18 lines
899 B
JSON
18 lines
899 B
JSON
{
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"name": "google-gemini-embeddings",
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"owner": {
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"name": "Jeremy Dawes",
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"email": "jeremy@jezweb.net"
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},
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"plugins": [
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{
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"name": "google-gemini-embeddings",
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"description": "Build RAG systems, semantic search, and document clustering with Gemini embeddings API (gemini-embedding-001). Generate 768-3072 dimension embeddings for vector search, integrate with Cloudflare Vectorize, and use 8 task types (RETRIEVAL_QUERY, RETRIEVAL_DOCUMENT, SEMANTIC_SIMILARITY) for optimized retrieval. Use when: implementing vector search with Google embeddings, building retrieval-augmented generation systems, creating semantic search features, clustering documents by meaning, integrating",
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"source": {
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"source": "url",
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"url": "https://git.waymay.us/zhongwei/gh-jezweb-claude-skills-skills-google-gemini-embeddings.git"
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},
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"strict": true
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}
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]
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} |