Memory Using SQLite

• Author: Heesun Moon

• Peer Review: harheem, gyjong

• Proofread : Juni Lee

• This is a part of LangChain Open Tutorial

Overview

This tutorial explains the SQLChatMessageHistory class, which allows storing chat history in any database supported by SQLAlchemy.

Structured Query Language (SQL) is a domain-specific language used in programming and designed for managing data held in a Relational Database Management System (RDBMS), or for stream processing in a Relational Data Stream Management System (RDSMS). It is particularly useful for handling structured data, including relationships between entities and variables.

SQLAlchemy is an open-source SQL toolkit and Object-Relational Mapper (ORM) for the Python programming language, released under the MIT License.

To use a database other than SQLite, please make sure to install the appropriate database driver first.

Table of Contents

• Overview

• Environment Setup

• Usage

• Chaining

References

• Wikipedia: SQL

• SQLAlchemy

---

Environment Setup

Set up the environment. You may refer to Environment Setup for more details.

[Note]

• langchain-opentutorial is a package that provides a set of easy-to-use environment setup, useful functions and utilities for tutorials.

• You can checkout the langchain-opentutorial for more details.

%%capture --no-stderr
%pip install langchain_opentutorial

# Install required packages
from langchain_opentutorial import package

package.install(
    [
        "langchain_community",
        "langchain_openai",
        "langchain_core",
        "SQLAlchemy",
    ],
    verbose=False,
    upgrade=False,
)

# Set environment variables
from langchain_opentutorial import set_env

set_env(
    {
        "OPENAI_API_KEY": "",
        "LANGCHAIN_API_KEY": "",
        "LANGCHAIN_TRACING_V2": "true",
        "LANGCHAIN_ENDPOINT": "https://api.smith.langchain.com",
        "LANGCHAIN_PROJECT": "MemoryUsingSQLite",
    }
)

You can alternatively set OPENAI_API_KEY in .env file and load it.

[Note] This is not necessary if you've already set OPENAI_API_KEY in previous steps.

from dotenv import load_dotenv

load_dotenv()

Usage

To use the storage, you need to provide only the following 2 things:

1. session_id - A unique identifier for the session, such as a user name, email, chat ID, etc.

2. connection - A string that specifies the database connection. This string will be passed to SQLAlchemy's create_engine function.

from langchain_community.chat_message_histories import SQLChatMessageHistory

# Initialize chat history with session ID and database connection.
chat_message_history = SQLChatMessageHistory(
    session_id="sql_history", connection="sqlite:///sqlite.db"
)

# Add a user message
chat_message_history.add_user_message(
    "Hello, nice to meet you! My name is Heesun :) I'm a LangChain developer. I look forward to working with you!"
)
# Add an AI message
chat_message_history.add_ai_message(
    "Hi, Heesun! Nice to meet you. I look forward to working with you too!"
)

Now, let's check the stored conversation history.

chat_message_history.messages

[HumanMessage(content="Hello, nice to meet you! My name is Heesun :) I'm a LangChain developer. I look forward to working with you!", additional_kwargs={}, response_metadata={}),
     AIMessage(content='Hi, Heesun! Nice to meet you. I look forward to working with you too!', additional_kwargs={}, response_metadata={})]

You can also clear the session memory from db:

# Clear the session memory
chat_message_history.clear()
chat_message_history.messages

[]

Adding Metadata

Metadata can be added by directly creating HumanMessage and AIMessage objects. This approach enables flexible data handling and logging.

Parameters:

• additional_kwargs - Stores custom tags or metadata, such as priority or task type.

• response_metadata - Captures AI response details, including model, timestamp, and token count.

These fields enhance debugging and task tracking through detailed data storage.

from langchain_core.messages import HumanMessage

# Add a user message with additional metadata.
user_message = HumanMessage(
    content="Can you help me summarize this text?",
    additional_kwargs={"task": "summarization"},
)

# Add the message to chat history.
chat_message_history.add_message(user_message)

chat_message_history.messages

[HumanMessage(content='Can you help me summarize this text?', additional_kwargs={'task': 'summarization'}, response_metadata={})]

from langchain_core.messages import AIMessage

# Add an AI message with response metadata.
ai_message = AIMessage(
    content="Sure! Here's the summary of the provided text.",
    response_metadata={"model": "gpt-4", "token_count": 30, "response_time": "150ms"},
)

# Add the message to chat history.
chat_message_history.add_message(ai_message)

chat_message_history.messages

[HumanMessage(content='Can you help me summarize this text?', additional_kwargs={'task': 'summarization'}, response_metadata={}),
     AIMessage(content="Sure! Here's the summary of the provided text.", additional_kwargs={}, response_metadata={'model': 'gpt-4', 'token_count': 30, 'response_time': '150ms'})]

Chaining

You can easily integrate this chat history class with LCEL Runnables.

from langchain_core.prompts import (
    ChatPromptTemplate,
    MessagesPlaceholder,
)
from langchain_core.runnables.history import RunnableWithMessageHistory
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import ChatOpenAI

prompt = ChatPromptTemplate.from_messages(
    [
        ("system", "You are a helpful assistant."),
        # Placeholder for chat history
        MessagesPlaceholder(variable_name="chat_history"),
        ("human", "{question}"),
    ]
)

# Chaining
chain = prompt | ChatOpenAI(model_name="gpt-4o") | StrOutputParser()

The following shows how to create a function that returns chat history from sqlite.db.

def get_chat_history(user_id, conversation_id):
    return SQLChatMessageHistory(
        table_name=user_id,
        session_id=conversation_id,
        connection="sqlite:///sqlite.db",
    )

Set config_fields to provide reference information when retrieving conversation details.

from langchain_core.runnables.utils import ConfigurableFieldSpec

config_fields = [
    ConfigurableFieldSpec(
        id="user_id",
        annotation=str,
        name="User ID",
        description="Unique identifier for a user.",
        default="",
        is_shared=True,
    ),
    ConfigurableFieldSpec(
        id="conversation_id",
        annotation=str,
        name="Conversation ID",
        description="Unique identifier for a conversation.",
        default="",
        is_shared=True,
    ),
]

chain_with_history = RunnableWithMessageHistory(
    chain,
    get_chat_history,
    input_messages_key="question",
    history_messages_key="chat_history",
    # Set parameters for retrieving chat history
    history_factory_config=config_fields,
)

Set the "user_id" and "conversation_id" key-value pairs under the "configurable" key.

# Config settings
config = {"configurable": {"user_id": "user1", "conversation_id": "conversation1"}}

Let's ask a question about the name. If there is any previously saved conversation history, it will provide the correct response.

• Use the invoke method of the chain_with_history object to generate an answer to the question.

• Pass a question dictionary and config settings to the invoke method as inputs.

# Execute by passing the question and config
chain_with_history.invoke(
    {"question": "Hi, nice to meet you. My name is Heesun."}, config
)

'Hi Heesun! Nice to meet you again. How can I help you today?'

# Execute a follow-up question
chain_with_history.invoke({"question": "What is my name?"}, config)

'Your name is Heesun.'

This time, set the same user_id but use a different value for conversation_id.

# Config settings
config = {"configurable": {"user_id": "user1", "conversation_id": "conversation2"}}

# Execute by passing the question and config
chain_with_history.invoke({"question": "What is my name?"}, config)

"I'm sorry, but I don't have access to personal information, so I don't know your name. If you'd like, you can tell me your name, and I can address you by it."