https://github.com/varunon9/rag-langchain-nodejs

Getting started with RAG system using Langchain in Node.js. This project uses OpenAI for embedding and Pinecone for Vector DB.
https://github.com/varunon9/rag-langchain-nodejs

ai langchain nodejs openai pineconedb rag retrieval-augmented-generation

Last synced: 16 days ago
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Getting started with RAG system using Langchain in Node.js. This project uses OpenAI for embedding and Pinecone for Vector DB.

• Host: GitHub
• URL: https://github.com/varunon9/rag-langchain-nodejs
• Owner: varunon9
• License: mit
• Created: 2024-08-24T14:16:56.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-08-24T18:07:53.000Z (4 months ago)
• Last Synced: 2024-10-31T03:42:40.709Z (2 months ago)
• Topics: ai, langchain, nodejs, openai, pineconedb, rag, retrieval-augmented-generation
• Language: JavaScript
• Homepage: https://www.youtube.com/watch?v=hAg6O0KdoVM
• Size: 799 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Getting Started with RAG system using Langchain

Welcome to the **RAG (Retrieval-Augmented Generation) System** repository! This project demonstrates how to implement a RAG system using Langchain in Node.js. The RAG system enhances text generation models by incorporating relevant information retrieved from external knowledge sources, such as documents or databases.

## Introduction

The **RAG System** leverages the power of **Langchain** to build a robust and scalable pipeline that retrieves relevant context from large documents or databases and uses this information to augment the responses generated by a language model.

## Features

1. **Document Parsing & Chunking**: Efficiently parse large documents and split them into manageable chunks.

2. **Embedding & Storage**: Generate embeddings for document chunks and store them in a vector database.

3. **Contextual Retrieval**: Retrieve the most relevant chunks based on user queries.

4. **Text Generation**: Generate responses by combining retrieved context with a language model.

5. **Flexible Integration**: Easily integrate additional data sources and models.

## Requirements

- Node.js

- yarn

- OpenAI API Key (for text generation and embedding)

- Vector Database (Pinecone)

## Installation

1. Clone the Repository:

```

git clone https://github.com/varunon9/rag-langchain-nodejs.git

cd rag-langchain-nodejs

```

2. Install Dependencies:

```

yarn install

```

3. Set Up Environment Variables:

Create a .env file in the root directory and add your environment variables:

```

OPENAI_API_KEY=your_openai_api_key

PINECONE_API_KEY=your_pinecone_api_key

```

## Usage

Run `node .`

- It will create index if not exists

- It will process sample PDF for the first time

- Processing PDF = Parsing, Chunking, Embeddings via OpenAI text-embedding-3-large model and storing embedding in Pinecone Vector db

- It will then keep accepting queries from terminal and generate answer from PDF

Check `index.js` for more details and to get started.

## Youtube Tutorial

https://www.youtube.com/watch?v=hAg6O0KdoVM