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 (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

• No citation tracking (unverifiable answers)

• Context too large (dilutes relevance)

• Single retrieval method (misses keyword matches)

• Not chunking long documents (context gets lost)

• Embedding queries differently than documents

• No fallback path in agentic RAG (workflow hangs)

• Infinite rewrite loops (no retry limit)

• Using wrong similarity metric (cosine vs euclidean)

• Not caching embeddings (recomputing unchanged content)

• 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