What problem does hybrid search solve?

Hybrid search combines vector similarity search with keyword-based search to close the recall gap left by pure semantic search, which is strong at capturing conceptual meaning but weak at surfacing exact matches for specific identifiers, proper nouns, product codes, and domain-specific vocabulary.

When should I use hybrid search in my RAG system?

Use hybrid search when you need improved recall in RAG systems, when handling queries with specific terms like names or codes, when working with domain-specific vocabulary that general embeddings may not capture well, or when you've observed pure vector search missing results users clearly expect to find.

What types of queries benefit most from hybrid search?

Queries that mix conceptual asks with hard constraints (like a question about a topic that also names a specific person or product), searches for exact identifiers or codes, and queries using domain-specific vocabulary that keyword matching would catch instantly but semantic search might miss.

Where can I find implementation guidance for hybrid search?

Detailed implementation patterns, worked examples, and step-by-step guidance are provided in the companion resource file `resources/implementation-playbook.md`.

Skill

Implement Hybrid Search for RAG Systems

Name: Hybrid Search Implementation
Availability: OnlineOnly
Author: Antigravity

Patterns for combining vector similarity and keyword search to fix RAG recall gaps on exact-term queries.

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Updated 4 days ago

Version 15.5.1

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Why it matters

Enhance Retrieval Augmented Generation (RAG) systems by implementing hybrid search patterns. This skill combines vector similarity with keyword matching to improve search recall and handle specific queries.

Outcomes

What it gets done

Implement patterns for combining vector and keyword search.

Improve recall in RAG systems.

Handle queries with specific terms and domain-specific vocabulary.

Combine semantic understanding with exact matching.

Install

Add it to your toolbox

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/ag-hybrid-search-implementation | bash

Overview

Hybrid Search Implementation

Patterns for combining vector similarity and keyword-based search to recover exact-term queries that pure semantic search misses. Use when building a RAG system that needs better recall on queries with specific terms or domain vocabulary.

What it does

A set of patterns for combining vector similarity search with keyword-based search in a single retrieval pipeline. It targets a specific failure mode of pure semantic search: queries containing exact terms, such as names or codes, that a vector embedding alone tends to miss even when its semantic match is otherwise strong. Combining both retrieval methods recovers those exact-match cases while keeping the recall benefits of semantic search, and is particularly useful for domain-specific vocabulary that embeddings don't represent well.

When to use - and when NOT to

Use when building a RAG system that needs improved recall, when queries mix semantic intent with specific terms like names or codes, when search needs to handle domain-specific vocabulary that embeddings represent poorly, or when pure vector search is demonstrably missing keyword matches a user expects to find. Not for tasks unrelated to hybrid search implementation - it explicitly stays out of scope for other retrieval or domain problems.

Inputs and outputs

Input is the retrieval requirement: a query type or domain where semantic-only search has known gaps. Output is a hybrid retrieval design combining vector similarity scoring with keyword-based matching. For detailed patterns and worked examples, it points to resources/implementation-playbook.md.

Integrations

References its own companion resource, resources/implementation-playbook.md, for detailed patterns and examples beyond this overview.

Who it's for

Engineers building or improving a RAG or search system who are seeing pure vector search miss exact-term queries and need a combined vector-plus-keyword retrieval approach.

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