feat: Adding PGChatMessageHistory

examples/pg_chat_message_history.ipynb

@@ -0,0 +1,314 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PGChatMessageHistory\n",
+    "\n",
+    "`PGChatMessageHistory` is a an implementation of the the LangChain ChatMessageHistory abstraction using `postgres` as the backend.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IR54BmgvdHT_"
+   },
+   "source": [
+    "## Install\n",
+    "\n",
+    "Install the `langchain-postgres` package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "0ZITIDE160OD",
+    "outputId": "e184bc0d-6541-4e0a-82d2-1e216db00a2d",
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet langchain-postgres"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QuQigs4UoFQ2"
+   },
+   "source": [
+    "## Create an engine\n",
+    "\n",
+    "The first step is to create a `PGEngine` instance, which does the following:\n",
+    "\n",
+    "1. Allows you to create tables for storing documents and embeddings.\n",
+    "2. Maintains a connection pool that manages connections to the database. This allows sharing of the connection pool and helps to reduce latency for database calls."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_postgres import PGEngine\n",
+    "\n",
+    "# See docker command above to launch a Postgres instance with pgvector enabled.\n",
+    "# Replace these values with your own configuration.\n",
+    "POSTGRES_USER = \"langchain\"\n",
+    "POSTGRES_PASSWORD = \"langchain\"\n",
+    "POSTGRES_HOST = \"localhost\"\n",
+    "POSTGRES_PORT = \"6024\"\n",
+    "POSTGRES_DB = \"langchain\"\n",
+    "\n",
+    "CONNECTION_STRING = (\n",
+    "    f\"postgresql+asyncpg://{POSTGRES_USER}:{POSTGRES_PASSWORD}@{POSTGRES_HOST}\"\n",
+    "    f\":{POSTGRES_PORT}/{POSTGRES_DB}\"\n",
+    ")\n",
+    "\n",
+    "pg_engine = PGEngine.from_connection_string(url=CONNECTION_STRING)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To use psycopg3 driver, set your connection string to `postgresql+psycopg://`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "D9Xs2qhm6X56"
+   },
+   "source": [
+    "### Initialize a table\n",
+    "The `PGChatMessageHistory` class requires a database table with a specific schema in order to store the chat message history.\n",
+    "\n",
+    "The `PGEngine` engine has a helper method `init_chat_history_table()` that can be used to create a table with the proper schema for you."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "TABLE_NAME = \"chat_history\"\n",
+    "\n",
+    "pg_engine.init_chat_history_table(table_name=TABLE_NAME)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### PGChatMessageHistory\n",
+    "\n",
+    "To initialize the `PGChatMessageHistory` class you need to provide only 3 things:\n",
+    "\n",
+    "1. `engine` - An instance of a `PGEngine` engine.\n",
+    "1. `session_id` - A unique identifier string that specifies an id for the session.\n",
+    "1. `table_name` : The name of the table within the PG database to store the chat message history.\n",
+    "1. `schema_name` : The name of the database schema containing the chat message history table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "z-AZyzAQ7bsf",
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_postgres import PGChatMessageHistory\n",
+    "\n",
+    "history = PGChatMessageHistory.create_sync(\n",
+    "    pg_engine,\n",
+    "    session_id=\"test_session\",\n",
+    "    table_name=TABLE_NAME,\n",
+    "    # schema_name=SCHEMA_NAME,\n",
+    ")\n",
+    "history.add_user_message(\"hi!\")\n",
+    "history.add_ai_message(\"whats up?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "history.messages"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Cleaning up\n",
+    "When the history of a specific session is obsolete and can be deleted, it can be done the following way.\n",
+    "\n",
+    "**Note:** Once deleted, the data is no longer stored in Postgres and is gone forever."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "history.clear()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "We can easily combine this message history class with [LCEL Runnables](https://python.langchain.com/docs/concepts/lcel/) such as `RunnableWithMessageHistory`.\n",
+    "\n",
+    "To create an agent or chain, you will need a model. This example will use one of [Google's Vertex AI chat models](https://python.langchain.com/docs/integrations/chat/google_vertex_ai_palm)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# enable Vertex AI API\n",
+    "!gcloud services enable aiplatform.googleapis.com"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
+    "from langchain_core.runnables.history import RunnableWithMessageHistory\n",
+    "from langchain_google_vertexai import ChatVertexAI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "## Please update the project id\n",
+    "GOOGLE_CLOUD_PROJECT_ID = \"\"\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", \"You are a helpful assistant.\"),\n",
+    "        MessagesPlaceholder(variable_name=\"history\"),\n",
+    "        (\"human\", \"{question}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "chain = prompt | ChatVertexAI(\n",
+    "    project=GOOGLE_CLOUD_PROJECT_ID, model_name=\"gemini-2.0-flash-exp\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "chain_with_history = RunnableWithMessageHistory(\n",
+    "    chain,\n",
+    "    lambda session_id: PGChatMessageHistory.create_sync(\n",
+    "        pg_engine,\n",
+    "        session_id=session_id,\n",
+    "        table_name=TABLE_NAME,\n",
+    "        # schema_name=SCHEMA_NAME,\n",
+    "    ),\n",
+    "    input_messages_key=\"question\",\n",
+    "    history_messages_key=\"history\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# This is where we configure the session id\n",
+    "config = {\"configurable\": {\"session_id\": \"test_session\"}}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "chain_with_history.invoke({\"question\": \"Hi! I'm bob\"}, config=config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "chain_with_history.invoke({\"question\": \"Whats my name\"}, config=config)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

langchain_postgres/__init__.py

@@ -2,6 +2,7 @@
 
 from langchain_postgres.chat_message_histories import PostgresChatMessageHistory
 from langchain_postgres.translator import PGVectorTranslator
+from langchain_postgres.v2.chat_message_history import PGChatMessageHistory
 from langchain_postgres.v2.engine import Column, ColumnDict, PGEngine
 from langchain_postgres.v2.vectorstores import PGVectorStore
 from langchain_postgres.vectorstores import PGVector
@@ -18,6 +19,7 @@
     "ColumnDict",
     "PGEngine",
     "PostgresChatMessageHistory",
+    "PGChatMessageHistory",
     "PGVector",
     "PGVectorStore",
     "PGVectorTranslator",