Build a production-grade AI-powered document Q&A system where users upload documents, ask questions, and get answers with citations — powered by RAG (Retrieval-Augmented Generation), vector search, and OpenAI.
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Node.js
Python
TypeScript
API Development
AI/ML Integration
Database Design
Set up the DocuChat project foundation. Configure the Express server with TypeScript and a layered architecture (controller → service → repository). Design the Prisma database schema with SQLite for development and PostgreSQL for production. Implement JWT authentication with refresh token rotation, …
Implement role-based access control using junction tables (not arrays) for role-permission mappings. Build background job processing with BullMQ and Redis for async work like document processing. Integrate external APIs with Axios interceptors, retry logic with exponential backoff, and circuit break…
Make DocuChat production-ready. Add Redis caching with cache-aside pattern and proper invalidation strategies. Implement per-user rate limiting with token bucket and sliding window algorithms, tiered by user role. Harden security with Helmet, CORS, input sanitization, and prompt injection defenses. …
Build the core AI pipeline that makes DocuChat what it is. Set up pgvector for vector storage with HNSW indexes. Upload documents (PDF, TXT) with multer, extract text, chunk intelligently, and generate embeddings with OpenAI's text-embedding-3-small. Build the RAG pipeline: embed user questions, ret…
Design AI systems that are safe and reliable in production. Build a human-in-the-loop review system with confidence scoring that routes low-confidence answers to a review queue. Implement agent autonomy guardrails — action whitelists, budget caps, kill switches, and supervisor patterns. Build ReAct …
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DocuChat: AI-Powered Document Q&A System
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