Multi AI Agent with RAG and WikiSearch

This project demonstrates the integration of multiple AI agents using Retrieval-Augmented Generation (RAG) and WikiSearch functionalities, along with Cassandra for data storage and retrieval. It leverages Llama 3.1 for natural language processing and "all-MiniLM-L6-v2" for generating embeddings.

Overview

This project, implemented in a Jupyter Notebook, showcases how to:

• Connect to Cassandra.
• Load and process data from web pages using WebBaseLoader.
• Convert text to vectors using the "all-MiniLM-L6-v2" Hugging Face embedding model.
• Perform document retrieval and question routing using LangChain and related libraries.
• Generate human-like responses using the Llama 3.1 LLM via GROQ.

Features

• Cassandra Integration: Connects to Cassandra for storing and retrieving document embeddings.
• LangChain: Utilizes LangChain for web page loading, text splitting, embedding, and vector store operations.
• WebBaseLoader: Uses WebBaseLoader to efficiently load content from web pages.
• Hugging Face Embeddings: Employs the "all-MiniLM-L6-v2" model for generating high-quality text embeddings.
• Llama 3.1 with GROQ: Integrates the Llama 3.1 LLM using GROQ for refined, human-like responses.
• WikiSearch: Integrates the Wikipedia API for answering questions not related to stored documents.
• Query Routing: Routes user queries to either the Cassandra vector store or WikiSearch based on relevance.