Key Takeaway
By the end of this blueprint you will have a production RAG pipeline using pgvector for dense retrieval, BM25 for sparse matching, a cross-encoder reranker for precision, and citation-aware prompt construction that lets users trace every claim back to a source document and page number.
Prerequisites
- PostgreSQL 16+ with the pgvector extension installed
- Python 3.11+ with familiarity with async patterns
- An embedding model API key (OpenAI, Cohere, or a local model via sentence-transformers)
- Basic understanding of vector similarity search concepts
- Docker for running PostgreSQL and any local embedding models
Why RAG Over Fine-Tuning?
Fine-tuning bakes knowledge into model weights, which works well for style and format but poorly for factual grounding. When your source data changes weekly or daily — internal docs, knowledge bases, product catalogs — fine-tuning cannot keep up. RAG keeps the LLM's reasoning capabilities intact while swapping the knowledge layer at query time. This means you can update your corpus without retraining, attribute every answer to a source document, and enforce access control on retrieval results without modifying the model.
RAG and fine-tuning are not mutually exclusive. Fine-tune for style and domain-specific reasoning patterns, then use RAG for factual grounding. The combination outperforms either approach alone for most enterprise use cases.
Architecture Overview
The pipeline is split into two paths: an offline ingestion path that processes documents through chunking, embedding, and indexing, and an online query path that retrieves, reranks, and synthesizes answers. A metadata store tracks document provenance so every generated response can cite its sources back to specific document sections and page numbers.
Document Ingestion and Chunking
Chunking strategy has an outsized impact on retrieval quality. Chunks that are too large dilute the signal with irrelevant context; chunks that are too small lose the context needed for coherent answers. The sweet spot depends on your content type: 200-400 tokens for dense technical documentation, 400-800 tokens for narrative content like reports and articles. We use recursive character splitting with overlap to maintain context across chunk boundaries.
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