RAG Retrieval

RAG Retrieval

Comprehensive patterns for building production RAG systems. Each category has individual rule files in rules/ loaded on-demand.

Quick Reference

Total: 30 rules across 9 categories

Core RAG

Fundamental patterns for retrieval, generation, and pipeline composition.

Embeddings

Embedding models, chunking strategies, and production optimization.

Contextual Retrieval

Anthropic's context-prepending technique — 67% fewer retrieval failures.

HyDE

Hypothetical Document Embeddings for bridging vocabulary gaps.

Agentic RAG

Self-correcting retrieval with LLM-driven decision making.

Multimodal RAG

Image + text retrieval with cross-modal search.

Query Decomposition

Breaking complex queries into concepts for parallel retrieval.

Reranking

Post-retrieval re-scoring for higher precision.

PGVector

Production hybrid search with PostgreSQL.

Quick Start Example

from openai import OpenAI

client = OpenAI()

async def rag_query(question: str, top_k: int = 5) -> dict:
    """Basic RAG with citations."""
    docs = await vector_db.search(question, limit=top_k)
    context = "\n\n".join([f"[{i+1}] {doc.text}" for i, doc in enumerate(docs)])

    response = await llm.chat([
        {"role": "system", "content": "Answer with inline citations [1], [2]. Use ONLY provided context."},
        {"role": "user", "content": f"Context:\n{context}\n\nQuestion: {question}"}
    ])

    return {"answer": response.content, "sources": [d.metadata['source'] for d in docs]}

Key Decisions

Common Mistakes

1. No citation tracking (unverifiable answers)
2. Context too large (dilutes relevance)
3. Single retrieval method (misses keyword matches)
4. Not chunking long documents (context gets lost)
5. Embedding queries differently than documents
6. No fallback path in agentic RAG (workflow hangs)
7. Infinite rewrite loops (no retry limit)
8. Using wrong similarity metric (cosine vs euclidean)
9. Not caching embeddings (recomputing unchanged content)
10. Missing image captions in multimodal RAG (limits text search)

Evaluations

See test-cases.json for 30 test cases across all categories.

Related Skills

• ork:langgraph - LangGraph workflow patterns (for agentic RAG workflows)
• caching - Cache RAG responses for repeated queries
• ork:golden-dataset - Evaluate retrieval quality
• ork:llm-integration - Local embeddings with nomic-embed-text
• vision-language-models - Image analysis for multimodal RAG
• ork:database-patterns - Schema design for vector search

Capability Details

retrieval-patterns

Keywords: retrieval, context, chunks, relevance, rag Solves:

• Retrieve relevant context for LLM
• Implement RAG pipeline with citations
• Optimize retrieval quality

hybrid-search

Keywords: hybrid, bm25, vector, fusion, rrf Solves:

• Combine keyword and semantic search
• Implement reciprocal rank fusion
• Balance precision and recall

embeddings

Keywords: embedding, text to vector, vectorize, chunk, similarity Solves:

• Convert text to vector embeddings
• Choose embedding models and dimensions
• Implement chunking strategies

contextual-retrieval

Keywords: contextual, anthropic, context-prepend, bm25 Solves:

• Prepend context to chunks for better retrieval
• Reduce retrieval failures by 67%
• Implement hybrid BM25+vector search

hyde

Keywords: hyde, hypothetical, vocabulary mismatch Solves:

• Bridge vocabulary gaps in semantic search
• Generate hypothetical documents for embedding
• Handle abstract or conceptual queries

agentic-rag

Keywords: self-rag, crag, corrective, adaptive, grading Solves:

• Build self-correcting RAG workflows
• Grade document relevance
• Implement web search fallback

multimodal-rag

Keywords: multimodal, image, clip, vision, pdf Solves:

• Build RAG with images and text
• Cross-modal search (text → image)
• Process PDFs with mixed content

query-decomposition

Keywords: decompose, multi-concept, complex query Solves:

• Break complex queries into concepts
• Parallel retrieval per concept
• Improve coverage for compound questions

reranking

Keywords: rerank, cross-encoder, precision, scoring Solves:

• Improve search precision post-retrieval
• Score relevance with cross-encoder or LLM
• Combine multiple scoring signals

pgvector-search

Keywords: pgvector, postgresql, hnsw, tsvector, hybrid Solves:

• Production hybrid search with PostgreSQL
• HNSW vs IVFFlat index selection
• SQL-based RRF fusion

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