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RAG at Petabyte Scale: Hybrid Dense-Sparse Vector Retrieval Systems

Combining BM25 lexical search with OpenAI text-embedding-3 vectors to eliminate hallucination in high-compliance financial and legal knowledge bases.

Hybrid Retrieval Architecture for Enterprise RAG

Pure dense vector retrieval often misses specific serial numbers, acronyms, or exact contractual phrasing. By engineering a Hybrid Dense-Sparse RAG Pipeline, we combine:

  1. Sparse Lexical Search: BM25 inverted index for exact keyword and identifier matching.
  2. Dense Semantic Search: High-dimensional vector embeddings for conceptual understanding.
  3. Reciprocal Rank Fusion (RRF): Merging both result streams before context injection.

Performance Benchmarks

  • Dense Only Precision: 84.1%
  • Sparse Only Precision: 76.4%
  • Hybrid RRF Precision: 98.6%