# Jamwithai Production Agentic RAG

> Production-grade RAG system patterns — keyword search foundations + vector hybrid retrieval, FastAPI, OpenSearch, Airflow pipelines. Dùng khi build RAG cho production.

- Skill: `yanacuti1121/jamwithai-production-agentic-rag` (Agent Skill)
- Install (CLI): `npx skillmds add yanacuti1121/jamwithai-production-agentic-rag`
- Raw SKILL.md: https://api.skillmd.com/api/skills/yanacuti1121/jamwithai-production-agentic-rag/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: yanacuti1121 (https://skillmd.com/u/yanacuti1121)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/yanacuti1121/jamwithai-production-agentic-rag

---


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

```python
# 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