rag-pipeline

name	rag-pipeline
description	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.

RAG Pipeline Skill

Quick Start Workflow

When building or maintaining the RAG pipeline:

1. 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

2. 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

3. 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