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RAG Without Vector Databases: When Structure Beats Embeddings

LlamaIndex

Claude

Key Takeaway

“Vector databases are not a prerequisite for effective RAG (Retrieval-Augmented Generation) systems; choosing retrieval strategies based on domain characteristics, query patterns, and infrastructure constraints often yields better cost-performance trade-offs than defaulting to semantic search.”

The Vector Database Overhead Problem

Vector databases introduce operational complexity that doesn't always justify their performance gains. Managing embedding pipelines, handling index updates, and maintaining similarity search infrastructure adds latency and cost overhead. For many production RAG systems, especially those with moderate corpus sizes or low query volumes, this infrastructure burden outweighs the benefits of semantic search. The assumption that embeddings are necessary for retrieval quality doesn't hold across all domains.

Alternative Retrieval Strategies

Different retrieval approaches offer distinct advantages depending on corpus characteristics and query patterns:

Method	Best For	Latency Impact	Infrastructure Cost
Keyword + LLM Re-ranking	Specialized domains with consistent terminology	Low	Minimal
Graph-based Retrieval	Multi-hop reasoning, connected concepts	Medium	Moderate
Structure-aware Search	Low-data domains (legal, biomedical)	Low	Minimal
On-demand Agent Retrieval	Low query volume relative to corpus	Variable	Very Low

Keyword search with BM25 (Best Matching 25) scoring followed by LLM-based re-ranking consistently outperforms semantic embeddings in specialized domains where terminology is precise and stable. Graph-based approaches excel when relationships between entities matter more than semantic similarity between isolated chunks.

When Document Structure Outperforms Embeddings

In domains like biomedical research or legal documents, inherent document structure provides stronger retrieval signals than learned embeddings. Table of contents hierarchies, section headings, and document metadata create natural navigation paths that match how domain experts actually search for information. A query about "adverse effects in clinical trials" benefits more from targeting methods and results sections than from semantic similarity across the entire corpus. This structure-aware retrieval eliminates the cold-start problem of embedding-based systems in low-data specialized domains.

Applied Insight: Choose Retrieval Architecture by Query Patterns

Vector databases make sense for high-volume systems with diverse natural language queries across broad domains. For specialized domains with consistent terminology, start with keyword search and LLM re-ranking. When your queries require multi-hop reasoning or relationship traversal, implement graph-based retrieval. In low-data specialized domains, leverage document structure and metadata before investing in embedding infrastructure. Evaluate retrieval quality against infrastructure cost, not just against other embedding models.

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