benchflow-ai / google-gemini-embeddings
Install for your project team
Run this command in your project directory to install the skill for your entire team:
mkdir -p .claude/skills/google-gemini-embeddings && curl -L -o skill.zip "https://fastmcp.me/Skills/Download/1404" && unzip -o skill.zip -d .claude/skills/google-gemini-embeddings && rm skill.zipProject Skills
This skill will be saved in .claude/skills/google-gemini-embeddings/ and checked into git. All team members will have access to it automatically.
Important: Please verify the skill by reviewing its instructions before using it.
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 embeddings with Cloudflare Vectorize, optimizing dimension sizes (128-3072), or troubleshooting dimension mismatch errors, incorrect task type selections, rate limit issues (100 RPM free tier), vector normalization mistakes, or text truncation errors (2,048 token limit).
