter/llm_connect.py b/biochatter/llm_connect.py index e375079f..db5f58a2 100644 --- a/biochatter/llm_connect.py +++ b/biochatter/llm_connect.py @@ -10,21 +10,19 @@ except ImportError: st = None -from abc import ABC, abstractmethod -from typing import Optional import json -import base64 import logging import urllib.parse +from abc import ABC, abstractmethod -from langchain_openai import ChatOpenAI, AzureChatOpenAI +import anthropic +import nltk +import openai from langchain_anthropic import ChatAnthropic -from langchain_core.messages import AIMessage, HumanMessage, SystemMessage from langchain_community.chat_models import ChatOllama from langchain_community.llms.huggingface_hub import HuggingFaceHub -import nltk -import openai -import anthropic +from langchain_core.messages import AIMessage, HumanMessage, SystemMessage +from langchain_openai import AzureChatOpenAI, ChatOpenAI from ._image import encode_image, encode_image_from_url from ._stats import get_stats @@ -59,9 +57,7 @@ class Conversation(ABC): - """ - - Use this class to set up a connection to an LLM API. Can be used to set the + """Use this class to set up a connection to an LLM API. Can be used to set the user name and API key, append specific messages for system, user, and AI roles (if available), set up the general context as well as manual and tool-based data inputs, and finally to query the API with prompts made by @@ -105,8 +101,7 @@ def set_user_name(self, user_name: str): self.user_name = user_name def set_rag_agent(self, agent: RagAgent): - """ - Update or insert rag_agent: if the rag_agent with the same mode already + """Update or insert rag_agent: if the rag_agent with the same mode already exists, it will be updated. Otherwise, the new rag_agent will be inserted. """ i, _ = self.find_rag_agent(agent.mode) @@ -124,7 +119,7 @@ def find_rag_agent(self, mode: str) -> tuple[int, RagAgent]: return -1, None @abstractmethod - def set_api_key(self, api_key: str, user: Optional[str] = None): + def set_api_key(self, api_key: str, user: str | None = None): pass def get_prompts(self): @@ -134,11 +129,12 @@ def set_prompts(self, prompts: dict): self.prompts = prompts def append_ai_message(self, message: str) -> None: - """ - Add a message from the AI to the conversation. + """Add a message from the AI to the conversation. Args: + ---- message (str): The message from the AI. + """ self.messages.append( AIMessage( @@ -147,11 +143,12 @@ def append_ai_message(self, message: str) -> None: ) def append_system_message(self, message: str) -> None: - """ - Add a system message to the conversation. + """Add a system message to the conversation. Args: + ---- message (str): The system message. + """ self.messages.append( SystemMessage( @@ -160,11 +157,12 @@ def append_system_message(self, message: str) -> None: ) def append_ca_message(self, message: str) -> None: - """ - Add a message to the correcting agent conversation. + """Add a message to the correcting agent conversation. Args: + ---- message (str): The message to the correcting agent. + """ self.ca_messages.append( SystemMessage( @@ -173,11 +171,12 @@ def append_ca_message(self, message: str) -> None: ) def append_user_message(self, message: str) -> None: - """ - Add a message from the user to the conversation. + """Add a message from the user to the conversation. Args: + ---- message (str): The message from the user. + """ self.messages.append( HumanMessage( @@ -186,27 +185,29 @@ def append_user_message(self, message: str) -> None: ) def append_image_message( - self, message: str, image_url: str, local: bool = False + self, + message: str, + image_url: str, + local: bool = False, ) -> None: - """ - Add a user message with an image to the conversation. Also checks, in + """Add a user message with an image to the conversation. Also checks, in addition to the `local` flag, if the image URL is a local file path. If it is local, the image will be encoded as a base64 string to be passed to the LLM. Args: + ---- message (str): The message from the user. image_url (str): The URL of the image. local (bool): Whether the image is local or not. If local, it will be encoded as a base64 string to be passed to the LLM. + """ parsed_url = urllib.parse.urlparse(image_url) if local or not parsed_url.netloc: image_url = f"data:image/jpeg;base64,{encode_image(image_url)}" else: - image_url = ( - f"data:image/jpeg;base64,{encode_image_from_url(image_url)}" - ) + image_url = f"data:image/jpeg;base64,{encode_image_from_url(image_url)}" self.messages.append( HumanMessage( @@ -218,9 +219,7 @@ def append_image_message( ) def setup(self, context: str): - """ - Set up the conversation with general prompts and a context. - """ + """Set up the conversation with general prompts and a context.""" for msg in self.prompts["primary_model_prompts"]: if msg: self.append_system_message(msg) @@ -247,22 +246,23 @@ def setup_data_input_tool(self, df, input_file_name: str): self.append_system_message(msg) def query(self, text: str, image_url: str = None) -> tuple[str, dict, str]: - """ - The main workflow for querying the LLM API. Appends the most recent + """The main workflow for querying the LLM API. Appends the most recent query to the conversation, optionally injects context from the RAG agent, and runs the primary query method of the child class. Args: + ---- text (str): The user query. image_url (str): The URL of an image to include in the conversation. Optional and only supported for models with vision capabilities. Returns: + ------- tuple: A tuple containing the response from the API, the token usage information, and the correction if necessary/desired. - """ + """ if not image_url: self.append_user_message(text) else: @@ -279,11 +279,7 @@ def query(self, text: str, image_url: str = None) -> tuple[str, dict, str]: if not self.correct: return (msg, token_usage, None) - cor_msg = ( - "Correcting (using single sentences) ..." - if self.split_correction - else "Correcting ..." - ) + cor_msg = "Correcting (using single sentences) ..." if self.split_correction else "Correcting ..." if st: with st.spinner(cor_msg): @@ -306,12 +302,12 @@ def _correct_query(self, msg: str): for sentence in sentences: correction = self._correct_response(sentence) - if not str(correction).lower() in ["ok", "ok."]: + if str(correction).lower() not in ["ok", "ok."]: corrections.append(correction) else: correction = self._correct_response(msg) - if not str(correction).lower() in ["ok", "ok."]: + if str(correction).lower() not in ["ok", "ok."]: corrections.append(correction) return corrections @@ -325,15 +321,12 @@ def _correct_response(self, msg: str): pass def _inject_context_by_ragagent_selector(self, text: str): - """ - Inject the context generated by RagAgentSelector, which will choose appropriate + """Inject the context generated by RagAgentSelector, which will choose appropriate rag agent to generate context according to user's question Args: text (str): The user query to be used for choosing rag agent """ - rag_agents: list[RagAgent] = [ - agent for agent in self.rag_agents if agent.use_prompt - ] + rag_agents: list[RagAgent] = [agent for agent in self.rag_agents if agent.use_prompt] decider_agent = RagAgentSelector( rag_agents=rag_agents, conversation_factory=lambda: self, @@ -343,16 +336,15 @@ def _inject_context_by_ragagent_selector(self, text: str): return result.tool_result # find rag agent selected rag_agent = next( - [agent for agent in rag_agents if agent.mode == result.answer], None + [agent for agent in rag_agents if agent.mode == result.answer], + None, ) if rag_agent is None: return None return rag_agent.generate_responses(text) def _inject_context(self, text: str): - """ - - Inject the context received from the RAG agent into the prompt. The RAG + """Inject the context received from the RAG agent into the prompt. The RAG agent will find the most similar n text fragments and add them to the message history object for usage in the next prompt. Uses the document summarisation prompt set to inject the context. The ultimate prompt @@ -360,10 +352,11 @@ def _inject_context(self, text: str): for formatting the string). Args: + ---- text (str): The user query to be used for similarity search. - """ - sim_msg = f"Performing similarity search to inject fragments ..." + """ + sim_msg = "Performing similarity search to inject fragments ..." if st: with st.spinner(sim_msg): @@ -397,35 +390,37 @@ def _inject_context(self, text: str): # if last prompt, format the statements into the prompt if i == len(prompts) - 1: self.append_system_message( - prompt.format(statements=statements) + prompt.format(statements=statements), ) else: self.append_system_message(prompt) def get_last_injected_context(self) -> list[dict]: - """ - Get a formatted list of the last context injected into the + """Get a formatted list of the last context injected into the conversation. Contains one dictionary for each RAG mode. - Returns: + Returns + ------- List[dict]: A list of dictionaries containing the mode and context for each RAG agent. + """ last_context = [] for agent in self.rag_agents: last_context.append( - {"mode": agent.mode, "context": agent.last_response} + {"mode": agent.mode, "context": agent.last_response}, ) return last_context def get_msg_json(self) -> str: - """ - Return a JSON representation (of a list of dicts) of the messages in + """Return a JSON representation (of a list of dicts) of the messages in the conversation. The keys of the dicts are the roles, the values are the messages. - Returns: + Returns + ------- str: A JSON representation of the messages in the conversation. + """ d = [] for msg in self.messages: @@ -443,10 +438,7 @@ def get_msg_json(self) -> str: return json.dumps(d) def reset(self): - """ - Resets the conversation to the initial state. - """ - + """Resets the conversation to the initial state.""" self.history = [] self.messages = [] self.ca_messages = [] @@ -461,9 +453,7 @@ def __init__( correct: bool = False, split_correction: bool = False, ): - """ - - This class is used to return the complete query as a string to be used + """This class is used to return the complete query as a string to be used in the frontend running the wasm model. It does not call the API itself, but updates the message history similarly to the other conversation classes. It overrides the `query` method from the `Conversation` class @@ -480,16 +470,18 @@ def __init__( ) def query(self, text: str) -> tuple: - """ - Return the entire message history as a single string. This is the + """Return the entire message history as a single string. This is the message that is sent to the wasm model. Args: + ---- text (str): The user query. Returns: + ------- tuple: A tuple containing the message history as a single string, and `None` for the second and third elements of the tuple. + """ self.append_user_message(text) @@ -498,22 +490,17 @@ def query(self, text: str) -> tuple: return (self._primary_query(), None, None) def _primary_query(self): - """ - Concatenate all messages in the conversation into a single string and + """Concatenate all messages in the conversation into a single string and return it. Currently discards information about roles (system, user). """ return "\n".join([m.content for m in self.messages]) def _correct_response(self, msg: str): - """ - This method is not used for the wasm model. - """ + """This method is not used for the wasm model.""" return "ok" def set_api_key(self, api_key: str, user: str | None = None): - """ - This method is not used for the wasm model. - """ + """This method is not used for the wasm model.""" return True @@ -526,15 +513,13 @@ def __init__( correct: bool = False, split_correction: bool = False, ): - """ - - Connect to an open-source LLM via the Xinference client library and set + """Connect to an open-source LLM via the Xinference client library and set up a conversation with the user. Also initialise a second conversational agent to provide corrections to the model output, if necessary. Args: - + ---- base_url (str): The base URL of the Xinference instance (should not include the /v1 part). @@ -597,13 +582,14 @@ def load_models(self): # return names def append_system_message(self, message: str): - """ - We override the system message addition because Xinference does not + """We override the system message addition because Xinference does not accept multiple system messages. We concatenate them if there are multiple. Args: + ---- message (str): The message to append. + """ # if there is not already a system message in self.messages if not any(isinstance(m, SystemMessage) for m in self.messages): @@ -620,9 +606,7 @@ def append_system_message(self, message: str): break def append_ca_message(self, message: str): - """ - - We also override the system message addition for the correcting agent, + """We also override the system message addition for the correcting agent, likewise because Xinference does not accept multiple system messages. We concatenate them if there are multiple. @@ -630,7 +614,9 @@ def append_ca_message(self, message: str): as the primary one. Args: + ---- message (str): The message to append. + """ # if there is not already a system message in self.messages if not any(isinstance(m, SystemMessage) for m in self.ca_messages): @@ -647,9 +633,7 @@ def append_ca_message(self, message: str): break def _primary_query(self): - """ - - Query the Xinference client API with the user's message and return the + """Query the Xinference client API with the user's message and return the response using the message history (flattery system messages, prior conversation) as context. Correct the response if necessary. @@ -660,8 +644,8 @@ def _primary_query(self): should be embedded into the first user message.' (from https://discuss.huggingface.co/t/issue-with-llama-2-chat-template-and-out-of-date-documentation/61645/3) - Returns: - + Returns + ------- tuple: A tuple containing the response from the Xinference API (formatted similarly to responses from the OpenAI API) and the token usage. @@ -706,17 +690,12 @@ def _primary_query(self): def _create_history(self): history = [] # extract text components from message contents - msg_texts = [ - m.content[0]["text"] if isinstance(m.content, list) else m.content - for m in self.messages - ] + msg_texts = [m.content[0]["text"] if isinstance(m.content, list) else m.content for m in self.messages] # check if last message is an image message is_image_message = False if isinstance(self.messages[-1].content, list): - is_image_message = ( - self.messages[-1].content[1]["type"] == "image_url" - ) + is_image_message = self.messages[-1].content[1]["type"] == "image_url" # find location of last AI message (if any) last_ai_message = None @@ -730,16 +709,16 @@ def _create_history(self): { "role": "user", "content": "\n".join( - [m for m in msg_texts[:last_ai_message]] + [m for m in msg_texts[:last_ai_message]], ), - } + }, ) # then append the last AI message history.append( { "role": "assistant", "content": msg_texts[last_ai_message], - } + }, ) # then concatenate all messages after that @@ -748,9 +727,9 @@ def _create_history(self): { "role": "user", "content": "\n".join( - [m for m in msg_texts[last_ai_message + 1 :]] + [m for m in msg_texts[last_ai_message + 1 :]], ), - } + }, ) # if there is no AI message, concatenate all messages into one user @@ -760,7 +739,7 @@ def _create_history(self): { "role": "user", "content": "\n".join([m for m in msg_texts[:]]), - } + }, ) # if the last message is an image message, add the image to the history @@ -770,24 +749,25 @@ def _create_history(self): { "type": "image_url", "image_url": { - "url": self.messages[-1].content[1]["image_url"]["url"] + "url": self.messages[-1].content[1]["image_url"]["url"], }, }, ] return history def _correct_response(self, msg: str): - """ - - Correct the response from the Xinference API by sending it to a + """Correct the response from the Xinference API by sending it to a secondary language model. Optionally split the response into single sentences and correct each sentence individually. Update usage stats. Args: + ---- msg (str): The response from the model. Returns: + ------- str: The corrected response (or OK if no correction necessary). + """ ca_messages = self.ca_messages.copy() ca_messages.append( @@ -797,8 +777,7 @@ def _correct_response(self, msg: str): ) ca_messages.append( SystemMessage( - content="If there is nothing to correct, please respond " - "with just 'OK', and nothing else!", + content="If there is nothing to correct, please respond with just 'OK', and nothing else!", ), ) history = [] @@ -824,42 +803,42 @@ def _correct_response(self, msg: str): return correction def _update_usage_stats(self, model: str, token_usage: dict): - """ - Update redis database with token usage statistics using the usage_stats + """Update redis database with token usage statistics using the usage_stats object with the increment method. Args: + ---- model (str): The model name. token_usage (dict): The token usage statistics. + """ - pass def set_api_key(self) -> bool: - """ - Try to get the Xinference model from the client API. If the model is + """Try to get the Xinference model from the client API. If the model is found, initialise the conversational agent. If the model is not found, `get_model` will raise a RuntimeError. - Returns: + Returns + ------- bool: True if the model is found, False otherwise. - """ + """ try: if self.model_name is None or self.model_name == "auto": self.model_name = self.list_models_by_type("chat")[0] self.model = self.client.get_model( - self.models[self.model_name]["id"] + self.models[self.model_name]["id"], ) if self.ca_model_name is None or self.ca_model_name == "auto": self.ca_model_name = self.list_models_by_type("chat")[0] self.ca_model = self.client.get_model( - self.models[self.ca_model_name]["id"] + self.models[self.ca_model_name]["id"], ) return True - except RuntimeError as e: + except RuntimeError: # TODO handle error, log? return False @@ -883,7 +862,7 @@ def list_models_by_type(self, type: str): class OllamaConversation(Conversation): - def set_api_key(self, api_key: str, user: Optional[str] = None): + def set_api_key(self, api_key: str, user: str | None = None): pass def __init__( @@ -894,15 +873,13 @@ def __init__( correct: bool = False, split_correction: bool = False, ): - """ - - Connect to an Ollama LLM via the Ollama/Langchain library and set + """Connect to an Ollama LLM via the Ollama/Langchain library and set up a conversation with the user. Also initialise a second conversational agent to provide corrections to the model output, if necessary. Args: - + ---- base_url (str): The base URL of the Ollama instance. prompts (dict): A dictionary of prompts to use for the conversation. @@ -924,23 +901,28 @@ def __init__( ) self.model_name = model_name self.model = ChatOllama( - base_url=base_url, model=self.model_name, temperature=0.0 + base_url=base_url, + model=self.model_name, + temperature=0.0, ) self.ca_model_name = "mixtral:latest" self.ca_model = ChatOllama( - base_url=base_url, model_name=self.ca_model_name, temperature=0.0 + base_url=base_url, + model_name=self.ca_model_name, + temperature=0.0, ) def append_system_message(self, message: str): - """ - We override the system message addition because Ollama does not + """We override the system message addition because Ollama does not accept multiple system messages. We concatenate them if there are multiple. Args: + ---- message (str): The message to append. + """ # if there is not already a system message in self.messages if not any(isinstance(m, SystemMessage) for m in self.messages): @@ -957,9 +939,7 @@ def append_system_message(self, message: str): break def append_ca_message(self, message: str): - """ - - We also override the system message addition for the correcting agent, + """We also override the system message addition for the correcting agent, likewise because Ollama does not accept multiple system messages. We concatenate them if there are multiple. @@ -967,7 +947,9 @@ def append_ca_message(self, message: str): as the primary one. Args: + ---- message (str): The message to append. + """ # if there is not already a system message in self.messages if not any(isinstance(m, SystemMessage) for m in self.ca_messages): @@ -984,14 +966,12 @@ def append_ca_message(self, message: str): break def _primary_query(self): - """ - - Query the Ollama client API with the user's message and return the + """Query the Ollama client API with the user's message and return the response using the message history (flattery system messages, prior conversation) as context. Correct the response if necessary. - Returns: - + Returns + ------- tuple: A tuple containing the response from the Ollama API (formatted similarly to responses from the OpenAI API) and the token usage. @@ -1000,7 +980,7 @@ def _primary_query(self): try: messages = self._create_history(self.messages) response = self.model.invoke( - messages + messages, # ,generate_config={"max_tokens": 2048, "temperature": 0}, ) except ( @@ -1043,17 +1023,18 @@ def _create_history(self, messages): return history def _correct_response(self, msg: str): - """ - - Correct the response from the Ollama API by sending it to a + """Correct the response from the Ollama API by sending it to a secondary language model. Optionally split the response into single sentences and correct each sentence individually. Update usage stats. Args: + ---- msg (str): The response from the model. Returns: + ------- str: The corrected response (or OK if no correction necessary). + """ ca_messages = self.ca_messages.copy() ca_messages.append( @@ -1063,12 +1044,11 @@ def _correct_response(self, msg: str): ) ca_messages.append( SystemMessage( - content="If there is nothing to correct, please respond " - "with just 'OK', and nothing else!", + content="If there is nothing to correct, please respond with just 'OK', and nothing else!", ), ) response = self.ca_model.invoke( - chat_history=self._create_history(self.messages) + chat_history=self._create_history(self.messages), ).dict() correction = response["content"] token_usage = response["eval_count"] @@ -1078,16 +1058,16 @@ def _correct_response(self, msg: str): return correction def _update_usage_stats(self, model: str, token_usage: dict): - """ - Update redis database with token usage statistics using the usage_stats + """Update redis database with token usage statistics using the usage_stats object with the increment method. Args: + ---- model (str): The model name. token_usage (dict): The token usage statistics. + """ - pass class AnthropicConversation(Conversation): @@ -1098,12 +1078,12 @@ def __init__( correct: bool = False, split_correction: bool = False, ): - """ - Connect to Anthropic's API and set up a conversation with the user. + """Connect to Anthropic's API and set up a conversation with the user. Also initialise a second conversational agent to provide corrections to the model output, if necessary. Args: + ---- model_name (str): The name of the model to use. prompts (dict): A dictionary of prompts to use for the conversation. @@ -1111,6 +1091,7 @@ def __init__( split_correction (bool): Whether to correct the model output by splitting the output into sentences and correcting each sentence individually. + """ super().__init__( model_name=model_name, @@ -1123,17 +1104,19 @@ def __init__( # TODO make accessible by drop-down def set_api_key(self, api_key: str, user: str) -> bool: - """ - Set the API key for the Anthropic API. If the key is valid, initialise the + """Set the API key for the Anthropic API. If the key is valid, initialise the conversational agent. Set the user for usage statistics. Args: + ---- api_key (str): The API key for the Anthropic API. user (str): The user for usage statistics. Returns: + ------- bool: True if the API key is valid, False otherwise. + """ client = anthropic.Anthropic( api_key=api_key, @@ -1157,18 +1140,19 @@ def set_api_key(self, api_key: str, user: str) -> bool: return True - except anthropic._exceptions.AuthenticationError as e: + except anthropic._exceptions.AuthenticationError: return False def _primary_query(self): - """ - Query the Anthropic API with the user's message and return the response + """Query the Anthropic API with the user's message and return the response using the message history (flattery system messages, prior conversation) as context. Correct the response if necessary. - Returns: + Returns + ------- tuple: A tuple containing the response from the Anthropic API and the token usage. + """ try: history = self._create_history() @@ -1201,17 +1185,12 @@ def _primary_query(self): def _create_history(self): history = [] # extract text components from message contents - msg_texts = [ - m.content[0]["text"] if isinstance(m.content, list) else m.content - for m in self.messages - ] + msg_texts = [m.content[0]["text"] if isinstance(m.content, list) else m.content for m in self.messages] # check if last message is an image message is_image_message = False if isinstance(self.messages[-1].content, list): - is_image_message = ( - self.messages[-1].content[1]["type"] == "image_url" - ) + is_image_message = self.messages[-1].content[1]["type"] == "image_url" # find location of last AI message (if any) last_ai_message = None @@ -1220,9 +1199,7 @@ def _create_history(self): last_ai_message = i # Aggregate system messages into one message at the beginning - system_messages = [ - m.content for m in self.messages if isinstance(m, SystemMessage) - ] + system_messages = [m.content for m in self.messages if isinstance(m, SystemMessage)] if system_messages: history.append( SystemMessage(content="\n".join(system_messages)), @@ -1247,7 +1224,7 @@ def _create_history(self): history.append( HumanMessage( content="\n".join( - [m for m in msg_texts[last_ai_message + 1 :]] + [m for m in msg_texts[last_ai_message + 1 :]], ), ), ) @@ -1261,7 +1238,7 @@ def _create_history(self): history.append( HumanMessage( content="\n".join( - [m for m in msg_texts[last_system_message + 1 :]] + [m for m in msg_texts[last_system_message + 1 :]], ), ), ) @@ -1273,23 +1250,25 @@ def _create_history(self): { "type": "image_url", "image_url": { - "url": self.messages[-1].content[1]["image_url"]["url"] + "url": self.messages[-1].content[1]["image_url"]["url"], }, }, ] return history def _correct_response(self, msg: str): - """ - Correct the response from the Anthropic API by sending it to a secondary + """Correct the response from the Anthropic API by sending it to a secondary language model. Optionally split the response into single sentences and correct each sentence individually. Update usage stats. Args: + ---- msg (str): The response from the Anthropic API. Returns: + ------- str: The corrected response (or OK if no correction necessary). + """ ca_messages = self.ca_messages.copy() ca_messages.append( @@ -1299,8 +1278,7 @@ def _correct_response(self, msg: str): ) ca_messages.append( SystemMessage( - content="If there is nothing to correct, please respond " - "with just 'OK', and nothing else!", + content="If there is nothing to correct, please respond with just 'OK', and nothing else!", ), ) @@ -1321,12 +1299,12 @@ def __init__( split_correction: bool = False, base_url: str = None, ): - """ - Connect to OpenAI's GPT API and set up a conversation with the user. + """Connect to OpenAI's GPT API and set up a conversation with the user. Also initialise a second conversational agent to provide corrections to the model output, if necessary. Args: + ---- model_name (str): The name of the model to use. prompts (dict): A dictionary of prompts to use for the conversation. @@ -1337,6 +1315,7 @@ def __init__( base_url (str): Optional OpenAI base_url value to use custom endpoint URL instead of default + """ super().__init__( model_name=model_name, @@ -1349,17 +1328,19 @@ def __init__( # TODO make accessible by drop-down def set_api_key(self, api_key: str, user: str) -> bool: - """ - Set the API key for the OpenAI API. If the key is valid, initialise the + """Set the API key for the OpenAI API. If the key is valid, initialise the conversational agent. Set the user for usage statistics. Args: + ---- api_key (str): The API key for the OpenAI API. user (str): The user for usage statistics. Returns: + ------- bool: True if the API key is valid, False otherwise. + """ client = openai.OpenAI( api_key=api_key, @@ -1386,18 +1367,19 @@ def set_api_key(self, api_key: str, user: str) -> bool: return True - except openai._exceptions.AuthenticationError as e: + except openai._exceptions.AuthenticationError: return False def _primary_query(self): - """ - Query the OpenAI API with the user's message and return the response + """Query the OpenAI API with the user's message and return the response using the message history (flattery system messages, prior conversation) as context. Correct the response if necessary. - Returns: + Returns + ------- tuple: A tuple containing the response from the OpenAI API and the token usage. + """ try: response = self.chat.generate([self.messages]) @@ -1429,16 +1411,18 @@ def _primary_query(self): return msg, token_usage def _correct_response(self, msg: str): - """ - Correct the response from the OpenAI API by sending it to a secondary + """Correct the response from the OpenAI API by sending it to a secondary language model. Optionally split the response into single sentences and correct each sentence individually. Update usage stats. Args: + ---- msg (str): The response from the OpenAI API. Returns: + ------- str: The corrected response (or OK if no correction necessary). + """ ca_messages = self.ca_messages.copy() ca_messages.append( @@ -1448,8 +1432,7 @@ def _correct_response(self, msg: str): ) ca_messages.append( SystemMessage( - content="If there is nothing to correct, please respond " - "with just 'OK', and nothing else!", + content="If there is nothing to correct, please respond with just 'OK', and nothing else!", ), ) @@ -1463,18 +1446,19 @@ def _correct_response(self, msg: str): return correction def _update_usage_stats(self, model: str, token_usage: dict): - """ - Update redis database with token usage statistics using the usage_stats + """Update redis database with token usage statistics using the usage_stats object with the increment method. Args: + ---- model (str): The model name. token_usage (dict): The token usage statistics. + """ if self.user == "community": self.usage_stats.increment( - f"usage:[date]:[user]", + "usage:[date]:[user]", {f"{k}:{model}": v for k, v in token_usage.items()}, ) @@ -1487,14 +1471,15 @@ def __init__( prompts: dict, correct: bool = False, split_correction: bool = False, - version: Optional[str] = None, - base_url: Optional[str] = None, + version: str | None = None, + base_url: str | None = None, ): - """ - Connect to Azure's GPT API and set up a conversation with the user. + """Connect to Azure's GPT API and set up a conversation with the user. + Extends GptConversation. Args: + ---- deployment_name (str): The name of the Azure deployment to use. model_name (str): The name of the model to use. This is distinct @@ -1511,6 +1496,7 @@ def __init__( version (str): The version of the Azure API to use. base_url (str): The base URL of the Azure API to use. + """ super().__init__( model_name=model_name, @@ -1523,18 +1509,21 @@ def __init__( self.base_url = base_url self.deployment_name = deployment_name - def set_api_key(self, api_key: str, user: Optional[str] = None) -> bool: - """ - Set the API key for the Azure API. If the key is valid, initialise the - conversational agent. No user stats on Azure. + def set_api_key(self, api_key: str) -> bool: + """Set the API key for the Azure API. + + If the key is valid, initialise the conversational agent. No user stats + on Azure. Args: + ---- api_key (str): The API key for the Azure API. Returns: + ------- bool: True if the API key is valid, False otherwise. - """ + """ try: self.chat = AzureChatOpenAI( deployment_name=self.deployment_name, @@ -1559,13 +1548,11 @@ def set_api_key(self, api_key: str, user: Optional[str] = None) -> bool: return True - except openai._exceptions.AuthenticationError as e: + except openai._exceptions.AuthenticationError: return False def _update_usage_stats(self, model: str, token_usage: dict): - """ - We do not track usage stats for Azure. - """ + """We do not track usage stats for Azure.""" return @@ -1576,9 +1563,7 @@ def __init__( prompts: dict, split_correction: bool, ): - """ - DEPRECATED: Superceded by XinferenceConversation. - """ + """DEPRECATED: Superceded by XinferenceConversation.""" super().__init__( model_name=model_name, prompts=prompts, @@ -1587,7 +1572,7 @@ def __init__( self.messages = [] - def set_api_key(self, api_key: str, user: Optional[str] = None): + def set_api_key(self, api_key: str, user: str | None = None): self.chat = HuggingFaceHub( repo_id=self.model_name, model_kwargs={"temperature": 1.0}, # "regular sampling" @@ -1598,13 +1583,11 @@ def set_api_key(self, api_key: str, user: Optional[str] = None): try: self.chat.generate(["Hello, I am a biomedical researcher."]) return True - except ValueError as e: + except ValueError: return False def _cast_messages(self, messages): - """ - Render the different roles of the chat-based conversation as plain text. - """ + """Render the different roles of the chat-based conversation as plain text.""" cast = "" for m in messages: if isinstance(m, SystemMessage): diff --git a/biochatter/podcast.py b/biochatter/podcast.py index 8ad5af26..eed04103 100644 --- a/biochatter/podcast.py +++ b/biochatter/podcast.py @@ -1,10 +1,10 @@ -from collections import deque import os +from collections import deque +import nltk from gtts import gTTS -from openai import OpenAI from langchain.schema import Document -import nltk +from openai import OpenAI from .llm_connect import GptConversation @@ -46,18 +46,17 @@ def __init__( document: Document, model_name: str = "gpt-3.5-turbo", ) -> None: - """ - Orchestrates the podcasting of a document. - """ + """Orchestrates the podcasting of a document.""" self.document = document self.model_name = model_name def generate_podcast(self, characters_per_paragraph: int) -> None: - """ - Podcasts the document. + """Podcasts the document. - TODO: + Todo: + ---- - chain of density prompting for variable summary length + """ full_text = self.document[0].page_content @@ -91,22 +90,22 @@ def generate_podcast(self, characters_per_paragraph: int) -> None: # summarise the summaries def _split_text(self, text: str) -> list[str]: - """ - Splits consecutive text into sentences. - """ + """Splits consecutive text into sentences.""" nltk.download("punkt") tokenizer = nltk.data.load("tokenizers/punkt/english.pickle") return tokenizer.tokenize(text) def _title_and_authors(self, text: str) -> str: - """ - Extracts title and authors from document. + """Extracts title and authors from document. Args: + ---- text (str): text to extract title and authors from Returns: + ------- str: title and authors + """ # first sentence - extract title, authors c_first = GptConversation( @@ -126,17 +125,19 @@ def _title_and_authors(self, text: str) -> str: return "A podcast by biochatter." def _process_section(self, text: str, summarise: bool = False) -> str: - """ - Processes a section of the document. Summarises if summarise is True, + """Processes a section of the document. Summarises if summarise is True, otherwise just makes the text more listenable. Args: + ---- text (str): text to summarise summarise (bool): whether to summarise the text Returns: + ------- str: summarised text + """ # summarise section c = GptConversation( @@ -153,21 +154,24 @@ def _process_section(self, text: str, summarise: bool = False) -> str: return msg def _process_sections( - self, sentences: list, characters_per_paragraph: int + self, + sentences: list, + characters_per_paragraph: int, ) -> list: - """ - - Processes sections of the document. Concatenates sentences until + """Processes sections of the document. Concatenates sentences until characters_per_paragraph is reached, removing each sentence from the list as it is added to the section to be processed. Args: + ---- sentences (list): list of sentences to summarise characters_per_paragraph (int): number of characters per paragraph Returns: + ------- list: list of processed sections + """ summarised_sections = [] section = "" @@ -181,10 +185,7 @@ def _process_sections( sentences.insert(0, sentence) summarised_section = self._process_section(section) # filter "no content" sections - if not ( - "no content" in summarised_section.lower() - and len(summarised_section) < 30 - ): + if not ("no content" in summarised_section.lower() and len(summarised_section) < 30): summarised_sections.append(summarised_section) section = "" @@ -196,10 +197,10 @@ def podcast_to_file( model: str = "gtts", voice: str = "alloy", ) -> None: - """ - Uses text-to-speech to generate audio for the summarised paper podcast. + """Uses text-to-speech to generate audio for the summarised paper podcast. Args: + ---- path (str): path to save audio file to model (str): model to use for text-to-speech. Currently supported: @@ -209,8 +210,8 @@ def podcast_to_file( voice (str): voice to use for text-to-speech. See OpenAI API documentation for available voices. - """ + """ full_text = self.podcast_to_text() if model == "gtts": @@ -224,10 +225,7 @@ def podcast_to_file( model=model, voice=voice, input=( - "You are listening to: \n\n" - + self.podcast_info - + "\n\n" - + " Text-to-speech generated by OpenAI." + "You are listening to: \n\n" + self.podcast_info + "\n\n" + " Text-to-speech generated by OpenAI." ), ) first_path = path.rsplit(".", 1)[0] + "_0.mp3" @@ -242,7 +240,7 @@ def podcast_to_file( nltk.download("punkt") tokenizer = nltk.data.load("tokenizers/punkt/english.pickle") sentences = deque( - tokenizer.tokenize(full_text) + tokenizer.tokenize(full_text), ) # Use a deque instead of a list # Split the text into sections by filling one section until it @@ -263,7 +261,7 @@ def podcast_to_file( # Last section: conclude the podcast sections.append( - f"This was {self.podcast_info}. Thank you for listening." + f"This was {self.podcast_info}. Thank you for listening.", ) # Save each section to a separate file with an integer suffix @@ -278,9 +276,7 @@ def podcast_to_file( response.stream_to_file(section_path) def podcast_to_text(self): - """ - Returns the summarised paper podcast as text. - """ + """Returns the summarised paper podcast as text.""" full_text = "You are listening to: " + self.podcast_info + "\n\n" for section in self.processed_sections: full_text += section + "\n\n" diff --git a/biochatter/prompts.py b/biochatter/prompts.py index 34c4b6db..6c0c6208 100644 --- a/biochatter/prompts.py +++ b/biochatter/prompts.py @@ -1,7 +1,6 @@ -from typing import Optional -from collections.abc import Callable -import os import json +import os +from collections.abc import Callable import yaml @@ -12,14 +11,12 @@ class BioCypherPromptEngine: def __init__( self, - schema_config_or_info_path: Optional[str] = None, - schema_config_or_info_dict: Optional[dict] = None, + schema_config_or_info_path: str | None = None, + schema_config_or_info_dict: dict | None = None, model_name: str = "gpt-3.5-turbo", - conversation_factory: Optional[Callable] = None, + conversation_factory: Callable | None = None, ) -> None: - """ - - Given a biocypher schema configuration, extract the entities and + """Given a biocypher schema configuration, extract the entities and relationships, and for each extract their mode of representation (node or edge), properties, and identifier namespace. Using these data, allow the generation of prompts for a large language model, informing it of @@ -27,6 +24,7 @@ def __init__( parameterisation of function calls to a knowledge graph. Args: + ---- schema_config_or_info_path: Path to a biocypher schema configuration file or the extended schema information output generated by BioCypher's `write_schema_info` function (preferred). @@ -43,30 +41,25 @@ def __init__( creating the KG query. If not provided, a default function is used (creating an OpenAI conversation with the specified model, see `_get_conversation`). - """ + """ if not schema_config_or_info_path and not schema_config_or_info_dict: raise ValueError( - "Please provide the schema configuration or schema info as a " - "path to a file or as a dictionary." + "Please provide the schema configuration or schema info as a path to a file or as a dictionary.", ) if schema_config_or_info_path and schema_config_or_info_dict: raise ValueError( "Please provide the schema configuration or schema info as a " - "path to a file or as a dictionary, not both." + "path to a file or as a dictionary, not both.", ) # set conversation factory or use default - self.conversation_factory = ( - conversation_factory - if conversation_factory is not None - else self._get_conversation - ) + self.conversation_factory = conversation_factory if conversation_factory is not None else self._get_conversation if schema_config_or_info_path: # read the schema configuration - with open(schema_config_or_info_path, "r") as f: + with open(schema_config_or_info_path) as f: schema_config = yaml.safe_load(f) elif schema_config_or_info_dict: schema_config = schema_config_or_info_dict @@ -82,26 +75,14 @@ def __init__( if not is_schema_info: for key, value in schema_config.items(): # hacky, better with biocypher output - name_indicates_relationship = ( - "interaction" in key.lower() or "association" in key.lower() - ) + name_indicates_relationship = "interaction" in key.lower() or "association" in key.lower() if "represented_as" in value: - if ( - value["represented_as"] == "node" - and not name_indicates_relationship - ): + if value["represented_as"] == "node" and not name_indicates_relationship: self.entities[sentencecase_to_pascalcase(key)] = value - elif ( - value["represented_as"] == "node" - and name_indicates_relationship - ): - self.relationships[sentencecase_to_pascalcase(key)] = ( - value - ) - elif value["represented_as"] == "edge": - self.relationships[sentencecase_to_pascalcase(key)] = ( - value - ) + elif (value["represented_as"] == "node" and name_indicates_relationship) or value[ + "represented_as" + ] == "edge": + self.relationships[sentencecase_to_pascalcase(key)] = value else: for key, value in schema_config.items(): if not isinstance(value, dict): @@ -124,57 +105,50 @@ def __init__( self.model_name = model_name def _capitalise_source_and_target(self, relationship: dict) -> dict: - """ - Make sources and targets PascalCase to match the entities. Sources and + """Make sources and targets PascalCase to match the entities. Sources and targets can be strings or lists of strings. """ if "source" in relationship: if isinstance(relationship["source"], str): relationship["source"] = sentencecase_to_pascalcase( - relationship["source"] + relationship["source"], ) elif isinstance(relationship["source"], list): - relationship["source"] = [ - sentencecase_to_pascalcase(s) - for s in relationship["source"] - ] + relationship["source"] = [sentencecase_to_pascalcase(s) for s in relationship["source"]] if "target" in relationship: if isinstance(relationship["target"], str): relationship["target"] = sentencecase_to_pascalcase( - relationship["target"] + relationship["target"], ) elif isinstance(relationship["target"], list): - relationship["target"] = [ - sentencecase_to_pascalcase(t) - for t in relationship["target"] - ] + relationship["target"] = [sentencecase_to_pascalcase(t) for t in relationship["target"]] return relationship def _select_graph_entities_from_question( - self, question: str, conversation: Conversation + self, + question: str, + conversation: Conversation, ) -> str: conversation.reset() success1 = self._select_entities( - question=question, conversation=conversation + question=question, + conversation=conversation, ) if not success1: raise ValueError( - "Entity selection failed. Please try again with a different " - "question." + "Entity selection failed. Please try again with a different question.", ) conversation.reset() success2 = self._select_relationships(conversation=conversation) if not success2: raise ValueError( - "Relationship selection failed. Please try again with a " - "different question." + "Relationship selection failed. Please try again with a different question.", ) conversation.reset() success3 = self._select_properties(conversation=conversation) if not success3: raise ValueError( - "Property selection failed. Please try again with a different " - "question." + "Property selection failed. Please try again with a different question.", ) def _generate_query_prompt( @@ -182,13 +156,13 @@ def _generate_query_prompt( entities: list, relationships: dict, properties: dict, - query_language: Optional[str] = "Cypher", + query_language: str | None = "Cypher", ) -> str: - """ - Generate a prompt for a large language model to generate a database + """Generate a prompt for a large language model to generate a database query based on the selected entities, relationships, and properties. Args: + ---- entities: A list of entities that are relevant to the question. relationships: A list of relationships that are relevant to the @@ -200,7 +174,9 @@ def _generate_query_prompt( query_language: The language of the query to generate. Returns: + ------- A prompt for a large language model to generate a database query. + """ msg = ( f"Generate a database query in {query_language} that answers " @@ -224,23 +200,28 @@ def _generate_query_prompt( return msg def generate_query_prompt( - self, question: str, query_language: Optional[str] = "Cypher" + self, + question: str, + query_language: str | None = "Cypher", ) -> str: - """ - Generate a prompt for a large language model to generate a database + """Generate a prompt for a large language model to generate a database query based on the user's question and class attributes informing about the schema. Args: + ---- question: A user's question. query_language: The language of the query to generate. Returns: + ------- A prompt for a large language model to generate a database query. + """ self._select_graph_entities_from_question( - question, self.conversation_factory() + question, + self.conversation_factory(), ) msg = self._generate_query_prompt( self.selected_entities, @@ -251,23 +232,27 @@ def generate_query_prompt( return msg def generate_query( - self, question: str, query_language: Optional[str] = "Cypher" + self, + question: str, + query_language: str | None = "Cypher", ) -> str: - """ - Wrap entity and property selection and query generation; return the + """Wrap entity and property selection and query generation; return the generated query. Args: + ---- question: A user's question. query_language: The language of the query to generate. Returns: + ------- A database query that could answer the user's question. - """ + """ self._select_graph_entities_from_question( - question, self.conversation_factory() + question, + self.conversation_factory(), ) return self._generate_query( @@ -280,62 +265,65 @@ def generate_query( ) def _get_conversation( - self, model_name: Optional[str] = None + self, + model_name: str | None = None, ) -> "Conversation": - """ - Create a conversation object given a model name. + """Create a conversation object given a model name. Args: + ---- model_name: The name of the model to use for the conversation. Returns: + ------- A BioChatter Conversation object for connecting to the LLM. Todo: + ---- Genericise to models outside of OpenAI. - """ + """ conversation = GptConversation( model_name=model_name or self.model_name, prompts={}, correct=False, ) conversation.set_api_key( - api_key=os.getenv("OPENAI_API_KEY"), user="test_user" + api_key=os.getenv("OPENAI_API_KEY"), + user="test_user", ) return conversation def _select_entities( - self, question: str, conversation: "Conversation" + self, + question: str, + conversation: "Conversation", ) -> bool: - """ - - Given a question, select the entities that are relevant to the question + """Given a question, select the entities that are relevant to the question and store them in `selected_entities` and `selected_relationships`. Use LLM conversation to do this. Args: + ---- question: A user's question. conversation: A BioChatter Conversation object for connecting to the LLM. Returns: + ------- True if at least one entity was selected, False otherwise. """ - self.question = question conversation.append_system_message( - ( - "You have access to a knowledge graph that contains " - f"these entity types: {', '.join(self.entities)}. Your task is " - "to select the entity types that are relevant to the user's question " - "for subsequent use in a query. Only return the entity types, " - "comma-separated, without any additional text. Do not return " - "entity names, relationships, or properties." - ) + "You have access to a knowledge graph that contains " + f"these entity types: {', '.join(self.entities)}. Your task is " + "to select the entity types that are relevant to the user's question " + "for subsequent use in a query. Only return the entity types, " + "comma-separated, without any additional text. Do not return " + "entity names, relationships, or properties.", ) msg, token_usage, correction = conversation.query(question) @@ -354,33 +342,34 @@ def _select_entities( return bool(result) def _select_relationships(self, conversation: "Conversation") -> bool: - """ - Given a question and the preselected entities, select relationships for + """Given a question and the preselected entities, select relationships for the query. Args: + ---- conversation: A BioChatter Conversation object for connecting to the LLM. Returns: + ------- True if at least one relationship was selected, False otherwise. Todo: + ---- Now we have the problem that we discard all relationships that do not have a source and target, if at least one relationship has a source and target. At least communicate this all-or-nothing behaviour to the user. - """ + """ if not self.question: raise ValueError( - "No question found. Please make sure to run entity selection " - "first." + "No question found. Please make sure to run entity selection first.", ) if not self.selected_entities: raise ValueError( - "No entities found. Please run the entity selection step first." + "No entities found. Please run the entity selection step first.", ) rels = {} @@ -397,7 +386,7 @@ def _select_relationships(self, conversation: "Conversation") -> bool: ( sentencecase_to_pascalcase(s), sentencecase_to_pascalcase(t), - ) + ), ) rels[key] = pairs source_and_target_present = True @@ -441,10 +430,7 @@ def _select_relationships(self, conversation: "Conversation") -> bool: continue for pair in value: - if ( - pair[0] in self.selected_entities - or pair[1] in self.selected_entities - ): + if pair[0] in self.selected_entities or pair[1] in self.selected_entities: selected_rels.append((key, pair)) rels = json.dumps(selected_rels) @@ -499,14 +485,14 @@ def _select_relationships(self, conversation: "Conversation") -> bool: continue if source not in self.selected_entities: self.selected_entities.append( - sentencecase_to_pascalcase(source) + sentencecase_to_pascalcase(source), ) for target in targets: if target is None: continue if target not in self.selected_entities: self.selected_entities.append( - sentencecase_to_pascalcase(target) + sentencecase_to_pascalcase(target), ) return bool(result) @@ -521,22 +507,19 @@ def _validate_json_str(json_str: str): return json_str.strip() def _select_properties(self, conversation: "Conversation") -> bool: - """ - - Given a question (optionally provided, but in the standard use case + """Given a question (optionally provided, but in the standard use case reused from the entity selection step) and the selected entities, select the properties that are relevant to the question and store them in the dictionary `selected_properties`. - Returns: + Returns + ------- True if at least one property was selected, False otherwise. """ - if not self.question: raise ValueError( - "No question found. Please make sure to run entity and " - "relationship selection first." + "No question found. Please make sure to run entity and relationship selection first.", ) if not self.selected_entities and not self.selected_relationships: @@ -544,21 +527,21 @@ def _select_properties(self, conversation: "Conversation") -> bool: "No entities or relationships provided, and none available " "from entity selection step. Please provide " "entities/relationships or run the entity selection " - "(`select_entities()`) step first." + "(`select_entities()`) step first.", ) e_props = {} for entity in self.selected_entities: if self.entities[entity].get("properties"): e_props[entity] = list( - self.entities[entity]["properties"].keys() + self.entities[entity]["properties"].keys(), ) r_props = {} for relationship in self.selected_relationships: if self.relationships[relationship].get("properties"): r_props[relationship] = list( - self.relationships[relationship]["properties"].keys() + self.relationships[relationship]["properties"].keys(), ) msg = ( @@ -595,11 +578,11 @@ def _generate_query( query_language: str, conversation: "Conversation", ) -> str: - """ - Generate a query in the specified query language that answers the user's + """Generate a query in the specified query language that answers the user's question. Args: + ---- question: A user's question. entities: A list of entities that are relevant to the question. @@ -616,7 +599,9 @@ def _generate_query( LLM. Returns: + ------- A database query that could answer the user's question. + """ msg = self._generate_query_prompt( entities, @@ -639,18 +624,18 @@ def _expand_pairs(self, relationship, values) -> None: if isinstance(values["target"], list): for target in values["target"]: self.rel_directions[relationship].append( - (source, target) + (source, target), ) else: self.rel_directions[relationship].append( - (source, values["target"]) + (source, values["target"]), ) elif isinstance(values["target"], list): for target in values["target"]: self.rel_directions[relationship].append( - (values["source"], target) + (values["source"], target), ) else: self.rel_directions[relationship].append( - (values["source"], values["target"]) + (values["source"], values["target"]), ) diff --git a/biochatter/query_interaction.py b/biochatter/query_interaction.py index 5d56e6dc..5d57d481 100644 --- a/biochatter/query_interaction.py +++ b/biochatter/query_interaction.py @@ -15,8 +15,8 @@ def __init__( # message each time... -> optional model_name: str = "gpt-3.5-turbo", ): - """ - Args: + """Args: + ---- query: A KG query generated by the LLM query_language: The language of the query. @@ -28,6 +28,7 @@ def __init__( kg_selected: A dictionary with a subset of KG entities, properties and relationships that are relevant to the question. + """ self.query = query self.query_lang = query_lang @@ -46,15 +47,14 @@ def _check_required_kg_keys(kg_dict): # Check if all required keys are present in the input dictionary if not all(key in kg_dict for key in required_keys): raise ValueError( - "The KG input dictionary is missing required keys." + "The KG input dictionary is missing required keys.", ) # TODO also check that entities and relationships is a dict, properties # is a dict of lists return True def explain_query(self): - """ - Explain the query - this can be called from the frontend IF the query + """Explain the query - this can be called from the frontend IF the query ran successfully. """ msg = ( @@ -76,7 +76,8 @@ def explain_query(self): ) conversation.set_api_key( - api_key=os.getenv("OPENAI_API_KEY"), user="query_interactor" + api_key=os.getenv("OPENAI_API_KEY"), + user="query_interactor", ) conversation.append_system_message(msg) @@ -86,9 +87,7 @@ def explain_query(self): return out_msg def update_query(self, update_request): - """ - Update the query to reflect a request from the user. - """ + """Update the query to reflect a request from the user.""" if not self.kg: self.kg = self.kg_selected msg = ( @@ -130,7 +129,8 @@ def update_query(self, update_request): ) conversation.set_api_key( - api_key=os.getenv("OPENAI_API_KEY"), user="query_interactor" + api_key=os.getenv("OPENAI_API_KEY"), + user="query_interactor", ) conversation.append_system_message(msg) diff --git a/biochatter/rag_agent.py b/biochatter/rag_agent.py index 0ac16374..6f0b402e 100644 --- a/biochatter/rag_agent.py +++ b/biochatter/rag_agent.py @@ -1,4 +1,3 @@ -from typing import Optional from collections.abc import Callable @@ -13,28 +12,27 @@ class RagAgent: def __init__( self, mode: str, - model_name: Optional[str] = "gpt-3.5-turbo", - connection_args: Optional[dict] = None, - n_results: Optional[int] = 3, - use_prompt: Optional[bool] = False, - schema_config_or_info_dict: Optional[dict] = None, - conversation_factory: Optional[Callable] = None, - embedding_func: Optional[object] = None, - documentids_workspace: Optional[list[str]] = None, - agent_desc: Optional[str] = None, - use_reflexion: Optional[bool] = False, + model_name: str | None = "gpt-3.5-turbo", + connection_args: dict | None = None, + n_results: int | None = 3, + use_prompt: bool | None = False, + schema_config_or_info_dict: dict | None = None, + conversation_factory: Callable | None = None, + embedding_func: object | None = None, + documentids_workspace: list[str] | None = None, + agent_desc: str | None = None, + use_reflexion: bool | None = False, ) -> None: ###### ##TO DO ###### # mode: 'api' for the case where the agent is querying an API # use_prompt: default TRUE for self.mode == api - - """ - Create a RAG agent that can return results from a database or vector + """Create a RAG agent that can return results from a database or vector store using a query engine. Args: + ---- mode (str): The mode of the agent. Either "kg" or "vectorstore". model_name (str): The name of the model to use. @@ -69,6 +67,7 @@ def __init__( use_reflexion (bool): Whether to use the ReflexionAgent to generate the query. + """ self.mode = mode self.model_name = model_name @@ -82,7 +81,7 @@ def __init__( if not connection_args: raise ValueError( - "Please provide connection args to connect to database." + "Please provide connection args to connect to database.", ) if not schema_config_or_info_dict: @@ -106,7 +105,7 @@ def __init__( if not connection_args: raise ValueError( - "Please provide connection args to connect to vector store." + "Please provide connection args to connect to vector store.", ) if not embedding_func: @@ -122,12 +121,12 @@ def __init__( self.query_func = self.agent.similarity_search elif self.mode == RagAgentModeEnum.API_BLAST: + from .api_agent.api_agent import APIAgent from .api_agent.blast import ( BlastFetcher, BlastInterpreter, BlastQueryBuilder, ) - from .api_agent.api_agent import APIAgent self.agent = APIAgent( conversation_factory=conversation_factory, @@ -137,12 +136,12 @@ def __init__( ) self.query_func = self.agent.execute elif self.mode == RagAgentModeEnum.API_ONCOKB: + from .api_agent.api_agent import APIAgent from .api_agent.oncokb import ( OncoKBFetcher, OncoKBInterpreter, OncoKBQueryBuilder, ) - from .api_agent.api_agent import APIAgent self.agent = APIAgent( conversation_factory=conversation_factory, @@ -153,7 +152,7 @@ def __init__( self.query_func = self.agent.execute else: raise ValueError( - "Invalid mode. Choose either 'kg', 'vectorstore', 'api_blast', or 'api_oncokb'." + "Invalid mode. Choose either 'kg', 'vectorstore', 'api_blast', or 'api_oncokb'.", ) @property @@ -161,23 +160,26 @@ def agent_description(self): return self._agent_desc @agent_description.setter - def agent_description(self, val: Optional[str] = None): + def agent_description(self, val: str | None = None): self._agent_desc = val def generate_responses(self, user_question: str) -> list[tuple]: - """ - Run the query function according to the mode and return the results in a + """Run the query function according to the mode and return the results in a uniform format (list of tuples, where the first element is the text for RAG and the second element is the metadata). Args: + ---- user_question (str): The user question. Returns: + ------- results (List[tuple]): A list of tuples containing the results. Todo: + ---- Which metadata are returned? + """ self.last_response = [] if not self.use_prompt: @@ -216,8 +218,7 @@ def generate_responses(self, user_question: str) -> list[tuple]: else: raise ValueError( - "Invalid mode. Choose either 'kg', 'vectorstore', 'api_blast', " - "or 'api_oncokb'." + "Invalid mode. Choose either 'kg', 'vectorstore', 'api_blast', or 'api_oncokb'.", ) self.last_response = response return response @@ -249,6 +250,5 @@ def get_description(self): return self.agent.get_description(tool_name, tool_desc) else: raise ValueError( - "Invalid mode. Choose either 'kg', 'vectorstore', 'api_blast', " - "or 'api_oncokb'." + "Invalid mode. Choose either 'kg', 'vectorstore', 'api_blast', or 'api_oncokb'.", ) diff --git a/biochatter/selector_agent.py b/biochatter/selector_agent.py index 457ad490..06a0b610 100644 --- a/biochatter/selector_agent.py +++ b/biochatter/selector_agent.py @@ -1,32 +1,28 @@ -from typing import Any, Dict -from datetime import datetime -from collections.abc import Callable import json +from collections.abc import Callable +from datetime import datetime -from langgraph.graph import END -from langchain_openai import ChatOpenAI -from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder -from langchain_core.messages import AIMessage, BaseMessage, ToolMessage -from langchain_core.pydantic_v1 import Field, BaseModel from langchain.output_parsers.openai_tools import ( - PydanticToolsParser, JsonOutputToolsParser, + PydanticToolsParser, ) +from langchain_core.messages import AIMessage, BaseMessage, ToolMessage +from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder +from langchain_core.pydantic_v1 import BaseModel, Field +from langchain_openai import ChatOpenAI +from langgraph.graph import END -from .rag_agent import RagAgent from .langgraph_agent_base import ( - EXECUTE_TOOL_NODE, ReflexionAgent, ReflexionAgentLogger, ReflexionAgentResult, ResponderWithRetries, ) +from .rag_agent import RagAgent class RagAgentSelectLogger(ReflexionAgentLogger): - """ - RagAgentSelector logger - """ + """RagAgentSelector logger""" def __init__(self) -> None: super().__init__() @@ -37,54 +33,56 @@ def log_step_message(self, step: int, node_name: str, output: BaseMessage): self._log_message(f"## {step}, {node_name}") self._log_message(f'Answer: {parsed_output[0]["args"]["answer"]}') self._log_message( - f'Reflection | Improving: {parsed_output[0]["args"]["reflection"]}' + f'Reflection | Improving: {parsed_output[0]["args"]["reflection"]}', ) if "revised_answer" in parsed_output[0]["args"]: self._log_message("Reflection | Revised Answer:") self._log_message(parsed_output[0]["args"]["revised_answer"]) self._log_message( - "-------------------------------- Node Output --------------------------------" + "-------------------------------- Node Output --------------------------------", ) - except Exception as e: + except Exception: self._log_message(str(output)[:100] + " ...", "error") def log_final_result(self, final_result: ReflexionAgentResult) -> None: self._log_message( - "\n\n-------------------------------- Final Generated Response --------------------------------" + "\n\n-------------------------------- Final Generated Response --------------------------------", ) obj = vars(final_result) self._log_message(json.dumps(obj)) class RagAgentChoiceModel(BaseModel): - "Choose RagAgent" + """Choose RagAgent""" answer: str = Field( - description="RagAgent name that is the most appropriate to provide supplementary information" + description="RagAgent name that is the most appropriate to provide supplementary information", ) reflection: str = Field( - description="Your reflection on the initial answer, critique of what to improve" + description="Your reflection on the initial answer, critique of what to improve", ) class RagAgentRevisionModel(RagAgentChoiceModel): - "Revise your previous answer" + """Revise your previous answer""" + revised_answer: str = Field(description="Revised RagAgent name") score: str = Field( description=( "the score for the chosen rag agent based on its inquired result" "and the relevance to user's question, " " with 0 representing the lowest score and 10 representing the highest score." - ) + ), ) class RagAgentSelector(ReflexionAgent): def __init__( - self, rag_agents: list[RagAgent], conversation_factory: Callable + self, + rag_agents: list[RagAgent], + conversation_factory: Callable, ): - """ - The class RagAgentSelector uses an LLM to choose the appropriate rag agent + """The class RagAgentSelector uses an LLM to choose the appropriate rag agent for a given user question. """ super().__init__( @@ -93,8 +91,7 @@ def __init__( ) self.rag_agents = rag_agents agent_desc = [ - f"{ix}. {rag_agents[ix].mode}: {rag_agents[ix].get_description()}" - for ix in range(len(rag_agents)) + f"{ix}. {rag_agents[ix].mode}: {rag_agents[ix].get_description()}" for ix in range(len(rag_agents)) ] rag_agents_desc = "\n\n".join(agent_desc) self.actor_prompt_template = ChatPromptTemplate.from_messages( @@ -115,7 +112,7 @@ def __init__( "system", "Only return rag agent name according to user's question above.", ), - ] + ], ).partial( time=lambda: datetime.now().isoformat(), rag_agents_desc=rag_agents_desc, @@ -123,19 +120,22 @@ def __init__( self.parser = JsonOutputToolsParser(return_id=True) def _create_initial_responder( - self, prompt: str | None = None + self, + prompt: str | None = None, ) -> ResponderWithRetries: llm: ChatOpenAI = self.conversation.chat initial_chain = self.actor_prompt_template.partial( instruction="", ) | llm.bind_tools( - tools=[RagAgentChoiceModel], tool_choice="RagAgentChoiceModel" + tools=[RagAgentChoiceModel], + tool_choice="RagAgentChoiceModel", ) validator = PydanticToolsParser(tools=[RagAgentChoiceModel]) return ResponderWithRetries(runnable=initial_chain, validator=validator) def _create_revise_responder( - self, prompt: str | None = None + self, + prompt: str | None = None, ) -> ResponderWithRetries: revision_instruction = """ Revise your previous chosen rag agent based on the result of the rag agent and follow the guidelines: @@ -144,7 +144,7 @@ def _create_revise_responder( """ llm = self.conversation.chat revision_chain = self.actor_prompt_template.partial( - instruction=revision_instruction + instruction=revision_instruction, ) | llm.bind_tools( tools=[RagAgentRevisionModel], tool_choice="RagAgentRevisionModel", @@ -165,8 +165,7 @@ def _tool_function(self, state: list[BaseMessage]) -> ToolMessage: parsed_args = parsed_msg["args"] agent_name = ( parsed_args["revised_answer"] - if "revised_answer" in parsed_args - and len(parsed_args["revised_answer"]) > 0 + if "revised_answer" in parsed_args and len(parsed_args["revised_answer"]) > 0 else parsed_args["answer"] ) # get agent according to agent name @@ -181,7 +180,7 @@ def _tool_function(self, state: list[BaseMessage]) -> ToolMessage: result = found_agent.generate_responses(user_question) if len(result) > 0: content = json.dumps( - {"rag_agent": found_agent.mode, "result": result} + {"rag_agent": found_agent.mode, "result": result}, ) break content = ( @@ -191,7 +190,7 @@ def _tool_function(self, state: list[BaseMessage]) -> ToolMessage: { "rag_agent": "", "result": [], - } + }, ) ) return ToolMessage(content=content, tool_call_id=parsed_msg["id"]) @@ -200,7 +199,8 @@ def _should_continue(self, state: list[BaseMessage]): return END # here we use one-pass loop for sake of performance def _parse_final_result( - self, messages: list[BaseMessage] + self, + messages: list[BaseMessage], ) -> ReflexionAgentResult: output = messages[-1] result = self.parser.invoke(output)[0]["args"] diff --git a/biochatter/vectorstore.py b/biochatter/vectorstore.py index 9e36acdb..b854ce42 100644 --- a/biochatter/vectorstore.py +++ b/biochatter/vectorstore.py @@ -1,18 +1,15 @@ -from typing import Optional - -from transformers import GPT2TokenizerFast +import fitz # this is PyMuPDF (PyPI pymupdf package, not fitz) +import openai from langchain.schema import Document from langchain.text_splitter import RecursiveCharacterTextSplitter +from langchain_community.document_loaders import TextLoader from langchain_community.embeddings import ( OllamaEmbeddings, XinferenceEmbeddings, ) -from langchain_community.vectorstores import Milvus -from langchain_community.document_loaders import TextLoader -from langchain_community.embeddings.openai import OpenAIEmbeddings from langchain_community.embeddings.azure_openai import AzureOpenAIEmbeddings -import fitz # this is PyMuPDF (PyPI pymupdf package, not fitz) -import openai +from langchain_community.embeddings.openai import OpenAIEmbeddings +from transformers import GPT2TokenizerFast from biochatter.vectorstore_agent import VectorDatabaseAgentMilvus @@ -25,31 +22,28 @@ def __init__( chunk_size: int = 1000, chunk_overlap: int = 0, split_by_characters: bool = True, - separators: Optional[list] = None, + separators: list | None = None, n_results: int = 3, - model: Optional[str] = "text-embedding-ada-002", - vector_db_vendor: Optional[str] = None, - connection_args: Optional[dict] = None, - embedding_collection_name: Optional[str] = None, - metadata_collection_name: Optional[str] = None, - api_key: Optional[str] = None, - is_azure: Optional[bool] = False, - azure_deployment: Optional[str] = None, - azure_endpoint: Optional[str] = None, - base_url: Optional[str] = None, - embeddings: Optional[ - OpenAIEmbeddings | XinferenceEmbeddings | OllamaEmbeddings - ] = None, - documentids_workspace: Optional[list[str]] = None, + model: str | None = "text-embedding-ada-002", + vector_db_vendor: str | None = None, + connection_args: dict | None = None, + embedding_collection_name: str | None = None, + metadata_collection_name: str | None = None, + api_key: str | None = None, + is_azure: bool | None = False, + azure_deployment: str | None = None, + azure_endpoint: str | None = None, + base_url: str | None = None, + embeddings: OpenAIEmbeddings | XinferenceEmbeddings | OllamaEmbeddings | None = None, + documentids_workspace: list[str] | None = None, ) -> None: - """ - Class that handles the retrieval-augmented generation (RAG) functionality + """Class that handles the retrieval-augmented generation (RAG) functionality of BioChatter. It splits text into chunks, embeds them, and stores them in a vector database. It can then be used to do similarity search on the database. Args: - + ---- used (bool, optional): whether RAG has been used (frontend setting). Defaults to False. @@ -113,20 +107,19 @@ def __init__( if embeddings: self.embeddings = embeddings - else: - if not self.online: - self.embeddings = ( - OpenAIEmbeddings(openai_api_key=api_key, model=model) - if not is_azure - else AzureOpenAIEmbeddings( - api_key=api_key, - azure_deployment=azure_deployment, - azure_endpoint=azure_endpoint, - model=model, - ) + elif not self.online: + self.embeddings = ( + OpenAIEmbeddings(openai_api_key=api_key, model=model) + if not is_azure + else AzureOpenAIEmbeddings( + api_key=api_key, + azure_deployment=azure_deployment, + azure_endpoint=azure_endpoint, + model=model, ) - else: - self.embeddings = None + ) + else: + self.embeddings = None # connection arguments self.connection_args = connection_args or { @@ -188,31 +181,25 @@ def _tokens_splitter(self) -> RecursiveCharacterTextSplitter: else: return RecursiveCharacterTextSplitter.from_tiktoken_encoder( encoding_name="", - model_name=( - DEFAULT_OPENAI_MODEL - if not self.model_name - else self.model_name - ), + model_name=(DEFAULT_OPENAI_MODEL if not self.model_name else self.model_name), chunk_size=self.chunk_size, chunk_overlap=self.chunk_overlap, separators=self.separators, ) def _text_splitter(self) -> RecursiveCharacterTextSplitter: - return ( - self._characters_splitter() - if self.split_by_characters - else self._tokens_splitter() - ) + return self._characters_splitter() if self.split_by_characters else self._tokens_splitter() def save_document(self, doc: list[Document]) -> str: - """ - This function saves document to the vector database + """This function saves document to the vector database Args: doc List[Document]: document content, read with DocumentReader load_document(), or document_from_pdf(), document_from_txt() - Returns: + + Returns + ------- str: document id, which can be used to remove an uploaded document with remove_document() + """ splitted = self._split_document(doc) return self._store_embeddings(splitted) @@ -229,12 +216,13 @@ def connect(self) -> None: def get_all_documents(self) -> list[dict]: return self.database_host.get_all_documents( - doc_ids=self.documentids_workspace + doc_ids=self.documentids_workspace, ) def remove_document(self, doc_id: str) -> None: return self.database_host.remove_document( - doc_id, self.documentids_workspace + doc_id, + self.documentids_workspace, ) @@ -245,23 +233,22 @@ def __init__( chunk_size: int = 1000, chunk_overlap: int = 0, split_by_characters: bool = True, - separators: Optional[list] = None, + separators: list | None = None, n_results: int = 3, - model: Optional[str] = "auto", - vector_db_vendor: Optional[str] = None, - connection_args: Optional[dict] = None, - embedding_collection_name: Optional[str] = None, - metadata_collection_name: Optional[str] = None, - api_key: Optional[str] = "none", - base_url: Optional[str] = None, - documentids_workspace: Optional[list[str]] = None, + model: str | None = "auto", + vector_db_vendor: str | None = None, + connection_args: dict | None = None, + embedding_collection_name: str | None = None, + metadata_collection_name: str | None = None, + api_key: str | None = "none", + base_url: str | None = None, + documentids_workspace: list[str] | None = None, ): - """ - Extension of the DocumentEmbedder class that uses Xinference for + """Extension of the DocumentEmbedder class that uses Xinference for embeddings. Args: - + ---- used (bool, optional): whether RAG has been used (frontend setting). chunk_size (int, optional): size of chunks to split text into. @@ -329,14 +316,14 @@ def __init__( api_key=api_key, base_url=base_url, embeddings=XinferenceEmbeddings( - server_url=base_url, model_uid=self.model_uid + server_url=base_url, + model_uid=self.model_uid, ), documentids_workspace=documentids_workspace, ) def load_models(self) -> None: - """ - Get all models that are currently available on the Xinference server and + """Get all models that are currently available on the Xinference server and write them to `self.models`. """ for id, model in self.client.list_models().items(): @@ -344,15 +331,17 @@ def load_models(self) -> None: self.models[model["model_name"]] = model def list_models_by_type(self, type: str) -> list[str]: - """ - Return all models of a certain type that are currently available on the + """Return all models of a certain type that are currently available on the Xinference server. Args: + ---- type (str): type of model to list (e.g. "embedding", "chat") Returns: + ------- List[str]: list of model names + """ names = [] for name, model in self.models.items(): @@ -371,23 +360,22 @@ def __init__( chunk_size: int = 1000, chunk_overlap: int = 0, split_by_characters: bool = True, - separators: Optional[list] = None, + separators: list | None = None, n_results: int = 3, - model: Optional[str] = "nomic-embed-text", - vector_db_vendor: Optional[str] = None, - connection_args: Optional[dict] = None, - embedding_collection_name: Optional[str] = None, - metadata_collection_name: Optional[str] = None, - api_key: Optional[str] = "none", - base_url: Optional[str] = None, - documentids_workspace: Optional[list[str]] = None, + model: str | None = "nomic-embed-text", + vector_db_vendor: str | None = None, + connection_args: dict | None = None, + embedding_collection_name: str | None = None, + metadata_collection_name: str | None = None, + api_key: str | None = "none", + base_url: str | None = None, + documentids_workspace: list[str] | None = None, ): - """ - Extension of the DocumentEmbedder class that uses Ollama for + """Extension of the DocumentEmbedder class that uses Ollama for embeddings. Args: - + ---- used (bool, optional): whether RAG has been used (frontend setting). chunk_size (int, optional): size of chunks to split text into. @@ -448,7 +436,8 @@ def __init__( api_key=api_key, base_url=base_url, embeddings=OllamaEmbeddings( - base_url=base_url, model=self.model_name + base_url=base_url, + model=self.model_name, ), documentids_workspace=documentids_workspace, ) @@ -456,15 +445,17 @@ def __init__( class DocumentReader: def load_document(self, path: str) -> list[Document]: - """ - Loads a document from a path; accepts txt and pdf files. Txt files are + """Loads a document from a path; accepts txt and pdf files. Txt files are loaded as-is, pdf files are converted to text using fitz. Args: + ---- path (str): path to document Returns: + ------- List[Document]: list of documents + """ if path.endswith(".txt"): loader = TextLoader(path) @@ -483,19 +474,21 @@ def load_document(self, path: str) -> list[Document]: Document( page_content=text, metadata=meta, - ) + ), ] def document_from_pdf(self, pdf: bytes) -> list[Document]: - """ - Receive a byte representation of a pdf file and return a list of Documents + """Receive a byte representation of a pdf file and return a list of Documents with metadata. Args: + ---- pdf (bytes): byte representation of pdf file Returns: + ------- List[Document]: list of documents + """ doc = fitz.open(stream=pdf, filetype="pdf") text = "" @@ -509,24 +502,26 @@ def document_from_pdf(self, pdf: bytes) -> list[Document]: Document( page_content=text, metadata=meta, - ) + ), ] def document_from_txt(self, txt: bytes) -> list[Document]: - """ - Receive a byte representation of a txt file and return a list of Documents + """Receive a byte representation of a txt file and return a list of Documents with metadata. Args: + ---- txt (bytes): byte representation of txt file Returns: + ------- List[Document]: list of documents + """ meta = {"source": "txt"} return [ Document( page_content=txt, metadata=meta, - ) + ), ] diff --git a/biochatter/vectorstore_agent.py b/biochatter/vectorstore_agent.py index 9717d21d..c16a04f7 100644 --- a/biochatter/vectorstore_agent.py +++ b/biochatter/vectorstore_agent.py @@ -1,20 +1,19 @@ -from typing import Tuple, Optional -import uuid -import random import logging +import random +import uuid +from langchain.schema import Document +from langchain_community.embeddings import OpenAIEmbeddings +from langchain_community.vectorstores import Milvus from pymilvus import ( - DataType, Collection, + CollectionSchema, + DataType, FieldSchema, MilvusException, - CollectionSchema, - utility, connections, + utility, ) -from langchain.schema import Document -from langchain_community.embeddings import OpenAIEmbeddings -from langchain_community.vectorstores import Milvus from .constants import MAX_AGENT_DESC_LENGTH @@ -40,23 +39,25 @@ def align_metadata( - metadata: list[dict], isDeleted: Optional[bool] = False + metadata: list[dict], + isDeleted: bool | None = False, ) -> list[list]: - """ - - Ensure that specific metadata fields are present; if not provided, fill with + """Ensure that specific metadata fields are present; if not provided, fill with "unknown". Also, add a random vector to each metadata item to simulate an embedding. Args: + ---- metadata (List[Dict]): List of metadata items isDeleted (Optional[bool], optional): Whether the document is deleted. Defaults to False. Returns: + ------- List[List]: List of metadata items, with each item being a list of metadata fields. + """ ret = [] fields = METADATA_FIELDS.copy() @@ -65,27 +66,26 @@ def align_metadata( ret.append([item[k] if k in item else "unknown" for item in metadata]) ret.append( - [ - [random.random() for _ in range(METADATA_VECTOR_DIM)] - for _ in range(len(metadata)) - ] + [[random.random() for _ in range(METADATA_VECTOR_DIM)] for _ in range(len(metadata))], ) ret.append([isDeleted for _ in metadata]) return ret def align_embeddings(docs: list[Document], meta_id: int) -> list[Document]: - """ - Ensure that the metadata id is present in each document. + """Ensure that the metadata id is present in each document. Args: + ---- docs (List[Document]): List of documents meta_id (int): Metadata id to assign to the documents Returns: + ------- List[Document]: List of documents, with each document having a metadata id. + """ ret = [] for doc in docs: @@ -93,12 +93,12 @@ def align_embeddings(docs: list[Document], meta_id: int) -> list[Document]: Document( page_content=doc.page_content, metadata={"meta_id": meta_id}, - ) + ), ) return ret -def validate_connection_args(connection_args: Optional[dict] = None): +def validate_connection_args(connection_args: dict | None = None): if connection_args is None: return { "host": "127.0.0.1", @@ -113,8 +113,7 @@ def validate_connection_args(connection_args: Optional[dict] = None): class VectorDatabaseAgentMilvus: - """ - The VectorDatabaseAgentMilvus class manages vector databases in a connected + """The VectorDatabaseAgentMilvus class manages vector databases in a connected host database. It manages an embedding collection `_col_embeddings:langchain.vectorstores.Milvus`, which is the main information on the embedded text fragments and the basis for similarity @@ -135,12 +134,12 @@ class VectorDatabaseAgentMilvus: def __init__( self, embedding_func: OpenAIEmbeddings, - connection_args: Optional[dict] = None, - embedding_collection_name: Optional[str] = None, - metadata_collection_name: Optional[str] = None, + connection_args: dict | None = None, + embedding_collection_name: str | None = None, + metadata_collection_name: str | None = None, ): - """ - Args: + """Args: + ---- embedding_func OpenAIEmbeddings: Function used to embed the text connection_args Optional dict: args to connect Vector Database @@ -148,21 +147,17 @@ def __init__( embedding_collection_name Optional str: exposed for test metadata_collection_name Optional str: exposed for test + """ self._embedding_func = embedding_func - self._col_embeddings: Optional[Milvus] = None - self._col_metadata: Optional[Collection] = None + self._col_embeddings: Milvus | None = None + self._col_metadata: Collection | None = None self._connection_args = validate_connection_args(connection_args) - self._embedding_name = ( - embedding_collection_name or DOCUMENT_EMBEDDINGS_COLLECTION_NAME - ) - self._metadata_name = ( - metadata_collection_name or DOCUMENT_METADATA_COLLECTION_NAME - ) + self._embedding_name = embedding_collection_name or DOCUMENT_EMBEDDINGS_COLLECTION_NAME + self._metadata_name = metadata_collection_name or DOCUMENT_METADATA_COLLECTION_NAME def connect(self) -> None: - """ - Connect to a host and read two document collections (the default names + """Connect to a host and read two document collections (the default names are `DocumentEmbeddings` and `DocumentMetadata`) in the currently active database (default database name is `default`); if those document collections don't exist, create the two collections. @@ -174,30 +169,39 @@ def _connect(self, host: str, port: str, user: str, password: str) -> None: self.alias = self._create_connection_alias(host, port, user, password) def _init_host(self) -> None: - """ - Initialize host. Will read/create document collection inside currently + """Initialize host. Will read/create document collection inside currently active database. """ self._create_collections() def _create_connection_alias( - self, host: str, port: str, user: str, password: str + self, + host: str, + port: str, + user: str, + password: str, ) -> str: - """ - Connect to host and create a connection alias for metadata collection + """Connect to host and create a connection alias for metadata collection using a random uuid. Args: + ---- host (str): host ip address port (str): host port Returns: + ------- str: connection alias + """ alias = uuid.uuid4().hex try: connections.connect( - host=host, port=port, user=user, password=password, alias=alias + host=host, + port=port, + user=user, + password=password, + alias=alias, ) logger.debug(f"Created new connection using: {alias}") return alias @@ -206,12 +210,12 @@ def _create_connection_alias( raise e def _create_collections(self) -> None: - """ - Create or load the embedding and metadata collections from the currently + """Create or load the embedding and metadata collections from the currently active database. """ embedding_exists = utility.has_collection( - self._embedding_name, using=self.alias + self._embedding_name, + using=self.alias, ) meta_exists = utility.has_collection( self._metadata_name, @@ -232,9 +236,7 @@ def _create_collections(self) -> None: self._col_metadata.load() def _load_embeddings_collection(self) -> None: - """ - Load embeddings collection from currently active database. - """ + """Load embeddings collection from currently active database.""" try: self._col_embeddings = Milvus( embedding_function=self._embedding_func, @@ -243,13 +245,12 @@ def _load_embeddings_collection(self) -> None: ) except MilvusException as e: logger.error( - f"Failed to load embeddings collection {self._embedding_name}." + f"Failed to load embeddings collection {self._embedding_name}.", ) raise e def _create_embeddings_collection(self) -> None: - """ - Create embedding collection. + """Create embedding collection. All fields: "meta_id", "vector" """ try: @@ -260,14 +261,12 @@ def _create_embeddings_collection(self) -> None: ) except MilvusException as e: logger.error( - f"Failed to create embeddings collection {self._embedding_name}" + f"Failed to create embeddings collection {self._embedding_name}", ) raise e def _load_metadata_collection(self) -> None: - """ - Load metadata collection from currently active database. - """ + """Load metadata collection from currently active database.""" self._col_metadata = Collection( self._metadata_name, using=self.alias, @@ -275,8 +274,7 @@ def _load_metadata_collection(self) -> None: self._col_metadata.load() def _create_metadata_collection(self) -> None: - """ - Create metadata collection. + """Create metadata collection. All fields: "id", "name", "author", "title", "format", "subject", "creator", "producer", "creationDate", "modDate", "source", "embedding", @@ -288,37 +286,60 @@ def _create_metadata_collection(self) -> None: """ MAX_LENGTH = 10000 doc_id = FieldSchema( - name="id", dtype=DataType.INT64, is_primary=True, auto_id=True + name="id", + dtype=DataType.INT64, + is_primary=True, + auto_id=True, ) doc_name = FieldSchema( - name="name", dtype=DataType.VARCHAR, max_length=MAX_LENGTH + name="name", + dtype=DataType.VARCHAR, + max_length=MAX_LENGTH, ) doc_author = FieldSchema( - name="author", dtype=DataType.VARCHAR, max_length=MAX_LENGTH + name="author", + dtype=DataType.VARCHAR, + max_length=MAX_LENGTH, ) doc_title = FieldSchema( - name="title", dtype=DataType.VARCHAR, max_length=MAX_LENGTH + name="title", + dtype=DataType.VARCHAR, + max_length=MAX_LENGTH, ) doc_format = FieldSchema( - name="format", dtype=DataType.VARCHAR, max_length=255 + name="format", + dtype=DataType.VARCHAR, + max_length=255, ) doc_subject = FieldSchema( - name="subject", dtype=DataType.VARCHAR, max_length=MAX_LENGTH + name="subject", + dtype=DataType.VARCHAR, + max_length=MAX_LENGTH, ) doc_creator = FieldSchema( - name="creator", dtype=DataType.VARCHAR, max_length=MAX_LENGTH + name="creator", + dtype=DataType.VARCHAR, + max_length=MAX_LENGTH, ) doc_producer = FieldSchema( - name="producer", dtype=DataType.VARCHAR, max_length=MAX_LENGTH + name="producer", + dtype=DataType.VARCHAR, + max_length=MAX_LENGTH, ) doc_creationDate = FieldSchema( - name="creationDate", dtype=DataType.VARCHAR, max_length=1024 + name="creationDate", + dtype=DataType.VARCHAR, + max_length=1024, ) doc_modDate = FieldSchema( - name="modDate", dtype=DataType.VARCHAR, max_length=1024 + name="modDate", + dtype=DataType.VARCHAR, + max_length=1024, ) doc_source = FieldSchema( - name="source", dtype=DataType.VARCHAR, max_length=MAX_LENGTH + name="source", + dtype=DataType.VARCHAR, + max_length=MAX_LENGTH, ) embedding = FieldSchema( name="embedding", @@ -347,20 +368,17 @@ def _create_metadata_collection(self) -> None: schema = CollectionSchema(fields=fields) try: self._col_metadata = Collection( - name=self._metadata_name, schema=schema, using=self.alias + name=self._metadata_name, + schema=schema, + using=self.alias, ) except MilvusException as e: logger.error(f"Failed to create collection {self._metadata_name}") raise e def _create_metadata_collection_index(self) -> None: - """ - Create index for metadata collection in currently active database. - """ - if ( - not isinstance(self._col_metadata, Collection) - or len(self._col_metadata.indexes) > 0 - ): + """Create index for metadata collection in currently active database.""" + if not isinstance(self._col_metadata, Collection) or len(self._col_metadata.indexes) > 0: return index_params = { @@ -377,22 +395,23 @@ def _create_metadata_collection_index(self) -> None: ) except MilvusException as e: logger.error( - "Failed to create index for meta collection " - f"{self._metadata_name}." + "Failed to create index for meta collection " f"{self._metadata_name}.", ) raise e def _insert_data(self, documents: list[Document]) -> str: - """ - Insert documents into the currently active database. + """Insert documents into the currently active database. Args: + ---- documents (List[Documents]): documents array, usually from DocumentReader.load_document, DocumentReader.document_from_pdf, DocumentReader.document_from_txt Returns: + ------- str: document id + """ if len(documents) == 0: return None @@ -403,7 +422,7 @@ def _insert_data(self, documents: list[Document]) -> str: meta_id = str(result.primary_keys[0]) self._col_metadata.flush() except MilvusException as e: - logger.error(f"Failed to insert meta data") + logger.error("Failed to insert meta data") raise e aligned_docs = align_embeddings(documents, meta_id) try: @@ -416,40 +435,44 @@ def _insert_data(self, documents: list[Document]) -> str: ) except MilvusException as e: logger.error( - "Failed to insert data to embedding collection " - f"{self._embedding_name}." + "Failed to insert data to embedding collection " f"{self._embedding_name}.", ) raise e return meta_id def store_embeddings(self, documents: list[Document]) -> str: - """ - Store documents in the currently active database. + """Store documents in the currently active database. Args: + ---- documents (List[Documents]): documents array, usually from DocumentReader.load_document, DocumentReader.document_from_pdf, DocumentReader.document_from_txt Returns: + ------- str: document id + """ if len(documents) == 0: - return + return None return self._insert_data(documents) def _build_embedding_search_expression( - self, meta_ids: list[dict] - ) -> Optional[str]: - """ - Build search expression for embedding collection. The generated + self, + meta_ids: list[dict], + ) -> str | None: + """Build search expression for embedding collection. The generated expression follows the pattern: "meta_id in [{id1}, {id2}, ...] Args: + ---- meta_ids: the array of metadata id in metadata collection Returns: + ------- str: search expression or None + """ if len(meta_ids) == 0: return "meta_id in []" @@ -462,25 +485,30 @@ def _build_embedding_search_expression( return built_expr def _join_embedding_and_metadata_results( - self, result_embedding: list[Document], result_meta: list[dict] + self, + result_embedding: list[Document], + result_meta: list[dict], ) -> list[Document]: - """ - Join the search results of embedding collection and results of metadata. + """Join the search results of embedding collection and results of metadata. Args: + ---- result_embedding (List[Document]): search result of embedding collection result_meta (List[Dict]): search result of metadata collection Returns: + ------- List[Document]: combined results like [{page_content: str, metadata: {...}}] + """ def _find_metadata_by_id( - metadata: list[dict], id: str - ) -> Optional[dict]: + metadata: list[dict], + id: str, + ) -> dict | None: for d in metadata: if str(d["id"]) == id: return d @@ -491,41 +519,41 @@ def _find_metadata_by_id( found = _find_metadata_by_id(result_meta, res.metadata["meta_id"]) if found is None: # discard logger.error( - f"Failed to join meta_id {res.metadata['meta_id']}" + f"Failed to join meta_id {res.metadata['meta_id']}", ) continue joined_docs.append( - Document(page_content=res.page_content, metadata=found) + Document(page_content=res.page_content, metadata=found), ) return joined_docs @staticmethod def _build_meta_col_query_expr_for_all_documents( - doc_ids: Optional[list[str]] = None, + doc_ids: list[str] | None = None, ) -> str: - """ - Build metadata collection query expression to obtain all documents. + """Build metadata collection query expression to obtain all documents. Args: + ---- doc_ids: the list of document ids (metadata ids), if thie argument is None, that is, the query is to get all undeleted documents in metadata collection. Otherwise, the query is to getr all undeleted documents form provided doc_ids Returns: + ------- query: str + """ - expr = ( - f"id in {doc_ids} and isDeleted == false" - if doc_ids is not None - else "isDeleted == false" - ) + expr = f"id in {doc_ids} and isDeleted == false" if doc_ids is not None else "isDeleted == false" return expr.replace('"', "").replace("'", "") def similarity_search( - self, query: str, k: int = 3, doc_ids: Optional[list[str]] = None + self, + query: str, + k: int = 3, + doc_ids: list[str] | None = None, ) -> list[Document]: - """ - Perform similarity search insider the currently active database + """Perform similarity search insider the currently active database according to the input query. This method will: @@ -535,6 +563,7 @@ def similarity_search( 3. combine metadata and embeddings Args: + ---- query (str): query string k (int): the number of results to return @@ -543,48 +572,57 @@ def similarity_search( similarity search across the specified documents Returns: + ------- List[Document]: search results + """ result_metadata = [] expr = VectorDatabaseAgentMilvus._build_meta_col_query_expr_for_all_documents( - doc_ids + doc_ids, ) result_metadata = self._col_metadata.query( - expr=expr, output_fields=METADATA_FIELDS + expr=expr, + output_fields=METADATA_FIELDS, ) expr = self._build_embedding_search_expression(result_metadata) result_embedding = self._col_embeddings.similarity_search( - query=query, k=k, expr=expr + query=query, + k=k, + expr=expr, ) return self._join_embedding_and_metadata_results( - result_embedding, result_metadata + result_embedding, + result_metadata, ) def remove_document( - self, doc_id: str, doc_ids: Optional[list[str]] = None + self, + doc_id: str, + doc_ids: list[str] | None = None, ) -> bool: - """ - Remove the document include meta data and its embeddings. + """Remove the document include meta data and its embeddings. Args: + ---- doc_id (str): the document to be deleted doc_ids (Optional[list[str]]): the list of document ids, defines documents scope within which remove operation occurs. Returns: + ------- bool: True if the document is deleted, False otherwise + """ if not self._col_metadata: return False - if doc_ids is not None and ( - len(doc_ids) == 0 or (len(doc_ids) > 0 and not doc_id in doc_ids) - ): + if doc_ids is not None and (len(doc_ids) == 0 or (len(doc_ids) > 0 and doc_id not in doc_ids)): return False try: expr = f"id in [{doc_id}]" res = self._col_metadata.query( - expr=expr, output_fields=METADATA_FIELDS + expr=expr, + output_fields=METADATA_FIELDS, ) if len(res) == 0: return False @@ -604,33 +642,37 @@ def remove_document( raise e def get_all_documents( - self, doc_ids: Optional[list[str]] = None + self, + doc_ids: list[str] | None = None, ) -> list[dict]: - """ - Get all non-deleted documents from the currently active database. + """Get all non-deleted documents from the currently active database. Args: + ---- doc_ids (List[str] optional): the list of document ids, defines documents scope within which the operation of obtaining all documents occurs Returns: + ------- List[Dict]: the metadata of all non-deleted documents in the form [{{id}, {author}, {source}, ...}] + """ try: expr = VectorDatabaseAgentMilvus._build_meta_col_query_expr_for_all_documents( - doc_ids + doc_ids, ) result_metadata = self._col_metadata.query( - expr=expr, output_fields=METADATA_FIELDS + expr=expr, + output_fields=METADATA_FIELDS, ) return result_metadata except MilvusException as e: logger.error(e) raise e - def get_description(self, doc_ids: Optional[list[str]] = None): + def get_description(self, doc_ids: list[str] | None = None): def get_name(meta: dict[str, str]): name_col = ["title", "name", "subject", "source"] for col in name_col: @@ -639,7 +681,7 @@ def get_name(meta: dict[str, str]): return "" expr = VectorDatabaseAgentMilvus._build_meta_col_query_expr_for_all_documents( - doc_ids + doc_ids, ) result = self._col_metadata.query( expr=expr, diff --git a/docs/scripts/hooks.py b/docs/scripts/hooks.py index 4df16075..a08f518a 100644 --- a/docs/scripts/hooks.py +++ b/docs/scripts/hooks.py @@ -1,22 +1,19 @@ +import math import os import re -import math -import seaborn as sns import colorcet as cc import matplotlib - import numpy as np +import seaborn as sns matplotlib.use("Agg") import matplotlib.pyplot as plt - import pandas as pd def on_pre_build(config, **kwargs) -> None: """This function is called when building the documentation.""" - result_files_path = "benchmark/results/" result_file_names = [ @@ -24,8 +21,8 @@ def on_pre_build(config, **kwargs) -> None: for f in os.listdir(result_files_path) if os.path.isfile(os.path.join(result_files_path, f)) and f.endswith(".csv") - and not "failure_mode" in f - and not "confidence" in f + and "failure_mode" not in f + and "confidence" not in f ] for file_name in result_file_names: @@ -50,9 +47,7 @@ def on_pre_build(config, **kwargs) -> None: def plot_text2cypher(): - """ - - Get entity_selection, relationship_selection, property_selection, + """Get entity_selection, relationship_selection, property_selection, property_exists, query_generation, and end_to_end_query_generation results files, combine and preprocess them and plot the accuracy for each model as a boxplot. @@ -61,7 +56,7 @@ def plot_text2cypher(): entity_selection = pd.read_csv("benchmark/results/entity_selection.csv") entity_selection["task"] = "entity_selection" relationship_selection = pd.read_csv( - "benchmark/results/relationship_selection.csv" + "benchmark/results/relationship_selection.csv", ) relationship_selection["task"] = "relationship_selection" property_selection = pd.read_csv("benchmark/results/property_selection.csv") @@ -71,7 +66,7 @@ def plot_text2cypher(): query_generation = pd.read_csv("benchmark/results/query_generation.csv") query_generation["task"] = "query_generation" end_to_end_query_generation = pd.read_csv( - "benchmark/results/end_to_end_query_generation.csv" + "benchmark/results/end_to_end_query_generation.csv", ) end_to_end_query_generation["task"] = "end_to_end_query_generation" @@ -84,28 +79,20 @@ def plot_text2cypher(): property_exists, query_generation, end_to_end_query_generation, - ] + ], ) # calculate accuracy results["score_possible"] = results["score"].apply( - lambda x: float(x.split("/")[1]) + lambda x: float(x.split("/")[1]), ) results["scores"] = results["score"].apply(lambda x: x.split("/")[0]) results["score_achieved"] = results["scores"].apply( - lambda x: ( - np.mean([float(score) for score in x.split(";")]) - if ";" in x - else float(x) - ) + lambda x: (np.mean([float(score) for score in x.split(";")]) if ";" in x else float(x)), ) results["accuracy"] = results["score_achieved"] / results["score_possible"] results["score_sd"] = results["scores"].apply( - lambda x: ( - np.std([float(score) for score in x.split(";")], ddof=1) - if ";" in x - else 0 - ) + lambda x: (np.std([float(score) for score in x.split(";")], ddof=1) if ";" in x else 0), ) results["model"] = results["model_name"].apply(lambda x: x.split(":")[0]) @@ -115,25 +102,19 @@ def plot_text2cypher(): lambda x: ( "openhermes" if "openhermes" in x - else ( - "llama-3" - if "llama-3" in x - else "gpt" if "gpt" in x else "other open source" - ) - ) + else ("llama-3" if "llama-3" in x else "gpt" if "gpt" in x else "other open source") + ), ) # order task by median accuracy ascending - task_order = ( - results.groupby("task")["accuracy"].median().sort_values().index - ) + task_order = results.groupby("task")["accuracy"].median().sort_values().index # order model_family by median accuracy ascending within each task results["model_family"] = results["model_family"].astype( pd.CategoricalDtype( categories=["other open source", "llama-3", "openhermes", "gpt"], ordered=True, - ) + ), ) # plot results per task @@ -156,9 +137,7 @@ def plot_text2cypher(): def plot_text2cypher_safety_only(): - """ - - Get entity_selection, relationship_selection, property_selection, + """Get entity_selection, relationship_selection, property_selection, property_exists, query_generation, and end_to_end_query_generation results files, combine and preprocess them and plot the accuracy for each model as a boxplot. Only use data that has 'safety' in the task name. @@ -167,7 +146,7 @@ def plot_text2cypher_safety_only(): entity_selection = pd.read_csv("benchmark/results/entity_selection.csv") entity_selection["task"] = "entity_selection" relationship_selection = pd.read_csv( - "benchmark/results/relationship_selection.csv" + "benchmark/results/relationship_selection.csv", ) relationship_selection["task"] = "relationship_selection" property_selection = pd.read_csv("benchmark/results/property_selection.csv") @@ -177,7 +156,7 @@ def plot_text2cypher_safety_only(): query_generation = pd.read_csv("benchmark/results/query_generation.csv") query_generation["task"] = "query_generation" end_to_end_query_generation = pd.read_csv( - "benchmark/results/end_to_end_query_generation.csv" + "benchmark/results/end_to_end_query_generation.csv", ) end_to_end_query_generation["task"] = "end_to_end_query_generation" @@ -190,7 +169,7 @@ def plot_text2cypher_safety_only(): property_exists, query_generation, end_to_end_query_generation, - ] + ], ) # only include rows where 'safety' is in the subtask name @@ -198,41 +177,28 @@ def plot_text2cypher_safety_only(): # calculate accuracy results["score_possible"] = results["score"].apply( - lambda x: float(x.split("/")[1]) + lambda x: float(x.split("/")[1]), ) results["scores"] = results["score"].apply(lambda x: x.split("/")[0]) results["score_achieved"] = results["scores"].apply( - lambda x: ( - np.mean([float(score) for score in x.split(";")]) - if ";" in x - else float(x) - ) + lambda x: (np.mean([float(score) for score in x.split(";")]) if ";" in x else float(x)), ) results["accuracy"] = results["score_achieved"] / results["score_possible"] results["score_sd"] = results["scores"].apply( - lambda x: ( - np.std([float(score) for score in x.split(";")], ddof=1) - if ";" in x - else 0 - ) + lambda x: (np.std([float(score) for score in x.split(";")], ddof=1) if ";" in x else 0), ) results["model"] = results["model_name"].apply(lambda x: x.split(":")[0]) # order task by median accuracy ascending - task_order = ( - results.groupby("task")["accuracy"].median().sort_values().index - ) + task_order = results.groupby("task")["accuracy"].median().sort_values().index # order model by median accuracy ascending within each task results["model"] = results["model"].astype( pd.CategoricalDtype( - categories=results.groupby("model")["accuracy"] - .median() - .sort_values() - .index, + categories=results.groupby("model")["accuracy"].median().sort_values().index, ordered=True, - ) + ), ) # plot results per task @@ -255,8 +221,7 @@ def plot_text2cypher_safety_only(): def plot_image_caption_confidence(): - """ - Get multimodal_answer_confidence.csv file, preprocess it and plot the + """Get multimodal_answer_confidence.csv file, preprocess it and plot the confidence scores for correct and incorrect answers as histograms. Correct answer confidence values are in the correct_confidence column and incorrect answer confidence values are in the incorrect_confidence column; both @@ -267,9 +232,7 @@ def plot_image_caption_confidence(): correct_values = results["correct_confidence"].to_list() incorrect_values = results["incorrect_confidence"].to_list() # flatten lists of confidence values - correct_values = [ - int(value) for sublist in correct_values for value in sublist.split(";") - ] + correct_values = [int(value) for sublist in correct_values for value in sublist.split(";")] for value in list(incorrect_values): if math.isnan(value): incorrect_values.remove(value) @@ -302,44 +265,37 @@ def plot_image_caption_confidence(): def preprocess_results_for_frontend( - raw_results: pd.DataFrame, path: str, file_name: str + raw_results: pd.DataFrame, + path: str, + file_name: str, ) -> None: """Preprocesses the results for the frontend and store them on disk. Args: + ---- raw_results (pd.DataFrame): The raw results. path (str): The path to the result files. file_name (str): The file name of the result file. + """ raw_results["score_possible"] = raw_results.apply( - lambda x: float(x["score"].split("/")[1]) * x["iterations"], axis=1 + lambda x: float(x["score"].split("/")[1]) * x["iterations"], + axis=1, ) raw_results["scores"] = raw_results["score"].apply( - lambda x: x.split("/")[0] + lambda x: x.split("/")[0], ) raw_results["score_achieved"] = raw_results["scores"].apply( - lambda x: ( - np.sum([float(score) for score in x.split(";")]) - if ";" in x - else float(x) - ) + lambda x: (np.sum([float(score) for score in x.split(";")]) if ";" in x else float(x)), ) # multiply score_achieved by iterations if no semicolon in scores # TODO remove once all benchmarks are in new format raw_results["score_achieved"] = raw_results.apply( - lambda x: ( - x["score_achieved"] * x["iterations"] - if ";" not in x["scores"] - else x["score_achieved"] - ), + lambda x: (x["score_achieved"] * x["iterations"] if ";" not in x["scores"] else x["score_achieved"]), axis=1, ) raw_results["score_sd"] = raw_results["scores"].apply( - lambda x: ( - np.std([float(score) for score in x.split(";")], ddof=1) - if ";" in x - else 0 - ) + lambda x: (np.std([float(score) for score in x.split(";")], ddof=1) if ";" in x else 0), ) aggregated_scores = raw_results.groupby(["model_name"]).agg( { @@ -347,21 +303,15 @@ def preprocess_results_for_frontend( "score_achieved": "sum", "score_sd": "sum", "iterations": "first", - } + }, ) aggregated_scores["Accuracy"] = aggregated_scores.apply( - lambda row: ( - row["score_achieved"] / row["score_possible"] - if row["score_possible"] != 0 - else 0 - ), + lambda row: (row["score_achieved"] / row["score_possible"] if row["score_possible"] != 0 else 0), axis=1, ) - aggregated_scores["Full model name"] = ( - aggregated_scores.index.get_level_values("model_name") - ) + aggregated_scores["Full model name"] = aggregated_scores.index.get_level_values("model_name") aggregated_scores["Score achieved"] = aggregated_scores["score_achieved"] aggregated_scores["Score possible"] = aggregated_scores["score_possible"] aggregated_scores["Score SD"] = aggregated_scores["score_sd"] @@ -386,32 +336,24 @@ def preprocess_results_for_frontend( def write_individual_extraction_task_results(raw_results: pd.DataFrame) -> None: - """ - Write one csv file per subtask for sourcedata_info_extraction results in the + """Write one csv file per subtask for sourcedata_info_extraction results in the same format as the other results files. """ raw_results["subtask"] = raw_results["subtask"].apply( - lambda x: x.split(":")[1] + lambda x: x.split(":")[1], ) raw_results["score_possible"] = raw_results.apply( - lambda x: float(x["score"].split("/")[1]) * x["iterations"], axis=1 + lambda x: float(x["score"].split("/")[1]) * x["iterations"], + axis=1, ) raw_results["scores"] = raw_results["score"].apply( - lambda x: x.split("/")[0] + lambda x: x.split("/")[0], ) raw_results["score_achieved"] = raw_results["scores"].apply( - lambda x: ( - np.sum([float(score) for score in x.split(";")]) - if ";" in x - else float(x) - ) + lambda x: (np.sum([float(score) for score in x.split(";")]) if ";" in x else float(x)), ) raw_results["score_sd"] = raw_results["scores"].apply( - lambda x: ( - np.std([float(score) for score in x.split(";")], ddof=1) - if ";" in x - else 0 - ) + lambda x: (np.std([float(score) for score in x.split(";")], ddof=1) if ";" in x else 0), ) aggregated_scores = raw_results.groupby(["model_name", "subtask"]).agg( { @@ -419,23 +361,17 @@ def write_individual_extraction_task_results(raw_results: pd.DataFrame) -> None: "score_achieved": "sum", "score_sd": "mean", "iterations": "first", - } + }, ) aggregated_scores["Accuracy"] = aggregated_scores.apply( - lambda row: ( - row["score_achieved"] / row["score_possible"] - if row["score_possible"] != 0 - else 0 - ), + lambda row: (row["score_achieved"] / row["score_possible"] if row["score_possible"] != 0 else 0), axis=1, ) - aggregated_scores["Full model name"] = ( - aggregated_scores.index.get_level_values("model_name") - ) + aggregated_scores["Full model name"] = aggregated_scores.index.get_level_values("model_name") aggregated_scores["Subtask"] = aggregated_scores.index.get_level_values( - "subtask" + "subtask", ) aggregated_scores["Score achieved"] = aggregated_scores["score_achieved"] aggregated_scores["Score possible"] = aggregated_scores["score_possible"] @@ -461,14 +397,14 @@ def write_individual_extraction_task_results(raw_results: pd.DataFrame) -> None: def create_overview_table(result_files_path: str, result_file_names: list[str]): - """ - - Creates an overview table for the frontend with y-axis = models and x-axis = + """Creates an overview table for the frontend with y-axis = models and x-axis = tasks. Args: + ---- result_files_path (str): The path to the result files. result_file_names (List[str]): The names of the result files. + """ subtask_results = [] for file in result_file_names: @@ -490,29 +426,19 @@ def create_overview_table(result_files_path: str, result_file_names: list[str]): ) overview_per_quantisation = overview - overview_per_quantisation["Full model name"] = ( - overview_per_quantisation.index - ) - overview_per_quantisation[ - ["Model name", "Size", "Version", "Quantisation"] - ] = overview_per_quantisation["Full model name"].str.split(":", expand=True) + overview_per_quantisation["Full model name"] = overview_per_quantisation.index + overview_per_quantisation[["Model name", "Size", "Version", "Quantisation"]] = overview_per_quantisation[ + "Full model name" + ].str.split(":", expand=True) # convert underscores in Size to commas - overview_per_quantisation["Size"] = overview_per_quantisation[ - "Size" - ].str.replace("_", ",") + overview_per_quantisation["Size"] = overview_per_quantisation["Size"].str.replace("_", ",") # add size 175 for gpt-3.5-turbo and Unknown for gpt-4 overview_per_quantisation["Size"] = overview_per_quantisation.apply( - lambda row: ( - "175" if "gpt-3.5-turbo" in row["Model name"] else row["Size"] - ), + lambda row: ("175" if "gpt-3.5-turbo" in row["Model name"] else row["Size"]), axis=1, ) overview_per_quantisation["Size"] = overview_per_quantisation.apply( - lambda row: ( - "Unknown" - if "gpt-4" in row["Model name"] or "claude" in row["Model name"] - else row["Size"] - ), + lambda row: ("Unknown" if "gpt-4" in row["Model name"] or "claude" in row["Model name"] else row["Size"]), axis=1, ) overview_per_quantisation = overview_per_quantisation[ @@ -526,12 +452,8 @@ def create_overview_table(result_files_path: str, result_file_names: list[str]): ] ] # round mean and sd to 2 decimal places - overview_per_quantisation.loc[:, "Median Accuracy"] = ( - overview_per_quantisation["Median Accuracy"].round(2) - ) - overview_per_quantisation.loc[:, "SD"] = overview_per_quantisation[ - "SD" - ].round(2) + overview_per_quantisation.loc[:, "Median Accuracy"] = overview_per_quantisation["Median Accuracy"].round(2) + overview_per_quantisation.loc[:, "SD"] = overview_per_quantisation["SD"].round(2) overview_per_quantisation.to_csv( f"{result_files_path}processed/overview-quantisation.csv", index=False, @@ -540,21 +462,20 @@ def create_overview_table(result_files_path: str, result_file_names: list[str]): # group by model name and size, aggregate different quantisations # keep models that do not have sizes overview_per_size = overview_per_quantisation.groupby( - ["Model name", "Size"] + ["Model name", "Size"], ).agg( { "Median Accuracy": "median", "SD": "mean", - } + }, ) # round mean and SD to 2 decimal places - overview_per_size["Median Accuracy"] = overview_per_size[ - "Median Accuracy" - ].round(2) + overview_per_size["Median Accuracy"] = overview_per_size["Median Accuracy"].round(2) overview_per_size["SD"] = overview_per_size["SD"].round(2) # sort by mean, descending overview_per_size = overview_per_size.sort_values( - by="Median Accuracy", ascending=False + by="Median Accuracy", + ascending=False, ) overview_per_size.to_csv( f"{result_files_path}processed/overview-model.csv", @@ -570,10 +491,13 @@ def plot_accuracy_per_model(overview) -> None: sns.set_theme(style="whitegrid") plt.figure(figsize=(10, 6)) sns.stripplot( - x="Model name", y="Accuracy", hue="Size", data=overview_melted + x="Model name", + y="Accuracy", + hue="Size", + data=overview_melted, ) plt.title( - "Strip plot across tasks, per Model, coloured by size (billions of parameters)" + "Strip plot across tasks, per Model, coloured by size (billions of parameters)", ) plt.ylim(-0.1, 1.1) plt.xticks(rotation=45, ha="right") @@ -592,7 +516,10 @@ def plot_accuracy_per_quantisation(overview) -> None: plt.figure(figsize=(10, 6)) sns.boxplot( - x="Model name", y="Accuracy", hue="Quantisation", data=overview_melted + x="Model name", + y="Accuracy", + hue="Quantisation", + data=overview_melted, ) plt.title("Boxplot across tasks, per Quantisation") plt.xticks(rotation=45) @@ -608,9 +535,7 @@ def plot_accuracy_per_task(overview): overview_melted = melt_and_process(overview) # concatenate model name and quantisation - overview_melted["Coarse model name"] = overview_melted[ - "Model name" - ].replace( + overview_melted["Coarse model name"] = overview_melted["Model name"].replace( { "gpt-3.5-turbo-0613": "gpt-3.5-turbo", "gpt-3.5-turbo-0125": "gpt-3.5-turbo", @@ -627,20 +552,18 @@ def plot_accuracy_per_task(overview): # Define the color palette palette = sns.color_palette( - "Set1", len(overview_melted["Coarse model name"].unique()) + "Set1", + len(overview_melted["Coarse model name"].unique()), ) # Calculate mean accuracy for each task - task_order = ( - overview_melted.groupby("Task")["Accuracy"] - .mean() - .sort_values() - .index[::-1] - ) + task_order = overview_melted.groupby("Task")["Accuracy"].mean().sort_values().index[::-1] # Sort the dataframe overview_melted["Task"] = pd.Categorical( - overview_melted["Task"], categories=task_order, ordered=True + overview_melted["Task"], + categories=task_order, + ordered=True, ) overview_melted = overview_melted.sort_values("Task") @@ -744,7 +667,9 @@ def plot_scatter_per_quantisation(overview): # Create a mask for 'openhermes' and closed models mask_openhermes = overview_melted["Model name"] == "openhermes-2.5" mask_closed = overview_melted["Model name"].str.contains( - "gpt|claude", case=False, regex=True + "gpt|claude", + case=False, + regex=True, ) # Do not add jitter for 'openhermes' model @@ -764,7 +689,9 @@ def plot_scatter_per_quantisation(overview): # For other models, add the original jitter x[~mask_openhermes & ~mask_closed] += np.random.normal( - 0, 0.1, size=len(x[~mask_openhermes & ~mask_closed]) + 0, + 0.1, + size=len(x[~mask_openhermes & ~mask_closed]), ) # Define the order of model names @@ -806,9 +733,7 @@ def plot_scatter_per_quantisation(overview): palette = sns.color_palette(cc.glasbey, n_colors=len(model_names_order)) # Define a dictionary mapping model names to colors using the order list - color_dict = { - model: palette[i] for i, model in enumerate(model_names_order) - } + color_dict = {model: palette[i] for i, model in enumerate(model_names_order)} # Use the dictionary as the palette argument in sns.scatterplot ax = sns.scatterplot( @@ -844,7 +769,7 @@ def plot_scatter_per_quantisation(overview): labels=overview_melted["Quantisation"].cat.categories, ) plt.title( - "Scatter plot across models, per quantisation, coloured by model name, size by model size (billions of parameters)" + "Scatter plot across models, per quantisation, coloured by model name, size by model size (billions of parameters)", ) plt.xticks(rotation=45) plt.savefig( @@ -887,17 +812,14 @@ def plot_rag_tasks(overview): [ "explicit_relevance_of_single_fragments", "implicit_relevance_of_multiple_fragments", - ] + ], ) ] # order models by median accuracy, inverse overview_melted["Model name"] = pd.Categorical( overview_melted["Model name"], - categories=overview_melted.groupby("Model name")["Median Accuracy"] - .median() - .sort_values() - .index[::-1], + categories=overview_melted.groupby("Model name")["Median Accuracy"].median().sort_values().index[::-1], ordered=True, ) @@ -934,55 +856,44 @@ def plot_rag_tasks(overview): def plot_extraction_tasks(): - """ - Load raw result file for sourcedata_info_extraction; aggregate based on the + """Load raw result file for sourcedata_info_extraction; aggregate based on the subtask name and calculate mean accuracy for each model. Plot a stripplot of the mean accuracy across models, coloured by subtask. """ sourcedata_info_extraction = pd.read_csv( - "benchmark/results/sourcedata_info_extraction.csv" + "benchmark/results/sourcedata_info_extraction.csv", ) # split subtask at colon and use second element - sourcedata_info_extraction["subtask"] = sourcedata_info_extraction[ - "subtask" - ].apply(lambda x: x.split(":")[1]) - sourcedata_info_extraction["score_possible"] = sourcedata_info_extraction[ - "score" - ].apply(lambda x: float(x.split("/")[1])) - sourcedata_info_extraction["scores"] = sourcedata_info_extraction[ - "score" - ].apply(lambda x: x.split("/")[0]) - sourcedata_info_extraction["score_achieved"] = sourcedata_info_extraction[ - "scores" - ].apply(lambda x: np.mean(float(x.split(";")[0])) if ";" in x else float(x)) - sourcedata_info_extraction["score_sd"] = sourcedata_info_extraction[ - "scores" - ].apply(lambda x: np.std(float(x.split(";")[0])) if ";" in x else 0) + sourcedata_info_extraction["subtask"] = sourcedata_info_extraction["subtask"].apply(lambda x: x.split(":")[1]) + sourcedata_info_extraction["score_possible"] = sourcedata_info_extraction["score"].apply( + lambda x: float(x.split("/")[1]), + ) + sourcedata_info_extraction["scores"] = sourcedata_info_extraction["score"].apply(lambda x: x.split("/")[0]) + sourcedata_info_extraction["score_achieved"] = sourcedata_info_extraction["scores"].apply( + lambda x: np.mean(float(x.split(";")[0])) if ";" in x else float(x), + ) + sourcedata_info_extraction["score_sd"] = sourcedata_info_extraction["scores"].apply( + lambda x: np.std(float(x.split(";")[0])) if ";" in x else 0, + ) aggregated_scores = sourcedata_info_extraction.groupby( - ["model_name", "subtask"] + ["model_name", "subtask"], ).agg( { "score_possible": "sum", "score_achieved": "sum", "score_sd": "first", "iterations": "first", - } + }, ) aggregated_scores["Accuracy"] = aggregated_scores.apply( - lambda row: ( - row["score_achieved"] / row["score_possible"] - if row["score_possible"] != 0 - else 0 - ), + lambda row: (row["score_achieved"] / row["score_possible"] if row["score_possible"] != 0 else 0), axis=1, ) - aggregated_scores["Full model name"] = ( - aggregated_scores.index.get_level_values("model_name") - ) + aggregated_scores["Full model name"] = aggregated_scores.index.get_level_values("model_name") aggregated_scores["Subtask"] = aggregated_scores.index.get_level_values( - "subtask" + "subtask", ) aggregated_scores["Score achieved"] = aggregated_scores["score_achieved"] aggregated_scores["Score possible"] = aggregated_scores["score_possible"] @@ -1021,44 +932,33 @@ def plot_extraction_tasks(): def plot_medical_exam(): - """ - Load raw result for medical_exam; aggregate based on the language and + """Load raw result for medical_exam; aggregate based on the language and calculate mean accuracy for each model. Plot a stripplot of the mean accuracy across models, coloured by language. """ medical_exam = pd.read_csv("benchmark/results/medical_exam.csv") medical_exam["score_possible"] = medical_exam["score"].apply( - lambda x: float(x.split("/")[1]) + lambda x: float(x.split("/")[1]), ) medical_exam["scores"] = medical_exam["score"].apply( - lambda x: x.split("/")[0] + lambda x: x.split("/")[0], ) medical_exam["score_achieved"] = medical_exam["scores"].apply( - lambda x: ( - np.mean([float(score) for score in x.split(";")]) - if ";" in x - else float(x) - ) - ) - medical_exam["accuracy"] = ( - medical_exam["score_achieved"] / medical_exam["score_possible"] + lambda x: (np.mean([float(score) for score in x.split(";")]) if ";" in x else float(x)), ) + medical_exam["accuracy"] = medical_exam["score_achieved"] / medical_exam["score_possible"] medical_exam["score_sd"] = medical_exam["scores"].apply( - lambda x: ( - np.std([float(score) for score in x.split(";")], ddof=1) - if ";" in x - else 0 - ) + lambda x: (np.std([float(score) for score in x.split(";")], ddof=1) if ";" in x else 0), ) medical_exam["task"] = medical_exam["subtask"].apply( - lambda x: x.split(":")[0] + lambda x: x.split(":")[0], ) medical_exam["domain"] = medical_exam["subtask"].apply( - lambda x: x.split(":")[1] + lambda x: x.split(":")[1], ) medical_exam["language"] = medical_exam["subtask"].apply( - lambda x: x.split(":")[2] + lambda x: x.split(":")[2], ) # processing: remove "short_words" task, not informative @@ -1066,12 +966,12 @@ def plot_medical_exam(): # plot language comparison aggregated_scores_language = medical_exam.groupby( - ["model_name", "language"] + ["model_name", "language"], ).agg( { "accuracy": "mean", "score_sd": "mean", - } + }, ) sns.set_theme(style="whitegrid") plt.figure(figsize=(6, 4)) @@ -1089,20 +989,20 @@ def plot_medical_exam(): # plot language comparison per domain aggregated_scores_language_domain = medical_exam.groupby( - ["model_name", "language", "domain"] + ["model_name", "language", "domain"], ).agg( { "accuracy": "mean", "score_sd": "mean", - } + }, ) # calculate mean accuracy per language and domain mean_accuracy = aggregated_scores_language_domain.groupby( - ["language", "domain"] + ["language", "domain"], )["accuracy"].mean() # sort domains by mean accuracy sorted_domains = mean_accuracy.sort_values( - ascending=False + ascending=False, ).index.get_level_values("domain") sns.set_theme(style="whitegrid") @@ -1127,7 +1027,7 @@ def plot_medical_exam(): { "accuracy": "mean", "score_sd": "mean", - } + }, ) # calculate mean accuracy per task mean_accuracy = aggregated_scores_task.groupby("task")["accuracy"].mean() @@ -1152,17 +1052,15 @@ def plot_medical_exam(): # plot domain comparison aggregated_scores_domain = medical_exam.groupby( - ["model_name", "domain"] + ["model_name", "domain"], ).agg( { "accuracy": "mean", "score_sd": "mean", - } + }, ) # calculate mean accuracy per domain - mean_accuracy = aggregated_scores_domain.groupby("domain")[ - "accuracy" - ].mean() + mean_accuracy = aggregated_scores_domain.groupby("domain")["accuracy"].mean() # sort domains by mean accuracy sorted_domains = mean_accuracy.sort_values(ascending=False).index @@ -1189,7 +1087,7 @@ def plot_comparison_naive_biochatter(overview): # select tasks naive_query_generation_using_schema and query_generation overview_melted = overview_melted[ overview_melted["Task"].isin( - ["naive_query_generation_using_schema", "query_generation"] + ["naive_query_generation_using_schema", "query_generation"], ) ] @@ -1277,12 +1175,8 @@ def calculate_stats(overview): # calculate p-value between naive and biochatter from scipy.stats import ttest_ind - biochatter = overview_melted[overview_melted["Task"] == "query_generation"][ - "Accuracy" - ].dropna() - naive = overview_melted[ - overview_melted["Task"] == "naive_query_generation_using_schema" - ]["Accuracy"].dropna() + biochatter = overview_melted[overview_melted["Task"] == "query_generation"]["Accuracy"].dropna() + naive = overview_melted[overview_melted["Task"] == "naive_query_generation_using_schema"]["Accuracy"].dropna() t_stat, p_value = ttest_ind(biochatter, naive) @@ -1298,7 +1192,7 @@ def calculate_stats(overview): # calculate correlation between LLM size and accuracy for all tasks # convert size to float, make Unknown = 300, replace commas with dots size = overview_melted["Size"].apply( - lambda x: 300 if x == "Unknown" else float(x.replace(",", ".")) + lambda x: 300 if x == "Unknown" else float(x.replace(",", ".")), ) from scipy.stats import pearsonr @@ -1331,7 +1225,7 @@ def calculate_stats(overview): # calculate correlation between quantisation and accuracy for all tasks # convert quantisation to float, make >= 16-bit* = 16, replace -bit with nothing quantisation = overview_melted["Quantisation"].apply( - lambda x: 16 if x == ">= 16-bit*" else float(x.replace("-bit", "")) + lambda x: 16 if x == ">= 16-bit*" else float(x.replace("-bit", "")), ) # Create a mask of rows where 'Accuracy' is not NaN mask = overview_melted["Accuracy"].notna() @@ -1341,7 +1235,8 @@ def calculate_stats(overview): accuracy = overview_melted["Accuracy"][mask] quant_accuracy_corr, quant_accuracy_p_value = pearsonr( - quantisation, accuracy + quantisation, + accuracy, ) # plot scatter plot plt.figure(figsize=(6, 4)) @@ -1364,13 +1259,13 @@ def calculate_stats(overview): with open("benchmark/results/processed/correlations.txt", "w") as f: f.write(f"Size vs accuracy Pearson correlation: {size_accuracy_corr}\n") f.write( - f"Size vs accuracy Pearson correlation p-value: {size_accuracy_p_value}\n" + f"Size vs accuracy Pearson correlation p-value: {size_accuracy_p_value}\n", ) f.write( - f"Quantisation vs accuracy Pearson correlation: {quant_accuracy_corr}\n" + f"Quantisation vs accuracy Pearson correlation: {quant_accuracy_corr}\n", ) f.write( - f"Quantisation vs accuracy Pearson correlation p-value: {quant_accuracy_p_value}\n" + f"Quantisation vs accuracy Pearson correlation p-value: {quant_accuracy_p_value}\n", ) @@ -1392,15 +1287,13 @@ def melt_and_process(overview): # unify quantisation names: 2-bit, 3-bit, etc digit_pattern = r"\d+" overview_melted["Quantisation"] = overview_melted["Quantisation"].apply( - lambda x: f"{re.findall(digit_pattern, x)[0]}-bit" if x else "None" + lambda x: f"{re.findall(digit_pattern, x)[0]}-bit" if x else "None", ) # set quantisation of gpt models to None overview_melted["Quantisation"] = overview_melted.apply( lambda row: ( ">= 16-bit*" - if "gpt-3.5-turbo" in row["Model name"] - or "gpt-4" in row["Model name"] - or "claude" in row["Model name"] + if "gpt-3.5-turbo" in row["Model name"] or "gpt-4" in row["Model name"] or "claude" in row["Model name"] else row["Quantisation"] ), axis=1, diff --git a/poetry.lock b/poetry.lock index 63fedbb3..7489f058 100644 --- a/poetry.lock +++ b/poetry.lock @@ -13,55 +13,34 @@ files = [ [[package]] name = "accelerate" -version = "0.33.0" +version = "1.1.1" description = "Accelerate" optional = 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-isort = "^5.10.1" -flake8 = "^6.1.0" pre-commit = ">=2.17.0" mkdocs-table-reader-plugin = "^2.0.3" scipy = "^1.12.0" @@ -87,48 +84,10 @@ mkdocs-redirects = "^1.2.1" requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" -[tool.black] -line-length = 80 -target-version = ['py310'] -include = '\.pyi?$' -exclude = ''' -( - /( - \.eggs - | \.git - | \.hg - | \.mypy_cache - | \.tox - | \.venv - | _build - | buck-out - | build - | dist - )/ -) -''' +[tool.ruff] +line-length = 120 +select = ["ALL"] +target-version = "py310" -[tool.isort] -from_first = true -line_length = 80 -multi_line_output = 3 -include_trailing_comma = true -use_parentheses = true -known_num="numpy,pandas" -sections = "FUTURE,STDLIB,THIRDPARTY,NUM,FIRSTPARTY,LOCALFOLDER" -no_lines_before="LOCALFOLDER" -balanced_wrapping = true -force_grid_wrap = 0 -length_sort = "1" -indent = " " -profile = "black" - -[tool.flake8] -ignore = ["E203", "D200", "D202", "D401", "D105", "W504"] -per-file-ignores = [ - "docs/source/conf.py:D100", - "tests/*:D100,D101,D102", - "*/__init__.py:F401" -] -max-line-length = 80 -count = true +[tool.ruff.lint.pycodestyle] +max-doc-length = 80 diff --git a/scripts/add_version_column.py b/scripts/add_version_column.py index d650215a..ea08125a 100644 --- a/scripts/add_version_column.py +++ b/scripts/add_version_column.py @@ -4,16 +4,11 @@ def _add_version_column(): - """ - Go through all benchmark result files and add a column for the version of + """Go through all benchmark result files and add a column for the version of biochatter, `biochatter_version`, to the results. Also add the value `0.4.10` as a default value to all rows. """ - result_files = [ - f"benchmark/results/{file}" - for file in os.listdir("benchmark/results") - if file.endswith(".csv") - ] + result_files = [f"benchmark/results/{file}" for file in os.listdir("benchmark/results") if file.endswith(".csv")] for file in result_files: result_file = pd.read_csv(file, header=0) diff --git a/scripts/ollama_conversation.py b/scripts/ollama_conversation.py index 78f6b05d..53fddbf5 100644 --- a/scripts/ollama_conversation.py +++ b/scripts/ollama_conversation.py @@ -1,5 +1,4 @@ -""" -Short helper script to quickly run a conversation with a self-hosted LLM via the +"""Short helper script to quickly run a conversation with a self-hosted LLM via the XinferenceConversation class. """ diff --git a/scripts/podcast_single_document.py b/scripts/podcast_single_document.py index 29fe6b2e..7b8d0afe 100755 --- a/scripts/podcast_single_document.py +++ b/scripts/podcast_single_document.py @@ -1,22 +1,26 @@ #!/usr/bin/env python3 -import os import argparse +import os from biochatter.podcast import Podcaster from biochatter.vectorstore import DocumentReader # Create the parser parser = argparse.ArgumentParser( - description="Generate a podcast from a document." + description="Generate a podcast from a document.", ) # Add the arguments parser.add_argument( - "input_path", type=str, help="The path to the input document." + "input_path", + type=str, + help="The path to the input document.", ) parser.add_argument( - "output_path", type=str, help="The path to the output document." + "output_path", + type=str, + help="The path to the output document.", ) try: args = parser.parse_args() diff --git a/scripts/update_benchmark_hashes.py b/scripts/update_benchmark_hashes.py index 6c09db12..d75dd9d8 100644 --- a/scripts/update_benchmark_hashes.py +++ b/scripts/update_benchmark_hashes.py @@ -1,15 +1,13 @@ import os -import yaml - import pandas as pd +import yaml from benchmark.load_dataset import _get_all_benchmark_files, _get_yaml_data def _update_hashes_in_results(): - """ - This function replaces the md5_hash in the benchmark results with the + """This function replaces the md5_hash in the benchmark results with the corresponding hash from the reference_hashes, matching the case in the subtask column. Do this only if the test has not changed, but for some reason the hash has changed without affecting the test. This is purely to @@ -20,7 +18,7 @@ def _update_hashes_in_results(): for file_path in files_in_directory: if file_path.endswith(".yaml"): - with open(file_path, "r") as stream: + with open(file_path) as stream: try: yaml_data = yaml.safe_load(stream) if "_data" in file_path: @@ -33,23 +31,18 @@ def _update_hashes_in_results(): if isinstance(yaml_data[key], list): for i in range(len(yaml_data[key])): if isinstance(yaml_data[key][i], dict): - reference_hashes[ - yaml_data[key][i]["case"] - ] = yaml_data[key][i]["hash"] + reference_hashes[yaml_data[key][i]["case"]] = yaml_data[key][i]["hash"] current_hashes.add( - yaml_data[key][i]["hash"] + yaml_data[key][i]["hash"], ) reference_hashes = { - key.replace("_", "").replace(":", ""): value - for key, value in reference_hashes.items() + key.replace("_", "").replace(":", ""): value for key, value in reference_hashes.items() } # get all result files result_files = [ - f"benchmark/results/{file}" - for file in os.listdir("benchmark/results") - if file.endswith(".csv") + f"benchmark/results/{file}" for file in os.listdir("benchmark/results") if file.endswith(".csv") ] # update hashes in results @@ -59,12 +52,8 @@ def _update_hashes_in_results(): # corresponding hash from reference_hashes, matching the # case in the subtask column for index, row in result_file.iterrows(): - subtask = ( - row["subtask"].replace("_", "").replace(":", "") - ) - result_file.at[index, "md5_hash"] = ( - reference_hashes[subtask] - ) + subtask = row["subtask"].replace("_", "").replace(":", "") + result_file.at[index, "md5_hash"] = reference_hashes[subtask] result_file.to_csv(file, index=False) diff --git a/scripts/xinference_conversation.py b/scripts/xinference_conversation.py index 5954ff0a..e3d581bd 100644 --- a/scripts/xinference_conversation.py +++ b/scripts/xinference_conversation.py @@ -1,5 +1,4 @@ -""" -Short helper script to quickly run a conversation with a self-hosted LLM via the +"""Short helper script to quickly run a conversation with a self-hosted LLM via the XinferenceConversation class. """ diff --git a/test/mock_langchain.py b/test/mock_langchain.py index 616030ed..1e85b263 100644 --- a/test/mock_langchain.py +++ b/test/mock_langchain.py @@ -1,7 +1,6 @@ -from ctypes import Union -from typing import Any, Optional -from unittest.mock import Mock, MagicMock import uuid +from typing import Any +from unittest.mock import Mock class Document: @@ -24,17 +23,15 @@ class OpenAIEmbeddings: pass -class Milvus(object): +class Milvus: def __init__( self, - embedding_function: Optional[OpenAIEmbeddings] = None, - collection_name: Optional[str] = "default", - connection_args: Optional[dict[str, Any]] = None, - documents: Optional[list[Document]] = None, + embedding_function: OpenAIEmbeddings | None = None, + collection_name: str | None = "default", + connection_args: dict[str, Any] | None = None, + documents: list[Document] | None = None, ) -> None: - self.documents: dict[list[Document]] = ( - {} if documents is None else {uuid.uuid4().hex: documents} - ) + self.documents: dict[list[Document]] = {} if documents is None else {uuid.uuid4().hex: documents} self.col = Mock() self.col.query = Mock() self.col.query.return_value = [] @@ -55,13 +52,14 @@ def remove_document(self, id: str): self.documents.pop(id) def similarity_search( - self, query: str, k: int, expr: Optional[str] = None + self, + query: str, + k: int, + expr: str | None = None, ) -> list[Document]: from random import randint - total_docs: list[Document] = [ - doc for id in self.documents.keys() for doc in self.documents[id] - ] + total_docs: list[Document] = [doc for id in self.documents.keys() for doc in self.documents[id]] if len(total_docs) < k: return [doc for doc in total_docs] ret_docs = [] diff --git a/test/mock_pymilvus.py b/test/mock_pymilvus.py index 7760e9b1..8c2e08f3 100644 --- a/test/mock_pymilvus.py +++ b/test/mock_pymilvus.py @@ -1,24 +1,24 @@ -from typing import Any, Union, Optional import uuid +from typing import Any from pymilvus import DataType, FieldSchema -def has_collection(name, using: Optional[str] = None): +def has_collection(name, using: str | None = None): return True -class CollectionSchema(object): +class CollectionSchema: def __init__(self, fields: list[FieldSchema]): pass -class CollectionRecord(object): +class CollectionRecord: def __init__(self, key: str) -> None: self.primary_keys = [key] -class Collection(object): +class Collection: def __init__( self, name: str, @@ -41,7 +41,7 @@ def delete(self, expr: str): def flush(self): pass - def similarity_search(self, query: str, k: int, expr: Optional[str] = None): + def similarity_search(self, query: str, k: int, expr: str | None = None): pass def load(self): @@ -55,15 +55,18 @@ def indexes(self): return [] def create_index( - self, field_name: str, index_params: dict[str, Any], using: str + self, + field_name: str, + index_params: dict[str, Any], + using: str, ): pass def insert( self, - data: Union[list, dict], - partition_name: Optional[str] = None, - timeout: Optional[float] = None, + data: list | dict, + partition_name: str | None = None, + timeout: float | None = None, **kwargs, ): id = uuid.uuid4().hex @@ -71,14 +74,14 @@ def insert( return CollectionRecord(id) -class Connections(object): +class Connections: def connect( self, host: str, port: str, - alias: Optional[str] = None, - user: Optional[str] = "", - password: Optional[str] = "", + alias: str | None = None, + user: str | None = "", + password: str | None = "", ): pass @@ -87,7 +90,7 @@ def connect( class Utility: - def has_collection(self, name: str, using: Optional[str] = None): + def has_collection(self, name: str, using: str | None = None): return True diff --git a/test/no_podcast.py b/test/no_podcast.py index 91e33c9c..d7c10f99 100644 --- a/test/no_podcast.py +++ b/test/no_podcast.py @@ -7,10 +7,7 @@ @pytest.mark.skip( - reason=( - "More of an integration test without asserts. " - "Currently used for debugging, to be later replaced." - ) + reason=("More of an integration test without asserts. Currently used for debugging, to be later replaced."), ) def test_podcast(): # runs long, requires OpenAI API key @@ -36,11 +33,7 @@ def test_podcast_to_text(): # test the podcast_to_text method expected_text = ( - "You are listening to: " - + podcaster.podcast_info - + "\n\n" - + "\n\n".join(podcaster.processed_sections) - + "\n\n" + "You are listening to: " + podcaster.podcast_info + "\n\n" + "\n\n".join(podcaster.processed_sections) + "\n\n" ) assert podcaster.podcast_to_text() == expected_text diff --git a/test/test_agent_selector.py b/test/test_agent_selector.py index 45201c5a..fccbc6d5 100644 --- a/test/test_agent_selector.py +++ b/test/test_agent_selector.py @@ -1,23 +1,20 @@ -from unittest.mock import MagicMock, patch -import os import json import logging +from unittest.mock import MagicMock +import neo4j_utils as nu +import shortuuid from dotenv import load_dotenv -from langchain_core.messages import AIMessage, BaseMessage from langchain.output_parsers.openai_tools import PydanticToolsParser -import pytest -import shortuuid -import neo4j_utils as nu +from langchain_core.messages import AIMessage, BaseMessage -from biochatter.rag_agent import RagAgent, RagAgentModeEnum -from biochatter.llm_connect import AzureGptConversation +from biochatter.langgraph_agent_base import ResponderWithRetries +from biochatter.rag_agent import RagAgentModeEnum from biochatter.selector_agent import ( - RagAgentSelector, RagAgentChoiceModel, RagAgentRevisionModel, + RagAgentSelector, ) -from biochatter.langgraph_agent_base import ResponderWithRetries load_dotenv() @@ -30,12 +27,7 @@ def find_schema_info_node(connection_args: dict): Look for a schema info node in the connected BioCypher graph and load the schema info if present. """ - db_uri = ( - "bolt://" - + connection_args.get("host") - + ":" - + connection_args.get("port") - ) + db_uri = "bolt://" + connection_args.get("host") + ":" + connection_args.get("port") neodriver = nu.Driver( db_name=connection_args.get("db_name") or "neo4j", db_uri=db_uri, @@ -70,14 +62,12 @@ def invoke(self, msg_obj: dict[str, list[BaseMessage]]) -> BaseMessage: { "id": id, "function": { - "arguments": '{"answer":"' - + f"{self.rag_agent}" - + '","reflection":"mock reflection"}', + "arguments": '{"answer":"' + f"{self.rag_agent}" + '","reflection":"mock reflection"}', "name": "RagAgentChoiceModel", }, "type": "function", - } - ] + }, + ], } msg.id = id msg.tool_calls = [ @@ -88,7 +78,7 @@ def invoke(self, msg_obj: dict[str, list[BaseMessage]]) -> BaseMessage: "reflection": "mock reflection", }, "id": id, - } + }, ] return msg @@ -113,8 +103,8 @@ def invoke(self, msg_obj: dict[str, list[BaseMessage]]) -> BaseMessage: "name": "RagAgentRevisionModel", }, "type": "function", - } - ] + }, + ], }, response_metadata={ "token_usage": { @@ -136,7 +126,7 @@ def invoke(self, msg_obj: dict[str, list[BaseMessage]]) -> BaseMessage: "sexual": {"filtered": False, "severity": "safe"}, "violence": {"filtered": False, "severity": "safe"}, }, - } + }, ], "finish_reason": "stop", "logprobs": None, @@ -154,7 +144,7 @@ def invoke(self, msg_obj: dict[str, list[BaseMessage]]) -> BaseMessage: "tool_result": "mock result", }, "id": "call_wTO40b9", - } + }, ], ) @@ -165,7 +155,8 @@ def __init__(self, rag_agents, conversation_factory, expected: str): self.expected_ragagent = expected def _create_initial_responder( - self, prompt: str | None = None + self, + prompt: str | None = None, ) -> ResponderWithRetries: runnable = InitialResponder(self.expected_ragagent) validator = PydanticToolsParser(tools=[RagAgentChoiceModel]) @@ -175,7 +166,8 @@ def _create_initial_responder( ) def _create_revise_responder( - self, prompt: str | None = None + self, + prompt: str | None = None, ) -> ResponderWithRetries: runnable = ReviseResponder(self.expected_ragagent) validator = PydanticToolsParser(tools=[RagAgentRevisionModel]) @@ -190,15 +182,15 @@ def create_agent_selector(expected_rag_agent: str): return_value=[ ('{"bp.name": "mock"}', {"cypher_query": "mock query"}), ('{"bp.name": "mock"}', {"cypher_query": "balahbalah"}), - ] + ], ) vectorstoreAgent = MagicMock() vectorstoreAgent.mode = RagAgentModeEnum.VectorStore vectorstoreAgent.get_description = MagicMock( - return_value="mock vector store" + return_value="mock vector store", ) vectorstoreAgent.generate_responses = MagicMock( - return_value=["mock content 1", "mock content 2"] + return_value=["mock content 1", "mock content 2"], ) blastAgent = MagicMock() blastAgent.mode = RagAgentModeEnum.API_BLAST @@ -207,7 +199,7 @@ def create_agent_selector(expected_rag_agent: str): return_value=[ "blast search result 1", "blast search result 2", - ] + ], ) oncokbAgent = MagicMock() oncokbAgent.mode = RagAgentModeEnum.API_ONCOKB @@ -216,7 +208,7 @@ def create_agent_selector(expected_rag_agent: str): return_value=[ "oncokb search result 1", "oncokb search result 2", - ] + ], ) return AgentSelectorMock( rag_agents=[dbAgent, vectorstoreAgent, blastAgent, oncokbAgent], @@ -228,7 +220,7 @@ def create_agent_selector(expected_rag_agent: str): def test_agent_selector_oncokb(): agent_selector = create_agent_selector(str(RagAgentModeEnum.API_ONCOKB)) result = agent_selector.execute( - "What is the oncogenic potential of BRAF V600E mutation?" + "What is the oncogenic potential of BRAF V600E mutation?", ) assert result.answer == "api_oncokb" @@ -236,7 +228,7 @@ def test_agent_selector_oncokb(): def test_agent_selector_blast(): agent_selector = create_agent_selector(str(RagAgentModeEnum.API_BLAST)) result = agent_selector.execute( - "Which organism does this sequence belong to? GATACGCGGCCACAGTACGACAATCTTCAG" + "Which organism does this sequence belong to? GATACGCGGCCACAGTACGACAATCTTCAG", ) assert result.answer == "api_blast" @@ -250,6 +242,6 @@ def test_agent_selector_kg(): def test_agent_selector_vectorstore(): agent_selector = create_agent_selector(str(RagAgentModeEnum.VectorStore)) result = agent_selector.execute( - "What is reported in the literature about the BRAF V600E mutation?" + "What is reported in the literature about the BRAF V600E mutation?", ) assert result.answer == "vectorstore" diff --git a/test/test_api_agent.py b/test/test_api_agent.py index fe4165c4..6c082658 100644 --- a/test/test_api_agent.py +++ b/test/test_api_agent.py @@ -1,17 +1,17 @@ -from typing import Any -from unittest.mock import Mock, MagicMock, patch -from collections.abc import Callable import os +from collections.abc import Callable +from typing import Any +from unittest.mock import MagicMock, patch -from pydantic import BaseModel import pytest +from pydantic import BaseModel -from biochatter.llm_connect import Conversation, GptConversation from biochatter.api_agent.abc import ( BaseFetcher, BaseInterpreter, BaseQueryBuilder, ) +from biochatter.api_agent.api_agent import APIAgent from biochatter.api_agent.blast import ( BLAST_QUERY_PROMPT, BLAST_SUMMARY_PROMPT, @@ -28,7 +28,7 @@ OncoKBQueryBuilder, OncoKBQueryParameters, ) -from biochatter.api_agent.api_agent import APIAgent +from biochatter.llm_connect import Conversation, GptConversation def conversation_factory(): @@ -43,12 +43,16 @@ def conversation_factory(): class TestQueryBuilder(BaseQueryBuilder): def create_runnable( - self, query_parameters: BaseModel, conversation: Conversation + self, + query_parameters: BaseModel, + conversation: Conversation, ) -> Callable[..., Any]: return "mock_runnable" def parameterise_query( - self, question: str, conversation: Conversation + self, + question: str, + conversation: Conversation, ) -> BaseModel: return "mock_result" @@ -75,22 +79,22 @@ class MockModel(BaseModel): field: str -@pytest.fixture +@pytest.fixture() def query_builder(): return TestQueryBuilder() -@pytest.fixture +@pytest.fixture() def fetcher(): return TestFetcher() -@pytest.fixture +@pytest.fixture() def interpreter(): return TestInterpreter() -@pytest.fixture +@pytest.fixture() def test_agent(query_builder, fetcher, interpreter): return APIAgent( conversation_factory=MagicMock(), @@ -130,7 +134,8 @@ def test_create_runnable(self, mock_create_runnable): # Act result = builder.create_runnable( - query_parameters=query_parameters, llm=None + query_parameters=query_parameters, + llm=None, ) # Assert @@ -157,7 +162,8 @@ def test_parameterise_query(self, mock_conversation, mock_create_runnable): # Act result = builder.parameterise_query( - question, mock_conversation_instance + question, + mock_conversation_instance, ) # Assert @@ -166,7 +172,7 @@ def test_parameterise_query(self, mock_conversation, mock_create_runnable): conversation=mock_conversation_instance, ) mock_runnable.invoke.assert_called_once_with( - {"input": f"Answer:\n{question} based on:\n {BLAST_QUERY_PROMPT}"} + {"input": f"Answer:\n{question} based on:\n {BLAST_QUERY_PROMPT}"}, ) assert result == mock_blast_query_parameters assert hasattr(result, "question_uuid") @@ -227,18 +233,19 @@ def test_submit_query_value_error(self, mock_post): # Act & Assert with pytest.raises( - ValueError, match="RID not found in BLAST submission response." + ValueError, + match="RID not found in BLAST submission response.", ): fetcher._submit_query(query_parameters) -@pytest.fixture +@pytest.fixture() def mock_conversation(): with patch("biochatter.llm_connect.GptConversation") as mock: yield mock -@pytest.fixture +@pytest.fixture() def mock_output_parser(): with patch("biochatter.api_agent.blast.StrOutputParser") as mock: mock_parser = MagicMock() @@ -246,16 +253,14 @@ def mock_output_parser(): yield mock_parser -@pytest.fixture +@pytest.fixture() def mock_chain(mock_conversation, mock_output_parser): with patch( - "biochatter.api_agent.blast.ChatPromptTemplate.from_messages" + "biochatter.api_agent.blast.ChatPromptTemplate.from_messages", ) as mock_prompt: mock_prompt_instance = MagicMock() mock_prompt.return_value = mock_prompt_instance - mock_chain = ( - mock_prompt_instance | mock_conversation.chat | mock_output_parser - ) + mock_chain = mock_prompt_instance | mock_conversation.chat | mock_output_parser yield mock_chain @@ -267,7 +272,8 @@ def test_summarise_results(mock_prompt, mock_conversation, mock_chain): question = "What is the best hit?" expected_context = "Mocked context from file" expected_summary_prompt = BLAST_SUMMARY_PROMPT.format( - question=question, context=expected_context + question=question, + context=expected_context, ) expected_answer = "Mocked answer" # Mock the methods and functions @@ -275,13 +281,15 @@ def test_summarise_results(mock_prompt, mock_conversation, mock_chain): # Act result = interpreter.summarise_results( - question, mock_conversation, expected_context + question, + mock_conversation, + expected_context, ) # Assert assert result == expected_answer mock_chain.invoke.assert_called_once_with( - {"input": {expected_summary_prompt}} + {"input": {expected_summary_prompt}}, ) @@ -300,7 +308,8 @@ def test_create_runnable(self, mock_create_runnable): # Act result = builder.create_runnable( - query_parameters=query_parameters, llm=None + query_parameters=query_parameters, + llm=None, ) # Assert @@ -327,7 +336,8 @@ def test_parameterise_query(self, mock_conversation, mock_create_runnable): # Act result = builder.parameterise_query( - question, mock_conversation_instance + question, + mock_conversation_instance, ) # Assert @@ -336,7 +346,7 @@ def test_parameterise_query(self, mock_conversation, mock_create_runnable): conversation=mock_conversation_instance, ) mock_runnable.invoke.assert_called_once_with( - {"input": f"Answer:\n{question} based on:\n {ONCOKB_QUERY_PROMPT}"} + {"input": f"Answer:\n{question} based on:\n {ONCOKB_QUERY_PROMPT}"}, ) assert result == mock_oncokb_query_parameters assert hasattr(result, "question_uuid") @@ -360,13 +370,13 @@ def test_fetch_results(self, mock_fetch_results): assert result == mock_response -@pytest.fixture +@pytest.fixture() def mock_conversation(): with patch("biochatter.llm_connect.GptConversation") as mock: yield mock -@pytest.fixture +@pytest.fixture() def mock_output_parser(): with patch("biochatter.api_agent.oncokb.StrOutputParser") as mock: mock_parser = MagicMock() @@ -374,16 +384,14 @@ def mock_output_parser(): yield mock_parser -@pytest.fixture +@pytest.fixture() def mock_chain(mock_conversation, mock_output_parser): with patch( - "biochatter.api_agent.oncokb.ChatPromptTemplate.from_messages" + "biochatter.api_agent.oncokb.ChatPromptTemplate.from_messages", ) as mock_prompt: mock_prompt_instance = MagicMock() mock_prompt.return_value = mock_prompt_instance - mock_chain = ( - mock_prompt_instance | mock_conversation.chat | mock_output_parser - ) + mock_chain = mock_prompt_instance | mock_conversation.chat | mock_output_parser yield mock_chain @@ -394,7 +402,8 @@ def test_summarise_results(mock_prompt, mock_conversation, mock_chain): question = "What is the best hit?" expected_context = "Mocked context from file" expected_summary_prompt = ONCOKB_SUMMARY_PROMPT.format( - question=question, context=expected_context + question=question, + context=expected_context, ) expected_answer = "Mocked answer" @@ -403,11 +412,13 @@ def test_summarise_results(mock_prompt, mock_conversation, mock_chain): # Act result = interpreter.summarise_results( - question, mock_conversation, expected_context + question, + mock_conversation, + expected_context, ) # Assert assert result == expected_answer mock_chain.invoke.assert_called_once_with( - {"input": {expected_summary_prompt}} + {"input": {expected_summary_prompt}}, ) diff --git a/test/test_benchmark_biocypher_query_generation.py b/test/test_benchmark_biocypher_query_generation.py index 871dd7d2..f78d94c3 100644 --- a/test/test_benchmark_biocypher_query_generation.py +++ b/test/test_benchmark_biocypher_query_generation.py @@ -29,15 +29,13 @@ def test_entity_selection(prompt_engine): None, ] mock_append_system_messages = Mock() - mock_conversation.return_value.append_system_message = ( - mock_append_system_messages - ) + mock_conversation.return_value.append_system_message = mock_append_system_messages success = prompt_engine._select_entities( question="Which genes are associated with mucoviscidosis?", conversation=mock_conversation.return_value, ) assert success - mock_append_system_messages.assert_called_once_with((system_msg)) + mock_append_system_messages.assert_called_once_with(system_msg) score = [] score.append("Gene" in prompt_engine.selected_entities) @@ -56,11 +54,9 @@ def test_relationship_selection(prompt_engine): None, ] mock_append_system_messages = Mock() - mock_conversation.return_value.append_system_message = ( - mock_append_system_messages - ) + mock_conversation.return_value.append_system_message = mock_append_system_messages success = prompt_engine._select_relationships( - conversation=mock_conversation.return_value + conversation=mock_conversation.return_value, ) assert success mock_append_system_messages.assert_called_once_with( @@ -71,36 +67,33 @@ def test_relationship_selection(prompt_engine): "targets, comma-separated, and without any additional text. Here " "are the possible relationships and their source and target " 'entities: [["GeneToPhenotypeAssociation", ["Disease", ' - '"Protein"]], ["GeneToPhenotypeAssociation", ["Disease", "Gene"]]].' + '"Protein"]], ["GeneToPhenotypeAssociation", ["Disease", "Gene"]]].', ) score = [] score.append( - prompt_engine.selected_relationships - == ["GeneToPhenotypeAssociation"] + prompt_engine.selected_relationships == ["GeneToPhenotypeAssociation"], ) score.append( - "PERTURBED" in prompt_engine.selected_relationship_labels.keys() + "PERTURBED" in prompt_engine.selected_relationship_labels.keys(), ) score.append( - "source" - in prompt_engine.selected_relationship_labels.get("PERTURBED") + "source" in prompt_engine.selected_relationship_labels.get("PERTURBED"), ) score.append( - "target" - in prompt_engine.selected_relationship_labels.get("PERTURBED") + "target" in prompt_engine.selected_relationship_labels.get("PERTURBED"), ) score.append( "Disease" in prompt_engine.selected_relationship_labels.get("PERTURBED").get( - "source" - ) + "source", + ), ) score.append( "Protein" in prompt_engine.selected_relationship_labels.get("PERTURBED").get( - "target" - ) + "target", + ), ) assert calculate_bool_vector_score(score) == (6, 6) @@ -128,15 +121,13 @@ def test_property_selection(prompt_engine): None, ] mock_append_system_messages = Mock() - mock_conversation.return_value.append_system_message = ( - mock_append_system_messages - ) + mock_conversation.return_value.append_system_message = mock_append_system_messages success = prompt_engine._select_properties( - conversation=mock_conversation.return_value + conversation=mock_conversation.return_value, ) assert success mock_append_system_messages.assert_called_once_with( - "You have access to a knowledge graph that contains entities and relationships. They have the following properties. Entities:{'Gene': ['id', 'name', 'taxon'], 'Disease': ['name', 'ICD10', 'DSM5']}, Relationships: {'GeneToPhenotypeAssociation': ['score', 'source', 'evidence']}. Your task is to select the properties that are relevant to the user's question for subsequent use in a query. Only return the entities and relationships with their relevant properties in compact JSON format, without any additional text. Return the entities/relationships as top-level dictionary keys, and their properties as dictionary values. Do not return properties that are not relevant to the question." + "You have access to a knowledge graph that contains entities and relationships. They have the following properties. Entities:{'Gene': ['id', 'name', 'taxon'], 'Disease': ['name', 'ICD10', 'DSM5']}, Relationships: {'GeneToPhenotypeAssociation': ['score', 'source', 'evidence']}. Your task is to select the properties that are relevant to the user's question for subsequent use in a query. Only return the entities and relationships with their relevant properties in compact JSON format, without any additional text. Return the entities/relationships as top-level dictionary keys, and their properties as dictionary values. Do not return properties that are not relevant to the question.", ) score = [] @@ -158,9 +149,7 @@ def test_query_generation(prompt_engine): None, ] mock_append_system_messages = Mock() - mock_conversation.return_value.append_system_message = ( - mock_append_system_messages - ) + mock_conversation.return_value.append_system_message = mock_append_system_messages query = prompt_engine._generate_query( question="Which genes are associated with mucoviscidosis?", entities=["Gene", "Disease"], @@ -168,7 +157,7 @@ def test_query_generation(prompt_engine): "PERTURBED": { "source": "Disease", "target": ["Protein", "Gene"], - } + }, }, properties={"Disease": ["name", "ICD10", "DSM5"]}, query_language="Cypher", @@ -183,7 +172,7 @@ def test_query_generation(prompt_engine): "'(:Disease)-(:PERTURBED)->(:Protein)', " "'(:Disease)-(:PERTURBED)->(:Gene)', generate a Cypher query using " "one of these combinations. Only return the query, without any " - "additional text, symbols or characters --- just the query statement." + "additional text, symbols or characters --- just the query statement.", ) score = [] score.append("MATCH" in query) @@ -192,10 +181,7 @@ def test_query_generation(prompt_engine): score.append("Disease" in query) score.append("mucoviscidosis" in query) score.append( - ( - "-[:PERTURBED]->(g:Gene)" in query - or "(g:Gene)<-[:PERTURBED]-" in query - ) + "-[:PERTURBED]->(g:Gene)" in query or "(g:Gene)<-[:PERTURBED]-" in query, ) score.append("WHERE" in query or "{name:" in query) diff --git a/test/test_benchmark_vectorstore.py b/test/test_benchmark_vectorstore.py index 0e38db9a..3dfa5cde 100644 --- a/test/test_benchmark_vectorstore.py +++ b/test/test_benchmark_vectorstore.py @@ -1,12 +1,11 @@ -from unittest.mock import patch import os +from unittest.mock import patch import pytest from benchmark.conftest import calculate_bool_vector_score from biochatter.rag_agent import RagAgent, RagAgentModeEnum -from biochatter.vectorstore import Document, DocumentReader, DocumentEmbedder -from biochatter.vectorstore_agent import VectorDatabaseAgentMilvus +from biochatter.vectorstore import Document, DocumentEmbedder, DocumentReader # setup milvus connection if os.getenv("DEVCONTAINER"): @@ -60,8 +59,7 @@ metadata={id: "1"}, ), Document( - page_content="BioCypher has been built with continuous consideration " - "of the FAIR and TRUST", + page_content="BioCypher has been built with continuous consideration of the FAIR and TRUST", metadata={id: "1"}, ), Document( @@ -87,26 +85,20 @@ def test_retrieval_augmented_generation(model, chunk_size): with ( patch( - "biochatter.vectorstore.OpenAIEmbeddings" + "biochatter.vectorstore.OpenAIEmbeddings", ) as mock_openaiembeddings, patch( - "biochatter.vectorstore_agent.VectorDatabaseAgentMilvus" + "biochatter.vectorstore_agent.VectorDatabaseAgentMilvus", ) as mock_host_1, patch("biochatter.vectorstore.VectorDatabaseAgentMilvus") as mock_host, patch( - "biochatter.vectorstore.RecursiveCharacterTextSplitter" + "biochatter.vectorstore.RecursiveCharacterTextSplitter", ) as mock_textsplitter, ): # mocking - mock_textsplitter.from_huggingface_tokenizer.return_value = ( - mock_textsplitter() - ) - mock_textsplitter.from_tiktoken_encoder.return_value = ( - mock_textsplitter() - ) - mock_textsplitter.return_value.split_documents.return_value = ( - splitted_docs - ) + mock_textsplitter.from_huggingface_tokenizer.return_value = mock_textsplitter() + mock_textsplitter.from_tiktoken_encoder.return_value = mock_textsplitter() + mock_textsplitter.return_value.split_documents.return_value = splitted_docs mock_host.return_value.store_embeddings.return_value = "1" mock_host_1.return_value.similarity_search.return_value = search_docs @@ -131,10 +123,7 @@ def test_retrieval_augmented_generation(model, chunk_size): correct = ["BioCypher" in result[0] for result in results] # delete embeddings - [ - doc_embedder.database_host.remove_document(doc_id) - for doc_id in doc_ids - ] + [doc_embedder.database_host.remove_document(doc_id) for doc_id in doc_ids] # record sum in CSV file assert calculate_bool_vector_score(correct) == (3, 3) diff --git a/test/test_database_agent.py b/test/test_database_agent.py index 6be6c60d..f23ab305 100644 --- a/test/test_database_agent.py +++ b/test/test_database_agent.py @@ -1,6 +1,6 @@ -import unittest.mock as mock +from unittest import mock -from biochatter.database_agent import Document, DatabaseAgent +from biochatter.database_agent import DatabaseAgent from biochatter.langgraph_agent_base import ReflexionAgentResult @@ -21,18 +21,16 @@ def test_get_query_results_with_reflexion(): db_agent.connect() # Call the connect method to initialize the driver with mock.patch( - "biochatter.database_agent.KGQueryReflexionAgent" + "biochatter.database_agent.KGQueryReflexionAgent", ) as mock_KGQueryReflexionAgent: - mock_KGQueryReflexionAgent.return_value.execute.return_value = ( - ReflexionAgentResult( - answer="test_query", - tool_result=[ - {"key": "value"}, - {"key": "value"}, - {"key": "value"}, - {"key": "value"}, - ], - ) + mock_KGQueryReflexionAgent.return_value.execute.return_value = ReflexionAgentResult( + answer="test_query", + tool_result=[ + {"key": "value"}, + {"key": "value"}, + {"key": "value"}, + {"key": "value"}, + ], ) # Mock the prompt_engine.generate_query method @@ -73,7 +71,9 @@ def test_get_query_results_without_reflexion(): # Mock the prompt_engine.generate_query method with mock.patch.object( - db_agent.prompt_engine, "generate_query", return_value="test_query" + db_agent.prompt_engine, + "generate_query", + return_value="test_query", ): # Mock the driver.query method with mock.patch.object( diff --git a/test/test_kg_langgraph_agent.py b/test/test_kg_langgraph_agent.py index cdb5ba5f..f2728167 100644 --- a/test/test_kg_langgraph_agent.py +++ b/test/test_kg_langgraph_agent.py @@ -1,17 +1,15 @@ -from types import UnionType -from typing import Any from collections.abc import Callable +from typing import Any -from langgraph.graph import END -from langchain_core.messages import AIMessage, BaseMessage -from langchain.output_parsers.openai_tools import PydanticToolsParser import pytest import shortuuid +from langchain.output_parsers.openai_tools import PydanticToolsParser +from langchain_core.messages import AIMessage, BaseMessage from biochatter.kg_langgraph_agent import ( - ReviseQuery, GenerateQuery, KGQueryReflexionAgent, + ReviseQuery, ) from biochatter.langgraph_agent_base import ResponderWithRetries @@ -31,8 +29,8 @@ def invoke(self, msg_obj: dict[str, list[BaseMessage]]) -> BaseMessage: "name": "GenerateQuery", }, "type": "function", - } - ] + }, + ], } msg.id = id msg.tool_calls = [ @@ -42,11 +40,11 @@ def invoke(self, msg_obj: dict[str, list[BaseMessage]]) -> BaseMessage: "answer": "MATCH (g:gene {name: 'EOMES'})-[:regulate]->(c:gene) RETURN DISTINCT c.name LIMIT 5", "reflection": "The query correctly identifies genes regulated by EOMES, which can help determine its primary and secondary functions based on the regulated genes' roles.", "search_queries": [ - "MATCH (g:gene {name: 'EOMES'})-[:regulate]->(c:gene) RETURN DISTINCT c.name LIMIT 5" + "MATCH (g:gene {name: 'EOMES'})-[:regulate]->(c:gene) RETURN DISTINCT c.name LIMIT 5", ], }, "id": id, - } + }, ] return msg @@ -68,8 +66,8 @@ def invoke(self, msg_obj: dict[str, list[BaseMessage]]) -> BaseMessage: "name": "ReviseQuery", }, "type": "function", - } - ] + }, + ], }, response_metadata={ "token_usage": { @@ -97,7 +95,7 @@ def invoke(self, msg_obj: dict[str, list[BaseMessage]]) -> BaseMessage: "severity": "safe", }, }, - } + }, ], "finish_reason": "stop", "logprobs": None, @@ -111,12 +109,12 @@ def invoke(self, msg_obj: dict[str, list[BaseMessage]]) -> BaseMessage: "answer": "MATCH (g:gene {name: 'EOMES'})-[:regulate]->(c:gene) RETURN DISTINCT c.name LIMIT 5", "reflection": "The initial query returned no results. To improve the query, I will remove the relationship constraint and try to find any connections or properties related to EOMES that might indicate its primary and secondary functions.", "search_queries": [ - "MATCH (g:gene {name: 'EOMES'})-[:regulate]->(c:gene) RETURN DISTINCT c.name LIMIT 5" + "MATCH (g:gene {name: 'EOMES'})-[:regulate]->(c:gene) RETURN DISTINCT c.name LIMIT 5", ], "revised_query": "MATCH (g:gene {name: 'EOMES'})-[]->(c) RETURN DISTINCT c.name, labels(c) LIMIT 5", }, "id": "call_wTO40b9rXHhNhxNEG3IHn3Nj", - } + }, ], ) else: @@ -131,8 +129,8 @@ def invoke(self, msg_obj: dict[str, list[BaseMessage]]) -> BaseMessage: "name": "ReviseQuery", }, "type": "function", - } - ] + }, + ], }, response_metadata={ "token_usage": { @@ -160,7 +158,7 @@ def invoke(self, msg_obj: dict[str, list[BaseMessage]]) -> BaseMessage: "severity": "safe", }, }, - } + }, ], "finish_reason": "stop", "logprobs": None, @@ -174,12 +172,12 @@ def invoke(self, msg_obj: dict[str, list[BaseMessage]]) -> BaseMessage: "answer": "MATCH (g:gene {name: 'EOMES'})-->(c:gene) RETURN DISTINCT c.name LIMIT 5;", "reflection": "The revised query successfully returned results by removing the relationship constraint. This indicates that EOMES is connected to other genes, but the specific 'regulate' relationship might not be explicitly defined in the database. The revised query is more flexible and provides the needed information.", "search_queries": [ - "MATCH (g:gene {name: 'EOMES'})-->(c:gene) RETURN DISTINCT c.name LIMIT 5;" + "MATCH (g:gene {name: 'EOMES'})-->(c:gene) RETURN DISTINCT c.name LIMIT 5;", ], "revised_query": "MATCH (g:gene {name: 'EOMES'})-->(c:gene) RETURN DISTINCT c.name LIMIT 5;", }, "id": "call_Eciqf2ZviAjYinzi4zpgiNej", - } + }, ], ) @@ -195,11 +193,15 @@ def __init__( recursion_limit: int | None = 20, ): super().__init__( - conversation_factory, connection_args, query_lang, recursion_limit + conversation_factory, + connection_args, + query_lang, + recursion_limit, ) def _create_initial_responder( - self, prompt: str | None = None + self, + prompt: str | None = None, ) -> ResponderWithRetries: runnable = InitialResponder() validator = PydanticToolsParser(tools=[GenerateQuery]) @@ -209,7 +211,8 @@ def _create_initial_responder( ) def _create_revise_responder( - self, prompt: str | None = None + self, + prompt: str | None = None, ) -> ResponderWithRetries: runnable = ReviseResponder() validator = PydanticToolsParser(tools=[ReviseQuery]) @@ -242,7 +245,7 @@ def _query_graph_database(self, query: str): "c.name": None, "labels(c)": ["ontology", "cell_line_or_tissue"], }, - ] + ], ] @@ -251,7 +254,7 @@ def __init__(self) -> None: self.chat = None -@pytest.fixture +@pytest.fixture() def kgQueryAgent(): return KGQueryReflexionAgentMock( connection_args={"host": "localhost", "port": "7687"}, @@ -262,7 +265,4 @@ def kgQueryAgent(): def test_execute(kgQueryAgent): question = "What genes does EOMES primarily regulate?" agent_result = kgQueryAgent.execute(question=question) - assert ( - agent_result.answer - == "MATCH (g:gene {name: 'EOMES'})-->(c:gene) RETURN DISTINCT c.name LIMIT 5;" - ) + assert agent_result.answer == "MATCH (g:gene {name: 'EOMES'})-->(c:gene) RETURN DISTINCT c.name LIMIT 5;" diff --git a/test/test_llm_connect.py b/test/test_llm_connect.py index 95b947a2..8361db16 100644 --- a/test/test_llm_connect.py +++ b/test/test_llm_connect.py @@ -1,52 +1,58 @@ -from unittest.mock import Mock, MagicMock, patch, mock_open +# ruff: noqa: S101 # Use of assert detected +# ruff: noqa: ANN201 # No docstring in public function +# ruff: noqa: D103 # Missing docstring in public function +# ruff: noqa: D100 # Missing docstring in public module + import os -import base64 +from unittest.mock import MagicMock, Mock, mock_open, patch -from PIL import Image -from xinference.client import Client -from openai._exceptions import NotFoundError import openai import pytest +from openai._exceptions import NotFoundError +from PIL import Image from biochatter._image import ( - encode_image, - process_image, - convert_to_png, + convert_and_resize_image, convert_to_pil_image, + convert_to_png, + encode_image, encode_image_from_url, - convert_and_resize_image, + process_image, ) from biochatter.llm_connect import ( AIMessage, + AnthropicConversation, + AzureGptConversation, + GptConversation, HumanMessage, + OllamaConversation, SystemMessage, - GptConversation, WasmConversation, - OllamaConversation, - AzureGptConversation, - AnthropicConversation, XinferenceConversation, ) @pytest.fixture(scope="module", autouse=True) -def manageTestContext(): +def manage_test_context(): import openai base_url = openai.base_url api_type = openai.api_type api_version = openai.api_version api_key = openai.api_key - # api_key_path = openai.api_key_path organization = openai.organization + proxy = getattr(openai, "proxy", None) yield True openai.base_url = base_url openai.api_type = api_type openai.api_version = api_version openai.api_key = api_key - # openai.api_key_path = api_key_path openai.organization = organization + if proxy is not None: + openai.proxy = proxy + elif hasattr(openai, "proxy"): + delattr(openai, "proxy") def test_empty_messages(): @@ -78,9 +84,7 @@ def test_multiple_messages(): convo.messages.append(HumanMessage(content="How are you?")) convo.messages.append(AIMessage(content="I'm doing well, thanks!")) assert convo.get_msg_json() == ( - '[{"system": "Hello, world!"}, ' - '{"user": "How are you?"}, ' - '{"ai": "I\'m doing well, thanks!"}]' + '[{"system": "Hello, world!"}, {"user": "How are you?"}, {"ai": "I\'m doing well, thanks!"}]' ) @@ -119,7 +123,14 @@ def test_openai_catches_authentication_error(mock_openai): assert not success -def test_azure_raises_request_error(): +@patch("biochatter.llm_connect.AzureChatOpenAI") +def test_azure_raises_request_error(mock_azure_chat): + mock_azure_chat.side_effect = NotFoundError( + message="Resource not found", + response=Mock(), + body=None, + ) + convo = AzureGptConversation( model_name="gpt-35-turbo", deployment_name="test_deployment", @@ -134,13 +145,12 @@ def test_azure_raises_request_error(): @patch("biochatter.llm_connect.AzureChatOpenAI") -def test_azure(mock_azurechat): - """ - Test OpenAI Azure endpoint functionality. Azure connectivity is enabled by - setting the corresponding environment variables. +def test_azure(mock_azure_chat): + """Test OpenAI Azure endpoint functionality. + + Azure connectivity is enabled by setting the corresponding environment + variables. """ - # mock_azurechat.return_value.generate = - openai.proxy = os.getenv("AZURE_TEST_OPENAI_PROXY") convo = AzureGptConversation( model_name=os.getenv("AZURE_TEST_OPENAI_MODEL_NAME"), deployment_name=os.getenv("AZURE_TEST_OPENAI_DEPLOYMENT_NAME"), @@ -150,6 +160,8 @@ def test_azure(mock_azurechat): base_url=os.getenv("AZURE_TEST_OPENAI_API_BASE"), ) + mock_azure_chat.return_value = Mock() + assert convo.set_api_key(os.getenv("AZURE_TEST_OPENAI_API_KEY")) @@ -185,13 +197,13 @@ def test_anthropic(): split_correction=False, ) assert conv.set_api_key( - api_key=os.getenv("ANTHROPIC_API_KEY"), user="test_user" + api_key=os.getenv("ANTHROPIC_API_KEY"), + user="test_user", ) def test_xinference_init(): - """ - Test generic LLM connectivity via the Xinference client. Currently depends + """Test generic LLM connectivity via the Xinference client. Currently depends on a test server. """ base_url = os.getenv("XINFERENCE_BASE_URL", "http://localhost:9997") @@ -221,7 +233,7 @@ def test_xinference_chatting(): "content": " Hello there, can you sing me a song?", }, "finish_reason": "stop", - } + }, ], "usage": { "prompt_tokens": 93, @@ -230,9 +242,7 @@ def test_xinference_chatting(): }, } mock_client.return_value.list_models.return_value = xinference_models - mock_client.return_value.get_model.return_value.chat.return_value = ( - response - ) + mock_client.return_value.get_model.return_value.chat.return_value = response convo = XinferenceConversation( base_url=base_url, prompts={}, @@ -313,9 +323,11 @@ def test_wasm_conversation(): assert result == test_query + "\nSystem message" -@pytest.fixture +@pytest.fixture() def xinference_conversation(): with patch("xinference.client.Client") as mock_client: + # Mock the authentication check + mock_client.return_value._check_cluster_authenticated.return_value = None mock_client.return_value.list_models.return_value = xinference_models mock_client.return_value.get_model.return_value.chat.return_value = ( {"choices": [{"message": {"content": "Human message"}}]}, @@ -326,10 +338,10 @@ def xinference_conversation(): prompts={}, correct=False, ) - return conversation + return conversation -def test_single_system_message_before_human(xinference_conversation): +def test_single_system_message_before_human(xinference_conversation: XinferenceConversation): xinference_conversation.messages = [ SystemMessage(content="System message"), HumanMessage(content="Human message"), @@ -341,7 +353,7 @@ def test_single_system_message_before_human(xinference_conversation): } -def test_multiple_system_messages_before_human(xinference_conversation): +def test_multiple_system_messages_before_human(xinference_conversation: XinferenceConversation): xinference_conversation.messages = [ SystemMessage(content="System message 1"), SystemMessage(content="System message 2"), @@ -355,7 +367,7 @@ def test_multiple_system_messages_before_human(xinference_conversation): def test_multiple_messages_including_ai_before_system_and_human( - xinference_conversation, + xinference_conversation: XinferenceConversation, ): xinference_conversation.messages = [ HumanMessage(content="Human message history"), @@ -407,7 +419,8 @@ def test_convert_to_pil_image_pdf(mock_convert_from_path): mock_convert_from_path.return_value = [Image.new("RGB", (1000, 1000))] with patch("biochatter._image.os.path.exists", return_value=True): with patch( - "biochatter._image.os.path.abspath", side_effect=lambda x: x + "biochatter._image.os.path.abspath", + side_effect=lambda x: x, ): img = convert_to_pil_image("test.pdf") assert isinstance(img, Image.Image) @@ -453,32 +466,23 @@ def test_encode_image_from_url(): mock_response = MagicMock() mock_response.read.return_value = b"image_data" mock_urlopen.return_value.__enter__.return_value = mock_response - mock_urlopen.return_value.info.return_value.get_content_type.return_value = ( - "image/jpeg" - ) + mock_urlopen.return_value.info.return_value.get_content_type.return_value = "image/jpeg" - with patch( - "biochatter._image.tempfile.NamedTemporaryFile", - new_callable=MagicMock, - ) as mock_tempfile: - mock_tempfile_instance = ( - mock_tempfile.return_value.__enter__.return_value - ) + with patch("tempfile.NamedTemporaryFile", new_callable=MagicMock) as mock_tempfile: + mock_tempfile_instance = mock_tempfile.return_value.__enter__.return_value mock_tempfile_instance.name = "test_temp_file" - # Using a simple Mock for the write method to avoid issues with MagicMock write_mock = Mock() mock_tempfile_instance.write = write_mock with patch("biochatter._image.encode_image") as mock_encode: mock_encode.return_value = "base64string" - with patch("biochatter._image.os.remove") as mock_remove: + with patch("os.remove") as mock_remove: encoded_str = encode_image_from_url( - "http://example.com/image.jpg" + "http://example.com/image.jpg", ) - # Ensure the temporary file write method was called with the correct data write_mock.assert_called_once_with(b"image_data") mock_remove.assert_called_once_with("test_temp_file") assert isinstance(encoded_str, str) @@ -496,7 +500,7 @@ def test_append_local_image_gpt(): convo.set_api_key(api_key=os.getenv("OPENAI_API_KEY"), user="test_user") convo.append_system_message( - "You are an editorial assistant to a journal in biomedical science." + "You are an editorial assistant to a journal in biomedical science.", ) convo.append_image_message( @@ -523,7 +527,7 @@ def test_local_image_query_gpt(): convo.set_api_key(api_key=os.getenv("OPENAI_API_KEY"), user="test_user") convo.append_system_message( - "You are an editorial assistant to a journal in biomedical science." + "You are an editorial assistant to a journal in biomedical science.", ) result, _, _ = convo.query( diff --git a/test/test_prompts.py b/test/test_prompts.py index 6e78f69e..01906ed4 100644 --- a/test/test_prompts.py +++ b/test/test_prompts.py @@ -1,18 +1,16 @@ from unittest.mock import Mock, patch -import os import pytest from biochatter.prompts import BioCypherPromptEngine -from biochatter.llm_connect import GptConversation ## THIS IS LARGELY BENCHMARK MATERIAL, TO BE MOCKED FOR UNIT TESTING -@pytest.fixture +@pytest.fixture() def prompt_engine(): return BioCypherPromptEngine( - schema_config_or_info_path="test/test_schema_info.yaml" + schema_config_or_info_path="test/test_schema_info.yaml", ) @@ -32,16 +30,11 @@ def test_biocypher_prompts(prompt_engine): ] assert "name" in prompt_engine.entities.get("Protein").get("properties") - assert ( - prompt_engine.relationships.get("Phosphorylation").get("represented_as") - == "edge" - ) + assert prompt_engine.relationships.get("Phosphorylation").get("represented_as") == "edge" def test_entity_selection(prompt_engine): - """ - - The first step is to identify the KG components that are relevant to the + """The first step is to identify the KG components that are relevant to the question. Requires only a question as input, entities and relationships are inferred from the schema. @@ -60,14 +53,12 @@ def test_entity_selection(prompt_engine): None, ] mock_append_system_message = Mock() - mock_conversation.return_value.append_system_message = ( - mock_append_system_message - ) + mock_conversation.return_value.append_system_message = mock_append_system_message success = prompt_engine._select_entities( question="Which genes are associated with mucoviscidosis?", conversation=mock_conversation.return_value, ) - mock_append_system_message.assert_called_once_with((system_msg)) + mock_append_system_message.assert_called_once_with(system_msg) assert success assert prompt_engine.selected_entities == ["Gene", "Disease"] @@ -82,11 +73,9 @@ def test_relationship_selection(prompt_engine): None, ] mock_append_system_messages = Mock() - mock_conversation.return_value.append_system_message = ( - mock_append_system_messages - ) + mock_conversation.return_value.append_system_message = mock_append_system_messages success = prompt_engine._select_relationships( - conversation=mock_conversation.return_value + conversation=mock_conversation.return_value, ) assert success mock_append_system_messages.assert_called_once_with( @@ -97,24 +86,24 @@ def test_relationship_selection(prompt_engine): "targets, comma-separated, and without any additional text. Here " "are the possible relationships and their source and target " 'entities: [["GeneToPhenotypeAssociation", ["Disease", "Protein"]],' - ' ["GeneToPhenotypeAssociation", ["Disease", "Gene"]]].' + ' ["GeneToPhenotypeAssociation", ["Disease", "Gene"]]].', ) assert prompt_engine.selected_relationships == [ - "GeneToPhenotypeAssociation" + "GeneToPhenotypeAssociation", ] assert "PERTURBED" in prompt_engine.selected_relationship_labels.keys() assert "source" in prompt_engine.selected_relationship_labels.get( - "PERTURBED" + "PERTURBED", ) assert "target" in prompt_engine.selected_relationship_labels.get( - "PERTURBED" + "PERTURBED", ) assert "Disease" in prompt_engine.selected_relationship_labels.get( - "PERTURBED" + "PERTURBED", ).get("source") assert "Protein" in prompt_engine.selected_relationship_labels.get( - "PERTURBED" + "PERTURBED", ).get("target") @@ -128,11 +117,9 @@ def test_relationship_selection_with_incomplete_entities(prompt_engine): None, ] mock_append_system_messages = Mock() - mock_conversation.return_value.append_system_message = ( - mock_append_system_messages - ) + mock_conversation.return_value.append_system_message = mock_append_system_messages success = prompt_engine._select_relationships( - conversation=mock_conversation.return_value + conversation=mock_conversation.return_value, ) assert success mock_append_system_messages.assert_called_once_with( @@ -144,7 +131,7 @@ def test_relationship_selection_with_incomplete_entities(prompt_engine): "are the possible relationships and their source and target " 'entities: [["GeneToPhenotypeAssociation", ["Disease", "Protein"]],' ' ["GeneToPhenotypeAssociation", ["Disease", "Gene"]], ' - '["GeneToDiseaseAssociation", ["Protein", "Disease"]]].' + '["GeneToDiseaseAssociation", ["Protein", "Disease"]]].', ) # TODO convert into benchmark to be independent of model call, mock to @@ -152,9 +139,7 @@ def test_relationship_selection_with_incomplete_entities(prompt_engine): # technically, these are wrong selections, but the test is also arbitrarily # wrong; in practice, this would be bad labelling. - assert ( - "GeneToDiseaseAssociation" in prompt_engine.selected_relationships - ) + assert "GeneToDiseaseAssociation" in prompt_engine.selected_relationships assert "Protein" in prompt_engine.selected_entities @@ -169,20 +154,16 @@ def test_relationship_selection_does_not_create_none_entities(prompt_engine): None, ] mock_append_system_messages = Mock() - mock_conversation.return_value.append_system_message = ( - mock_append_system_messages - ) + mock_conversation.return_value.append_system_message = mock_append_system_messages success = prompt_engine._select_relationships( - conversation=mock_conversation.return_value + conversation=mock_conversation.return_value, ) assert success assert None not in prompt_engine.selected_entities def test_property_selection(prompt_engine): - """ - - The second step is to identify the properties of the relevant KG components + """The second step is to identify the properties of the relevant KG components to enable constraining the query. Requires the pre-selected entities and relationships as input. The question is reused from the entity selection (but can optionally be provided), and the properties are inferred from the @@ -217,24 +198,20 @@ def test_property_selection(prompt_engine): None, ] mock_append_system_messages = Mock() - mock_conversation.return_value.append_system_message = ( - mock_append_system_messages - ) + mock_conversation.return_value.append_system_message = mock_append_system_messages success = prompt_engine._select_properties( - conversation=mock_conversation.return_value + conversation=mock_conversation.return_value, ) assert success mock_append_system_messages.assert_called_once_with( - "You have access to a knowledge graph that contains entities and relationships. They have the following properties. Entities:{'Gene': ['id', 'name', 'taxon'], 'Disease': ['name', 'ICD10', 'DSM5']}, Relationships: {'GeneToPhenotypeAssociation': ['score', 'source', 'evidence']}. Your task is to select the properties that are relevant to the user's question for subsequent use in a query. Only return the entities and relationships with their relevant properties in compact JSON format, without any additional text. Return the entities/relationships as top-level dictionary keys, and their properties as dictionary values. Do not return properties that are not relevant to the question." + "You have access to a knowledge graph that contains entities and relationships. They have the following properties. Entities:{'Gene': ['id', 'name', 'taxon'], 'Disease': ['name', 'ICD10', 'DSM5']}, Relationships: {'GeneToPhenotypeAssociation': ['score', 'source', 'evidence']}. Your task is to select the properties that are relevant to the user's question for subsequent use in a query. Only return the entities and relationships with their relevant properties in compact JSON format, without any additional text. Return the entities/relationships as top-level dictionary keys, and their properties as dictionary values. Do not return properties that are not relevant to the question.", ) assert "Disease" in prompt_engine.selected_properties.keys() assert "name" in prompt_engine.selected_properties.get("Disease") def test_cypher_query_generation(prompt_engine): - """ - - The third step is to generate a query that will retrieve the information + """The third step is to generate a query that will retrieve the information that is relevant to the user's question from the KG. Requires a question, the pre-selected entities and relationships, and the pre-selected properties as input. The query language can be inferred from the dedicated extended @@ -265,9 +242,7 @@ def test_cypher_query_generation(prompt_engine): None, ] mock_append_system_messages = Mock() - mock_conversation.return_value.append_system_message = ( - mock_append_system_messages - ) + mock_conversation.return_value.append_system_message = mock_append_system_messages query = prompt_engine._generate_query( question="Which genes are associated with mucoviscidosis?", entities=["Gene", "Disease"], @@ -275,7 +250,7 @@ def test_cypher_query_generation(prompt_engine): "PERTURBED": { "source": "Disease", "target": ["Protein", "Gene"], - } + }, }, properties={"Disease": ["name", "ICD10", "DSM5"]}, query_language="Cypher", @@ -291,23 +266,19 @@ def test_cypher_query_generation(prompt_engine): "'(:Disease)-(:PERTURBED)->(:Protein)', " "'(:Disease)-(:PERTURBED)->(:Gene)', generate a Cypher query using " "one of these combinations. Only return the query, without any " - "additional text, symbols or characters --- just the query statement." + "additional text, symbols or characters --- just the query statement.", ) assert "MATCH" in query assert "RETURN" in query assert "Gene" in query assert "Disease" in query assert "mucoviscidosis" in query - assert ( - "-[:PERTURBED]->(g:Gene)" in query - or "(g:Gene)<-[:PERTURBED]-" in query - ) + assert "-[:PERTURBED]->(g:Gene)" in query or "(g:Gene)<-[:PERTURBED]-" in query assert "WHERE" in query or "{name:" in query def test_sql_query_generation(prompt_engine): - """ - Same as before but test the generation of an SQL query. + """Same as before but test the generation of an SQL query. Note: not as representative, because normally in a relational DB there would be a joining table linking genes and diseases - that info @@ -331,9 +302,7 @@ def test_sql_query_generation(prompt_engine): None, ] mock_append_system_messages = Mock() - mock_conversation.return_value.append_system_message = ( - mock_append_system_messages - ) + mock_conversation.return_value.append_system_message = mock_append_system_messages query = prompt_engine._generate_query( question="Which genes are associated with mucoviscidosis?", entities=["Gene", "Disease"], @@ -341,7 +310,7 @@ def test_sql_query_generation(prompt_engine): "PERTURBED": { "source": "Disease", "target": ["Protein", "Gene"], - } + }, }, properties={"Disease": ["name", "ICD10", "DSM5"]}, query_language="SQL", @@ -357,7 +326,7 @@ def test_sql_query_generation(prompt_engine): "'(:Disease)-(:PERTURBED)->(:Protein)', " "'(:Disease)-(:PERTURBED)->(:Gene)', generate a SQL query using " "one of these combinations. Only return the query, without any " - "additional text, symbols or characters --- just the query statement." + "additional text, symbols or characters --- just the query statement.", ) assert "SELECT" in query assert "Gene" in query @@ -378,7 +347,5 @@ def test_end_to_end_query_generation(prompt_engine): assert "Gene" in query assert "Disease" in query assert "mucoviscidosis" in query - assert ( - "-[:PERTURBED]->(g:Gene)" in query or "(g:Gene)<-[:PERTURBED]-" in query - ) + assert "-[:PERTURBED]->(g:Gene)" in query or "(g:Gene)<-[:PERTURBED]-" in query assert "WHERE" in query or "{name:" in query diff --git a/test/test_query_interaction.py b/test/test_query_interaction.py index 480eb59f..a41d99f8 100644 --- a/test/test_query_interaction.py +++ b/test/test_query_interaction.py @@ -1,5 +1,5 @@ -from unittest.mock import Mock, patch import re +from unittest.mock import Mock, patch import pytest @@ -100,38 +100,31 @@ }, }, "relationships": { - "PERTURBED": {"source": "Disease", "target": ["Protein", "Gene"]} + "PERTURBED": {"source": "Disease", "target": ["Protein", "Gene"]}, }, } -@pytest.fixture +@pytest.fixture() def query_handler_basic(): - query = ( - "MATCH (d:Disease {name: 'mucoviscidosis'})-[:PERTURBED]->(g:Gene) " - "RETURN g.name AS AssociatedGenes" - ) + query = "MATCH (d:Disease {name: 'mucoviscidosis'})-[:PERTURBED]->(g:Gene) RETURN g.name AS AssociatedGenes" query_lang = "Cypher" question = "Which genes are associated with mucoviscidosis?" return BioCypherQueryHandler(query, query_lang, _kg_selected, question) -@pytest.fixture +@pytest.fixture() def query_handler_with_kg(): - query = ( - "MATCH (d:Disease {name: 'mucoviscidosis'})-[:PERTURBED]->(g:Gene) " - "RETURN g.name AS AssociatedGenes" - ) + query = "MATCH (d:Disease {name: 'mucoviscidosis'})-[:PERTURBED]->(g:Gene) RETURN g.name AS AssociatedGenes" query_lang = "Cypher" question = "Which genes are associated with mucoviscidosis?" return BioCypherQueryHandler(query, query_lang, _kg_selected, question, _kg) -@pytest.fixture +@pytest.fixture() def query_with_limit_handler(): query = ( - "MATCH (d:Disease {name: 'mucoviscidosis'})-[:PERTURBED]->(g:Gene) " - "RETURN g.name AS AssociatedGenes LIMIT 25" + "MATCH (d:Disease {name: 'mucoviscidosis'})-[:PERTURBED]->(g:Gene) RETURN g.name AS AssociatedGenes LIMIT 25" ) query_lang = "Cypher" question = "Which genes are associated with mucoviscidosis?" @@ -146,9 +139,7 @@ def test_explain(query_handler, request): mock_gptconv.return_value.query.return_value = [resultMsg, Mock(), None] mock_append_system_messages = Mock() - mock_gptconv.return_value.append_system_message = ( - mock_append_system_messages - ) + mock_gptconv.return_value.append_system_message = mock_append_system_messages explanation = query_handler.explain_query() mock_append_system_messages.assert_called_once_with( @@ -162,7 +153,7 @@ def test_explain(query_handler, request): "relationships: " f"{list(query_handler.kg_selected['relationships'].keys())}, and " f"properties: {query_handler.kg_selected['properties']}. " - "Only return the explanation, without any additional text." + "Only return the explanation, without any additional text.", ) @@ -195,7 +186,9 @@ def test_update_limit(query_with_limit_handler): update_request = "Return maximum 10 results" updated_query = update_limit(query_with_limit_handler, update_request) assert updated_query == re.sub( - r"LIMIT [\d]+", "LIMIT 10", query_with_limit_handler.query + r"LIMIT [\d]+", + "LIMIT 10", + query_with_limit_handler.query, ) @@ -207,13 +200,11 @@ def update_limit(query_handler, update_request): ) mock_gptconv.return_value.query.return_value = [resultMsg, Mock(), None] mock_append_system_messages = Mock() - mock_gptconv.return_value.append_system_message = ( - mock_append_system_messages - ) + mock_gptconv.return_value.append_system_message = mock_append_system_messages updated_query = query_handler.update_query(update_request) mock_append_system_messages.assert_called_once_with( - system_msg_for_update(query_handler) + system_msg_for_update(query_handler), ) return updated_query.strip() @@ -229,13 +220,11 @@ def test_update_sorting(query_handler, request): ) mock_gptconv.return_value.query.return_value = [resultMsg, Mock(), None] mock_append_system_messages = Mock() - mock_gptconv.return_value.append_system_message = ( - mock_append_system_messages - ) + mock_gptconv.return_value.append_system_message = mock_append_system_messages updated_query = query_handler.update_query(update_request) mock_append_system_messages.assert_called_once_with( - system_msg_for_update(query_handler) + system_msg_for_update(query_handler), ) assert query_handler.query in updated_query.strip() assert "ORDER BY g.name ASC" in updated_query diff --git a/test/test_rag_agent.py b/test/test_rag_agent.py index 2513619d..5d1c7895 100644 --- a/test/test_rag_agent.py +++ b/test/test_rag_agent.py @@ -1,11 +1,11 @@ -from unittest.mock import MagicMock, patch -import os import json +import os +from unittest.mock import MagicMock, patch import pytest -from biochatter.rag_agent import RagAgent, RagAgentModeEnum from biochatter.llm_connect import GptConversation +from biochatter.rag_agent import RagAgent, RagAgentModeEnum from biochatter.vectorstore_agent import Document @@ -31,7 +31,7 @@ def test_rag_agent_kg_mode(): "name": "result1", "description": "result1 description", "type": "result1 type", - } + }, }, ), metadata={ @@ -45,7 +45,7 @@ def test_rag_agent_kg_mode(): "name": "result2", "description": "result2 description", "type": "result2 type", - } + }, }, ), metadata={ @@ -59,7 +59,7 @@ def test_rag_agent_kg_mode(): "name": "result3", "description": "result3 description", "type": "result3 type", - } + }, }, ), metadata={ @@ -73,7 +73,7 @@ def test_rag_agent_kg_mode(): model_name="test_model", connection_args={"host": "xxx", "port": "xxx"}, schema_config_or_info_dict={ - "schema_config_or_info_dict": "test_schema_config_or_info_dict" + "schema_config_or_info_dict": "test_schema_config_or_info_dict", }, ) assert agent.mode == RagAgentModeEnum.KG @@ -88,7 +88,7 @@ def test_rag_agent_kg_mode(): def test_rag_agent_vectorstore_mode(): with patch( - "biochatter.vectorstore_agent.VectorDatabaseAgentMilvus" + "biochatter.vectorstore_agent.VectorDatabaseAgentMilvus", ) as MockVectorDatabaseAgentMilvus: mock_agent = MockVectorDatabaseAgentMilvus.return_value mock_agent.similarity_search.return_value = [ @@ -129,14 +129,18 @@ def test_rag_agent_vectorstore_mode(): assert type(result[0]) == tuple MockVectorDatabaseAgentMilvus.assert_called_once() mock_agent.similarity_search.assert_called_once_with( - "test question", 3, doc_ids=None + "test question", + 3, + doc_ids=None, ) def test_rag_agent_invalid_mode(): with pytest.raises(ValueError) as excinfo: RagAgent( - mode="invalid_mode", model_name="test_model", connection_args={} + mode="invalid_mode", + model_name="test_model", + connection_args={}, ) assert "Invalid mode. Choose either" in str(excinfo.value) @@ -154,9 +158,7 @@ def test_rag_agent_api_oncokb_mode( mock_fetcher, mock_interpreter, ): - """ - Test the API agent in 'api_oncokb' mode. - """ + """Test the API agent in 'api_oncokb' mode.""" # Create an instance of RagAgent in 'api_oncokb' mode rag_agent = RagAgent( mode=RagAgentModeEnum.API_ONCOKB, @@ -164,9 +166,7 @@ def test_rag_agent_api_oncokb_mode( use_prompt=True, # Ensure prompts are used to get responses conversation_factory=conversation_factory, ) - assert ( - rag_agent.mode == RagAgentModeEnum.API_ONCOKB - ), "Agent mode should be 'api_oncokb'" + assert rag_agent.mode == RagAgentModeEnum.API_ONCOKB, "Agent mode should be 'api_oncokb'" # Define the test question question = "What is the oncogenic potential of BRAF mutation?" @@ -175,9 +175,7 @@ def test_rag_agent_api_oncokb_mode( responses = rag_agent.generate_responses(question) assert responses, "No responses generated" assert isinstance(responses, list), "Responses should be a list" - assert all( - isinstance(response, tuple) for response in responses - ), "Each response should be a tuple" + assert all(isinstance(response, tuple) for response in responses), "Each response should be a tuple" if responses: print("Test response:", responses[0][1]) @@ -189,9 +187,7 @@ def test_rag_agent_api_oncokb_mode( @pytest.mark.skip(reason="Live test for development purposes") def test_rag_agent_api_mode_no_mock(): - """ - Test the API agent with a specific DNA sequence question. - """ + """Test the API agent with a specific DNA sequence question.""" # Define the test question question = "Which organism does the DNA sequence come from: TTCATCGGTCTGAGCAGAGGATGAAGTTGCAAATGATGCAAGCAAAACAGCTCAAAGATGAAGAGGAAAAGGCTATACACAACAGGAGCAATGTAGATACAGAAGGT" @@ -208,9 +204,7 @@ def test_rag_agent_api_mode_no_mock(): responses = api_agent.generate_responses(question) assert responses, "No responses generated" assert isinstance(responses, list), "Responses should be a list" - assert all( - isinstance(response, tuple) for response in responses - ), "Each response should be a tuple" + assert all(isinstance(response, tuple) for response in responses), "Each response should be a tuple" if responses: print("Test response:", responses[0][1]) diff --git a/test/test_vectorstore.py b/test/test_vectorstore.py index 93333c18..810473e1 100644 --- a/test/test_vectorstore.py +++ b/test/test_vectorstore.py @@ -1,12 +1,10 @@ -from unittest.mock import MagicMock, patch import os - -from xinference.client import Client +from unittest.mock import patch from biochatter.vectorstore import ( Document, - DocumentReader, DocumentEmbedder, + DocumentReader, OllamaDocumentEmbedder, XinferenceDocumentEmbedder, ) @@ -55,12 +53,12 @@ @patch("biochatter.vectorstore.VectorDatabaseAgentMilvus") @patch("biochatter.vectorstore.RecursiveCharacterTextSplitter") def test_retrieval_augmented_generation( - mock_textsplitter, mock_host, mock_openaiembeddings + mock_textsplitter, + mock_host, + mock_openaiembeddings, ): # mocking - mock_textsplitter.from_huggingface_tokenizer.return_value = ( - mock_textsplitter() - ) + mock_textsplitter.from_huggingface_tokenizer.return_value = mock_textsplitter() mock_textsplitter.from_tiktoken_encoder.return_value = mock_textsplitter() mock_textsplitter.return_value.split_documents.return_value = splitted_docs mock_host.return_value.store_embeddings.return_value = "1" @@ -101,12 +99,13 @@ def test_retrieval_augmented_generation( @patch("biochatter.vectorstore.VectorDatabaseAgentMilvus") @patch("biochatter.vectorstore.RecursiveCharacterTextSplitter") def test_retrieval_augmented_generation_xinference_api( - mock_textsplitter, mock_host, mock_embeddings, mock_client + mock_textsplitter, + mock_host, + mock_embeddings, + mock_client, ): # mocking - mock_textsplitter.from_huggingface_tokenizer.return_value = ( - mock_textsplitter() - ) + mock_textsplitter.from_huggingface_tokenizer.return_value = mock_textsplitter() mock_textsplitter.from_tiktoken_encoder.return_value = mock_textsplitter() mock_textsplitter.return_value.split_documents.return_value = splitted_docs mock_host.return_value.store_embeddings.return_value = "1" @@ -120,7 +119,7 @@ def test_retrieval_augmented_generation_xinference_api( "max_tokens": 512, "language": ["en"], "model_revision": "", - } + }, } pdf_path = "test/bc_summary.pdf" @@ -133,7 +132,8 @@ def test_retrieval_augmented_generation_xinference_api( rag_agent = XinferenceDocumentEmbedder( base_url=os.getenv( - "GENERIC_TEST_OPENAI_BASE_URL", "http://localhost:9997" + "GENERIC_TEST_OPENAI_BASE_URL", + "http://localhost:9997", ), embedding_collection_name="ollama_embedding_test", metadata_collection_name="xinference_metadata_test", @@ -164,12 +164,13 @@ def test_retrieval_augmented_generation_xinference_api( @patch("biochatter.vectorstore.VectorDatabaseAgentMilvus") @patch("biochatter.vectorstore.RecursiveCharacterTextSplitter") def test_retrieval_augmented_generation_ollama_api( - mock_textsplitter, mock_host, mock_embeddings, mock_client + mock_textsplitter, + mock_host, + mock_embeddings, + mock_client, ): # mocking - mock_textsplitter.from_huggingface_tokenizer.return_value = ( - mock_textsplitter() - ) + mock_textsplitter.from_huggingface_tokenizer.return_value = mock_textsplitter() mock_textsplitter.from_tiktoken_encoder.return_value = mock_textsplitter() mock_textsplitter.return_value.split_documents.return_value = splitted_docs mock_host.return_value.store_embeddings.return_value = "1" @@ -183,7 +184,7 @@ def test_retrieval_augmented_generation_ollama_api( "max_tokens": 512, "language": ["en"], "model_revision": "", - } + }, } pdf_path = "test/bc_summary.pdf" @@ -283,9 +284,7 @@ def check_document_splitter( @patch("biochatter.vectorstore.RecursiveCharacterTextSplitter") def test_split_by_characters(mock_textsplitter, mock_host, mock_embeddings): # character splitter - mock_textsplitter.from_huggingface_tokenizer.return_value = ( - mock_textsplitter() - ) + mock_textsplitter.from_huggingface_tokenizer.return_value = mock_textsplitter() mock_textsplitter.from_tiktoken_encoder.return_value = mock_textsplitter() mock_textsplitter.return_value.split_documents.return_value = splitted_docs rag_agent = DocumentEmbedder( @@ -293,11 +292,15 @@ def test_split_by_characters(mock_textsplitter, mock_host, mock_embeddings): chunk_overlap=CHUNK_OVERLAP, ) check_document_splitter( - rag_agent, "test/bc_summary.pdf", len(splitted_docs) + rag_agent, + "test/bc_summary.pdf", + len(splitted_docs), ) check_document_splitter(rag_agent, "test/dcn.pdf", len(splitted_docs)) check_document_splitter( - rag_agent, "test/bc_summary.txt", len(splitted_docs) + rag_agent, + "test/bc_summary.txt", + len(splitted_docs), ) # tiktoken @@ -307,13 +310,17 @@ def test_split_by_characters(mock_textsplitter, mock_host, mock_embeddings): chunk_overlap=CHUNK_OVERLAP, ) check_document_splitter( - rag_agent, "test/bc_summary.pdf", len(splitted_docs) + rag_agent, + "test/bc_summary.pdf", + len(splitted_docs), ) mock_textsplitter.from_tiktoken_encoder.assert_called_once() check_document_splitter(rag_agent, "test/dcn.pdf", len(splitted_docs)) assert mock_textsplitter.from_tiktoken_encoder.call_count == 2 check_document_splitter( - rag_agent, "test/bc_summary.txt", len(splitted_docs) + rag_agent, + "test/bc_summary.txt", + len(splitted_docs), ) assert mock_textsplitter.from_tiktoken_encoder.call_count == 3 @@ -325,12 +332,16 @@ def test_split_by_characters(mock_textsplitter, mock_host, mock_embeddings): chunk_overlap=CHUNK_OVERLAP, ) check_document_splitter( - rag_agent, "test/bc_summary.pdf", len(splitted_docs) + rag_agent, + "test/bc_summary.pdf", + len(splitted_docs), ) mock_textsplitter.from_huggingface_tokenizer.assert_called_once() check_document_splitter(rag_agent, "test/dcn.pdf", len(splitted_docs)) assert mock_textsplitter.from_huggingface_tokenizer.call_count == 2 check_document_splitter( - rag_agent, "test/bc_summary.txt", len(splitted_docs) + rag_agent, + "test/bc_summary.txt", + len(splitted_docs), ) assert mock_textsplitter.from_huggingface_tokenizer.call_count == 3 diff --git a/test/test_vectorstore_agent.py b/test/test_vectorstore_agent.py index 38dc024d..4fa05252 100644 --- a/test/test_vectorstore_agent.py +++ b/test/test_vectorstore_agent.py @@ -1,10 +1,8 @@ -""" -This test needs OPENAI_API_KEY in the environment and a local milvus server. -""" +"""This test needs OPENAI_API_KEY in the environment and a local milvus server.""" -from unittest.mock import patch import os import uuid +from unittest.mock import patch import pytest @@ -12,15 +10,16 @@ # from pymilvus import utility, Collection, connections # from langchain.embeddings import OpenAIEmbeddings from biochatter.vectorstore_agent import VectorDatabaseAgentMilvus + +from .mock_langchain import Document, Milvus, OpenAIEmbeddings from .mock_pymilvus import ( - DataType, Collection, - FieldSchema, CollectionSchema, - utility, + DataType, + FieldSchema, connections, + utility, ) -from .mock_langchain import Milvus, Document, OpenAIEmbeddings # setup milvus connection if os.getenv("DEVCONTAINER"): @@ -130,15 +129,16 @@ "representation. KGs are d" ), metadata={"source": "test/bc_summary.txt"}, - ) + ), ] -@pytest.fixture +@pytest.fixture() def dbHost(): with ( patch( - "biochatter.vectorstore_agent.OpenAIEmbeddings", OpenAIEmbeddings + "biochatter.vectorstore_agent.OpenAIEmbeddings", + OpenAIEmbeddings, ), patch("biochatter.vectorstore_agent.Document", Document), patch("biochatter.vectorstore_agent.Milvus", Milvus), @@ -148,7 +148,8 @@ def dbHost(): patch("biochatter.vectorstore_agent.DataType", DataType), patch("biochatter.vectorstore_agent.FieldSchema", FieldSchema), patch( - "biochatter.vectorstore_agent.CollectionSchema", CollectionSchema + "biochatter.vectorstore_agent.CollectionSchema", + CollectionSchema, ), ): # create dbHost @@ -213,6 +214,6 @@ def test_build_meta_col_query_expr_for_all_documents(): ] for test_data in data: expr = VectorDatabaseAgentMilvus._build_meta_col_query_expr_for_all_documents( - test_data[0] + test_data[0], ) assert expr == test_data[1]