rag-retrieval

name	rag-retrieval
license	MIT
compatibility	Claude Code 2.1.183+.
description	Retrieval-Augmented Generation patterns for grounded LLM responses. Use when building RAG pipelines, embedding documents, implementing hybrid search, contextual retrieval, HyDE, agentic RAG, multimodal RAG, query decomposition, reranking, or pgvector search.
tags	["rag","retrieval","llm","context","grounding","embeddings","hyde","reranking","pgvector","multimodal"]
context	fork
agent	data-pipeline-engineer
version	2.0.0
author	OrchestKit
user-invocable	false
disable-model-invocation	true
complexity	high
persuasion-type	reference
effort	high
metadata	{"category":"mcp-enhancement"}
allowed-tools	["Read","Glob","Grep","WebFetch","WebSearch"]
path_patterns	["**/rag/**","**/retrieval/**","**/embeddings/**","**/vector/**"]

RAG Retrieval

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

Quick Reference

Category	Rules	Impact	When to Use
Core RAG	4	CRITICAL	Basic RAG, citations, hybrid search, context management
Embeddings	3	HIGH	Model selection, chunking, batch/cache optimization
Contextual Retrieval	3	HIGH	Context-prepending, hybrid BM25+vector, pipeline
HyDE	3	HIGH	Vocabulary mismatch, hypothetical document generation
Agentic RAG	4	HIGH	Self-RAG, CRAG, knowledge graphs, adaptive routing
Multimodal RAG	3	MEDIUM	Image+text retrieval, PDF chunking, cross-modal search
Query Decomposition	3	MEDIUM	Multi-concept queries, parallel retrieval, RRF fusion
Reranking	3	MEDIUM	Cross-encoder, LLM scoring, combined signals
PGVector	4	HIGH	PostgreSQL hybrid search, HNSW indexes, schema design

Total: 30 rules across 9 categories

Core RAG

Fundamental patterns for retrieval, generation, and pipeline composition.

Rule	File	Key Pattern
Basic RAG	rules/core-basic-rag.md	Retrieve + context + generate with citations
Hybrid Search	rules/core-hybrid-search.md	RRF fusion (k=60) for semantic + keyword
Context Management	rules/core-context-management.md	Token budgeting + sufficiency check
Pipeline Composition	rules/core-pipeline-composition.md	Composable Decompose → HyDE → Retrieve → Rerank

Embeddings

Embedding models, chunking strategies, and production optimization.

Rule	File	Key Pattern
Models & API	rules/embeddings-models.md	Model selection, batch API, similarity
Chunking	rules/embeddings-chunking.md	Semantic boundary splitting, 512 token sweet spot
Advanced	rules/embeddings-advanced.md	Redis cache, Matryoshka dims, batch processing

Contextual Retrieval

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

Rule	File	Key Pattern
Context Prepending	rules/contextual-prepend.md	LLM-generated context + prompt caching
Hybrid Search	rules/contextual-hybrid.md	40% BM25 / 60% vector weight split
Complete Pipeline	rules/contextual-pipeline.md	End-to-end indexing + hybrid retrieval

HyDE

Hypothetical Document Embeddings for bridging vocabulary gaps.

Rule	File	Key Pattern
Generation	rules/hyde-generation.md	Embed hypothetical doc, not query
Per-Concept	rules/hyde-per-concept.md	Parallel HyDE for multi-topic queries
Fallback	rules/hyde-fallback.md	2-3s timeout → direct embedding fallback

Agentic RAG

Self-correcting retrieval with LLM-driven decision making.

Rule	File	Key Pattern
Self-RAG	rules/agentic-self-rag.md	Binary document grading for relevance
Corrective RAG	rules/agentic-corrective-rag.md	CRAG workflow with web fallback
Knowledge Graph	rules/agentic-knowledge-graph.md	KG + vector hybrid for entity-rich domains
Adaptive Retrieval	rules/agentic-adaptive-retrieval.md	Query routing to optimal strategy

Multimodal RAG

Image + text retrieval with cross-modal search.

Rule	File	Key Pattern
Embeddings	rules/multimodal-embeddings.md	CLIP, SigLIP 2, Voyage multimodal-3
Chunking	rules/multimodal-chunking.md	PDF extraction preserving images
Pipeline	rules/multimodal-pipeline.md	Dedup + hybrid retrieval + generation

Query Decomposition

Breaking complex queries into concepts for parallel retrieval.

Rule	File	Key Pattern
Detection	rules/query-detection.md	Heuristic indicators (<1ms fast path)
Decompose + RRF	rules/query-decompose.md	LLM concept extraction + parallel retrieval
HyDE Combo	rules/query-hyde-combo.md	Decompose + HyDE for maximum coverage

Reranking

Post-retrieval re-scoring for higher precision.

Rule	File	Key Pattern
Cross-Encoder	rules/reranking-cross-encoder.md	ms-marco-MiniLM (~50ms, free)
LLM Reranking	rules/reranking-llm.md	Batch scoring + Cohere API
Combined	rules/reranking-combined.md	Multi-signal weighted scoring

PGVector

Production hybrid search with PostgreSQL.

Rule	File	Key Pattern
Schema	rules/pgvector-schema.md	HNSW index + pre-computed tsvector
Hybrid Search	rules/pgvector-hybrid-search.md	SQLAlchemy RRF with FULL OUTER JOIN
Indexing	rules/pgvector-indexing.md	HNSW (17x faster) vs IVFFlat
Metadata	rules/pgvector-metadata.md	Filtering, boosting, Redis 8 comparison

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

Decision	Recommendation
Embedding model	text-embedding-3-small (general), voyage-3.5 (production)
Chunk size	256-1024 tokens (512 typical)
Hybrid weight	40% BM25 / 60% vector
Top-k	3-10 documents
Temperature	0.1-0.3 (factual)
Context budget	4K-8K tokens
Reranking	Retrieve 50, rerank to 10
Vector index	HNSW (production), IVFFlat (high-volume)
HyDE timeout	2-3 seconds with fallback
Query decomposition	Heuristic first, LLM only if multi-concept

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