multi-vector-search
OfficialSearch across multiple fields to boost relevance.
System Documentation
What problem does it solve?
This Skill enables searching across multiple semantic facets of each item, such as title, description, reviews, and images, to provide more accurate results than single-vector search.
Core Features & Use Cases
- Separate field embeddings for each semantic aspect (title, description, reviews, image) to preserve individual meaning.
- Flexible fusion strategies (RRF or weighted rankers) to tailor results based on user intent.
- Use cases include product catalogs, document repositories, and Q&A systems where different parts of an item contribute differently to relevance.
Quick Start
Index your data with separate fields and embeddings (title, description, reviews, image). Then perform a multi-vector search by providing a query and selecting a mode (balanced, title, description, or visual). Example: "Find products matching 'smartphone with good camera' across title and reviews while including image similarity."
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: multi-vector-search Download link: https://github.com/zilliztech/milvus-marketplace/archive/main.zip#multi-vector-search Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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