RAG Agent Project

This project demonstrates the creation of a Retrieval-Augmented Generation (RAG) agent using LangChain. The agent can dynamically determine which actions to take and in what order to achieve a desired outcome.

Setup

1. Create and activate a virtual environment using Conda:

   conda create --name .venv python=3.10
   conda activate .venv

2. Install the necessary Python libraries:

   pip install -U langgraph langchain langchain-openai langchainhub langchain-community tavily-python faiss-cpu httpx python-dotenv streamlit

3. Set up API keys in a .env file:

   OPENAI_API_KEY=your_openai_api_key_here
   SERPAPI_API_KEY=your_serpapi_api_key_here
   

Running the Streamlit App

To start the Streamlit application, run the following command:

streamlit run main.py