Agentic RAG PDF Chatbot
Agent-Orchestrated Document Intelligence
Advanced agent-orchestrated retrieval-augmented generation (Agentic RAG) chatbot that intelligently reasons over user queries, decides when to retrieve document context, and generates grounded answers. Unlike traditional RAG systems that blindly retrieve for every query, this agent uses an LLM-driven decision engine to selectively invoke document retrieval tools. Supports multiple document formats (PDF, TXT, DOCX) with semantic chunking, vector similarity search using FAISS and Sentence-Transformer embeddings, and maintains conversational memory. Features source-aware answers with page numbers, voice-enabled AI (STT + TTS), query analytics, and real-time streaming responses. Built with OpenAI Agents SDK, LangChain, and Groq LLMs.
Why it mattered
Traditional RAG systems retrieve context for every question, even when retrieval is unnecessary or the question needs reasoning first.
What shipped
An agentic RAG chatbot where the agent decides whether to retrieve documents, use memory, ask for clarification, or answer directly.
Proof signal
Agent-orchestrated decision making; Multi-format document support; Real-time streaming; Voice-enabled interaction; Source attribution with page numbers
System flow
The user query enters an agent decision layer. The agent can call document retrieval tools, stream an answer, use conversational memory, and return source-aware responses.
Honest maturity snapshot
Manual questions, scattered context, and slow follow-up.
An agent workflow that routes intent, calls tools, and returns useful next actions.
Features
- LLM-driven retrieval decision making.
- PDF, TXT, and DOCX support.
- FAISS and sentence-transformer retrieval.
- Streaming responses for better UX.
- Voice-enabled interaction with STT/TTS.
- Source attribution with page references.
Engineering challenges
- Designing tool rules that make retrieval useful instead of noisy.
- Keeping responses grounded while preserving conversational flow.
- Managing multi-format ingestion without breaking the user experience.
Results
- Demonstrates a mature leap from standard RAG into agentic retrieval.
- Strong fit for legal, academic, and internal knowledge-base use cases.
- Shows end-to-end AI product thinking, not only notebook experimentation.
What I learned
- Tradeoff learned: Designing tool rules that make retrieval useful instead of noisy.
- Proof learned: Demonstrates a mature leap from standard RAG into agentic retrieval.
- Next iteration: Add document-level access permissions.