Session management for multi-user RAG in LangChain - Mini Project: Build & Apply

Session management for multi-user RAG

📖 Scenario: You are building a chatbot application that uses Retrieval-Augmented Generation (RAG) to answer questions from multiple users. Each user should have their own session to keep track of their conversation history and retrieved documents.This helps the chatbot give better answers by remembering what each user asked before and what information was retrieved.

🎯 Goal: Create a simple session management system using LangChain that stores conversation history and retrieved documents separately for each user.This will allow the chatbot to handle multiple users independently without mixing their data.

📋 What You'll Learn

Create a dictionary to hold sessions for multiple users

Add a configuration variable for maximum history length per session

Implement a function to add user queries and retrieved documents to the correct session

Complete the session management by adding a function to retrieve the current session data for a user

💡 Why This Matters

🌍 Real World

Multi-user chatbots need to keep track of each user's conversation separately to provide personalized and accurate answers. This session management system helps achieve that by storing conversation history and retrieved documents per user.

💼 Career

Understanding session management is important for building scalable chatbots and conversational AI systems that serve many users simultaneously without mixing their data.

1

Create the sessions dictionary

Create a dictionary called user_sessions that will hold session data for multiple users. Initialize it as an empty dictionary.

LangChain

# Create the user_sessions dictionary
# Your code here

Need a hint?

Think of user_sessions as a place to store each user's chat history and documents separately.

2

Add max history length configuration

Create a variable called max_history_length and set it to 5. This will limit how many past messages we keep per user session.

LangChain

user_sessions = {}
# Set max_history_length to 5
# Your code here

Need a hint?

This variable helps keep the session data small by remembering only the last few messages.

3

Add function to update user session

Write a function called update_session that takes user_id, query, and retrieved_docs as parameters. Inside the function, if the user_id is not in user_sessions, initialize it with empty lists for history and docs. Then append the query to the user's history list and retrieved_docs to the user's docs list. Finally, trim both lists to keep only the last max_history_length items.

LangChain

user_sessions = {}
max_history_length = 5

# Define update_session function
# Your code here

Need a hint?

This function keeps each user's conversation and documents separate and limits their size.

4

Add function to get user session data

Write a function called get_session_data that takes user_id as a parameter and returns the session data for that user from user_sessions. If the user_id is not found, return {'history': [], 'docs': []} as an empty session.

LangChain

user_sessions = {}
max_history_length = 5

def update_session(user_id, query, retrieved_docs):
    if user_id not in user_sessions:
        user_sessions[user_id] = {'history': [], 'docs': []}
    user_sessions[user_id]['history'].append(query)
    user_sessions[user_id]['docs'].append(retrieved_docs)
    user_sessions[user_id]['history'] = user_sessions[user_id]['history'][-max_history_length:]
    user_sessions[user_id]['docs'] = user_sessions[user_id]['docs'][-max_history_length:]

# Define get_session_data function
# Your code here

Need a hint?

This function helps retrieve the current conversation and documents for a user safely.