Beyond Naive RAG in Enterprise Systems

Retrieval-Augmented Generation (RAG) is the backbone of modern enterprise AI systems. However, standard naive RAG pipelines—which rely solely on basic cosine similarity search across naive text chunks—frequently fail in production due to context fragmentation and keyword misses.

---

The Power of Hybrid Dense + Sparse Search

To achieve 99%+ context retrieval accuracy, Throughout Technologies implements Hybrid Search combining:

1. Dense Vector Embeddings: Semantic understanding via OpenAI text-embedding-3 or Hugging Face models.
2. Sparse Keyword Matching (BM25): Exact matching for part numbers, SKU codes, acronyms, and legal identifiers.
3. Reciprocal Rank Fusion (RRF): Re-ranking results from both retrieval engines into an optimal context payload.

---

Implementing pgvector for Colocated Data & Embeddings

By utilizing PostgreSQL's pgvector extension, enterprises eliminate the overhead of maintaining synchronized external vector databases, keeping relational data and high-dimensional vectors in a single ACID-compliant database.