jamwithai/production-agentic-rag-course resource

Health v2 · maintenance only

Adoption not part of the score

8499 stars · 1875 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A learner-focused course repository that walks through building a production-grade RAG system (an arXiv paper curator) week by week, covering infrastructure, data pipelines, BM25 and hybrid search, local LLMs, monitoring, and agentic RAG with LangGraph. It is Python-based, using FastAPI, OpenSearch, PostgreSQL, Airflow, Docker Compose, Langfuse, Redis, and a Telegram bot interface.

Use cases

• learn to build a production RAG system from scratch
• build a research assistant that answers questions about arXiv papers
• implement hybrid keyword and semantic search with BM25 and vectors
• set up an automated pipeline to fetch and parse academic papers
• add observability and tracing to a RAG application with Langfuse
• build an agentic RAG workflow with LangGraph
• deploy a RAG stack locally with Docker Compose and a local LLM

When to choose

• you want a structured, hands-on curriculum for learning production RAG engineering
• you prefer learning search fundamentals (BM25) before vector and hybrid retrieval
• you want a complete reference project including pipelines, monitoring, caching, and a chat interface
• you want to run the whole stack locally with open-source tools

When to avoid

• you need a ready-made production RAG service to drop into your app rather than a learning project
• you want a minimal vector-search-only tutorial without infrastructure overhead
• you are not comfortable with Docker, Python, and self-hosting multiple services

Facets

learning-resource · maturity active

rag search-engine llm-inference agent-framework etl monitoring caching chatbot large-language-models tutorials python self-hosted cross-platform course rag hybrid-search bm25 opensearch fastapi langgraph langfuse arxiv telegram-bot airflow hands-on-learning retrieval-augmented-generation ai-agents natural-language-processing search data-engineering docker

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markdown · JSON · MCP: product_card(name="jamwithai/production-agentic-rag-course")