I will your rag chatbot or document qna system

The Docs Q&A engine transforms static documentation into an interactive, conversational knowledge base. It allows users to upload complex PDF documents and receive instant, context-aware answers to natural language questions based strictly on the uploaded text.

How It Works Behind the Scenes

• Document Parsing & Text Extraction: Leverages pypdf to programmatically extract and clean text data directly from multi-page document layouts.
• Semantic Analysis & Knowledge Retrieval: (Designed for future RAG/Vector expansion) Uses lightweight text processing to handle content blocks seamlessly before sending context to the AI model.
• Contextual LLM Orchestration: Integrates with the Google Gemini API (google-genai / google-generativeai) using advanced prompt engineering to ensure responses are factually anchored to the uploaded source material, mitigating AI hallucinations.
• Asynchronous Communication: Managed via FastAPI endpoints, allowing the UI to handle file uploads and streaming data formats without blocking user interaction.