RAG & Vector DB Patterns

When to Load This Skill

Load when working with: Qdrant, pgvector, embeddings, chunking, retrieval-augmented generation, semantic search, knowledge bases, document ingestion pipelines.

Vector DB Choice

Option	When to Use
Qdrant	Default choice. Standalone service, excellent filtering, production-ready, Docker-friendly
pgvector	Already have PostgreSQL, simple use case, don't want extra service
In-memory (numpy)	Prototyping only, < 10k documents

Qdrant Setup

services:
  qdrant:
    image: qdrant/qdrant:latest
    ports:
      - "6333:6333"
    volumes:
      - qdrant_data:/qdrant/storage

volumes:
  qdrant_data:

Full adapter implementation:

resources/qdrant-adapter.md

Embeddings

Two options:

• OpenRouter (

  text-embedding-3-small

  ) — API-based, no local GPU required
• sentence-transformers (

  multilingual-e5-base

  , 768 dim, ~280MB) — local, free, good for Russian

Full implementations:

resources/embeddings.md

Chunking

Chunking is the most critical RAG quality parameter. Default: paragraph-based, 512 tokens, 1-sentence overlap.

Full strategy +

Chunk

dataclass:

resources/chunking-strategies.md

RAG Query Pipeline

class RAGService:
    def __init__(
        self,
        vector_db: QdrantAdapter,
        embedder: LocalEmbeddingAdapter,
        llm_adapter,
    ) -> None:
        self._db = vector_db
        self._embedder = embedder
        self._llm = llm_adapter

    async def answer(self, question: str, top_k: int = 5) -> dict:
        query_embedding = self._embedder.embed([question])[0]
        retrieved = await self._db.search(query_embedding, top_k=top_k)

        if not retrieved:
            return {"answer": "No information found in knowledge base.", "sources": []}

        context = "\n\n---\n\n".join(r["text"] for r in retrieved)
        sources = list({r["source"] for r in retrieved})

        answer = await self._llm.invoke(
            system="Answer based only on the provided context. If the answer is not in the context, say so explicitly.",
            messages=[{"role": "user", "content": f"Context:\n{context}\n\nQuestion: {question}"}],
        )

        return {"answer": answer, "sources": sources, "retrieved_count": len(retrieved)}

Document Ingestion

Full

IngestionService

(PDF + DOCX):

resources/ingestion-pipeline.md

pgvector Alternative

SQL setup + search function:

resources/pgvector-alternative.md

Further Resources

• resources/reranking.md

  — cross-encoder reranking for precision improvement

• resources/eval-ragas.md

  — RAG quality evaluation with RAGAS framework