12 lines
691 B
JSON
12 lines
691 B
JSON
{
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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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"version": "1.0.0",
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"author": {
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"name": "Jeremy Dawes",
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"email": "jeremy@jezweb.net"
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},
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"skills": [
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"./"
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]
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} |