I built the Enterprise Agentic RAG AI Assistant, an enterprise knowledge-base assistant combining retrieval-augmented generation with agentic AI for document analysis and multi-step reasoning.
- I upload and index PDFs with FAISS vector search, retrieve information using similarity scoring, and provide source citations with relevance tracking.
- I describe the system as planning, executing, reflecting, and synthesizing responses, going beyond simple RAG for document analysis.
Building this project deepened my understanding of agentic AI patterns and state management, vector databases and semantic search, multi-step reasoning workflows, error handling and recovery, enterprise security, and modern full-stack development. I shared a GitHub repository link and invited thoughts and feedback, but did not report benchmarks, implementation details, or evaluation results.
- The backend uses FastAPI with Python, Groq LLM, FAISS and SentenceTransformers for embeddings, LangGraph, and SQLite for conversation memory. The frontend uses Next.js 14 with TypeScript, Tailwind CSS, React Markdown, and a real-time chat interface. The infrastructure includes Docker containerization and a RESTful API.
- Security features include JWT authentication and secure token management, per-user document isolation, rate limiting, request validation, security headers, and CORS protection.
- I use LangGraph to orchestrate multi-step reasoning, dynamically routing among Calculator, Time, RAG, and Web Search tools. The workflow decomposes tasks, synthesizes results, evaluates answer quality through reflection, retries when needed, and recovers from errors using exponential backoff.
- The assistant supports answers grounded in uploaded documents, web search for real-time information, conversation history, and session management. One example query is “Calculate 15 * 8 and tell me the current time”; I did not report the system’s result for that query.