Retrieval-Augmented Generation (RAG) is the foundational architecture for enterprise AI applications that require accurate, grounded responses from proprietary documents and domain knowledge bases. However, standard vector-only semantic search often fails on precise technical terms, part numbers, abbreviations, and exact keyword matches.
At Wellbit Technologies, we engineer enterprise RAG systems utilizing hybrid search and multi-stage reranking pipelines built directly on PostgreSQL with pgvector.
Why Pure Vector Search Falls Short
Dense embedding models project text into high-dimensional semantic spaces. While they excel at broad topical similarity, they struggle with:
- Exact alphanumeric identifiers (e.g., product SKUs, invoice numbers, policy codes).
- Specialized technical terminology not heavily represented in training corpuses.
- High-precision keyword filtering across structured metadata.
The Hybrid Search Architecture
To achieve state-of-the-art retrieval accuracy, our architecture combines two distinct search paradigms:
1. Dense Semantic Embeddings (pgvector)
We compute dense vector embeddings using state-of-the-art embedding models and index them using HNSW (Hierarchical Navigable Small World) indexes inside PostgreSQL via pgvector. This captures semantic intent and conceptual questions.
2. Sparse Full-Text Search (BM25 / tsvector)
Simultaneously, PostgreSQL full-text search (tsvector and tsquery) indexes exact tokens, stems, and lexemes with BM25-style ranking. This guarantees that exact keyword queries and entity identifiers are never missed.
3. Reciprocal Rank Fusion (RRF) & Cross-Encoder Reranking
Results from dense and sparse queries are merged using Reciprocal Rank Fusion (RRF). The top candidate documents are then passed to a high-precision cross-encoder reranking model, which scores the exact relevance of each passage against the user query before passing context to the LLM.
Production Impact
By implementing hybrid search with cross-encoder reranking, retrieval precision typically increases by 25–40% compared to basic vector search, while eliminating hallucinated policy responses and ensuring reliable enterprise question-answering.