I’m looking for an experienced FastAPI developer who has worked on AI chatbots and data orchestration. I already have a chatbot built with FastAPI that integrates three main features: RAG-based responses from my document dataset. Database-driven Q&A — users can ask natural language questions, and the chatbot fetches data from the database accordingly. Dynamic chart, graph, and report generation based on database results. The core system is built and running, but I’m facing some orchestration issues: Data isn’t being fetched from the correct sources/files. NLP query parsing and “safe query” handling need refinement. Dynamic graph and report generation need debugging/improvement. What I Need: Someone who can review the current codebase and fix orchestration issues. Ensure RAG, DB, and visualization modules work smoothly together. Optimize FastAPI endpoints and backend logic for reliability and performance. Required Skills: FastAPI (advanced) LangChain or similar frameworks (for RAG) SQLAlchemy / database query handling Data visualization (Plotly, Matplotlib, or similar) Experience with NLP and safe query generation Scope • Trace the current report-generation flow (FastAPI router → DB query → RAG response composer). • Identify and fix the bug that blocks or corrupts real-time reports. • Ensure each report streams the required user interaction logs in the expected JSON format. Deliverables • Clean, commented code patch or pull request. • Quick test script or cURL examples that prove real-time reports work. • Short changelog so I can reproduce the fix in staging. Tech stack in play: FastAPI, async SQL driver (SQLAlchemy/psycopg), Python 3.11, Redis cache, OpenAI & LangChain for RAG. If you can jump in, debug efficiently, and keep existing architecture untouched aside from the necessary fix, let’s get started.