rag-pipeline

Agent Building

Use when building Retrieval-Augmented Generation systems. Covers document chunking, embedding generation, vector indexing, semantic search, context building, and integration of retrieval with LLM completion for accurate Q&A on Physical AI textbook content.

QUICK START

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Source SKILL.md: https://github.com/majiayu000/claude-skill-registry/blob/HEAD/skills/data/rag-pipeline/SKILL.md

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RAG Pipeline Skill

Quick Start Workflow

When building or maintaining the RAG pipeline:

Content Ingestion (One-time setup)
- Read all Docusaurus markdown files from /docs
- Chunk text (800 chars, 200 overlap)
- Generate embeddings with OpenAI ada-002
- Upsert to Qdrant with metadata
Query Flow (Runtime)
- Receive user question
- Generate query embedding
- Search Qdrant (top 5 results, score >= 0.7)
- Build context from relevant chunks
- Pass to OpenAI GPT-4 with context
- Return answer + sources
Continuous Improvement
- Monitor search quality (are results relevant?)
- Adjust chunk size if needed
- Update score thresholds
- Add filters for specific chapters

Standard Architecture

User Question
    ↓
[Generate Embedding]
    ↓ 
[Search Qdrant]
    ↓
[Extract Top 5 Chunks]
    ↓
[Build Context String]
    ↓
[GPT-4 with Context]
    ↓
AI Answer + Sources

Key Parameters

Chunk size: 800 characters
Overlap: 200 characters
Embedding model: text-embedding-ada-002
LLM model: gpt-4 or gpt-3.5-turbo
Search limit: 5 chunks
Score threshold: 0.7
Context window: ~3000 tokens max

Best Practices

For Physical AI textbook RAG:

Preserve code blocks when chunking
Include chapter/section in metadata
Cite sources in responses
Cache embeddings for popular queries
Log all queries for analytics
Handle "no results" gracefully

Knowledge Base

Detailed guides available:

Chunking Strategies → references/chunking.md
Ingestion Script → references/ingestion-script.md
Query Pipeline → references/query-pipeline.md
Context Building → references/context-building.md
Error Handling → references/error-handling.md