RAG and Knowledge Pipelines

RAG and Knowledge Pipelines

Retrieval-augmented generation connects an agent to external knowledge. A good RAG pipeline is more than a vector database: it is a full lifecycle from source material to evaluated answers.

Pipeline Stages

1. Ingestion: collect documents, records, transcripts, or structured sources.
2. Cleaning: remove duplicates, normalize formats, and preserve provenance.
3. Chunking: split content into retrieval-friendly units.
4. Metadata: add source, date, category, audience, permissions, and quality signals.
5. Embeddings: turn chunks into vectors using a chosen embedding model.
6. Storage: write vectors and metadata to a vector database or hybrid search system.
7. Retrieval: query relevant chunks for a task.
8. Reranking: reorder candidates for precision.
9. Context injection: pass selected evidence to the model with clear instructions.
10. Evaluation: measure groundedness, recall, and answer quality.

Design Notes

Metadata often matters as much as embeddings. It lets retrieval filter by source, permissions, freshness, and project scope before the model sees context.

Related pages: Local LLMs and Embeddings and Langfuse Observability.