Hybrid Retrieval

Hybrid retrieval combines semantic similarity search with explicit relationship traversal. Typically, a vector index first identifies relevant chunks before the graph adds related entities, paths, or rule dependencies. This creates a retrieval pipeline that blends semantic breadth with relational depth and fits many production-oriented GraphRAG scenarios.

Use this profile to understand the building block briefly, place it in the model, and open related building blocks.

Content type

Concept

Theoretical construct: explains a term, principle, or mental model.

Classification level

Structure

What organizes, connects, or makes decisions possible.

In brief

Hybrid Retrieval in brief

Hybrid retrieval combines semantic vector search with exact or structural search to find relevant information more reliably.

Where does it come from?

The pattern addresses a limit of single search methods: vectors capture meaning but can miss terms or filters; keyword and graph queries are precise yet tied to wording. Shared ranking combines their strengths.

The mental model

A query passes through several retrievers. One finds semantic similarity, another exact terms or metadata, and a third can use graph relationships. Results are then normalized, weighted, and reranked. Quality therefore depends on indexes as well as fusion, filtering, and evaluation.

Key building blocks

Retrievers

Different search paths produce candidates by meaning, terms, or relationships.

Fusion

A method combines result lists and reconciles their scores or ranks.

Evaluation

Test queries and judged results show whether the combination improves retrieval.

When is it useful?

Hybrid retrieval helps knowledge assistants and graph applications containing specialized terms. Check index coverage, filters, rank fusion, and recall/precision with representative queries.

Neo4j Vector Indexes