SKILL.md
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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