https://github.com/himanshubhosale25/custom-web-rag-chatbot
This project implements a Retrieval-Augmented Generation (RAG) chatbot using LangChain, OpenAI embeddings, and FAISS for vector storage. It scrapes web data, generates contextual responses, and provides a Flask API for integration with a React frontend.
https://github.com/himanshubhosale25/custom-web-rag-chatbot
chatbot flask langchain openai rag
Last synced: 3 months ago
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This project implements a Retrieval-Augmented Generation (RAG) chatbot using LangChain, OpenAI embeddings, and FAISS for vector storage. It scrapes web data, generates contextual responses, and provides a Flask API for integration with a React frontend.
- Host: GitHub
- URL: https://github.com/himanshubhosale25/custom-web-rag-chatbot
- Owner: HimanshuBhosale25
- License: mit
- Created: 2025-02-08T13:52:34.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-25T21:14:07.000Z (over 1 year ago)
- Last Synced: 2025-06-07T23:05:21.310Z (about 1 year ago)
- Topics: chatbot, flask, langchain, openai, rag
- Language: CSS
- Homepage:
- Size: 829 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🤖 LangChain RAG Chatbot
A **Retrieval-Augmented Generation (RAG) chatbot** built using **LangChain, OpenAI, FAISS, and Flask**. This chatbot can scrape web data, store embeddings, and retrieve relevant information to generate intelligent responses.
## 🚀 Features
- 🌐 **Web Scraping**: Uses `WebBaseLoader` to collect and preprocess data.
- 🧠 **Vector Storage**: Stores embeddings using FAISS for efficient retrieval.
- 🔍 **Contextual Search**: Enhances responses with relevant context.
- 🗣️ **Flask REST API**: Serves responses via a backend API.
- ⚡ **Optimized Processing**: Avoids redundant embedding generations.
## 📷 Screenshots
### **Chat Interface**
### **Chat Interface**
### **Chat Interface**
### **Chat Interface**
## 🏗️ Tech Stack
- **Backend**: Python, Flask, LangChain
- **Embeddings**: OpenAI `text-embedding-3-large`
- **Vector Storage**: FAISS
- **Frontend**: React (for chatbot UI)
## 🔧 Setup & Installation
### 1️⃣ **Clone the Repository**
```sh
git clone https://github.com/HimanshuBhosale25/langchain-rag-chatbot.git
cd langchain-rag-chatbot
```
### 2️⃣ **Backend Setup**
```sh
cd backend
conda activate your_env_name
pip install -r requirements.txt
python app.py
```
### 3️⃣ **Frontend Setup**
```sh
cd frontend
npm install
npm run dev
```