mirror of https://github.com/hwchase17/langchain
parent
a682f0d12b
commit
2fa7266ebb
@ -1,21 +0,0 @@
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MIT License
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Copyright (c) 2024 LangChain, Inc.
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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@ -1,56 +0,0 @@
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.PHONY: all format lint test tests integration_tests docker_tests help extended_tests
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# Default target executed when no arguments are given to make.
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all: help
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# Define a variable for the test file path.
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TEST_FILE ?= tests/unit_tests/
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integration_test integration_tests: TEST_FILE = tests/integration_tests/
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test tests integration_test integration_tests:
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poetry run pytest $(TEST_FILE)
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######################
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# LINTING AND FORMATTING
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######################
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# Define a variable for Python and notebook files.
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PYTHON_FILES=.
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MYPY_CACHE=.mypy_cache
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lint format: PYTHON_FILES=.
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lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/postgres --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
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lint_package: PYTHON_FILES=langchain_postgres
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lint_tests: PYTHON_FILES=tests
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lint_tests: MYPY_CACHE=.mypy_cache_test
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lint lint_diff lint_package lint_tests:
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poetry run ruff .
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poetry run ruff format $(PYTHON_FILES) --diff
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poetry run ruff --select I $(PYTHON_FILES)
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mkdir -p $(MYPY_CACHE); poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
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format format_diff:
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poetry run ruff format $(PYTHON_FILES)
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poetry run ruff --select I --fix $(PYTHON_FILES)
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spell_check:
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poetry run codespell --toml pyproject.toml
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spell_fix:
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poetry run codespell --toml pyproject.toml -w
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check_imports: $(shell find langchain_postgres -name '*.py')
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poetry run python ./scripts/check_imports.py $^
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######################
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# HELP
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######################
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help:
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@echo '----'
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@echo 'check_imports - check imports'
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@echo 'format - run code formatters'
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@echo 'lint - run linters'
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@echo 'test - run unit tests'
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@echo 'tests - run unit tests'
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@echo 'test TEST_FILE=<test_file> - run all tests in file'
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from importlib import metadata
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from langchain_postgres.chat_message_histories import PostgresChatMessageHistory
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from langchain_postgres.checkpoint import (
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CheckpointSerializer,
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PickleCheckpointSerializer,
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PostgresCheckpoint,
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)
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try:
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__version__ = metadata.version(__package__)
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except metadata.PackageNotFoundError:
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# Case where package metadata is not available.
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__version__ = ""
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__all__ = [
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"__version__",
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"CheckpointSerializer",
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"PostgresChatMessageHistory",
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"PostgresCheckpoint",
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"PickleCheckpointSerializer",
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]
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"""Copied over from langchain_community.
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This code should be moved to langchain proper or removed entirely.
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"""
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import logging
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from typing import List, Union
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import numpy as np
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logger = logging.getLogger(__name__)
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Matrix = Union[List[List[float]], List[np.ndarray], np.ndarray]
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def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray:
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"""Row-wise cosine similarity between two equal-width matrices."""
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if len(X) == 0 or len(Y) == 0:
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return np.array([])
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X = np.array(X)
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Y = np.array(Y)
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if X.shape[1] != Y.shape[1]:
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raise ValueError(
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f"Number of columns in X and Y must be the same. X has shape {X.shape} "
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f"and Y has shape {Y.shape}."
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)
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try:
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import simsimd as simd # type: ignore
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X = np.array(X, dtype=np.float32)
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Y = np.array(Y, dtype=np.float32)
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Z = 1 - simd.cdist(X, Y, metric="cosine")
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if isinstance(Z, float):
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return np.array([Z])
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return np.array(Z)
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except ImportError:
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logger.debug(
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"Unable to import simsimd, defaulting to NumPy implementation. If you want "
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"to use simsimd please install with `pip install simsimd`."
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)
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X_norm = np.linalg.norm(X, axis=1)
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Y_norm = np.linalg.norm(Y, axis=1)
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# Ignore divide by zero errors run time warnings as those are handled below.
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with np.errstate(divide="ignore", invalid="ignore"):
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similarity = np.dot(X, Y.T) / np.outer(X_norm, Y_norm)
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similarity[np.isnan(similarity) | np.isinf(similarity)] = 0.0
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return similarity
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def maximal_marginal_relevance(
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query_embedding: np.ndarray,
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embedding_list: list,
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lambda_mult: float = 0.5,
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k: int = 4,
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) -> List[int]:
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"""Calculate maximal marginal relevance."""
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if min(k, len(embedding_list)) <= 0:
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return []
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if query_embedding.ndim == 1:
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query_embedding = np.expand_dims(query_embedding, axis=0)
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similarity_to_query = cosine_similarity(query_embedding, embedding_list)[0]
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most_similar = int(np.argmax(similarity_to_query))
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idxs = [most_similar]
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selected = np.array([embedding_list[most_similar]])
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while len(idxs) < min(k, len(embedding_list)):
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best_score = -np.inf
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idx_to_add = -1
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similarity_to_selected = cosine_similarity(embedding_list, selected)
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for i, query_score in enumerate(similarity_to_query):
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if i in idxs:
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continue
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redundant_score = max(similarity_to_selected[i])
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equation_score = (
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lambda_mult * query_score - (1 - lambda_mult) * redundant_score
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)
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if equation_score > best_score:
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best_score = equation_score
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idx_to_add = i
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idxs.append(idx_to_add)
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selected = np.append(selected, [embedding_list[idx_to_add]], axis=0)
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return idxs
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"""Client for persisting chat message history in a Postgres database.
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This client provides support for both sync and async via psycopg 3.
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"""
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from __future__ import annotations
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import json
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import logging
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import re
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import uuid
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from typing import List, Optional, Sequence
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import psycopg
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from langchain_core.chat_history import BaseChatMessageHistory
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from langchain_core.messages import BaseMessage, message_to_dict, messages_from_dict
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from psycopg import sql
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logger = logging.getLogger(__name__)
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def _create_table_and_index(table_name: str) -> List[sql.Composed]:
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"""Make a SQL query to create a table."""
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index_name = f"idx_{table_name}_session_id"
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statements = [
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sql.SQL(
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"""
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CREATE TABLE IF NOT EXISTS {table_name} (
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id SERIAL PRIMARY KEY,
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session_id UUID NOT NULL,
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message JSONB NOT NULL,
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created_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
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);
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"""
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).format(table_name=sql.Identifier(table_name)),
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sql.SQL(
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"""
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CREATE INDEX IF NOT EXISTS {index_name} ON {table_name} (session_id);
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"""
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).format(
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table_name=sql.Identifier(table_name), index_name=sql.Identifier(index_name)
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),
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]
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return statements
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def _get_messages_query(table_name: str) -> sql.Composed:
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"""Make a SQL query to get messages for a given session."""
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return sql.SQL(
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"SELECT message "
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"FROM {table_name} "
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"WHERE session_id = %(session_id)s "
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"ORDER BY id;"
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).format(table_name=sql.Identifier(table_name))
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def _delete_by_session_id_query(table_name: str) -> sql.Composed:
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"""Make a SQL query to delete messages for a given session."""
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return sql.SQL(
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"DELETE FROM {table_name} WHERE session_id = %(session_id)s;"
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).format(table_name=sql.Identifier(table_name))
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def _delete_table_query(table_name: str) -> sql.Composed:
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"""Make a SQL query to delete a table."""
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return sql.SQL("DROP TABLE IF EXISTS {table_name};").format(
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table_name=sql.Identifier(table_name)
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)
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def _insert_message_query(table_name: str) -> sql.Composed:
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"""Make a SQL query to insert a message."""
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return sql.SQL(
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"INSERT INTO {table_name} (session_id, message) VALUES (%s, %s)"
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).format(table_name=sql.Identifier(table_name))
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class PostgresChatMessageHistory(BaseChatMessageHistory):
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def __init__(
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self,
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table_name: str,
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session_id: str,
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/,
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*,
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sync_connection: Optional[psycopg.Connection] = None,
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async_connection: Optional[psycopg.AsyncConnection] = None,
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) -> None:
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"""Client for persisting chat message history in a Postgres database,
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This client provides support for both sync and async via psycopg >=3.
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The client can create schema in the database and provides methods to
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add messages, get messages, and clear the chat message history.
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The schema has the following columns:
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- id: A serial primary key.
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- session_id: The session ID for the chat message history.
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- message: The JSONB message content.
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- created_at: The timestamp of when the message was created.
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Messages are retrieved for a given session_id and are sorted by
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the id (which should be increasing monotonically), and correspond
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to the order in which the messages were added to the history.
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The "created_at" column is not returned by the interface, but
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has been added for the schema so the information is available in the database.
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A session_id can be used to separate different chat histories in the same table,
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the session_id should be provided when initializing the client.
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This chat history client takes in a psycopg connection object (either
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Connection or AsyncConnection) and uses it to interact with the database.
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This design allows to reuse the underlying connection object across
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multiple instantiations of this class, making instantiation fast.
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This chat history client is designed for prototyping applications that
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involve chat and are based on Postgres.
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As your application grows, you will likely need to extend the schema to
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handle more complex queries. For example, a chat application
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may involve multiple tables like a user table, a table for storing
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chat sessions / conversations, and this table for storing chat messages
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for a given session. The application will require access to additional
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endpoints like deleting messages by user id, listing conversations by
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user id or ordering them based on last message time, etc.
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Feel free to adapt this implementation to suit your application's needs.
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Args:
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session_id: The session ID to use for the chat message history
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table_name: The name of the database table to use
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sync_connection: An existing psycopg connection instance
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async_connection: An existing psycopg async connection instance
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Usage:
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- Use the create_schema or acreate_schema method to set up the table
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schema in the database.
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- Initialize the class with the appropriate session ID, table name,
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and database connection.
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- Add messages to the database using add_messages or aadd_messages.
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- Retrieve messages with get_messages or aget_messages.
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- Clear the session history with clear or aclear when needed.
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Note:
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- At least one of sync_connection or async_connection must be provided.
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Examples:
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.. code-block:: python
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import uuid
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from langchain_core.messages import SystemMessage, AIMessage, HumanMessage
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from langchain_postgres import PostgresChatMessageHistory
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import psycopg
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# Establish a synchronous connection to the database
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# (or use psycopg.AsyncConnection for async)
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sync_connection = psycopg2.connect(conn_info)
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# Create the table schema (only needs to be done once)
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table_name = "chat_history"
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PostgresChatMessageHistory.create_schema(sync_connection, table_name)
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session_id = str(uuid.uuid4())
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# Initialize the chat history manager
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chat_history = PostgresChatMessageHistory(
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table_name,
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session_id,
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sync_connection=sync_connection
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)
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# Add messages to the chat history
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chat_history.add_messages([
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SystemMessage(content="Meow"),
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AIMessage(content="woof"),
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HumanMessage(content="bark"),
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])
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print(chat_history.messages)
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"""
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if not sync_connection and not async_connection:
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raise ValueError("Must provide sync_connection or async_connection")
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self._connection = sync_connection
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self._aconnection = async_connection
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# Validate that session id is a UUID
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try:
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uuid.UUID(session_id)
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except ValueError:
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raise ValueError(
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f"Invalid session id. Session id must be a valid UUID. Got {session_id}"
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)
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self._session_id = session_id
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if not re.match(r"^\w+$", table_name):
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raise ValueError(
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"Invalid table name. Table name must contain only alphanumeric "
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"characters and underscores."
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)
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self._table_name = table_name
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@staticmethod
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def create_schema(
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connection: psycopg.Connection,
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table_name: str,
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/,
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) -> None:
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"""Create the table schema in the database and create relevant indexes."""
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queries = _create_table_and_index(table_name)
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logger.info("Creating schema for table %s", table_name)
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with connection.cursor() as cursor:
|
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for query in queries:
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cursor.execute(query)
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connection.commit()
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||||
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||||
@staticmethod
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||||
async def acreate_schema(
|
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connection: psycopg.AsyncConnection, table_name: str, /
|
||||
) -> None:
|
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"""Create the table schema in the database and create relevant indexes."""
|
||||
queries = _create_table_and_index(table_name)
|
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logger.info("Creating schema for table %s", table_name)
|
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async with connection.cursor() as cur:
|
||||
for query in queries:
|
||||
await cur.execute(query)
|
||||
await connection.commit()
|
||||
|
||||
@staticmethod
|
||||
def drop_table(connection: psycopg.Connection, table_name: str, /) -> None:
|
||||
"""Delete the table schema in the database.
|
||||
|
||||
WARNING:
|
||||
This will delete the given table from the database including
|
||||
all the database in the table and the schema of the table.
|
||||
|
||||
Args:
|
||||
connection: The database connection.
|
||||
table_name: The name of the table to create.
|
||||
"""
|
||||
|
||||
query = _delete_table_query(table_name)
|
||||
logger.info("Dropping table %s", table_name)
|
||||
with connection.cursor() as cursor:
|
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cursor.execute(query)
|
||||
connection.commit()
|
||||
|
||||
@staticmethod
|
||||
async def adrop_table(
|
||||
connection: psycopg.AsyncConnection, table_name: str, /
|
||||
) -> None:
|
||||
"""Delete the table schema in the database.
|
||||
|
||||
WARNING:
|
||||
This will delete the given table from the database including
|
||||
all the database in the table and the schema of the table.
|
||||
|
||||
Args:
|
||||
connection: Async database connection.
|
||||
table_name: The name of the table to create.
|
||||
"""
|
||||
query = _delete_table_query(table_name)
|
||||
logger.info("Dropping table %s", table_name)
|
||||
|
||||
async with connection.cursor() as acur:
|
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await acur.execute(query)
|
||||
await connection.commit()
|
||||
|
||||
def add_messages(self, messages: Sequence[BaseMessage]) -> None:
|
||||
"""Add messages to the chat message history."""
|
||||
if self._connection is None:
|
||||
raise ValueError(
|
||||
"Please initialize the PostgresChatMessageHistory "
|
||||
"with a sync connection or use the aadd_messages method instead."
|
||||
)
|
||||
|
||||
values = [
|
||||
(self._session_id, json.dumps(message_to_dict(message)))
|
||||
for message in messages
|
||||
]
|
||||
|
||||
query = _insert_message_query(self._table_name)
|
||||
|
||||
with self._connection.cursor() as cursor:
|
||||
cursor.executemany(query, values)
|
||||
self._connection.commit()
|
||||
|
||||
async def aadd_messages(self, messages: Sequence[BaseMessage]) -> None:
|
||||
"""Add messages to the chat message history."""
|
||||
if self._aconnection is None:
|
||||
raise ValueError(
|
||||
"Please initialize the PostgresChatMessageHistory "
|
||||
"with an async connection or use the sync add_messages method instead."
|
||||
)
|
||||
|
||||
values = [
|
||||
(self._session_id, json.dumps(message_to_dict(message)))
|
||||
for message in messages
|
||||
]
|
||||
|
||||
query = _insert_message_query(self._table_name)
|
||||
async with self._aconnection.cursor() as cursor:
|
||||
await cursor.executemany(query, values)
|
||||
await self._aconnection.commit()
|
||||
|
||||
def get_messages(self) -> List[BaseMessage]:
|
||||
"""Retrieve messages from the chat message history."""
|
||||
if self._connection is None:
|
||||
raise ValueError(
|
||||
"Please initialize the PostgresChatMessageHistory "
|
||||
"with a sync connection or use the async aget_messages method instead."
|
||||
)
|
||||
|
||||
query = _get_messages_query(self._table_name)
|
||||
|
||||
with self._connection.cursor() as cursor:
|
||||
cursor.execute(query, {"session_id": self._session_id})
|
||||
items = [record[0] for record in cursor.fetchall()]
|
||||
|
||||
messages = messages_from_dict(items)
|
||||
return messages
|
||||
|
||||
async def aget_messages(self) -> List[BaseMessage]:
|
||||
"""Retrieve messages from the chat message history."""
|
||||
if self._aconnection is None:
|
||||
raise ValueError(
|
||||
"Please initialize the PostgresChatMessageHistory "
|
||||
"with an async connection or use the sync get_messages method instead."
|
||||
)
|
||||
|
||||
query = _get_messages_query(self._table_name)
|
||||
async with self._aconnection.cursor() as cursor:
|
||||
await cursor.execute(query, {"session_id": self._session_id})
|
||||
items = [record[0] for record in await cursor.fetchall()]
|
||||
|
||||
messages = messages_from_dict(items)
|
||||
return messages
|
||||
|
||||
@property # type: ignore[override]
|
||||
def messages(self) -> List[BaseMessage]:
|
||||
"""The abstraction required a property."""
|
||||
return self.get_messages()
|
||||
|
||||
def clear(self) -> None:
|
||||
"""Clear the chat message history for the GIVEN session."""
|
||||
if self._connection is None:
|
||||
raise ValueError(
|
||||
"Please initialize the PostgresChatMessageHistory "
|
||||
"with a sync connection or use the async clear method instead."
|
||||
)
|
||||
|
||||
query = _delete_by_session_id_query(self._table_name)
|
||||
with self._connection.cursor() as cursor:
|
||||
cursor.execute(query, {"session_id": self._session_id})
|
||||
self._connection.commit()
|
||||
|
||||
async def aclear(self) -> None:
|
||||
"""Clear the chat message history for the GIVEN session."""
|
||||
if self._aconnection is None:
|
||||
raise ValueError(
|
||||
"Please initialize the PostgresChatMessageHistory "
|
||||
"with an async connection or use the sync clear method instead."
|
||||
)
|
||||
|
||||
query = _delete_by_session_id_query(self._table_name)
|
||||
async with self._aconnection.cursor() as cursor:
|
||||
await cursor.execute(query, {"session_id": self._session_id})
|
||||
await self._aconnection.commit()
|
@ -1,565 +0,0 @@
|
||||
"""Implementation of a langgraph checkpoint saver using Postgres."""
|
||||
import abc
|
||||
import pickle
|
||||
from contextlib import asynccontextmanager, contextmanager
|
||||
from typing import AsyncGenerator, AsyncIterator, Generator, Optional, Union, cast
|
||||
|
||||
import psycopg
|
||||
from langchain_core.runnables import ConfigurableFieldSpec, RunnableConfig
|
||||
from langgraph.checkpoint import BaseCheckpointSaver
|
||||
from langgraph.checkpoint.base import Checkpoint, CheckpointThreadTs, CheckpointTuple
|
||||
from psycopg_pool import AsyncConnectionPool, ConnectionPool
|
||||
|
||||
|
||||
class CheckpointSerializer(abc.ABC):
|
||||
"""A serializer for serializing and deserializing objects to and from bytes."""
|
||||
|
||||
@abc.abstractmethod
|
||||
def dumps(self, obj: Checkpoint) -> bytes:
|
||||
"""Serialize an object to bytes."""
|
||||
|
||||
@abc.abstractmethod
|
||||
def loads(self, data: bytes) -> Checkpoint:
|
||||
"""Deserialize an object from bytes."""
|
||||
|
||||
|
||||
class PickleCheckpointSerializer(CheckpointSerializer):
|
||||
"""Use the pickle module to serialize and deserialize objects.
|
||||
|
||||
This serializer uses the pickle module to serialize and deserialize objects.
|
||||
|
||||
While pickling can serialize a wide range of Python objects, it may fail
|
||||
de-serializable objects upon updates of the Python version or the python
|
||||
environment (e.g., the object's class definition changes in LangGraph).
|
||||
|
||||
*Security Warning*: The pickle module can deserialize malicious payloads,
|
||||
only use this serializer with trusted data; e.g., data that you
|
||||
have serialized yourself and can guarantee the integrity of.
|
||||
"""
|
||||
|
||||
def dumps(self, obj: Checkpoint) -> bytes:
|
||||
"""Serialize an object to bytes."""
|
||||
return pickle.dumps(obj)
|
||||
|
||||
def loads(self, data: bytes) -> Checkpoint:
|
||||
"""Deserialize an object from bytes."""
|
||||
return cast(Checkpoint, pickle.loads(data))
|
||||
|
||||
|
||||
class PostgresCheckpoint(BaseCheckpointSaver):
|
||||
"""LangGraph checkpoint saver for Postgres.
|
||||
|
||||
This implementation of a checkpoint saver uses a Postgres database to save
|
||||
and retrieve checkpoints. It uses the psycopg3 package to interact with the
|
||||
Postgres database.
|
||||
|
||||
The checkpoint accepts either a sync_connection in the form of a psycopg.Connection
|
||||
or a psycopg.ConnectionPool object, or an async_connection in the form of a
|
||||
psycopg.AsyncConnection or psycopg.AsyncConnectionPool object.
|
||||
|
||||
Usage:
|
||||
|
||||
1. First time use: create schema in the database using the `create_schema` method or
|
||||
the async version `acreate_schema` method.
|
||||
2. Create a PostgresCheckpoint object with a serializer and an appropriate
|
||||
connection object.
|
||||
It's recommended to use a connection pool object for the connection.
|
||||
If using a connection object, you are responsible for closing the connection
|
||||
when done.
|
||||
|
||||
Examples:
|
||||
|
||||
|
||||
Sync usage with a connection pool:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from psycopg_pool import ConnectionPool
|
||||
from langchain_postgres import (
|
||||
PostgresCheckpoint, PickleCheckpointSerializer
|
||||
)
|
||||
|
||||
pool = ConnectionPool(
|
||||
# Example configuration
|
||||
conninfo="postgresql://user:password@localhost:5432/dbname",
|
||||
max_size=20,
|
||||
)
|
||||
|
||||
# Uses the pickle module for serialization
|
||||
# Make sure that you're only de-serializing trusted data
|
||||
# (e.g., payloads that you have serialized yourself).
|
||||
# Or implement a custom serializer.
|
||||
checkpoint = PostgresCheckpoint(
|
||||
serializer=PickleCheckpointSerializer(),
|
||||
sync_connection=pool,
|
||||
)
|
||||
|
||||
# Use the checkpoint object to put, get, list checkpoints, etc.
|
||||
|
||||
|
||||
Async usage with a connection pool:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from psycopg_pool import AsyncConnectionPool
|
||||
from langchain_postgres import (
|
||||
PostgresCheckpoint, PickleCheckpointSerializer
|
||||
)
|
||||
|
||||
pool = AsyncConnectionPool(
|
||||
# Example configuration
|
||||
conninfo="postgresql://user:password@localhost:5432/dbname",
|
||||
max_size=20,
|
||||
)
|
||||
|
||||
# Uses the pickle module for serialization
|
||||
# Make sure that you're only de-serializing trusted data
|
||||
# (e.g., payloads that you have serialized yourself).
|
||||
# Or implement a custom serializer.
|
||||
checkpoint = PostgresCheckpoint(
|
||||
serializer=PickleCheckpointSerializer(),
|
||||
async_connection=pool,
|
||||
)
|
||||
|
||||
# Use the checkpoint object to put, get, list checkpoints, etc.
|
||||
|
||||
|
||||
Async usage with a connection object:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from psycopg import AsyncConnection
|
||||
from langchain_postgres import (
|
||||
PostgresCheckpoint, PickleCheckpointSerializer
|
||||
)
|
||||
|
||||
conninfo="postgresql://user:password@localhost:5432/dbname"
|
||||
# Take care of closing the connection when done
|
||||
async with AsyncConnection(conninfo=conninfo) as conn:
|
||||
# Uses the pickle module for serialization
|
||||
# Make sure that you're only de-serializing trusted data
|
||||
# (e.g., payloads that you have serialized yourself).
|
||||
# Or implement a custom serializer.
|
||||
checkpoint = PostgresCheckpoint(
|
||||
serializer=PickleCheckpointSerializer(),
|
||||
async_connection=conn,
|
||||
)
|
||||
|
||||
# Use the checkpoint object to put, get, list checkpoints, etc.
|
||||
...
|
||||
"""
|
||||
|
||||
serializer: CheckpointSerializer
|
||||
"""The serializer for serializing and deserializing objects to and from bytes."""
|
||||
|
||||
sync_connection: Optional[Union[psycopg.Connection, ConnectionPool]] = None
|
||||
"""The synchronous connection or pool to the Postgres database.
|
||||
|
||||
If providing a connection object, please ensure that the connection is open
|
||||
and remember to close the connection when done.
|
||||
"""
|
||||
async_connection: Optional[
|
||||
Union[psycopg.AsyncConnection, AsyncConnectionPool]
|
||||
] = None
|
||||
"""The asynchronous connection or pool to the Postgres database.
|
||||
|
||||
If providing a connection object, please ensure that the connection is open
|
||||
and remember to close the connection when done.
|
||||
"""
|
||||
|
||||
class Config:
|
||||
arbitrary_types_allowed = True
|
||||
extra = "forbid"
|
||||
|
||||
@property
|
||||
def config_specs(self) -> list[ConfigurableFieldSpec]:
|
||||
"""Return the configuration specs for this runnable."""
|
||||
return [
|
||||
ConfigurableFieldSpec(
|
||||
id="thread_id",
|
||||
annotation=Optional[str],
|
||||
name="Thread ID",
|
||||
description=None,
|
||||
default=None,
|
||||
is_shared=True,
|
||||
),
|
||||
CheckpointThreadTs,
|
||||
]
|
||||
|
||||
@contextmanager
|
||||
def _get_sync_connection(self) -> Generator[psycopg.Connection, None, None]:
|
||||
"""Get the connection to the Postgres database."""
|
||||
if isinstance(self.sync_connection, psycopg.Connection):
|
||||
yield self.sync_connection
|
||||
elif isinstance(self.sync_connection, ConnectionPool):
|
||||
with self.sync_connection.connection() as conn:
|
||||
yield conn
|
||||
else:
|
||||
raise ValueError(
|
||||
"Invalid sync connection object. Please initialize the check pointer "
|
||||
f"with an appropriate sync connection object. "
|
||||
f"Got {type(self.sync_connection)}."
|
||||
)
|
||||
|
||||
@asynccontextmanager
|
||||
async def _get_async_connection(
|
||||
self,
|
||||
) -> AsyncGenerator[psycopg.AsyncConnection, None]:
|
||||
"""Get the connection to the Postgres database."""
|
||||
if isinstance(self.async_connection, psycopg.AsyncConnection):
|
||||
yield self.async_connection
|
||||
elif isinstance(self.async_connection, AsyncConnectionPool):
|
||||
async with self.async_connection.connection() as conn:
|
||||
yield conn
|
||||
else:
|
||||
raise ValueError(
|
||||
"Invalid async connection object. Please initialize the check pointer "
|
||||
f"with an appropriate async connection object. "
|
||||
f"Got {type(self.async_connection)}."
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def create_schema(connection: psycopg.Connection, /) -> None:
|
||||
"""Create the schema for the checkpoint saver."""
|
||||
with connection.cursor() as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
CREATE TABLE IF NOT EXISTS checkpoints (
|
||||
thread_id TEXT NOT NULL,
|
||||
checkpoint BYTEA NOT NULL,
|
||||
thread_ts TIMESTAMPTZ NOT NULL,
|
||||
parent_ts TIMESTAMPTZ,
|
||||
PRIMARY KEY (thread_id, thread_ts)
|
||||
);
|
||||
"""
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
async def acreate_schema(connection: psycopg.AsyncConnection, /) -> None:
|
||||
"""Create the schema for the checkpoint saver."""
|
||||
async with connection.cursor() as cur:
|
||||
await cur.execute(
|
||||
"""
|
||||
CREATE TABLE IF NOT EXISTS checkpoints (
|
||||
thread_id TEXT NOT NULL,
|
||||
checkpoint BYTEA NOT NULL,
|
||||
thread_ts TIMESTAMPTZ NOT NULL,
|
||||
parent_ts TIMESTAMPTZ,
|
||||
PRIMARY KEY (thread_id, thread_ts)
|
||||
);
|
||||
"""
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def drop_schema(connection: psycopg.Connection, /) -> None:
|
||||
"""Drop the table for the checkpoint saver."""
|
||||
with connection.cursor() as cur:
|
||||
cur.execute("DROP TABLE IF EXISTS checkpoints;")
|
||||
|
||||
@staticmethod
|
||||
async def adrop_schema(connection: psycopg.AsyncConnection, /) -> None:
|
||||
"""Drop the table for the checkpoint saver."""
|
||||
async with connection.cursor() as cur:
|
||||
await cur.execute("DROP TABLE IF EXISTS checkpoints;")
|
||||
|
||||
def put(self, config: RunnableConfig, checkpoint: Checkpoint) -> RunnableConfig:
|
||||
"""Put the checkpoint for the given configuration.
|
||||
|
||||
Args:
|
||||
config: The configuration for the checkpoint.
|
||||
A dict with a `configurable` key which is a dict with
|
||||
a `thread_id` key and an optional `thread_ts` key.
|
||||
For example, { 'configurable': { 'thread_id': 'test_thread' } }
|
||||
checkpoint: The checkpoint to persist.
|
||||
|
||||
Returns:
|
||||
The RunnableConfig that describes the checkpoint that was just created.
|
||||
It'll contain the `thread_id` and `thread_ts` of the checkpoint.
|
||||
"""
|
||||
thread_id = config["configurable"]["thread_id"]
|
||||
parent_ts = config["configurable"].get("thread_ts")
|
||||
|
||||
with self._get_sync_connection() as conn:
|
||||
with conn.cursor() as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
INSERT INTO checkpoints
|
||||
(thread_id, thread_ts, parent_ts, checkpoint)
|
||||
VALUES
|
||||
(%(thread_id)s, %(thread_ts)s, %(parent_ts)s, %(checkpoint)s)
|
||||
ON CONFLICT (thread_id, thread_ts)
|
||||
DO UPDATE SET checkpoint = EXCLUDED.checkpoint;
|
||||
""",
|
||||
{
|
||||
"thread_id": thread_id,
|
||||
"thread_ts": checkpoint["ts"],
|
||||
"parent_ts": parent_ts if parent_ts else None,
|
||||
"checkpoint": self.serializer.dumps(checkpoint),
|
||||
},
|
||||
)
|
||||
|
||||
return {
|
||||
"configurable": {
|
||||
"thread_id": thread_id,
|
||||
"thread_ts": checkpoint["ts"],
|
||||
},
|
||||
}
|
||||
|
||||
async def aput(
|
||||
self, config: RunnableConfig, checkpoint: Checkpoint
|
||||
) -> RunnableConfig:
|
||||
"""Put the checkpoint for the given configuration.
|
||||
|
||||
Args:
|
||||
config: The configuration for the checkpoint.
|
||||
A dict with a `configurable` key which is a dict with
|
||||
a `thread_id` key and an optional `thread_ts` key.
|
||||
For example, { 'configurable': { 'thread_id': 'test_thread' } }
|
||||
checkpoint: The checkpoint to persist.
|
||||
|
||||
Returns:
|
||||
The RunnableConfig that describes the checkpoint that was just created.
|
||||
It'll contain the `thread_id` and `thread_ts` of the checkpoint.
|
||||
"""
|
||||
thread_id = config["configurable"]["thread_id"]
|
||||
parent_ts = config["configurable"].get("thread_ts")
|
||||
async with self._get_async_connection() as conn:
|
||||
async with conn.cursor() as cur:
|
||||
await cur.execute(
|
||||
"""
|
||||
INSERT INTO
|
||||
checkpoints (thread_id, thread_ts, parent_ts, checkpoint)
|
||||
VALUES
|
||||
(%(thread_id)s, %(thread_ts)s, %(parent_ts)s, %(checkpoint)s)
|
||||
ON CONFLICT (thread_id, thread_ts)
|
||||
DO UPDATE SET checkpoint = EXCLUDED.checkpoint;
|
||||
""",
|
||||
{
|
||||
"thread_id": thread_id,
|
||||
"thread_ts": checkpoint["ts"],
|
||||
"parent_ts": parent_ts if parent_ts else None,
|
||||
"checkpoint": self.serializer.dumps(checkpoint),
|
||||
},
|
||||
)
|
||||
|
||||
return {
|
||||
"configurable": {
|
||||
"thread_id": thread_id,
|
||||
"thread_ts": checkpoint["ts"],
|
||||
},
|
||||
}
|
||||
|
||||
def list(self, config: RunnableConfig) -> Generator[CheckpointTuple, None, None]:
|
||||
"""Get all the checkpoints for the given configuration."""
|
||||
with self._get_sync_connection() as conn:
|
||||
with conn.cursor() as cur:
|
||||
thread_id = config["configurable"]["thread_id"]
|
||||
cur.execute(
|
||||
"SELECT checkpoint, thread_ts, parent_ts "
|
||||
"FROM checkpoints "
|
||||
"WHERE thread_id = %(thread_id)s "
|
||||
"ORDER BY thread_ts DESC",
|
||||
{
|
||||
"thread_id": thread_id,
|
||||
},
|
||||
)
|
||||
for value in cur:
|
||||
yield CheckpointTuple(
|
||||
{
|
||||
"configurable": {
|
||||
"thread_id": thread_id,
|
||||
"thread_ts": value[1].isoformat(),
|
||||
}
|
||||
},
|
||||
self.serializer.loads(value[0]),
|
||||
{
|
||||
"configurable": {
|
||||
"thread_id": thread_id,
|
||||
"thread_ts": value[2].isoformat(),
|
||||
}
|
||||
}
|
||||
if value[2]
|
||||
else None,
|
||||
)
|
||||
|
||||
async def alist(self, config: RunnableConfig) -> AsyncIterator[CheckpointTuple]:
|
||||
"""Get all the checkpoints for the given configuration."""
|
||||
async with self._get_async_connection() as conn:
|
||||
async with conn.cursor() as cur:
|
||||
thread_id = config["configurable"]["thread_id"]
|
||||
await cur.execute(
|
||||
"SELECT checkpoint, thread_ts, parent_ts "
|
||||
"FROM checkpoints "
|
||||
"WHERE thread_id = %(thread_id)s "
|
||||
"ORDER BY thread_ts DESC",
|
||||
{
|
||||
"thread_id": thread_id,
|
||||
},
|
||||
)
|
||||
async for value in cur:
|
||||
yield CheckpointTuple(
|
||||
{
|
||||
"configurable": {
|
||||
"thread_id": thread_id,
|
||||
"thread_ts": value[1].isoformat(),
|
||||
}
|
||||
},
|
||||
self.serializer.loads(value[0]),
|
||||
{
|
||||
"configurable": {
|
||||
"thread_id": thread_id,
|
||||
"thread_ts": value[2].isoformat(),
|
||||
}
|
||||
}
|
||||
if value[2]
|
||||
else None,
|
||||
)
|
||||
|
||||
def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
|
||||
"""Get the checkpoint tuple for the given configuration.
|
||||
|
||||
Args:
|
||||
config: The configuration for the checkpoint.
|
||||
A dict with a `configurable` key which is a dict with
|
||||
a `thread_id` key and an optional `thread_ts` key.
|
||||
For example, { 'configurable': { 'thread_id': 'test_thread' } }
|
||||
|
||||
Returns:
|
||||
The checkpoint tuple for the given configuration if it exists,
|
||||
otherwise None.
|
||||
|
||||
If thread_ts is None, the latest checkpoint is returned if it exists.
|
||||
"""
|
||||
thread_id = config["configurable"]["thread_id"]
|
||||
thread_ts = config["configurable"].get("thread_ts")
|
||||
with self._get_sync_connection() as conn:
|
||||
with conn.cursor() as cur:
|
||||
if thread_ts:
|
||||
cur.execute(
|
||||
"SELECT checkpoint, parent_ts "
|
||||
"FROM checkpoints "
|
||||
"WHERE thread_id = %(thread_id)s AND thread_ts = %(thread_ts)s",
|
||||
{
|
||||
"thread_id": thread_id,
|
||||
"thread_ts": thread_ts,
|
||||
},
|
||||
)
|
||||
value = cur.fetchone()
|
||||
if value:
|
||||
return CheckpointTuple(
|
||||
config,
|
||||
self.serializer.loads(value[0]),
|
||||
{
|
||||
"configurable": {
|
||||
"thread_id": thread_id,
|
||||
"thread_ts": value[1].isoformat(),
|
||||
}
|
||||
}
|
||||
if value[1]
|
||||
else None,
|
||||
)
|
||||
else:
|
||||
cur.execute(
|
||||
"SELECT checkpoint, thread_ts, parent_ts "
|
||||
"FROM checkpoints "
|
||||
"WHERE thread_id = %(thread_id)s "
|
||||
"ORDER BY thread_ts DESC LIMIT 1",
|
||||
{
|
||||
"thread_id": thread_id,
|
||||
},
|
||||
)
|
||||
value = cur.fetchone()
|
||||
if value:
|
||||
return CheckpointTuple(
|
||||
config={
|
||||
"configurable": {
|
||||
"thread_id": thread_id,
|
||||
"thread_ts": value[1].isoformat(),
|
||||
}
|
||||
},
|
||||
checkpoint=self.serializer.loads(value[0]),
|
||||
parent_config={
|
||||
"configurable": {
|
||||
"thread_id": thread_id,
|
||||
"thread_ts": value[2].isoformat(),
|
||||
}
|
||||
}
|
||||
if value[2]
|
||||
else None,
|
||||
)
|
||||
return None
|
||||
|
||||
async def aget_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
|
||||
"""Get the checkpoint tuple for the given configuration.
|
||||
|
||||
Args:
|
||||
config: The configuration for the checkpoint.
|
||||
A dict with a `configurable` key which is a dict with
|
||||
a `thread_id` key and an optional `thread_ts` key.
|
||||
For example, { 'configurable': { 'thread_id': 'test_thread' } }
|
||||
|
||||
Returns:
|
||||
The checkpoint tuple for the given configuration if it exists,
|
||||
otherwise None.
|
||||
|
||||
If thread_ts is None, the latest checkpoint is returned if it exists.
|
||||
"""
|
||||
thread_id = config["configurable"]["thread_id"]
|
||||
thread_ts = config["configurable"].get("thread_ts")
|
||||
async with self._get_async_connection() as conn:
|
||||
async with conn.cursor() as cur:
|
||||
if thread_ts:
|
||||
await cur.execute(
|
||||
"SELECT checkpoint, parent_ts "
|
||||
"FROM checkpoints "
|
||||
"WHERE thread_id = %(thread_id)s AND thread_ts = %(thread_ts)s",
|
||||
{
|
||||
"thread_id": thread_id,
|
||||
"thread_ts": thread_ts,
|
||||
},
|
||||
)
|
||||
value = await cur.fetchone()
|
||||
if value:
|
||||
return CheckpointTuple(
|
||||
config,
|
||||
self.serializer.loads(value[0]),
|
||||
{
|
||||
"configurable": {
|
||||
"thread_id": thread_id,
|
||||
"thread_ts": value[1].isoformat(),
|
||||
}
|
||||
}
|
||||
if value[1]
|
||||
else None,
|
||||
)
|
||||
else:
|
||||
await cur.execute(
|
||||
"SELECT checkpoint, thread_ts, parent_ts "
|
||||
"FROM checkpoints "
|
||||
"WHERE thread_id = %(thread_id)s "
|
||||
"ORDER BY thread_ts DESC LIMIT 1",
|
||||
{
|
||||
"thread_id": thread_id,
|
||||
},
|
||||
)
|
||||
value = await cur.fetchone()
|
||||
if value:
|
||||
return CheckpointTuple(
|
||||
config={
|
||||
"configurable": {
|
||||
"thread_id": thread_id,
|
||||
"thread_ts": value[1].isoformat(),
|
||||
}
|
||||
},
|
||||
checkpoint=self.serializer.loads(value[0]),
|
||||
parent_config={
|
||||
"configurable": {
|
||||
"thread_id": thread_id,
|
||||
"thread_ts": value[2].isoformat(),
|
||||
}
|
||||
}
|
||||
if value[2]
|
||||
else None,
|
||||
)
|
||||
|
||||
return None
|
File diff suppressed because it is too large
Load Diff
@ -1,987 +0,0 @@
|
||||
# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
|
||||
|
||||
[[package]]
|
||||
name = "annotated-types"
|
||||
version = "0.6.0"
|
||||
description = "Reusable constraint types to use with typing.Annotated"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "annotated_types-0.6.0-py3-none-any.whl", hash = "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43"},
|
||||
{file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "certifi"
|
||||
version = "2024.2.2"
|
||||
description = "Python package for providing Mozilla's CA Bundle."
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
files = [
|
||||
{file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"},
|
||||
{file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "charset-normalizer"
|
||||
version = "3.3.2"
|
||||
description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
|
||||
optional = false
|
||||
python-versions = ">=3.7.0"
|
||||
files = [
|
||||
{file = "charset-normalizer-3.3.2.tar.gz", hash = "sha256:f30c3cb33b24454a82faecaf01b19c18562b1e89558fb6c56de4d9118a032fd5"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:25baf083bf6f6b341f4121c2f3c548875ee6f5339300e08be3f2b2ba1721cdd3"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:06435b539f889b1f6f4ac1758871aae42dc3a8c0e24ac9e60c2384973ad73027"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9063e24fdb1e498ab71cb7419e24622516c4a04476b17a2dab57e8baa30d6e03"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6897af51655e3691ff853668779c7bad41579facacf5fd7253b0133308cf000d"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1d3193f4a680c64b4b6a9115943538edb896edc190f0b222e73761716519268e"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:cd70574b12bb8a4d2aaa0094515df2463cb429d8536cfb6c7ce983246983e5a6"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8465322196c8b4d7ab6d1e049e4c5cb460d0394da4a27d23cc242fbf0034b6b5"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a9a8e9031d613fd2009c182b69c7b2c1ef8239a0efb1df3f7c8da66d5dd3d537"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:beb58fe5cdb101e3a055192ac291b7a21e3b7ef4f67fa1d74e331a7f2124341c"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:e06ed3eb3218bc64786f7db41917d4e686cc4856944f53d5bdf83a6884432e12"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:2e81c7b9c8979ce92ed306c249d46894776a909505d8f5a4ba55b14206e3222f"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:572c3763a264ba47b3cf708a44ce965d98555f618ca42c926a9c1616d8f34269"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:fd1abc0d89e30cc4e02e4064dc67fcc51bd941eb395c502aac3ec19fab46b519"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-win32.whl", hash = "sha256:3d47fa203a7bd9c5b6cee4736ee84ca03b8ef23193c0d1ca99b5089f72645c73"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-win_amd64.whl", hash = "sha256:10955842570876604d404661fbccbc9c7e684caf432c09c715ec38fbae45ae09"},
|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:802fe99cca7457642125a8a88a084cef28ff0cf9407060f7b93dca5aa25480db"},
|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:573f6eac48f4769d667c4442081b1794f52919e7edada77495aaed9236d13a96"},
|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:549a3a73da901d5bc3ce8d24e0600d1fa85524c10287f6004fbab87672bf3e1e"},
|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f27273b60488abe721a075bcca6d7f3964f9f6f067c8c4c605743023d7d3944f"},
|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1ceae2f17a9c33cb48e3263960dc5fc8005351ee19db217e9b1bb15d28c02574"},
|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:65f6f63034100ead094b8744b3b97965785388f308a64cf8d7c34f2f2e5be0c4"},
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||||
{file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:753f10e867343b4511128c6ed8c82f7bec3bd026875576dfd88483c5c73b2fd8"},
|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4a78b2b446bd7c934f5dcedc588903fb2f5eec172f3d29e52a9096a43722adfc"},
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||||
{file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:e537484df0d8f426ce2afb2d0f8e1c3d0b114b83f8850e5f2fbea0e797bd82ae"},
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||||
{file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:eb6904c354526e758fda7167b33005998fb68c46fbc10e013ca97f21ca5c8887"},
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||||
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||||
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||||
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|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-win32.whl", hash = "sha256:7cd13a2e3ddeed6913a65e66e94b51d80a041145a026c27e6bb76c31a853c6ab"},
|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-win_amd64.whl", hash = "sha256:663946639d296df6a2bb2aa51b60a2454ca1cb29835324c640dafb5ff2131a77"},
|
||||
{file = "charset_normalizer-3.3.2-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:0b2b64d2bb6d3fb9112bafa732def486049e63de9618b5843bcdd081d8144cd8"},
|
||||
{file = "charset_normalizer-3.3.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:ddbb2551d7e0102e7252db79ba445cdab71b26640817ab1e3e3648dad515003b"},
|
||||
{file = "charset_normalizer-3.3.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:55086ee1064215781fff39a1af09518bc9255b50d6333f2e4c74ca09fac6a8f6"},
|
||||
{file = "charset_normalizer-3.3.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8f4a014bc36d3c57402e2977dada34f9c12300af536839dc38c0beab8878f38a"},
|
||||
{file = "charset_normalizer-3.3.2-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:a10af20b82360ab00827f916a6058451b723b4e65030c5a18577c8b2de5b3389"},
|
||||
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|
||||
{file = "charset_normalizer-3.3.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:90d558489962fd4918143277a773316e56c72da56ec7aa3dc3dbbe20fdfed15b"},
|
||||
{file = "charset_normalizer-3.3.2-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:6ac7ffc7ad6d040517be39eb591cac5ff87416c2537df6ba3cba3bae290c0fed"},
|
||||
{file = "charset_normalizer-3.3.2-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:7ed9e526742851e8d5cc9e6cf41427dfc6068d4f5a3bb03659444b4cabf6bc26"},
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{file = "charset_normalizer-3.3.2-cp312-cp312-win32.whl", hash = "sha256:d965bba47ddeec8cd560687584e88cf699fd28f192ceb452d1d7ee807c5597b7"},
|
||||
{file = "charset_normalizer-3.3.2-cp312-cp312-win_amd64.whl", hash = "sha256:96b02a3dc4381e5494fad39be677abcb5e6634bf7b4fa83a6dd3112607547001"},
|
||||
{file = "charset_normalizer-3.3.2-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:95f2a5796329323b8f0512e09dbb7a1860c46a39da62ecb2324f116fa8fdc85c"},
|
||||
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||||
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|
||||
{file = "charset_normalizer-3.3.2-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3287761bc4ee9e33561a7e058c72ac0938c4f57fe49a09eae428fd88aafe7bb6"},
|
||||
{file = "charset_normalizer-3.3.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:42cb296636fcc8b0644486d15c12376cb9fa75443e00fb25de0b8602e64c1714"},
|
||||
{file = "charset_normalizer-3.3.2-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:0a55554a2fa0d408816b3b5cedf0045f4b8e1a6065aec45849de2d6f3f8e9786"},
|
||||
{file = "charset_normalizer-3.3.2-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:c083af607d2515612056a31f0a8d9e0fcb5876b7bfc0abad3ecd275bc4ebc2d5"},
|
||||
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|
||||
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|
||||
{file = "charset_normalizer-3.3.2-cp37-cp37m-musllinux_1_1_s390x.whl", hash = "sha256:c180f51afb394e165eafe4ac2936a14bee3eb10debc9d9e4db8958fe36afe711"},
|
||||
{file = "charset_normalizer-3.3.2-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:8c622a5fe39a48f78944a87d4fb8a53ee07344641b0562c540d840748571b811"},
|
||||
{file = "charset_normalizer-3.3.2-cp37-cp37m-win32.whl", hash = "sha256:db364eca23f876da6f9e16c9da0df51aa4f104a972735574842618b8c6d999d4"},
|
||||
{file = "charset_normalizer-3.3.2-cp37-cp37m-win_amd64.whl", hash = "sha256:86216b5cee4b06df986d214f664305142d9c76df9b6512be2738aa72a2048f99"},
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|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
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||||
{file = "SQLAlchemy-2.0.29-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:99a1e69d4e26f71e750e9ad6fdc8614fbddb67cfe2173a3628a2566034e223c7"},
|
||||
{file = "SQLAlchemy-2.0.29-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5ef3fbccb4058355053c51b82fd3501a6e13dd808c8d8cd2561e610c5456013c"},
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
{file = "SQLAlchemy-2.0.29-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a13b917b4ffe5a0a31b83d051d60477819ddf18276852ea68037a144a506efb9"},
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||||
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||||
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||||
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||||
{file = "SQLAlchemy-2.0.29-cp311-cp311-win32.whl", hash = "sha256:b5d7ed79df55a731749ce65ec20d666d82b185fa4898430b17cb90c892741520"},
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||||
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||||
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||||
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||||
{file = "SQLAlchemy-2.0.29-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5c7b02525ede2a164c5fa5014915ba3591730f2cc831f5be9ff3b7fd3e30958e"},
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||||
{file = "SQLAlchemy-2.0.29-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0dfefdb3e54cd15f5d56fd5ae32f1da2d95d78319c1f6dfb9bcd0eb15d603d5d"},
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||||
{file = "SQLAlchemy-2.0.29-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:a88913000da9205b13f6f195f0813b6ffd8a0c0c2bd58d499e00a30eb508870c"},
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||||
{file = "SQLAlchemy-2.0.29-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:fecd5089c4be1bcc37c35e9aa678938d2888845a134dd016de457b942cf5a758"},
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||||
{file = "SQLAlchemy-2.0.29-cp312-cp312-win32.whl", hash = "sha256:8197d6f7a3d2b468861ebb4c9f998b9df9e358d6e1cf9c2a01061cb9b6cf4e41"},
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||||
{file = "SQLAlchemy-2.0.29-cp312-cp312-win_amd64.whl", hash = "sha256:9b19836ccca0d321e237560e475fd99c3d8655d03da80c845c4da20dda31b6e1"},
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||||
{file = "SQLAlchemy-2.0.29-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:87a1d53a5382cdbbf4b7619f107cc862c1b0a4feb29000922db72e5a66a5ffc0"},
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||||
{file = "SQLAlchemy-2.0.29-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2a0732dffe32333211801b28339d2a0babc1971bc90a983e3035e7b0d6f06b93"},
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||||
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||||
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||||
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||||
{file = "SQLAlchemy-2.0.29-cp37-cp37m-win32.whl", hash = "sha256:e5bbe55e8552019c6463709b39634a5fc55e080d0827e2a3a11e18eb73f5cdbd"},
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||||
{file = "SQLAlchemy-2.0.29-cp37-cp37m-win_amd64.whl", hash = "sha256:c2f9c762a2735600654c654bf48dad388b888f8ce387b095806480e6e4ff6907"},
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||||
{file = "SQLAlchemy-2.0.29-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:7e614d7a25a43a9f54fcce4675c12761b248547f3d41b195e8010ca7297c369c"},
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||||
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||||
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||||
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||||
{file = "SQLAlchemy-2.0.29-cp38-cp38-win32.whl", hash = "sha256:bab41acf151cd68bc2b466deae5deeb9e8ae9c50ad113444151ad965d5bf685b"},
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||||
{file = "SQLAlchemy-2.0.29-cp38-cp38-win_amd64.whl", hash = "sha256:52c8011088305476691b8750c60e03b87910a123cfd9ad48576d6414b6ec2a1d"},
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||||
{file = "SQLAlchemy-2.0.29-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:3071ad498896907a5ef756206b9dc750f8e57352113c19272bdfdc429c7bd7de"},
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||||
{file = "SQLAlchemy-2.0.29-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:dba622396a3170974f81bad49aacebd243455ec3cc70615aeaef9e9613b5bca5"},
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||||
{file = "SQLAlchemy-2.0.29-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7b184e3de58009cc0bf32e20f137f1ec75a32470f5fede06c58f6c355ed42a72"},
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||||
{file = "SQLAlchemy-2.0.29-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8c37f1050feb91f3d6c32f864d8e114ff5545a4a7afe56778d76a9aec62638ba"},
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||||
{file = "SQLAlchemy-2.0.29-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:bda7ce59b06d0f09afe22c56714c65c957b1068dee3d5e74d743edec7daba552"},
|
||||
{file = "SQLAlchemy-2.0.29-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:25664e18bef6dc45015b08f99c63952a53a0a61f61f2e48a9e70cec27e55f699"},
|
||||
{file = "SQLAlchemy-2.0.29-cp39-cp39-win32.whl", hash = "sha256:77d29cb6c34b14af8a484e831ab530c0f7188f8efed1c6a833a2c674bf3c26ec"},
|
||||
{file = "SQLAlchemy-2.0.29-cp39-cp39-win_amd64.whl", hash = "sha256:04c487305ab035a9548f573763915189fc0fe0824d9ba28433196f8436f1449c"},
|
||||
{file = "SQLAlchemy-2.0.29-py3-none-any.whl", hash = "sha256:dc4ee2d4ee43251905f88637d5281a8d52e916a021384ec10758826f5cbae305"},
|
||||
{file = "SQLAlchemy-2.0.29.tar.gz", hash = "sha256:bd9566b8e58cabd700bc367b60e90d9349cd16f0984973f98a9a09f9c64e86f0"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
greenlet = {version = "!=0.4.17", markers = "platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\""}
|
||||
typing-extensions = ">=4.6.0"
|
||||
|
||||
[package.extras]
|
||||
aiomysql = ["aiomysql (>=0.2.0)", "greenlet (!=0.4.17)"]
|
||||
aioodbc = ["aioodbc", "greenlet (!=0.4.17)"]
|
||||
aiosqlite = ["aiosqlite", "greenlet (!=0.4.17)", "typing_extensions (!=3.10.0.1)"]
|
||||
asyncio = ["greenlet (!=0.4.17)"]
|
||||
asyncmy = ["asyncmy (>=0.2.3,!=0.2.4,!=0.2.6)", "greenlet (!=0.4.17)"]
|
||||
mariadb-connector = ["mariadb (>=1.0.1,!=1.1.2,!=1.1.5)"]
|
||||
mssql = ["pyodbc"]
|
||||
mssql-pymssql = ["pymssql"]
|
||||
mssql-pyodbc = ["pyodbc"]
|
||||
mypy = ["mypy (>=0.910)"]
|
||||
mysql = ["mysqlclient (>=1.4.0)"]
|
||||
mysql-connector = ["mysql-connector-python"]
|
||||
oracle = ["cx_oracle (>=8)"]
|
||||
oracle-oracledb = ["oracledb (>=1.0.1)"]
|
||||
postgresql = ["psycopg2 (>=2.7)"]
|
||||
postgresql-asyncpg = ["asyncpg", "greenlet (!=0.4.17)"]
|
||||
postgresql-pg8000 = ["pg8000 (>=1.29.1)"]
|
||||
postgresql-psycopg = ["psycopg (>=3.0.7)"]
|
||||
postgresql-psycopg2binary = ["psycopg2-binary"]
|
||||
postgresql-psycopg2cffi = ["psycopg2cffi"]
|
||||
postgresql-psycopgbinary = ["psycopg[binary] (>=3.0.7)"]
|
||||
pymysql = ["pymysql"]
|
||||
sqlcipher = ["sqlcipher3_binary"]
|
||||
|
||||
[[package]]
|
||||
name = "tenacity"
|
||||
version = "8.2.3"
|
||||
description = "Retry code until it succeeds"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "tenacity-8.2.3-py3-none-any.whl", hash = "sha256:ce510e327a630c9e1beaf17d42e6ffacc88185044ad85cf74c0a8887c6a0f88c"},
|
||||
{file = "tenacity-8.2.3.tar.gz", hash = "sha256:5398ef0d78e63f40007c1fb4c0bff96e1911394d2fa8d194f77619c05ff6cc8a"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
doc = ["reno", "sphinx", "tornado (>=4.5)"]
|
||||
|
||||
[[package]]
|
||||
name = "tomli"
|
||||
version = "2.0.1"
|
||||
description = "A lil' TOML parser"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "tomli-2.0.1-py3-none-any.whl", hash = "sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc"},
|
||||
{file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "typing-extensions"
|
||||
version = "4.11.0"
|
||||
description = "Backported and Experimental Type Hints for Python 3.8+"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "typing_extensions-4.11.0-py3-none-any.whl", hash = "sha256:c1f94d72897edaf4ce775bb7558d5b79d8126906a14ea5ed1635921406c0387a"},
|
||||
{file = "typing_extensions-4.11.0.tar.gz", hash = "sha256:83f085bd5ca59c80295fc2a82ab5dac679cbe02b9f33f7d83af68e241bea51b0"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tzdata"
|
||||
version = "2024.1"
|
||||
description = "Provider of IANA time zone data"
|
||||
optional = false
|
||||
python-versions = ">=2"
|
||||
files = [
|
||||
{file = "tzdata-2024.1-py2.py3-none-any.whl", hash = "sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"},
|
||||
{file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "urllib3"
|
||||
version = "2.2.1"
|
||||
description = "HTTP library with thread-safe connection pooling, file post, and more."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "urllib3-2.2.1-py3-none-any.whl", hash = "sha256:450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d"},
|
||||
{file = "urllib3-2.2.1.tar.gz", hash = "sha256:d0570876c61ab9e520d776c38acbbb5b05a776d3f9ff98a5c8fd5162a444cf19"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
|
||||
h2 = ["h2 (>=4,<5)"]
|
||||
socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
|
||||
zstd = ["zstandard (>=0.18.0)"]
|
||||
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.9"
|
||||
content-hash = "02a20cf8f1209824252361c78bffcdfa960bf92ef3214807cc9f494eb533b7e4"
|
@ -1,94 +0,0 @@
|
||||
[tool.poetry]
|
||||
name = "langchain-postgres"
|
||||
version = "0.0.1"
|
||||
description = "An integration package connecting Postgres and LangChain"
|
||||
authors = []
|
||||
readme = "README.md"
|
||||
repository = "https://github.com/langchain-ai/langchain"
|
||||
license = "MIT"
|
||||
|
||||
[tool.poetry.urls]
|
||||
"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/postgres"
|
||||
|
||||
[tool.poetry.dependencies]
|
||||
python = "^3.9"
|
||||
langchain-core = "^0.1"
|
||||
psycopg = "^3.1.18"
|
||||
langgraph = "^0.0.32"
|
||||
psycopg-pool = "^3.2.1"
|
||||
sqlalchemy = "^2.0.29"
|
||||
pgvector = "^0.2.5"
|
||||
numpy = "^1.26.4"
|
||||
|
||||
[tool.poetry.group.test]
|
||||
optional = true
|
||||
|
||||
[tool.poetry.group.test.dependencies]
|
||||
pytest = "^7.4.3"
|
||||
pytest-asyncio = "^0.23.2"
|
||||
langchain-core = {path = "../../core", develop = true}
|
||||
|
||||
[tool.poetry.group.codespell]
|
||||
optional = true
|
||||
|
||||
[tool.poetry.group.codespell.dependencies]
|
||||
codespell = "^2.2.6"
|
||||
|
||||
[tool.poetry.group.test_integration]
|
||||
optional = true
|
||||
|
||||
[tool.poetry.group.test_integration.dependencies]
|
||||
|
||||
[tool.poetry.group.lint]
|
||||
optional = true
|
||||
|
||||
[tool.poetry.group.lint.dependencies]
|
||||
ruff = "^0.1.8"
|
||||
|
||||
[tool.poetry.group.typing.dependencies]
|
||||
mypy = "^1.7.1"
|
||||
langchain-core = {path = "../../core", develop = true}
|
||||
|
||||
[tool.poetry.group.dev]
|
||||
optional = true
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
langchain-core = {path = "../../core", develop = true}
|
||||
|
||||
[tool.ruff.lint]
|
||||
select = [
|
||||
"E", # pycodestyle
|
||||
"F", # pyflakes
|
||||
"I", # isort
|
||||
"T201", # print
|
||||
]
|
||||
|
||||
[tool.mypy]
|
||||
disallow_untyped_defs = "True"
|
||||
|
||||
[tool.coverage.run]
|
||||
omit = [
|
||||
"tests/*",
|
||||
]
|
||||
|
||||
[build-system]
|
||||
requires = ["poetry-core>=1.0.0"]
|
||||
build-backend = "poetry.core.masonry.api"
|
||||
|
||||
[tool.pytest.ini_options]
|
||||
# --strict-markers will raise errors on unknown marks.
|
||||
# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks
|
||||
#
|
||||
# https://docs.pytest.org/en/7.1.x/reference/reference.html
|
||||
# --strict-config any warnings encountered while parsing the `pytest`
|
||||
# section of the configuration file raise errors.
|
||||
#
|
||||
# https://github.com/tophat/syrupy
|
||||
# --snapshot-warn-unused Prints a warning on unused snapshots rather than fail the test suite.
|
||||
addopts = "--strict-markers --strict-config --durations=5"
|
||||
# Registering custom markers.
|
||||
# https://docs.pytest.org/en/7.1.x/example/markers.html#registering-markers
|
||||
markers = [
|
||||
"compile: mark placeholder test used to compile integration tests without running them",
|
||||
]
|
||||
asyncio_mode = "auto"
|
@ -1,17 +0,0 @@
|
||||
import sys
|
||||
import traceback
|
||||
from importlib.machinery import SourceFileLoader
|
||||
|
||||
if __name__ == "__main__":
|
||||
files = sys.argv[1:]
|
||||
has_failure = False
|
||||
for file in files:
|
||||
try:
|
||||
SourceFileLoader("x", file).load_module()
|
||||
except Exception:
|
||||
has_faillure = True
|
||||
print(file) # noqa: T201
|
||||
traceback.print_exc()
|
||||
print() # noqa: T201
|
||||
|
||||
sys.exit(1 if has_failure else 0)
|
@ -1,27 +0,0 @@
|
||||
#!/bin/bash
|
||||
#
|
||||
# This script searches for lines starting with "import pydantic" or "from pydantic"
|
||||
# in tracked files within a Git repository.
|
||||
#
|
||||
# Usage: ./scripts/check_pydantic.sh /path/to/repository
|
||||
|
||||
# Check if a path argument is provided
|
||||
if [ $# -ne 1 ]; then
|
||||
echo "Usage: $0 /path/to/repository"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
repository_path="$1"
|
||||
|
||||
# Search for lines matching the pattern within the specified repository
|
||||
result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic')
|
||||
|
||||
# Check if any matching lines were found
|
||||
if [ -n "$result" ]; then
|
||||
echo "ERROR: The following lines need to be updated:"
|
||||
echo "$result"
|
||||
echo "Please replace the code with an import from langchain_core.pydantic_v1."
|
||||
echo "For example, replace 'from pydantic import BaseModel'"
|
||||
echo "with 'from langchain_core.pydantic_v1 import BaseModel'"
|
||||
exit 1
|
||||
fi
|
@ -1,18 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
set -eu
|
||||
|
||||
# Initialize a variable to keep track of errors
|
||||
errors=0
|
||||
|
||||
# make sure not importing from langchain, langchain_experimental, or langchain_community
|
||||
git --no-pager grep '^from langchain\.' . && errors=$((errors+1))
|
||||
git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1))
|
||||
git --no-pager grep '^from langchain_community\.' . && errors=$((errors+1))
|
||||
|
||||
# Decide on an exit status based on the errors
|
||||
if [ "$errors" -gt 0 ]; then
|
||||
exit 1
|
||||
else
|
||||
exit 0
|
||||
fi
|
@ -1,28 +0,0 @@
|
||||
"""Copied from community."""
|
||||
from typing import List
|
||||
|
||||
from langchain_core.embeddings import Embeddings
|
||||
|
||||
fake_texts = ["foo", "bar", "baz"]
|
||||
|
||||
|
||||
class FakeEmbeddings(Embeddings):
|
||||
"""Fake embeddings functionality for testing."""
|
||||
|
||||
def embed_documents(self, texts: List[str]) -> List[List[float]]:
|
||||
"""Return simple embeddings.
|
||||
Embeddings encode each text as its index."""
|
||||
return [[float(1.0)] * 9 + [float(i)] for i in range(len(texts))]
|
||||
|
||||
async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
|
||||
return self.embed_documents(texts)
|
||||
|
||||
def embed_query(self, text: str) -> List[float]:
|
||||
"""Return constant query embeddings.
|
||||
Embeddings are identical to embed_documents(texts)[0].
|
||||
Distance to each text will be that text's index,
|
||||
as it was passed to embed_documents."""
|
||||
return [float(1.0)] * 9 + [float(0.0)]
|
||||
|
||||
async def aembed_query(self, text: str) -> List[float]:
|
||||
return self.embed_query(text)
|
@ -1,218 +0,0 @@
|
||||
"""Module needs to move to a stasndalone package."""
|
||||
from langchain_core.documents import Document
|
||||
|
||||
metadatas = [
|
||||
{
|
||||
"name": "adam",
|
||||
"date": "2021-01-01",
|
||||
"count": 1,
|
||||
"is_active": True,
|
||||
"tags": ["a", "b"],
|
||||
"location": [1.0, 2.0],
|
||||
"id": 1,
|
||||
"height": 10.0, # Float column
|
||||
"happiness": 0.9, # Float column
|
||||
"sadness": 0.1, # Float column
|
||||
},
|
||||
{
|
||||
"name": "bob",
|
||||
"date": "2021-01-02",
|
||||
"count": 2,
|
||||
"is_active": False,
|
||||
"tags": ["b", "c"],
|
||||
"location": [2.0, 3.0],
|
||||
"id": 2,
|
||||
"height": 5.7, # Float column
|
||||
"happiness": 0.8, # Float column
|
||||
"sadness": 0.1, # Float column
|
||||
},
|
||||
{
|
||||
"name": "jane",
|
||||
"date": "2021-01-01",
|
||||
"count": 3,
|
||||
"is_active": True,
|
||||
"tags": ["b", "d"],
|
||||
"location": [3.0, 4.0],
|
||||
"id": 3,
|
||||
"height": 2.4, # Float column
|
||||
"happiness": None,
|
||||
# Sadness missing intentionally
|
||||
},
|
||||
]
|
||||
texts = ["id {id}".format(id=metadata["id"]) for metadata in metadatas]
|
||||
|
||||
DOCUMENTS = [
|
||||
Document(page_content=text, metadata=metadata)
|
||||
for text, metadata in zip(texts, metadatas)
|
||||
]
|
||||
|
||||
|
||||
TYPE_1_FILTERING_TEST_CASES = [
|
||||
# These tests only involve equality checks
|
||||
(
|
||||
{"id": 1},
|
||||
[1],
|
||||
),
|
||||
# String field
|
||||
(
|
||||
# check name
|
||||
{"name": "adam"},
|
||||
[1],
|
||||
),
|
||||
# Boolean fields
|
||||
(
|
||||
{"is_active": True},
|
||||
[1, 3],
|
||||
),
|
||||
(
|
||||
{"is_active": False},
|
||||
[2],
|
||||
),
|
||||
# And semantics for top level filtering
|
||||
(
|
||||
{"id": 1, "is_active": True},
|
||||
[1],
|
||||
),
|
||||
(
|
||||
{"id": 1, "is_active": False},
|
||||
[],
|
||||
),
|
||||
]
|
||||
|
||||
TYPE_2_FILTERING_TEST_CASES = [
|
||||
# These involve equality checks and other operators
|
||||
# like $ne, $gt, $gte, $lt, $lte, $not
|
||||
(
|
||||
{"id": 1},
|
||||
[1],
|
||||
),
|
||||
(
|
||||
{"id": {"$ne": 1}},
|
||||
[2, 3],
|
||||
),
|
||||
(
|
||||
{"id": {"$gt": 1}},
|
||||
[2, 3],
|
||||
),
|
||||
(
|
||||
{"id": {"$gte": 1}},
|
||||
[1, 2, 3],
|
||||
),
|
||||
(
|
||||
{"id": {"$lt": 1}},
|
||||
[],
|
||||
),
|
||||
(
|
||||
{"id": {"$lte": 1}},
|
||||
[1],
|
||||
),
|
||||
# Repeat all the same tests with name (string column)
|
||||
(
|
||||
{"name": "adam"},
|
||||
[1],
|
||||
),
|
||||
(
|
||||
{"name": "bob"},
|
||||
[2],
|
||||
),
|
||||
(
|
||||
{"name": {"$eq": "adam"}},
|
||||
[1],
|
||||
),
|
||||
(
|
||||
{"name": {"$ne": "adam"}},
|
||||
[2, 3],
|
||||
),
|
||||
# And also gt, gte, lt, lte relying on lexicographical ordering
|
||||
(
|
||||
{"name": {"$gt": "jane"}},
|
||||
[],
|
||||
),
|
||||
(
|
||||
{"name": {"$gte": "jane"}},
|
||||
[3],
|
||||
),
|
||||
(
|
||||
{"name": {"$lt": "jane"}},
|
||||
[1, 2],
|
||||
),
|
||||
(
|
||||
{"name": {"$lte": "jane"}},
|
||||
[1, 2, 3],
|
||||
),
|
||||
(
|
||||
{"is_active": {"$eq": True}},
|
||||
[1, 3],
|
||||
),
|
||||
(
|
||||
{"is_active": {"$ne": True}},
|
||||
[2],
|
||||
),
|
||||
# Test float column.
|
||||
(
|
||||
{"height": {"$gt": 5.0}},
|
||||
[1, 2],
|
||||
),
|
||||
(
|
||||
{"height": {"$gte": 5.0}},
|
||||
[1, 2],
|
||||
),
|
||||
(
|
||||
{"height": {"$lt": 5.0}},
|
||||
[3],
|
||||
),
|
||||
(
|
||||
{"height": {"$lte": 5.8}},
|
||||
[2, 3],
|
||||
),
|
||||
]
|
||||
|
||||
TYPE_3_FILTERING_TEST_CASES = [
|
||||
# These involve usage of AND and OR operators
|
||||
(
|
||||
{"$or": [{"id": 1}, {"id": 2}]},
|
||||
[1, 2],
|
||||
),
|
||||
(
|
||||
{"$or": [{"id": 1}, {"name": "bob"}]},
|
||||
[1, 2],
|
||||
),
|
||||
(
|
||||
{"$and": [{"id": 1}, {"id": 2}]},
|
||||
[],
|
||||
),
|
||||
(
|
||||
{"$or": [{"id": 1}, {"id": 2}, {"id": 3}]},
|
||||
[1, 2, 3],
|
||||
),
|
||||
]
|
||||
|
||||
TYPE_4_FILTERING_TEST_CASES = [
|
||||
# These involve special operators like $in, $nin, $between
|
||||
# Test between
|
||||
(
|
||||
{"id": {"$between": (1, 2)}},
|
||||
[1, 2],
|
||||
),
|
||||
(
|
||||
{"id": {"$between": (1, 1)}},
|
||||
[1],
|
||||
),
|
||||
(
|
||||
{"name": {"$in": ["adam", "bob"]}},
|
||||
[1, 2],
|
||||
),
|
||||
]
|
||||
|
||||
TYPE_5_FILTERING_TEST_CASES = [
|
||||
# These involve special operators like $like, $ilike that
|
||||
# may be specified to certain databases.
|
||||
(
|
||||
{"name": {"$like": "a%"}},
|
||||
[1],
|
||||
),
|
||||
(
|
||||
{"name": {"$like": "%a%"}}, # adam and jane
|
||||
[1, 3],
|
||||
),
|
||||
]
|
@ -1,123 +0,0 @@
|
||||
import uuid
|
||||
|
||||
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
|
||||
|
||||
from langchain_postgres.chat_message_histories import PostgresChatMessageHistory
|
||||
from tests.utils import asyncpg_client, syncpg_client
|
||||
|
||||
|
||||
def test_sync_chat_history() -> None:
|
||||
table_name = "chat_history"
|
||||
session_id = str(uuid.UUID(int=123))
|
||||
with syncpg_client() as sync_connection:
|
||||
PostgresChatMessageHistory.drop_table(sync_connection, table_name)
|
||||
PostgresChatMessageHistory.create_schema(sync_connection, table_name)
|
||||
|
||||
chat_history = PostgresChatMessageHistory(
|
||||
table_name, session_id, sync_connection=sync_connection
|
||||
)
|
||||
|
||||
messages = chat_history.messages
|
||||
assert messages == []
|
||||
|
||||
assert chat_history is not None
|
||||
|
||||
# Get messages from the chat history
|
||||
messages = chat_history.messages
|
||||
assert messages == []
|
||||
|
||||
chat_history.add_messages(
|
||||
[
|
||||
SystemMessage(content="Meow"),
|
||||
AIMessage(content="woof"),
|
||||
HumanMessage(content="bark"),
|
||||
]
|
||||
)
|
||||
|
||||
# Get messages from the chat history
|
||||
messages = chat_history.messages
|
||||
assert len(messages) == 3
|
||||
assert messages == [
|
||||
SystemMessage(content="Meow"),
|
||||
AIMessage(content="woof"),
|
||||
HumanMessage(content="bark"),
|
||||
]
|
||||
|
||||
chat_history.add_messages(
|
||||
[
|
||||
SystemMessage(content="Meow"),
|
||||
AIMessage(content="woof"),
|
||||
HumanMessage(content="bark"),
|
||||
]
|
||||
)
|
||||
|
||||
messages = chat_history.messages
|
||||
assert len(messages) == 6
|
||||
assert messages == [
|
||||
SystemMessage(content="Meow"),
|
||||
AIMessage(content="woof"),
|
||||
HumanMessage(content="bark"),
|
||||
SystemMessage(content="Meow"),
|
||||
AIMessage(content="woof"),
|
||||
HumanMessage(content="bark"),
|
||||
]
|
||||
|
||||
chat_history.clear()
|
||||
assert chat_history.messages == []
|
||||
|
||||
|
||||
async def test_async_chat_history() -> None:
|
||||
"""Test the async chat history."""
|
||||
async with asyncpg_client() as async_connection:
|
||||
table_name = "chat_history"
|
||||
session_id = str(uuid.UUID(int=125))
|
||||
await PostgresChatMessageHistory.adrop_table(async_connection, table_name)
|
||||
await PostgresChatMessageHistory.acreate_schema(async_connection, table_name)
|
||||
|
||||
chat_history = PostgresChatMessageHistory(
|
||||
table_name, session_id, async_connection=async_connection
|
||||
)
|
||||
|
||||
messages = await chat_history.aget_messages()
|
||||
assert messages == []
|
||||
|
||||
# Add messages
|
||||
await chat_history.aadd_messages(
|
||||
[
|
||||
SystemMessage(content="Meow"),
|
||||
AIMessage(content="woof"),
|
||||
HumanMessage(content="bark"),
|
||||
]
|
||||
)
|
||||
# Get the messages
|
||||
messages = await chat_history.aget_messages()
|
||||
assert len(messages) == 3
|
||||
assert messages == [
|
||||
SystemMessage(content="Meow"),
|
||||
AIMessage(content="woof"),
|
||||
HumanMessage(content="bark"),
|
||||
]
|
||||
|
||||
# Add more messages
|
||||
await chat_history.aadd_messages(
|
||||
[
|
||||
SystemMessage(content="Meow"),
|
||||
AIMessage(content="woof"),
|
||||
HumanMessage(content="bark"),
|
||||
]
|
||||
)
|
||||
# Get the messages
|
||||
messages = await chat_history.aget_messages()
|
||||
assert len(messages) == 6
|
||||
assert messages == [
|
||||
SystemMessage(content="Meow"),
|
||||
AIMessage(content="woof"),
|
||||
HumanMessage(content="bark"),
|
||||
SystemMessage(content="Meow"),
|
||||
AIMessage(content="woof"),
|
||||
HumanMessage(content="bark"),
|
||||
]
|
||||
|
||||
# clear
|
||||
await chat_history.aclear()
|
||||
assert await chat_history.aget_messages() == []
|
@ -1,326 +0,0 @@
|
||||
from collections import defaultdict
|
||||
|
||||
from langgraph.checkpoint import Checkpoint
|
||||
from langgraph.checkpoint.base import CheckpointTuple
|
||||
|
||||
from langchain_postgres.checkpoint import PickleCheckpointSerializer, PostgresCheckpoint
|
||||
from tests.utils import asyncpg_client, syncpg_client
|
||||
|
||||
|
||||
async def test_async_checkpoint() -> None:
|
||||
"""Test the async chat history."""
|
||||
async with asyncpg_client() as async_connection:
|
||||
await PostgresCheckpoint.adrop_schema(async_connection)
|
||||
await PostgresCheckpoint.acreate_schema(async_connection)
|
||||
checkpoint_saver = PostgresCheckpoint(
|
||||
async_connection=async_connection, serializer=PickleCheckpointSerializer()
|
||||
)
|
||||
checkpoint_tuple = [
|
||||
c
|
||||
async for c in checkpoint_saver.alist(
|
||||
{
|
||||
"configurable": {
|
||||
"thread_id": "test_thread",
|
||||
}
|
||||
}
|
||||
)
|
||||
]
|
||||
assert len(checkpoint_tuple) == 0
|
||||
|
||||
# Add a checkpoint
|
||||
sample_checkpoint: Checkpoint = {
|
||||
"v": 1,
|
||||
"ts": "2021-09-01T00:00:00+00:00",
|
||||
"channel_values": {},
|
||||
"channel_versions": defaultdict(),
|
||||
"versions_seen": defaultdict(),
|
||||
}
|
||||
|
||||
await checkpoint_saver.aput(
|
||||
{
|
||||
"configurable": {
|
||||
"thread_id": "test_thread",
|
||||
}
|
||||
},
|
||||
sample_checkpoint,
|
||||
)
|
||||
|
||||
checkpoints = [
|
||||
c
|
||||
async for c in checkpoint_saver.alist(
|
||||
{
|
||||
"configurable": {
|
||||
"thread_id": "test_thread",
|
||||
}
|
||||
}
|
||||
)
|
||||
]
|
||||
|
||||
assert len(checkpoints) == 1
|
||||
assert checkpoints[0].checkpoint == sample_checkpoint
|
||||
|
||||
# Add another checkpoint
|
||||
sample_checkpoint2: Checkpoint = {
|
||||
"v": 1,
|
||||
"ts": "2021-09-02T00:00:00+00:00",
|
||||
"channel_values": {},
|
||||
"channel_versions": defaultdict(),
|
||||
"versions_seen": defaultdict(),
|
||||
}
|
||||
|
||||
await checkpoint_saver.aput(
|
||||
{
|
||||
"configurable": {
|
||||
"thread_id": "test_thread",
|
||||
}
|
||||
},
|
||||
sample_checkpoint2,
|
||||
)
|
||||
|
||||
# Try aget
|
||||
checkpoints = [
|
||||
c
|
||||
async for c in checkpoint_saver.alist(
|
||||
{
|
||||
"configurable": {
|
||||
"thread_id": "test_thread",
|
||||
}
|
||||
}
|
||||
)
|
||||
]
|
||||
|
||||
assert len(checkpoints) == 2
|
||||
# Should be sorted by timestamp desc
|
||||
assert checkpoints[0].checkpoint == sample_checkpoint2
|
||||
assert checkpoints[1].checkpoint == sample_checkpoint
|
||||
|
||||
assert await checkpoint_saver.aget_tuple(
|
||||
{
|
||||
"configurable": {
|
||||
"thread_id": "test_thread",
|
||||
}
|
||||
}
|
||||
) == CheckpointTuple(
|
||||
config={
|
||||
"configurable": {
|
||||
"thread_id": "test_thread",
|
||||
"thread_ts": "2021-09-02T00:00:00+00:00",
|
||||
}
|
||||
},
|
||||
checkpoint={
|
||||
"v": 1,
|
||||
"ts": "2021-09-02T00:00:00+00:00",
|
||||
"channel_values": {},
|
||||
"channel_versions": {}, # type: ignore
|
||||
"versions_seen": {}, # type: ignore
|
||||
},
|
||||
parent_config=None,
|
||||
)
|
||||
|
||||
# Check aget_tuple with thread_ts
|
||||
assert await checkpoint_saver.aget_tuple(
|
||||
{
|
||||
"configurable": {
|
||||
"thread_id": "test_thread",
|
||||
"thread_ts": "2021-09-01T00:00:00+00:00",
|
||||
}
|
||||
}
|
||||
) == CheckpointTuple(
|
||||
config={
|
||||
"configurable": {
|
||||
"thread_id": "test_thread",
|
||||
"thread_ts": "2021-09-01T00:00:00+00:00",
|
||||
}
|
||||
},
|
||||
checkpoint={
|
||||
"v": 1,
|
||||
"ts": "2021-09-01T00:00:00+00:00",
|
||||
"channel_values": {},
|
||||
"channel_versions": {}, # type: ignore
|
||||
"versions_seen": {}, # type: ignore
|
||||
},
|
||||
parent_config=None,
|
||||
)
|
||||
|
||||
|
||||
def test_sync_checkpoint() -> None:
|
||||
"""Test the sync check point implementation."""
|
||||
with syncpg_client() as sync_connection:
|
||||
PostgresCheckpoint.drop_schema(sync_connection)
|
||||
PostgresCheckpoint.create_schema(sync_connection)
|
||||
checkpoint_saver = PostgresCheckpoint(
|
||||
sync_connection=sync_connection, serializer=PickleCheckpointSerializer()
|
||||
)
|
||||
checkpoint_tuple = [
|
||||
c
|
||||
for c in checkpoint_saver.list(
|
||||
{
|
||||
"configurable": {
|
||||
"thread_id": "test_thread",
|
||||
}
|
||||
}
|
||||
)
|
||||
]
|
||||
assert len(checkpoint_tuple) == 0
|
||||
|
||||
# Add a checkpoint
|
||||
sample_checkpoint: Checkpoint = {
|
||||
"v": 1,
|
||||
"ts": "2021-09-01T00:00:00+00:00",
|
||||
"channel_values": {},
|
||||
"channel_versions": defaultdict(),
|
||||
"versions_seen": defaultdict(),
|
||||
}
|
||||
|
||||
checkpoint_saver.put(
|
||||
{
|
||||
"configurable": {
|
||||
"thread_id": "test_thread",
|
||||
}
|
||||
},
|
||||
sample_checkpoint,
|
||||
)
|
||||
|
||||
checkpoints = [
|
||||
c
|
||||
for c in checkpoint_saver.list(
|
||||
{
|
||||
"configurable": {
|
||||
"thread_id": "test_thread",
|
||||
}
|
||||
}
|
||||
)
|
||||
]
|
||||
|
||||
assert len(checkpoints) == 1
|
||||
assert checkpoints[0].checkpoint == sample_checkpoint
|
||||
|
||||
# Add another checkpoint
|
||||
sample_checkpoint_2: Checkpoint = {
|
||||
"v": 1,
|
||||
"ts": "2021-09-02T00:00:00+00:00",
|
||||
"channel_values": {},
|
||||
"channel_versions": defaultdict(),
|
||||
"versions_seen": defaultdict(),
|
||||
}
|
||||
|
||||
checkpoint_saver.put(
|
||||
{
|
||||
"configurable": {
|
||||
"thread_id": "test_thread",
|
||||
}
|
||||
},
|
||||
sample_checkpoint_2,
|
||||
)
|
||||
|
||||
# Try aget
|
||||
checkpoints = [
|
||||
c
|
||||
for c in checkpoint_saver.list(
|
||||
{
|
||||
"configurable": {
|
||||
"thread_id": "test_thread",
|
||||
}
|
||||
}
|
||||
)
|
||||
]
|
||||
|
||||
assert len(checkpoints) == 2
|
||||
# Should be sorted by timestamp desc
|
||||
assert checkpoints[0].checkpoint == sample_checkpoint_2
|
||||
assert checkpoints[1].checkpoint == sample_checkpoint
|
||||
|
||||
assert checkpoint_saver.get_tuple(
|
||||
{
|
||||
"configurable": {
|
||||
"thread_id": "test_thread",
|
||||
}
|
||||
}
|
||||
) == CheckpointTuple(
|
||||
config={
|
||||
"configurable": {
|
||||
"thread_id": "test_thread",
|
||||
"thread_ts": "2021-09-02T00:00:00+00:00",
|
||||
}
|
||||
},
|
||||
checkpoint={
|
||||
"v": 1,
|
||||
"ts": "2021-09-02T00:00:00+00:00",
|
||||
"channel_values": {},
|
||||
"channel_versions": defaultdict(),
|
||||
"versions_seen": defaultdict(),
|
||||
},
|
||||
parent_config=None,
|
||||
)
|
||||
|
||||
|
||||
async def test_on_conflict_aput() -> None:
|
||||
async with asyncpg_client() as async_connection:
|
||||
await PostgresCheckpoint.adrop_schema(async_connection)
|
||||
await PostgresCheckpoint.acreate_schema(async_connection)
|
||||
checkpoint_saver = PostgresCheckpoint(
|
||||
async_connection=async_connection, serializer=PickleCheckpointSerializer()
|
||||
)
|
||||
|
||||
# aput with twice on the same (thread_id, thread_ts) should not raise any error
|
||||
sample_checkpoint: Checkpoint = {
|
||||
"v": 1,
|
||||
"ts": "2021-09-01T00:00:00+00:00",
|
||||
"channel_values": {},
|
||||
"channel_versions": defaultdict(),
|
||||
"versions_seen": defaultdict(),
|
||||
}
|
||||
new_checkpoint: Checkpoint = {
|
||||
"v": 2,
|
||||
"ts": "2021-09-01T00:00:00+00:00",
|
||||
"channel_values": {},
|
||||
"channel_versions": defaultdict(),
|
||||
"versions_seen": defaultdict(),
|
||||
}
|
||||
await checkpoint_saver.aput(
|
||||
{
|
||||
"configurable": {
|
||||
"thread_id": "test_thread",
|
||||
"thread_ts": "2021-09-01T00:00:00+00:00",
|
||||
}
|
||||
},
|
||||
sample_checkpoint,
|
||||
)
|
||||
await checkpoint_saver.aput(
|
||||
{
|
||||
"configurable": {
|
||||
"thread_id": "test_thread",
|
||||
"thread_ts": "2021-09-01T00:00:00+00:00",
|
||||
}
|
||||
},
|
||||
new_checkpoint,
|
||||
)
|
||||
# Check aget_tuple with thread_ts
|
||||
assert await checkpoint_saver.aget_tuple(
|
||||
{
|
||||
"configurable": {
|
||||
"thread_id": "test_thread",
|
||||
"thread_ts": "2021-09-01T00:00:00+00:00",
|
||||
}
|
||||
}
|
||||
) == CheckpointTuple(
|
||||
config={
|
||||
"configurable": {
|
||||
"thread_id": "test_thread",
|
||||
"thread_ts": "2021-09-01T00:00:00+00:00",
|
||||
}
|
||||
},
|
||||
checkpoint={
|
||||
"v": 2,
|
||||
"ts": "2021-09-01T00:00:00+00:00",
|
||||
"channel_values": {},
|
||||
"channel_versions": defaultdict(None, {}),
|
||||
"versions_seen": defaultdict(None, {}),
|
||||
},
|
||||
parent_config={
|
||||
"configurable": {
|
||||
"thread_id": "test_thread",
|
||||
"thread_ts": "2021-09-01T00:00:00+00:00",
|
||||
}
|
||||
},
|
||||
)
|
@ -1,7 +0,0 @@
|
||||
import pytest
|
||||
|
||||
|
||||
@pytest.mark.compile
|
||||
def test_placeholder() -> None:
|
||||
"""Used for compiling integration tests without running any real tests."""
|
||||
pass
|
@ -1,505 +0,0 @@
|
||||
"""Test PGVector functionality."""
|
||||
|
||||
import os
|
||||
from typing import Any, Dict, Generator, List
|
||||
|
||||
import pytest
|
||||
import sqlalchemy
|
||||
from langchain_core.documents import Document
|
||||
from sqlalchemy.orm import Session
|
||||
|
||||
from langchain_postgres.vectorstores import (
|
||||
SUPPORTED_OPERATORS,
|
||||
PGVector,
|
||||
)
|
||||
from tests.integration_tests.fake_embeddings import FakeEmbeddings
|
||||
from tests.integration_tests.fixtures.filtering_test_cases import (
|
||||
DOCUMENTS,
|
||||
TYPE_1_FILTERING_TEST_CASES,
|
||||
TYPE_2_FILTERING_TEST_CASES,
|
||||
TYPE_3_FILTERING_TEST_CASES,
|
||||
TYPE_4_FILTERING_TEST_CASES,
|
||||
TYPE_5_FILTERING_TEST_CASES,
|
||||
)
|
||||
|
||||
# The connection string matches the default settings in the docker-compose file
|
||||
# located in the root of the repository: [root]/docker/docker-compose.yml
|
||||
# Non-standard ports are used to avoid conflicts with other local postgres
|
||||
# instances.
|
||||
# To spin up postgres with the pgvector extension:
|
||||
# cd [root]/docker/docker-compose.yml
|
||||
# docker compose up pgvector
|
||||
CONNECTION_STRING = PGVector.connection_string_from_db_params(
|
||||
driver=os.environ.get("TEST_PGVECTOR_DRIVER", "psycopg"),
|
||||
host=os.environ.get("TEST_PGVECTOR_HOST", "localhost"),
|
||||
port=int(os.environ.get("TEST_PGVECTOR_PORT", "6024")),
|
||||
database=os.environ.get("TEST_PGVECTOR_DATABASE", "langchain"),
|
||||
user=os.environ.get("TEST_PGVECTOR_USER", "langchain"),
|
||||
password=os.environ.get("TEST_PGVECTOR_PASSWORD", "langchain"),
|
||||
)
|
||||
|
||||
ADA_TOKEN_COUNT = 1536
|
||||
|
||||
|
||||
class FakeEmbeddingsWithAdaDimension(FakeEmbeddings):
|
||||
"""Fake embeddings functionality for testing."""
|
||||
|
||||
def embed_documents(self, texts: List[str]) -> List[List[float]]:
|
||||
"""Return simple embeddings."""
|
||||
return [
|
||||
[float(1.0)] * (ADA_TOKEN_COUNT - 1) + [float(i)] for i in range(len(texts))
|
||||
]
|
||||
|
||||
def embed_query(self, text: str) -> List[float]:
|
||||
"""Return simple embeddings."""
|
||||
return [float(1.0)] * (ADA_TOKEN_COUNT - 1) + [float(0.0)]
|
||||
|
||||
|
||||
def test_pgvector(pgvector: PGVector) -> None:
|
||||
"""Test end to end construction and search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
docsearch = PGVector.from_texts(
|
||||
texts=texts,
|
||||
collection_name="test_collection",
|
||||
embedding=FakeEmbeddingsWithAdaDimension(),
|
||||
connection_string=CONNECTION_STRING,
|
||||
pre_delete_collection=True,
|
||||
)
|
||||
output = docsearch.similarity_search("foo", k=1)
|
||||
assert output == [Document(page_content="foo")]
|
||||
|
||||
|
||||
def test_pgvector_embeddings() -> None:
|
||||
"""Test end to end construction with embeddings and search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
text_embeddings = FakeEmbeddingsWithAdaDimension().embed_documents(texts)
|
||||
text_embedding_pairs = list(zip(texts, text_embeddings))
|
||||
docsearch = PGVector.from_embeddings(
|
||||
text_embeddings=text_embedding_pairs,
|
||||
collection_name="test_collection",
|
||||
embedding=FakeEmbeddingsWithAdaDimension(),
|
||||
connection_string=CONNECTION_STRING,
|
||||
pre_delete_collection=True,
|
||||
)
|
||||
output = docsearch.similarity_search("foo", k=1)
|
||||
assert output == [Document(page_content="foo")]
|
||||
|
||||
|
||||
def test_pgvector_with_metadatas() -> None:
|
||||
"""Test end to end construction and search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
metadatas = [{"page": str(i)} for i in range(len(texts))]
|
||||
docsearch = PGVector.from_texts(
|
||||
texts=texts,
|
||||
collection_name="test_collection",
|
||||
embedding=FakeEmbeddingsWithAdaDimension(),
|
||||
metadatas=metadatas,
|
||||
connection_string=CONNECTION_STRING,
|
||||
pre_delete_collection=True,
|
||||
)
|
||||
output = docsearch.similarity_search("foo", k=1)
|
||||
assert output == [Document(page_content="foo", metadata={"page": "0"})]
|
||||
|
||||
|
||||
def test_pgvector_with_metadatas_with_scores() -> None:
|
||||
"""Test end to end construction and search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
metadatas = [{"page": str(i)} for i in range(len(texts))]
|
||||
docsearch = PGVector.from_texts(
|
||||
texts=texts,
|
||||
collection_name="test_collection",
|
||||
embedding=FakeEmbeddingsWithAdaDimension(),
|
||||
metadatas=metadatas,
|
||||
connection_string=CONNECTION_STRING,
|
||||
pre_delete_collection=True,
|
||||
)
|
||||
output = docsearch.similarity_search_with_score("foo", k=1)
|
||||
assert output == [(Document(page_content="foo", metadata={"page": "0"}), 0.0)]
|
||||
|
||||
|
||||
def test_pgvector_with_filter_match() -> None:
|
||||
"""Test end to end construction and search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
metadatas = [{"page": str(i)} for i in range(len(texts))]
|
||||
docsearch = PGVector.from_texts(
|
||||
texts=texts,
|
||||
collection_name="test_collection_filter",
|
||||
embedding=FakeEmbeddingsWithAdaDimension(),
|
||||
metadatas=metadatas,
|
||||
connection_string=CONNECTION_STRING,
|
||||
pre_delete_collection=True,
|
||||
)
|
||||
output = docsearch.similarity_search_with_score("foo", k=1, filter={"page": "0"})
|
||||
assert output == [(Document(page_content="foo", metadata={"page": "0"}), 0.0)]
|
||||
|
||||
|
||||
def test_pgvector_with_filter_distant_match() -> None:
|
||||
"""Test end to end construction and search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
metadatas = [{"page": str(i)} for i in range(len(texts))]
|
||||
docsearch = PGVector.from_texts(
|
||||
texts=texts,
|
||||
collection_name="test_collection_filter",
|
||||
embedding=FakeEmbeddingsWithAdaDimension(),
|
||||
metadatas=metadatas,
|
||||
connection_string=CONNECTION_STRING,
|
||||
pre_delete_collection=True,
|
||||
)
|
||||
output = docsearch.similarity_search_with_score("foo", k=1, filter={"page": "2"})
|
||||
assert output == [
|
||||
(Document(page_content="baz", metadata={"page": "2"}), 0.0013003906671379406)
|
||||
]
|
||||
|
||||
|
||||
def test_pgvector_with_filter_no_match() -> None:
|
||||
"""Test end to end construction and search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
metadatas = [{"page": str(i)} for i in range(len(texts))]
|
||||
docsearch = PGVector.from_texts(
|
||||
texts=texts,
|
||||
collection_name="test_collection_filter",
|
||||
embedding=FakeEmbeddingsWithAdaDimension(),
|
||||
metadatas=metadatas,
|
||||
connection_string=CONNECTION_STRING,
|
||||
pre_delete_collection=True,
|
||||
)
|
||||
output = docsearch.similarity_search_with_score("foo", k=1, filter={"page": "5"})
|
||||
assert output == []
|
||||
|
||||
|
||||
def test_pgvector_collection_with_metadata() -> None:
|
||||
"""Test end to end collection construction"""
|
||||
pgvector = PGVector(
|
||||
collection_name="test_collection",
|
||||
collection_metadata={"foo": "bar"},
|
||||
embedding_function=FakeEmbeddingsWithAdaDimension(),
|
||||
connection_string=CONNECTION_STRING,
|
||||
pre_delete_collection=True,
|
||||
)
|
||||
session = Session(pgvector._create_engine())
|
||||
collection = pgvector.get_collection(session)
|
||||
if collection is None:
|
||||
assert False, "Expected a CollectionStore object but received None"
|
||||
else:
|
||||
assert collection.name == "test_collection"
|
||||
assert collection.cmetadata == {"foo": "bar"}
|
||||
|
||||
|
||||
def test_pgvector_with_filter_in_set() -> None:
|
||||
"""Test end to end construction and search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
metadatas = [{"page": str(i)} for i in range(len(texts))]
|
||||
docsearch = PGVector.from_texts(
|
||||
texts=texts,
|
||||
collection_name="test_collection_filter",
|
||||
embedding=FakeEmbeddingsWithAdaDimension(),
|
||||
metadatas=metadatas,
|
||||
connection_string=CONNECTION_STRING,
|
||||
pre_delete_collection=True,
|
||||
)
|
||||
output = docsearch.similarity_search_with_score(
|
||||
"foo", k=2, filter={"page": {"IN": ["0", "2"]}}
|
||||
)
|
||||
assert output == [
|
||||
(Document(page_content="foo", metadata={"page": "0"}), 0.0),
|
||||
(Document(page_content="baz", metadata={"page": "2"}), 0.0013003906671379406),
|
||||
]
|
||||
|
||||
|
||||
def test_pgvector_with_filter_nin_set() -> None:
|
||||
"""Test end to end construction and search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
metadatas = [{"page": str(i)} for i in range(len(texts))]
|
||||
docsearch = PGVector.from_texts(
|
||||
texts=texts,
|
||||
collection_name="test_collection_filter",
|
||||
embedding=FakeEmbeddingsWithAdaDimension(),
|
||||
metadatas=metadatas,
|
||||
connection_string=CONNECTION_STRING,
|
||||
pre_delete_collection=True,
|
||||
)
|
||||
output = docsearch.similarity_search_with_score(
|
||||
"foo", k=2, filter={"page": {"NIN": ["1"]}}
|
||||
)
|
||||
assert output == [
|
||||
(Document(page_content="foo", metadata={"page": "0"}), 0.0),
|
||||
(Document(page_content="baz", metadata={"page": "2"}), 0.0013003906671379406),
|
||||
]
|
||||
|
||||
|
||||
def test_pgvector_delete_docs() -> None:
|
||||
"""Add and delete documents."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
metadatas = [{"page": str(i)} for i in range(len(texts))]
|
||||
docsearch = PGVector.from_texts(
|
||||
texts=texts,
|
||||
collection_name="test_collection_filter",
|
||||
embedding=FakeEmbeddingsWithAdaDimension(),
|
||||
metadatas=metadatas,
|
||||
ids=["1", "2", "3"],
|
||||
connection_string=CONNECTION_STRING,
|
||||
pre_delete_collection=True,
|
||||
)
|
||||
docsearch.delete(["1", "2"])
|
||||
with docsearch._make_session() as session:
|
||||
records = list(session.query(docsearch.EmbeddingStore).all())
|
||||
# ignoring type error since mypy cannot determine whether
|
||||
# the list is sortable
|
||||
assert sorted(record.custom_id for record in records) == ["3"] # type: ignore
|
||||
|
||||
docsearch.delete(["2", "3"]) # Should not raise on missing ids
|
||||
with docsearch._make_session() as session:
|
||||
records = list(session.query(docsearch.EmbeddingStore).all())
|
||||
# ignoring type error since mypy cannot determine whether
|
||||
# the list is sortable
|
||||
assert sorted(record.custom_id for record in records) == [] # type: ignore
|
||||
|
||||
|
||||
def test_pgvector_relevance_score() -> None:
|
||||
"""Test to make sure the relevance score is scaled to 0-1."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
metadatas = [{"page": str(i)} for i in range(len(texts))]
|
||||
docsearch = PGVector.from_texts(
|
||||
texts=texts,
|
||||
collection_name="test_collection",
|
||||
embedding=FakeEmbeddingsWithAdaDimension(),
|
||||
metadatas=metadatas,
|
||||
connection_string=CONNECTION_STRING,
|
||||
pre_delete_collection=True,
|
||||
)
|
||||
|
||||
output = docsearch.similarity_search_with_relevance_scores("foo", k=3)
|
||||
assert output == [
|
||||
(Document(page_content="foo", metadata={"page": "0"}), 1.0),
|
||||
(Document(page_content="bar", metadata={"page": "1"}), 0.9996744261675065),
|
||||
(Document(page_content="baz", metadata={"page": "2"}), 0.9986996093328621),
|
||||
]
|
||||
|
||||
|
||||
def test_pgvector_retriever_search_threshold() -> None:
|
||||
"""Test using retriever for searching with threshold."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
metadatas = [{"page": str(i)} for i in range(len(texts))]
|
||||
docsearch = PGVector.from_texts(
|
||||
texts=texts,
|
||||
collection_name="test_collection",
|
||||
embedding=FakeEmbeddingsWithAdaDimension(),
|
||||
metadatas=metadatas,
|
||||
connection_string=CONNECTION_STRING,
|
||||
pre_delete_collection=True,
|
||||
)
|
||||
|
||||
retriever = docsearch.as_retriever(
|
||||
search_type="similarity_score_threshold",
|
||||
search_kwargs={"k": 3, "score_threshold": 0.999},
|
||||
)
|
||||
output = retriever.get_relevant_documents("summer")
|
||||
assert output == [
|
||||
Document(page_content="foo", metadata={"page": "0"}),
|
||||
Document(page_content="bar", metadata={"page": "1"}),
|
||||
]
|
||||
|
||||
|
||||
def test_pgvector_retriever_search_threshold_custom_normalization_fn() -> None:
|
||||
"""Test searching with threshold and custom normalization function"""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
metadatas = [{"page": str(i)} for i in range(len(texts))]
|
||||
docsearch = PGVector.from_texts(
|
||||
texts=texts,
|
||||
collection_name="test_collection",
|
||||
embedding=FakeEmbeddingsWithAdaDimension(),
|
||||
metadatas=metadatas,
|
||||
connection_string=CONNECTION_STRING,
|
||||
pre_delete_collection=True,
|
||||
relevance_score_fn=lambda d: d * 0,
|
||||
)
|
||||
|
||||
retriever = docsearch.as_retriever(
|
||||
search_type="similarity_score_threshold",
|
||||
search_kwargs={"k": 3, "score_threshold": 0.5},
|
||||
)
|
||||
output = retriever.get_relevant_documents("foo")
|
||||
assert output == []
|
||||
|
||||
|
||||
def test_pgvector_max_marginal_relevance_search() -> None:
|
||||
"""Test max marginal relevance search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
docsearch = PGVector.from_texts(
|
||||
texts=texts,
|
||||
collection_name="test_collection",
|
||||
embedding=FakeEmbeddingsWithAdaDimension(),
|
||||
connection_string=CONNECTION_STRING,
|
||||
pre_delete_collection=True,
|
||||
)
|
||||
output = docsearch.max_marginal_relevance_search("foo", k=1, fetch_k=3)
|
||||
assert output == [Document(page_content="foo")]
|
||||
|
||||
|
||||
def test_pgvector_max_marginal_relevance_search_with_score() -> None:
|
||||
"""Test max marginal relevance search with relevance scores."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
docsearch = PGVector.from_texts(
|
||||
texts=texts,
|
||||
collection_name="test_collection",
|
||||
embedding=FakeEmbeddingsWithAdaDimension(),
|
||||
connection_string=CONNECTION_STRING,
|
||||
pre_delete_collection=True,
|
||||
)
|
||||
output = docsearch.max_marginal_relevance_search_with_score("foo", k=1, fetch_k=3)
|
||||
assert output == [(Document(page_content="foo"), 0.0)]
|
||||
|
||||
|
||||
def test_pgvector_with_custom_connection() -> None:
|
||||
"""Test construction using a custom connection."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
engine = sqlalchemy.create_engine(CONNECTION_STRING)
|
||||
with engine.connect() as connection:
|
||||
docsearch = PGVector.from_texts(
|
||||
texts=texts,
|
||||
collection_name="test_collection",
|
||||
embedding=FakeEmbeddingsWithAdaDimension(),
|
||||
connection_string=CONNECTION_STRING,
|
||||
pre_delete_collection=True,
|
||||
connection=connection,
|
||||
)
|
||||
output = docsearch.similarity_search("foo", k=1)
|
||||
assert output == [Document(page_content="foo")]
|
||||
|
||||
|
||||
def test_pgvector_with_custom_engine_args() -> None:
|
||||
"""Test construction using custom engine arguments."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
engine_args = {
|
||||
"pool_size": 5,
|
||||
"max_overflow": 10,
|
||||
"pool_recycle": -1,
|
||||
"pool_use_lifo": False,
|
||||
"pool_pre_ping": False,
|
||||
"pool_timeout": 30,
|
||||
}
|
||||
docsearch = PGVector.from_texts(
|
||||
texts=texts,
|
||||
collection_name="test_collection",
|
||||
embedding=FakeEmbeddingsWithAdaDimension(),
|
||||
connection_string=CONNECTION_STRING,
|
||||
pre_delete_collection=True,
|
||||
engine_args=engine_args,
|
||||
)
|
||||
output = docsearch.similarity_search("foo", k=1)
|
||||
assert output == [Document(page_content="foo")]
|
||||
|
||||
|
||||
# We should reuse this test-case across other integrations
|
||||
# Add database fixture using pytest
|
||||
@pytest.fixture
|
||||
def pgvector() -> Generator[PGVector, None, None]:
|
||||
"""Create a PGVector instance."""
|
||||
store = PGVector.from_documents(
|
||||
documents=DOCUMENTS,
|
||||
collection_name="test_collection",
|
||||
embedding=FakeEmbeddingsWithAdaDimension(),
|
||||
connection_string=CONNECTION_STRING,
|
||||
pre_delete_collection=True,
|
||||
relevance_score_fn=lambda d: d * 0,
|
||||
use_jsonb=True,
|
||||
)
|
||||
try:
|
||||
yield store
|
||||
# Do clean up
|
||||
finally:
|
||||
store.drop_tables()
|
||||
|
||||
|
||||
@pytest.mark.parametrize("test_filter, expected_ids", TYPE_1_FILTERING_TEST_CASES[:1])
|
||||
def test_pgvector_with_with_metadata_filters_1(
|
||||
pgvector: PGVector,
|
||||
test_filter: Dict[str, Any],
|
||||
expected_ids: List[int],
|
||||
) -> None:
|
||||
"""Test end to end construction and search."""
|
||||
docs = pgvector.similarity_search("meow", k=5, filter=test_filter)
|
||||
assert [doc.metadata["id"] for doc in docs] == expected_ids, test_filter
|
||||
|
||||
|
||||
@pytest.mark.parametrize("test_filter, expected_ids", TYPE_2_FILTERING_TEST_CASES)
|
||||
def test_pgvector_with_with_metadata_filters_2(
|
||||
pgvector: PGVector,
|
||||
test_filter: Dict[str, Any],
|
||||
expected_ids: List[int],
|
||||
) -> None:
|
||||
"""Test end to end construction and search."""
|
||||
docs = pgvector.similarity_search("meow", k=5, filter=test_filter)
|
||||
assert [doc.metadata["id"] for doc in docs] == expected_ids, test_filter
|
||||
|
||||
|
||||
@pytest.mark.parametrize("test_filter, expected_ids", TYPE_3_FILTERING_TEST_CASES)
|
||||
def test_pgvector_with_with_metadata_filters_3(
|
||||
pgvector: PGVector,
|
||||
test_filter: Dict[str, Any],
|
||||
expected_ids: List[int],
|
||||
) -> None:
|
||||
"""Test end to end construction and search."""
|
||||
docs = pgvector.similarity_search("meow", k=5, filter=test_filter)
|
||||
assert [doc.metadata["id"] for doc in docs] == expected_ids, test_filter
|
||||
|
||||
|
||||
@pytest.mark.parametrize("test_filter, expected_ids", TYPE_4_FILTERING_TEST_CASES)
|
||||
def test_pgvector_with_with_metadata_filters_4(
|
||||
pgvector: PGVector,
|
||||
test_filter: Dict[str, Any],
|
||||
expected_ids: List[int],
|
||||
) -> None:
|
||||
"""Test end to end construction and search."""
|
||||
docs = pgvector.similarity_search("meow", k=5, filter=test_filter)
|
||||
assert [doc.metadata["id"] for doc in docs] == expected_ids, test_filter
|
||||
|
||||
|
||||
@pytest.mark.parametrize("test_filter, expected_ids", TYPE_5_FILTERING_TEST_CASES)
|
||||
def test_pgvector_with_with_metadata_filters_5(
|
||||
pgvector: PGVector,
|
||||
test_filter: Dict[str, Any],
|
||||
expected_ids: List[int],
|
||||
) -> None:
|
||||
"""Test end to end construction and search."""
|
||||
docs = pgvector.similarity_search("meow", k=5, filter=test_filter)
|
||||
assert [doc.metadata["id"] for doc in docs] == expected_ids, test_filter
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"invalid_filter",
|
||||
[
|
||||
["hello"],
|
||||
{
|
||||
"id": 2,
|
||||
"$name": "foo",
|
||||
},
|
||||
{"$or": {}},
|
||||
{"$and": {}},
|
||||
{"$between": {}},
|
||||
{"$eq": {}},
|
||||
],
|
||||
)
|
||||
def test_invalid_filters(pgvector: PGVector, invalid_filter: Any) -> None:
|
||||
"""Verify that invalid filters raise an error."""
|
||||
with pytest.raises(ValueError):
|
||||
pgvector._create_filter_clause(invalid_filter)
|
||||
|
||||
|
||||
def test_validate_operators() -> None:
|
||||
"""Verify that all operators have been categorized."""
|
||||
assert sorted(SUPPORTED_OPERATORS) == [
|
||||
"$and",
|
||||
"$between",
|
||||
"$eq",
|
||||
"$gt",
|
||||
"$gte",
|
||||
"$ilike",
|
||||
"$in",
|
||||
"$like",
|
||||
"$lt",
|
||||
"$lte",
|
||||
"$ne",
|
||||
"$nin",
|
||||
"$or",
|
||||
]
|
@ -1,14 +0,0 @@
|
||||
from langchain_postgres import __all__
|
||||
|
||||
EXPECTED_ALL = [
|
||||
"__version__",
|
||||
"CheckpointSerializer",
|
||||
"PostgresChatMessageHistory",
|
||||
"PostgresCheckpoint",
|
||||
"PickleCheckpointSerializer",
|
||||
]
|
||||
|
||||
|
||||
def test_all_imports() -> None:
|
||||
"""Test that __all__ is correctly defined."""
|
||||
assert sorted(EXPECTED_ALL) == sorted(__all__)
|
@ -1,42 +0,0 @@
|
||||
"""Get fixtures for the database connection."""
|
||||
import os
|
||||
from contextlib import asynccontextmanager, contextmanager
|
||||
|
||||
import psycopg
|
||||
from typing_extensions import AsyncGenerator, Generator
|
||||
|
||||
PG_USER = os.environ.get("PG_USER", "langchain")
|
||||
PG_HOST = os.environ.get("PG_HOST", "localhost")
|
||||
PG_PASSWORD = os.environ.get("PG_PASSWORD", "langchain")
|
||||
PG_DATABASE = os.environ.get("PG_DATABASE", "langchain")
|
||||
|
||||
# Using a different port for testing than the default 5432
|
||||
# to avoid conflicts with a running PostgreSQL instance
|
||||
# This port matches the convention in langchain/docker/docker-compose.yml
|
||||
# To spin up a PostgreSQL instance for testing, run:
|
||||
# docker-compose -f docker/docker-compose.yml up -d postgres
|
||||
PG_PORT = os.environ.get("PG_PORT", "6023")
|
||||
|
||||
DSN = f"postgresql://{PG_USER}:{PG_PASSWORD}@{PG_HOST}:{PG_PORT}/{PG_DATABASE}"
|
||||
|
||||
|
||||
@asynccontextmanager
|
||||
async def asyncpg_client() -> AsyncGenerator[psycopg.AsyncConnection, None]:
|
||||
# Establish a connection to your test database
|
||||
conn = await psycopg.AsyncConnection.connect(conninfo=DSN)
|
||||
try:
|
||||
yield conn
|
||||
finally:
|
||||
# Cleanup: close the connection after the test is done
|
||||
await conn.close()
|
||||
|
||||
|
||||
@contextmanager
|
||||
def syncpg_client() -> Generator[psycopg.Connection, None, None]:
|
||||
# Establish a connection to your test database
|
||||
conn = psycopg.connect(conninfo=DSN)
|
||||
try:
|
||||
yield conn
|
||||
finally:
|
||||
# Cleanup: close the connection after the test is done
|
||||
conn.close()
|
Loading…
Reference in New Issue