LangChain.js Tutorial: Building an Advanced Retrieval Chain with Conversation History

Introduction

This tutorial is designed to make working with LangChain.js as easy and approachable as possible. It provides a hands-on introduction to LangChain, a powerful library for building language model applications. With step-by-step guidance, you will learn how to harness the power of AI and language models in JavaScript without requiring advanced knowledge.

The core concept demonstrated here is the enhancement of a simple retrieval system by adding conversation memory. This allows users to have fluid conversations with the AI, where it remembers prior interactions and delivers context-aware responses.

Use Cases

The advanced retrieval chain with conversation memory can be used in multiple scenarios:

• Customer Support: Allow customers to have fluid and natural conversations with chatbots that remember past queries, providing faster and more accurate responses.
• Personal Assistants: Build personal AI assistants that recall your previous conversations to assist with follow-up tasks and reminders.
• Educational Tools: Create AI tutors that keep track of learners' progress and adapt their answers based on past interactions.
• Research Assistance: Use the system to recall previously retrieved information and provide detailed, context-driven follow-ups.

File Structure

Here's a brief overview of the important files in the src directory:

• src/llms.js: Handles the initialization of language models used for processing queries.
• src/prompt-templates.js: Contains templates for creating structured prompts for the language model.
• src/output-parsers.js: Defines parsers to interpret the output of the language model and format responses.
• src/retrieval-chain.js: Implements a basic retrieval chain, querying the vector database.
• src/conversation-retrieval-chain.js: Enhances the basic retrieval chain by incorporating conversation history for more accurate responses.
• src/agent.js: Defines the agent responsible for managing the query pipeline and interaction with different modules.
• src/memory.js: Manages conversation memory, keeping track of user interactions and responses.

Installation

To set up and run the project locally, follow these steps:

Prerequisites

Ensure you have the following installed on your machine:

• Node.js (version 16 or higher)
• NPM (comes with Node.js)

Steps

1. Clone the Repository:

   git clone https://github.com/Bhavik-Jikadara/langchain-js-tutorial.git
   cd langchain-js-tutorial

2. Install Dependencies Run the following command to install all required node modules:

   npm install

3. Set Up Environment Variables Create a .env file in the root directory and add the following (replace placeholders with actual values):

   OPENAI_API_KEY=""
   OPENAI_MODEL_NAME=gpt-3.5-turbo
   TAVILY_API_KEY=""

How to Run

1. Run the Application: After setting up your environment variables, start the app using the following command:

   node src/filename.js

2. Test the Application: The system is now set up to handle conversation-based queries and memory-enhanced retrieval. You can run tests by interacting with the console or integrating the code with a frontend interface.

Future Enhancements

• Frontend Integration: Connect the conversation retrieval chain to a web or mobile interface to provide a seamless user experience.
• Database Enhancements: Add support for other vector databases or integrate with knowledge graphs to expand retrieval capabilities.
• Custom Prompts: Fine-tune the prompt templates and models for specific domains (e.g., customer support, medical assistance).

Conclusion

This project provides a foundational understanding of building advanced AI applications using LangChain.js. By incorporating conversation memory into a retrieval system, we enable fluid and contextual conversations, making language models even more powerful and useful in real-world applications.

For more detailed documentation and future updates, refer to the LangChain.js documentation.