weaviate
CommunityMaster Weaviate for semantic search.
Software Engineering#schema design#semantic search#vector database#RAG#hybrid search#weaviate#multi-modal
AuthorRepairYourTech
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill provides comprehensive guidance for leveraging Weaviate, a powerful vector database, to build advanced search and retrieval systems, especially for semantic search, RAG, and multi-modal applications.
Core Features & Use Cases
- Schema Design: Define classes, properties, and vectorizer configurations.
- Search Patterns: Implement vector, BM25, hybrid, filtered, and generative search.
- Vectorizer Modules: Configure and utilize various embedding modules (OpenAI, Cohere, HuggingFace, CLIP).
- Driver Setup: Connect using Python and JavaScript/TypeScript clients.
- Security & Coordination: Understand multi-tenancy, API keys, OIDC, and cross-store sync.
Quick Start
Use the weaviate skill to create a new 'Document' collection with OpenAI text vectorization and add 'title' and 'content' properties.
Dependency Matrix
Required Modules
None requiredComponents
references
💻 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: weaviate Download link: https://github.com/RepairYourTech/cfsa-antigravity/archive/main.zip#weaviate Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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