This repository contains a simple RAG (Retrieval-Augmented Generation) chatbot built using LangChain, LangGraph, ChromaDB, LangSmith, OPenAI, and Flask. This is a basic RAG application where you can add data from various files (pdf, json, csv, txt, etc) and through web links.

Make sure you have python 3.12 or higher installed on your system.

git clone https://github.com/siddhi47/RAG-chatbot.git

OpenAI API key is required to run the chatbot. You can set it as an environment in .env file. Refer to the .env.example file for the required variables. You can get your OpenAI API key from OpenAI.

Optionally, you can set up LangSmith API key to log the interactions with the chatbot. You can get your LangSmith API key from LangSmith.

You can set up a virtual environment to run the project. You can use venv or virtualenv to create a virtual environment. Here is an example using venv:

virtualenv venv
source venv/bin/activate
pip install -e .

Once you have activated the environment, you can run the application using the following command:

python app.py

This will run the app on port 5000 by default. You can access the chatbot at http://localhost:5000.

Make sure you have Docker installed on your system. Refer to the Docker documentation for installation instructions.

Make sure you have the .env file in the root directory of the project with the required environment variables.

Run the following command to build the Docker image:

docker build -t rag-chatbot .
docker run -p 5000:5000 --env-file .env rag-chatbot