This tutorial is designed to make working with LangChain.js as easy and approachable as possible.
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.
The advanced retrieval chain with conversation memory can be used in multiple scenarios:
Here's a brief overview of the important files in the src directory:
To set up and run the project locally, follow these steps:
Ensure you have the following installed on your machine:
Clone the Repository:
git clone https://github.com/Bhavik-Jikadara/langchain-js-tutorial.git
cd langchain-js-tutorial
Install Dependencies Run the following command to install all required node modules:
npm install
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=""
Run the Application: After setting up your environment variables, start the app using the following command:
node src/filename.js
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.
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.