[LangChain] Add example programs and notebooks

framework/langchain/conversational_memory.ipynb

@@ -0,0 +1,359 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# CrateDB Chat Message History\n",
+    "\n",
+    "This notebook demonstrates how to use the `CrateDBChatMessageHistory`\n",
+    "to manage chat history in CrateDB, for supporting conversational memory."
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "f22eab3f84cbeb37"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Prerequisites\n",
+    "\n",
+    "Install required packages."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "outputs": [],
+   "source": [
+    "#!pip install -r requirements.txt"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Configuration\n",
+    "\n",
+    "To use the storage wrapper, you will need to configure two details.\n",
+    "\n",
+    "1. Session Id - a unique identifier of the session, like user name, email, chat id etc.\n",
+    "2. Database connection string: An SQLAlchemy-compatible URI that specifies the database\n",
+    "   connection. It will be passed to SQLAlchemy create_engine function."
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "f8f2830ee9ca1e01"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "outputs": [],
+   "source": [
+    "from langchain.memory.chat_message_histories import CrateDBChatMessageHistory\n",
+    "\n",
+    "CONNECTION_STRING = \"crate://crate@localhost/?schema=notebook\"\n",
+    "\n",
+    "chat_message_history = CrateDBChatMessageHistory(\n",
+    "\tsession_id=\"test_session\",\n",
+    "\tconnection_string=CONNECTION_STRING\n",
+    ")"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Basic Usage"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "outputs": [],
+   "source": [
+    "chat_message_history.add_user_message(\"Hello\")\n",
+    "chat_message_history.add_ai_message(\"Hi\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-28T10:04:38.077748Z",
+     "start_time": "2023-08-28T10:04:36.105894Z"
+    }
+   },
+   "id": "4576e914a866fb40"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "[HumanMessage(content='Hello', additional_kwargs={}, example=False),\n AIMessage(content='Hi', additional_kwargs={}, example=False)]"
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat_message_history.messages"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-28T10:04:38.929396Z",
+     "start_time": "2023-08-28T10:04:38.915727Z"
+    }
+   },
+   "id": "b476688cbb32ba90"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Custom Storage Model\n",
+    "\n",
+    "The default data model, which stores information about conversation messages only\n",
+    "has two slots for storing message details, the session id, and the message dictionary.\n",
+    "\n",
+    "If you want to store additional information, like message date, author, language etc.,\n",
+    "please provide an implementation for a custom message converter.\n",
+    "\n",
+    "This example demonstrates how to create a custom message converter, by implementing\n",
+    "the `BaseMessageConverter` interface."
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "2e5337719d5614fd"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "outputs": [],
+   "source": [
+    "from datetime import datetime\n",
+    "from typing import Any\n",
+    "\n",
+    "from langchain.memory.chat_message_histories.cratedb import generate_autoincrement_identifier\n",
+    "from langchain.memory.chat_message_histories.sql import BaseMessageConverter\n",
+    "from langchain.schema import AIMessage, BaseMessage, HumanMessage, SystemMessage\n",
+    "\n",
+    "import sqlalchemy as sa\n",
+    "from sqlalchemy.orm import declarative_base\n",
+    "\n",
+    "\n",
+    "Base = declarative_base()\n",
+    "\n",
+    "\n",
+    "class CustomMessage(Base):\n",
+    "\t__tablename__ = \"custom_message_store\"\n",
+    "\n",
+    "\tid = sa.Column(sa.BigInteger, primary_key=True, default=generate_autoincrement_identifier)\n",
+    "\tsession_id = sa.Column(sa.Text)\n",
+    "\ttype = sa.Column(sa.Text)\n",
+    "\tcontent = sa.Column(sa.Text)\n",
+    "\tcreated_at = sa.Column(sa.DateTime)\n",
+    "\tauthor_email = sa.Column(sa.Text)\n",
+    "\n",
+    "\n",
+    "class CustomMessageConverter(BaseMessageConverter):\n",
+    "\tdef __init__(self, author_email: str):\n",
+    "\t\tself.author_email = author_email\n",
+    "\t\n",
+    "\tdef from_sql_model(self, sql_message: Any) -> BaseMessage:\n",
+    "\t\tif sql_message.type == \"human\":\n",
+    "\t\t\treturn HumanMessage(\n",
+    "\t\t\t\tcontent=sql_message.content,\n",
+    "\t\t\t)\n",
+    "\t\telif sql_message.type == \"ai\":\n",
+    "\t\t\treturn AIMessage(\n",
+    "\t\t\t\tcontent=sql_message.content,\n",
+    "\t\t\t)\n",
+    "\t\telif sql_message.type == \"system\":\n",
+    "\t\t\treturn SystemMessage(\n",
+    "\t\t\t\tcontent=sql_message.content,\n",
+    "\t\t\t)\n",
+    "\t\telse:\n",
+    "\t\t\traise ValueError(f\"Unknown message type: {sql_message.type}\")\n",
+    "\t\n",
+    "\tdef to_sql_model(self, message: BaseMessage, session_id: str) -> Any:\n",
+    "\t\tnow = datetime.now()\n",
+    "\t\treturn CustomMessage(\n",
+    "\t\t\tsession_id=session_id,\n",
+    "\t\t\ttype=message.type,\n",
+    "\t\t\tcontent=message.content,\n",
+    "\t\t\tcreated_at=now,\n",
+    "\t\t\tauthor_email=self.author_email\n",
+    "\t\t)\n",
+    "\t\n",
+    "\tdef get_sql_model_class(self) -> Any:\n",
+    "\t\treturn CustomMessage\n",
+    "\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "\n",
+    "\tBase.metadata.drop_all(bind=sa.create_engine(CONNECTION_STRING))\n",
+    "\n",
+    "\tchat_message_history = CrateDBChatMessageHistory(\n",
+    "\t\tsession_id=\"test_session\",\n",
+    "\t\tconnection_string=CONNECTION_STRING,\n",
+    "\t\tcustom_message_converter=CustomMessageConverter(\n",
+    "\t\t\tauthor_email=\"test@example.com\"\n",
+    "\t\t)\n",
+    "\t)\n",
+    "\n",
+    "\tchat_message_history.add_user_message(\"Hello\")\n",
+    "\tchat_message_history.add_ai_message(\"Hi\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-28T10:04:41.510498Z",
+     "start_time": "2023-08-28T10:04:41.494912Z"
+    }
+   },
+   "id": "fdfde84c07d071bb"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "[HumanMessage(content='Hello', additional_kwargs={}, example=False),\n AIMessage(content='Hi', additional_kwargs={}, example=False)]"
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat_message_history.messages"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-28T10:04:43.497990Z",
+     "start_time": "2023-08-28T10:04:43.492517Z"
+    }
+   },
+   "id": "4a6a54d8a9e2856f"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Custom Name for Session Column\n",
+    "\n",
+    "The session id, a unique token identifying the session, is an important property of\n",
+    "this subsystem. If your database table stores it in a different column, you can use\n",
+    "the `session_id_field_name` keyword argument to adjust the name correspondingly."
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "622aded629a1adeb"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import typing as t\n",
+    "\n",
+    "from langchain.memory.chat_message_histories.cratedb import generate_autoincrement_identifier, CrateDBMessageConverter\n",
+    "from langchain.schema import _message_to_dict\n",
+    "\n",
+    "\n",
+    "Base = declarative_base()\n",
+    "\n",
+    "class MessageWithDifferentSessionIdColumn(Base):\n",
+    "\t__tablename__ = \"message_store_different_session_id\"\n",
+    "\tid = sa.Column(sa.BigInteger, primary_key=True, default=generate_autoincrement_identifier)\n",
+    "\tcustom_session_id = sa.Column(sa.Text)\n",
+    "\tmessage = sa.Column(sa.Text)\n",
+    "\n",
+    "\n",
+    "class CustomMessageConverterWithDifferentSessionIdColumn(CrateDBMessageConverter):\n",
+    "    def __init__(self):\n",
+    "        self.model_class = MessageWithDifferentSessionIdColumn\n",
+    "\n",
+    "    def to_sql_model(self, message: BaseMessage, custom_session_id: str) -> t.Any:\n",
+    "        return self.model_class(\n",
+    "            custom_session_id=custom_session_id, message=json.dumps(_message_to_dict(message))\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "\tBase.metadata.drop_all(bind=sa.create_engine(CONNECTION_STRING))\n",
+    "\n",
+    "\tchat_message_history = CrateDBChatMessageHistory(\n",
+    "\t\tsession_id=\"test_session\",\n",
+    "\t\tconnection_string=CONNECTION_STRING,\n",
+    "\t\tcustom_message_converter=CustomMessageConverterWithDifferentSessionIdColumn(),\n",
+    "\t\tsession_id_field_name=\"custom_session_id\",\n",
+    "\t)\n",
+    "\n",
+    "\tchat_message_history.add_user_message(\"Hello\")\n",
+    "\tchat_message_history.add_ai_message(\"Hi\")"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "[HumanMessage(content='Hello', additional_kwargs={}, example=False),\n AIMessage(content='Hi', additional_kwargs={}, example=False)]"
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat_message_history.messages"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

framework/langchain/conversational_memory.py

@@ -0,0 +1,34 @@
+"""
+Demonstrate conversational memory with CrateDB.
+
+Synopsis::
+
+    # Install prerequisites.
+    pip install -r requirements.txt
+
+    # Start database.
+    docker run --rm -it --publish=4200:4200 crate/crate:nightly
+
+    # Run program.
+    export CRATEDB_CONNECTION_STRING="crate://crate@localhost/?schema=doc"
+    python conversational_memory.py
+"""
+import os
+from pprint import pprint
+
+from langchain.memory.chat_message_histories import CrateDBChatMessageHistory
+
+
+def main():
+
+    chat_message_history = CrateDBChatMessageHistory(
+        session_id="test_session",
+        connection_string=os.environ.get("CRATEDB_CONNECTION_STRING")
+    )
+    chat_message_history.add_user_message("Hello")
+    chat_message_history.add_ai_message("Hi")
+    pprint(chat_message_history.messages)
+
+
+if __name__ == "__main__":
+    main()