rag-learning

name	rag-learning
description	Learn RAG by building real applications. Use when (1) building first RAG from scratch, (2) understanding RAG component design, (3) debugging retrieval problems, (4) optimizing retrieval quality, (5) comparing RAG frameworks.

RAG Learning

Learning Path

Level 1: Minimal RAG

Build working RAG with LangChain + Chroma in 30 minutes.

For step-by-step: See references/level1-minimal.md

Level 2: Core Components

Understand and experiment with each RAG component.

For details: See references/level2-components.md

Level 3: Real Documents

Handle PDF, web pages, tables. Build personal knowledge base.

For details: See references/level3-documents.md

Level 4: Optimization

Implement Hybrid Search, Reranking, better prompts.

For details: See references/level4-optimization.md

Level 5: Production

Compare frameworks, choose vector DB, add caching.

For details: See references/level5-production.md

Quick Reference

Frameworks by Use Case

Use Case	Framework	Why
Quick prototype	LangChain	Most examples, easy start
Data-heavy apps	LlamaIndex	Best data connectors
No prompting	DSPy	Programmatic LLM
Production RAG	RAGFlow	End-to-end solution

Common Issues

Problem	Solution
Chunk too large/small	Adjust chunk_size (500-1000 tokens)
Wrong embedding model	Try domain-specific model
Retrieved but wrong answer	Check prompt template
No results	Lower similarity threshold
Slow retrieval	Add caching, use hybrid search

Resources

• LangChain - 124k stars
• LlamaIndex - 46k stars
• RAGFlow - 71k stars
• DSPy - 31k stars
• Chonkie - Lightweight chunking