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Remove postgres package (#20207)

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Package moved
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Eugene Yurtsev 3 months ago committed by GitHub
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21
libs/partners/postgres/LICENSE
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MIT License
Copyright (c) 2024 LangChain, Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

56
libs/partners/postgres/Makefile
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.PHONY: all format lint test tests integration_tests docker_tests help extended_tests
# Default target executed when no arguments are given to make.
all: help
# Define a variable for the test file path.
TEST_FILE ?= tests/unit_tests/
integration_test integration_tests: TEST_FILE = tests/integration_tests/
test tests integration_test integration_tests:
poetry run pytest $(TEST_FILE)
######################
# LINTING AND FORMATTING
######################
# Define a variable for Python and notebook files.
PYTHON_FILES=.
MYPY_CACHE=.mypy_cache
lint format: PYTHON_FILES=.
lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/postgres --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
lint_package: PYTHON_FILES=langchain_postgres
lint_tests: PYTHON_FILES=tests
lint_tests: MYPY_CACHE=.mypy_cache_test
lint lint_diff lint_package lint_tests:
poetry run ruff .
poetry run ruff format $(PYTHON_FILES) --diff
poetry run ruff --select I $(PYTHON_FILES)
mkdir -p $(MYPY_CACHE); poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
format format_diff:
poetry run ruff format $(PYTHON_FILES)
poetry run ruff --select I --fix $(PYTHON_FILES)
spell_check:
poetry run codespell --toml pyproject.toml
spell_fix:
poetry run codespell --toml pyproject.toml -w
check_imports: $(shell find langchain_postgres -name '*.py')
poetry run python ./scripts/check_imports.py $^
######################
# HELP
######################
help:
@echo '----'
@echo 'check_imports - check imports'
@echo 'format - run code formatters'
@echo 'lint - run linters'
@echo 'test - run unit tests'
@echo 'tests - run unit tests'
@echo 'test TEST_FILE=<test_file> - run all tests in file'

123
libs/partners/postgres/README.md
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# langchain-postgres
This package has moved!
The `langchain-postgres` package is an integration package managed by the core LangChain team.
https://github.com/langchain-ai/langchain-postgres/
This package contains implementations of core abstractions using `Postgres`.
The package is released under the MIT license.
Feel free to use the abstraction as provided or else modify them / extend them as appropriate for your own application.
## Installation
```bash
pip install -U langchain-postgres
```
## Usage
### ChatMessageHistory
The chat message history abstraction helps to persist chat message history
in a postgres table.
PostgresChatMessageHistory is parameterized using a `table_name` and a `session_id`.
The `table_name` is the name of the table in the database where
the chat messages will be stored.
The `session_id` is a unique identifier for the chat session. It can be assigned
by the caller using `uuid.uuid4()`.
```python
import uuid
from langchain_core.messages import SystemMessage, AIMessage, HumanMessage
from langchain_postgres import PostgresChatMessageHistory
import psycopg
# Establish a synchronous connection to the database
# (or use psycopg.AsyncConnection for async)
conn_info = ... # Fill in with your connection info
sync_connection = psycopg.connect(conn_info)
# Create the table schema (only needs to be done once)
table_name = "chat_history"
PostgresChatMessageHistory.create_schema(sync_connection, table_name)
session_id = str(uuid.uuid4())
# Initialize the chat history manager
chat_history = PostgresChatMessageHistory(
table_name,
session_id,
sync_connection=sync_connection
)
# Add messages to the chat history
chat_history.add_messages([
SystemMessage(content="Meow"),
AIMessage(content="woof"),
HumanMessage(content="bark"),
])
print(chat_history.messages)
```
### PostgresCheckpoint
An implementation of the `Checkpoint` abstraction in LangGraph using Postgres.
Async Usage:
```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.
```
Sync Usage:
```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.
```

22
libs/partners/postgres/langchain_postgres/__init__.py
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from importlib import metadata
from langchain_postgres.chat_message_histories import PostgresChatMessageHistory
from langchain_postgres.checkpoint import (
CheckpointSerializer,
PickleCheckpointSerializer,
PostgresCheckpoint,
)
try:
__version__ = metadata.version(__package__)
except metadata.PackageNotFoundError:
# Case where package metadata is not available.
__version__ = ""
__all__ = [
"__version__",
"CheckpointSerializer",
"PostgresChatMessageHistory",
"PostgresCheckpoint",
"PickleCheckpointSerializer",
]

82
libs/partners/postgres/langchain_postgres/_utils.py
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"""Copied over from langchain_community.
This code should be moved to langchain proper or removed entirely.
"""
import logging
from typing import List, Union
import numpy as np
logger = logging.getLogger(__name__)
Matrix = Union[List[List[float]], List[np.ndarray], np.ndarray]
def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray:
"""Row-wise cosine similarity between two equal-width matrices."""
if len(X) == 0 or len(Y) == 0:
return np.array([])
X = np.array(X)
Y = np.array(Y)
if X.shape[1] != Y.shape[1]:
raise ValueError(
f"Number of columns in X and Y must be the same. X has shape {X.shape} "
f"and Y has shape {Y.shape}."
)
try:
import simsimd as simd # type: ignore
X = np.array(X, dtype=np.float32)
Y = np.array(Y, dtype=np.float32)
Z = 1 - simd.cdist(X, Y, metric="cosine")
if isinstance(Z, float):
return np.array([Z])
return np.array(Z)
except ImportError:
logger.debug(
"Unable to import simsimd, defaulting to NumPy implementation. If you want "
"to use simsimd please install with `pip install simsimd`."
)
X_norm = np.linalg.norm(X, axis=1)
Y_norm = np.linalg.norm(Y, axis=1)
# Ignore divide by zero errors run time warnings as those are handled below.
with np.errstate(divide="ignore", invalid="ignore"):
similarity = np.dot(X, Y.T) / np.outer(X_norm, Y_norm)
similarity[np.isnan(similarity) | np.isinf(similarity)] = 0.0
return similarity
def maximal_marginal_relevance(
query_embedding: np.ndarray,
embedding_list: list,
lambda_mult: float = 0.5,
k: int = 4,
) -> List[int]:
"""Calculate maximal marginal relevance."""
if min(k, len(embedding_list)) <= 0:
return []
if query_embedding.ndim == 1:
query_embedding = np.expand_dims(query_embedding, axis=0)
similarity_to_query = cosine_similarity(query_embedding, embedding_list)[0]
most_similar = int(np.argmax(similarity_to_query))
idxs = [most_similar]
selected = np.array([embedding_list[most_similar]])
while len(idxs) < min(k, len(embedding_list)):
best_score = -np.inf
idx_to_add = -1
similarity_to_selected = cosine_similarity(embedding_list, selected)
for i, query_score in enumerate(similarity_to_query):
if i in idxs:
continue
redundant_score = max(similarity_to_selected[i])
equation_score = (
lambda_mult * query_score - (1 - lambda_mult) * redundant_score
)
if equation_score > best_score:
best_score = equation_score
idx_to_add = i
idxs.append(idx_to_add)
selected = np.append(selected, [embedding_list[idx_to_add]], axis=0)
return idxs

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libs/partners/postgres/langchain_postgres/chat_message_histories.py
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"""Client for persisting chat message history in a Postgres database.
This client provides support for both sync and async via psycopg 3.
"""
from __future__ import annotations
import json
import logging
import re
import uuid
from typing import List, Optional, Sequence
import psycopg
from langchain_core.chat_history import BaseChatMessageHistory
from langchain_core.messages import BaseMessage, message_to_dict, messages_from_dict
from psycopg import sql
logger = logging.getLogger(__name__)
def _create_table_and_index(table_name: str) -> List[sql.Composed]:
"""Make a SQL query to create a table."""
index_name = f"idx_{table_name}_session_id"
statements = [
sql.SQL(
"""
CREATE TABLE IF NOT EXISTS {table_name} (
id SERIAL PRIMARY KEY,
session_id UUID NOT NULL,
message JSONB NOT NULL,
created_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
);
"""
).format(table_name=sql.Identifier(table_name)),
sql.SQL(
"""
CREATE INDEX IF NOT EXISTS {index_name} ON {table_name} (session_id);
"""
).format(
table_name=sql.Identifier(table_name), index_name=sql.Identifier(index_name)
),
]
return statements
def _get_messages_query(table_name: str) -> sql.Composed:
"""Make a SQL query to get messages for a given session."""
return sql.SQL(
"SELECT message "
"FROM {table_name} "
"WHERE session_id = %(session_id)s "
"ORDER BY id;"
).format(table_name=sql.Identifier(table_name))
def _delete_by_session_id_query(table_name: str) -> sql.Composed:
"""Make a SQL query to delete messages for a given session."""
return sql.SQL(
"DELETE FROM {table_name} WHERE session_id = %(session_id)s;"
).format(table_name=sql.Identifier(table_name))
def _delete_table_query(table_name: str) -> sql.Composed:
"""Make a SQL query to delete a table."""
return sql.SQL("DROP TABLE IF EXISTS {table_name};").format(
table_name=sql.Identifier(table_name)
)
def _insert_message_query(table_name: str) -> sql.Composed:
"""Make a SQL query to insert a message."""
return sql.SQL(
"INSERT INTO {table_name} (session_id, message) VALUES (%s, %s)"
).format(table_name=sql.Identifier(table_name))
class PostgresChatMessageHistory(BaseChatMessageHistory):
def __init__(
self,
table_name: str,
session_id: str,
/,
*,
sync_connection: Optional[psycopg.Connection] = None,
async_connection: Optional[psycopg.AsyncConnection] = None,
) -> None:
"""Client for persisting chat message history in a Postgres database,
This client provides support for both sync and async via psycopg >=3.
The client can create schema in the database and provides methods to
add messages, get messages, and clear the chat message history.
The schema has the following columns:
- id: A serial primary key.
- session_id: The session ID for the chat message history.
- message: The JSONB message content.
- created_at: The timestamp of when the message was created.
Messages are retrieved for a given session_id and are sorted by
the id (which should be increasing monotonically), and correspond
to the order in which the messages were added to the history.
The "created_at" column is not returned by the interface, but
has been added for the schema so the information is available in the database.
A session_id can be used to separate different chat histories in the same table,
the session_id should be provided when initializing the client.
This chat history client takes in a psycopg connection object (either
Connection or AsyncConnection) and uses it to interact with the database.
This design allows to reuse the underlying connection object across
multiple instantiations of this class, making instantiation fast.
This chat history client is designed for prototyping applications that
involve chat and are based on Postgres.
As your application grows, you will likely need to extend the schema to
handle more complex queries. For example, a chat application
may involve multiple tables like a user table, a table for storing
chat sessions / conversations, and this table for storing chat messages
for a given session. The application will require access to additional
endpoints like deleting messages by user id, listing conversations by
user id or ordering them based on last message time, etc.
Feel free to adapt this implementation to suit your application's needs.
Args:
session_id: The session ID to use for the chat message history
table_name: The name of the database table to use
sync_connection: An existing psycopg connection instance
async_connection: An existing psycopg async connection instance
Usage:
- Use the create_schema or acreate_schema method to set up the table
schema in the database.
- Initialize the class with the appropriate session ID, table name,
and database connection.
- Add messages to the database using add_messages or aadd_messages.
- Retrieve messages with get_messages or aget_messages.
- Clear the session history with clear or aclear when needed.
Note:
- At least one of sync_connection or async_connection must be provided.
Examples:
.. code-block:: python
import uuid
from langchain_core.messages import SystemMessage, AIMessage, HumanMessage
from langchain_postgres import PostgresChatMessageHistory
import psycopg
# Establish a synchronous connection to the database
# (or use psycopg.AsyncConnection for async)
sync_connection = psycopg2.connect(conn_info)
# Create the table schema (only needs to be done once)
table_name = "chat_history"
PostgresChatMessageHistory.create_schema(sync_connection, table_name)
session_id = str(uuid.uuid4())
# Initialize the chat history manager
chat_history = PostgresChatMessageHistory(
table_name,
session_id,
sync_connection=sync_connection
)
# Add messages to the chat history
chat_history.add_messages([
SystemMessage(content="Meow"),
AIMessage(content="woof"),
HumanMessage(content="bark"),
])
print(chat_history.messages)
"""
if not sync_connection and not async_connection:
raise ValueError("Must provide sync_connection or async_connection")
self._connection = sync_connection
self._aconnection = async_connection
# Validate that session id is a UUID
try:
uuid.UUID(session_id)
except ValueError:
raise ValueError(
f"Invalid session id. Session id must be a valid UUID. Got {session_id}"
)
self._session_id = session_id
if not re.match(r"^\w+$", table_name):
raise ValueError(
"Invalid table name. Table name must contain only alphanumeric "
"characters and underscores."
)
self._table_name = table_name
@staticmethod
def create_schema(
connection: psycopg.Connection,
table_name: str,
/,
) -> None:
"""Create the table schema in the database and create relevant indexes."""
queries = _create_table_and_index(table_name)
logger.info("Creating schema for table %s", table_name)
with connection.cursor() as cursor:
for query in queries:
cursor.execute(query)
connection.commit()
@staticmethod
async def acreate_schema(
connection: psycopg.AsyncConnection, table_name: str, /
) -> None:
"""Create the table schema in the database and create relevant indexes."""
queries = _create_table_and_index(table_name)
logger.info("Creating schema for table %s", table_name)
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:
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:
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()

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libs/partners/postgres/langchain_postgres/checkpoint.py
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"""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

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{file = "SQLAlchemy-2.0.29-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:dba622396a3170974f81bad49aacebd243455ec3cc70615aeaef9e9613b5bca5"},
{file = "SQLAlchemy-2.0.29-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7b184e3de58009cc0bf32e20f137f1ec75a32470f5fede06c58f6c355ed42a72"},
{file = "SQLAlchemy-2.0.29-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8c37f1050feb91f3d6c32f864d8e114ff5545a4a7afe56778d76a9aec62638ba"},
{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"

94
libs/partners/postgres/pyproject.toml
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@ -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"

17
libs/partners/postgres/scripts/check_imports.py
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@ -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)

27
libs/partners/postgres/scripts/check_pydantic.sh
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@ -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

18
libs/partners/postgres/scripts/lint_imports.sh
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@ -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

0
libs/partners/postgres/tests/__init__.py
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0
libs/partners/postgres/tests/integration_tests/__init__.py
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28
libs/partners/postgres/tests/integration_tests/fake_embeddings.py
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@ -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)

0
libs/partners/postgres/tests/integration_tests/fixtures/__init__.py
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218
libs/partners/postgres/tests/integration_tests/fixtures/filtering_test_cases.py
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@ -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],
),
]

123
libs/partners/postgres/tests/integration_tests/test_chat_histories.py
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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() == []

326
libs/partners/postgres/tests/integration_tests/test_checkpointer.py
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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",
}
},
)

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libs/partners/postgres/tests/integration_tests/test_compile.py
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@ -1,7 +0,0 @@
import pytest
@pytest.mark.compile
def test_placeholder() -> None:
"""Used for compiling integration tests without running any real tests."""
pass

505
libs/partners/postgres/tests/integration_tests/test_vectorstore.py
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@ -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",
]

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libs/partners/postgres/tests/unit_tests/__init__.py
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libs/partners/postgres/tests/unit_tests/test_imports.py
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@ -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__)

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libs/partners/postgres/tests/utils.py
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"""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()
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