RAG Retrieval

RAG Retrieval

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

House thresholds, fusion ordering, and the latency and quality budgets we assert live in House delta below. Vendor documentation is linked, not restated (see Upstream coverage).

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

Upstream coverage (do not restate)

Fetch these from the source instead of restating them here. Where a row says the house subset stays, that named file carries only the tuned values and the reason, not a tutorial.