( Case study )
AI Agent & RAG System
AI Agent & RAG System - A comprehensive implementation of intelligent agents using LangChain and LangGraph, featuring tool-based reasoning, document retrieval, and real-time web search with DuckDuckGo integration.
Problem
An intelligent AI Agent that automatically decides whether to answer from a local document knowledge base or retrieve real-time information from the web, solving the limitations of document-only RAG systems.
Approach
User Query → AI Agent → Intelligent Routing → Document Retrieval (FAISS) / Web Search (DuckDuckGo) → Context Integration → Llama 3.1 → Final Answer
Results
Result - Built an intelligent **AI Agent** capable of automatically routing queries to the appropriate information source. - Enabled **hybrid knowledge retrieval** by combining local document search with real-time DuckDuckGo web search. - Generated **context-aware responses** using retrieved information and Llama 3.1, reducing the limitation of document-only RAG systems.
Key Learnings
Key Learnings - Gained practical experience in building **Agentic RAG systems** using LangChain and LangGraph. - Learned how to implement **document processing, embeddings, and FAISS vector retrieval** for semantic search. - Learned to integrate **external tools such as DuckDuckGo** with AI agents for real-time information retrieval. - Understood **agent-based query routing**, where the system selects the appropriate tool based on the user's query. - Improved understanding of **LLM orchestration and context-aware response generation** using Llama 3.1.
