5. Chat Model Save Message History

In this section, you will learn how to connect your LangChain chat assistant to a local SQLite database to store and retrieve conversation history.

Objectives

Understand SQL-backed persistent history tracking.
Store message loops dynamically in a local SQLite file using SQLChatMessageHistory.
Retrieve and output logs for a specific session ID from a completely separate script.

SQLite Persistence

Unlike cloud databases, SQLite requires zero credentials or network configurations. It saves all logs locally inside a standard .db database file.

Goal

Implement a live terminal chat loop that saves queries to a local SQLite database, and create a second script to retrieve and print those saved conversation logs.

Sample Input

SESSION_ID = "user_session_sqlite"
DB_CONNECTION = "sqlite:///chat_history.db"

Sample Output

Automatic storage of messages inside the local SQLite database.

Plan

Saver Script:
1. Import SQLChatMessageHistory from langchain_community.chat_message_histories.
2. Instantiate the history object using the local database connection.
3. Start a console chat loop, adding inputs and replies to the history.
Retriever Script:
1. Connect to the same SQLite database.
2. Iterate through and print the history messages.

Code Implementation

1. Conversation Loop & Saver (5a_chat_model_save_message_history_sqlite.py)

Let’s build the SQLite saver application incrementally step-by-step:

Step 1: Imports and Setup

Plan:

Import environment variable loader load_dotenv from dotenv.
Import SQLChatMessageHistory from langchain_community.chat_message_histories.
Import the unified model initializer init_chat_model from langchain.chat_models.

Code Implementation:

from dotenv import load_dotenv
from langchain_community.chat_message_histories import SQLChatMessageHistory
from langchain.chat_models import init_chat_model

load_dotenv()

Step 2: Initialize Database Connection

Plan:

Set up a unique SESSION_ID string to identify this chat session.
Define the local connection URI sqlite:///chat_history.db.
Instantiate the SQLChatMessageHistory object.

Code Implementation:

# Setup SQLite Connection and Session
SESSION_ID = "user_session_sqlite"
DB_CONNECTION = "sqlite:///chat_history.db"

# Initialize SQL-backed Message History
chat_history = SQLChatMessageHistory(
    session_id=SESSION_ID,
    connection_string=DB_CONNECTION
)

Step 3: Setup Chat Model

Plan:

Initialize a Groq-provided Llama model (llama-3.3-70b-versatile) using the core abstraction helper init_chat_model.

Code Implementation:

# Initialize Chat Model using core abstractions (Groq Llama model)
model = init_chat_model("llama-3.3-70b-versatile", model_provider="groq")

Step 4: Execute Interactive Chat Loop

Plan:

Prompt user queries from the console in a while True loop.
Call .add_user_message() to persist the input in the SQL database.
Call model.invoke() passing the current full message array.
Call .add_ai_message() to persist the model’s answer.

Code Implementation:

print("Start chatting with the AI. Type 'exit' to quit.")

while True:
    human_input = input("User: ")
    if human_input.lower() == "exit":
        break

    # 1. Add user query to SQL database
    chat_history.add_user_message(human_input)

    # 2. Invoke model on complete session history
    ai_response = model.invoke(chat_history.messages)

    # 3. Add AI reply to SQL database
    chat_history.add_ai_message(ai_response.content)

    print(f"AI: {ai_response.content}")

Combined Saver Code

Combining all the steps above gives the final completed script:

from dotenv import load_dotenv
from langchain_community.chat_message_histories import SQLChatMessageHistory
from langchain.chat_models import init_chat_model

load_dotenv()

# Setup SQLite Connection and Session
SESSION_ID = "user_session_sqlite"
DB_CONNECTION = "sqlite:///chat_history.db"

# Initialize SQL-backed Message History
chat_history = SQLChatMessageHistory(
    session_id=SESSION_ID,
    connection_string=DB_CONNECTION
)

# Initialize Chat Model using core abstractions (Groq Llama model)
model = init_chat_model("llama-3.3-70b-versatile", model_provider="groq")

print("Start chatting with the AI. Type 'exit' to quit.")

while True:
    human_input = input("User: ")
    if human_input.lower() == "exit":
        break

    # 1. Add user query to SQL database
    chat_history.add_user_message(human_input)

    # 2. Invoke model on complete session history
    ai_response = model.invoke(chat_history.messages)

    # 3. Add AI reply to SQL database
    chat_history.add_ai_message(ai_response.content)

    print(f"AI: {ai_response.content}")

2. Session Retriever Script (5b_chat_model_retrieve_message_history_sqlite.py)

Below is the retriever script that loads the SQL log independently and outputs the session chat logs:

from langchain_community.chat_message_histories import SQLChatMessageHistory

# Setup SQLite Connection and Session (must match the saver script)
SESSION_ID = "user_session_sqlite"
DB_CONNECTION = "sqlite:///chat_history.db"

# Load the SQL-backed Message History
chat_history = SQLChatMessageHistory(
    session_id=SESSION_ID,
    connection_string=DB_CONNECTION
)

print(f"--- Stored Logs for Session: {SESSION_ID} ---")
for message in chat_history.messages:
    role = "User" if message.type == "human" else "AI"
    print(f"{role}: {message.content}")

Exercise: Session Config Checker 🔍

Goal

Write a python utility function check_logs(session_id: str) that connects to our local SQLite database and prints how many human vs AI messages are currently stored for the given session.

Sample Input

check_logs("user_session_sqlite")

Sample Output

Total Messages: 4 (User: 2, AI: 2)

Plan

Instantiate the SQLChatMessageHistory inside the function.
Iterate through .messages and count human and ai instances.
Print the formatted totals.

Solution

from langchain_community.chat_message_histories import SQLChatMessageHistory

def check_logs(session_id: str):
    chat_history = SQLChatMessageHistory(
        session_id=session_id,
        connection_string="sqlite:///chat_history.db"
    )
    
    msgs = chat_history.messages
    user_count = sum(1 for m in msgs if m.type == "human")
    ai_count = sum(1 for m in msgs if m.type == "ai")
    
    print(f"Total Messages: {len(msgs)} (User: {user_count}, AI: {ai_count})")

check_logs("user_session_sqlite")

Practice & Exercises

To practice, open the interactive notebook:

Practice & Exercises

Practice configuring SQL databases locally for persistent conversation sessions.💻 VS Code | 🚀 Colab | 📥 Download

Introduction

Chat Models

Prompt Templates

Chains

RAG

Tools and Agents

5. Chat Model Save Message History

Objectives

SQLite Persistence

Goal

Sample Input

Sample Output

Plan

Code Implementation

1. Conversation Loop & Saver (5a_chat_model_save_message_history_sqlite.py)

Step 1: Imports and Setup

Step 2: Initialize Database Connection

Step 3: Setup Chat Model

Step 4: Execute Interactive Chat Loop

Combined Saver Code

2. Session Retriever Script (5b_chat_model_retrieve_message_history_sqlite.py)

Exercise: Session Config Checker 🔍

Goal

Sample Input

Sample Output

Plan

Practice & Exercises

Practice & Exercises

​Objectives

​SQLite Persistence

​Goal

​Sample Input

​Sample Output

​Plan

​Code Implementation

1. Conversation Loop & Saver (5a_chat_model_save_message_history_sqlite.py)

Step 1: Imports and Setup

Step 2: Initialize Database Connection

Step 3: Setup Chat Model

Step 4: Execute Interactive Chat Loop

Combined Saver Code

2. Session Retriever Script (5b_chat_model_retrieve_message_history_sqlite.py)

​Exercise: Session Config Checker 🔍

​Goal

​Sample Input

​Sample Output

​Plan

​Practice & Exercises

Practice & Exercises

Objectives

SQLite Persistence

Goal

Sample Input

Sample Output

Plan

Code Implementation

Exercise: Session Config Checker 🔍

Goal

Sample Input

Sample Output

Plan

Practice & Exercises