jamwithai-production-agentic-rag

name	jamwithai--production-agentic-rag
description	Production-grade RAG system patterns — keyword search foundations + vector hybrid retrieval, FastAPI, OpenSearch, Airflow pipelines. Dùng khi build RAG cho production.
allowed-tools	Bash, Read, Write
user-invocable	true

Production RAG system architecture từ "The Mother of AI Project" course.

Core principle

Build RAG the professional way: solid keyword search foundations first, then enhance with vectors — not AI-first approaches that skip search fundamentals.

Stack

FastAPI          — API layer
PostgreSQL       — metadata store
OpenSearch       — BM25 keyword + vector hybrid search
Airflow          — data pipeline orchestration
Docker Compose   — full local stack

Architecture phases

1. Infrastructure — Docker, FastAPI, PostgreSQL, OpenSearch, Airflow
2. Data pipeline — automated fetch + parse (arXiv papers or any domain)
3. BM25 keyword search — production search with filtering + relevance scoring
4. Vector search — semantic embeddings layer on top of keyword
5. Hybrid retrieval — combine BM25 + vector for best results
6. Agentic RAG — agent loop with tool use on top of retrieval

Key patterns

# Hybrid search: keyword + semantic
results = hybrid_search(
    query=user_query,
    bm25_weight=0.4,
    vector_weight=0.6,
    top_k=10
)

Source

https://github.com/jamwithai/production-agentic-rag-course