Agentic RAG Project

====================== Retrieval Augmented Generation for Efficient PDF Question Answering The Agentic RAG Project is a cutting-edge application that leverages multiple agents to provide a seamless user experience when querying multiple PDF documents with diverse content.

Overview

This project utilizes a distributed architecture, assigning each agent ownership of a single PDF document. This approach enables faster question-answer sessions and reduces the number of tokens sent to the AI model, resulting in significant cost savings.

Key Features: -Multi-Agent Architecture: Efficiently handles multiple PDF documents -Faster Response Times: Optimized question-answer sessions -Cost-Effective: Reduced token usage minimizes AI model expenses

Technology Stack: *CrewAI *LLaMA *OLLaMA *Gemini *Embeddings *Streamlit

Getting Started To run the Agentic RAG Project, follow these steps: Prerequisites: Create a .streamlit/secrets.toml file with the required API keys: GROQ_API_KEY GEMINI_API_KEY

Run the Application: Install required dependencies Run streamlit run rag_agents_crew.py