{
  "name": "google-gemini-embeddings",
  "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",
  "version": "1.0.0",
  "author": {
    "name": "Jeremy Dawes",
    "email": "jeremy@jezweb.net"
  },
  "skills": [
    "./"
  ]
}