google-gemini-embeddings

Semantic search with Gemini embeddings converts text into vector representations, then retrieves similar documents by comparing vectors. Generate embeddings using the gemini-embedding-001 model, index them in a vector database like Cloudflare Vectorize, and query by embedding your search text to find semantically related results.

Gemini embeddings support flexible dimensions from 128 to 3072, letting you trade latency and storage for precision. Your chosen dimension must match your vector index exactly—dimension mismatch prevents retrieval. Smaller dimensions (128–256) are faster; larger ones (768–3072) capture finer semantic detail.

Yes. Gemini embeddings support eight task types including RETRIEVAL_DOCUMENT, RETRIEVAL_QUERY, CLUSTERING, and SEMANTIC_SIMILARITY. Specify the task type when generating embeddings to optimize vectors for your use case—RAG pipelines, clustering workflows, or similarity comparisons all use the same API with task-specific tuning.

Implement exponential backoff to handle rate limits gracefully. Batch multiple documents together in a single request to improve throughput and reduce API calls. The Gemini SDK and REST endpoints both support batch processing, letting you embed hundreds of documents efficiently while respecting rate constraints.

Gemini embeddings integrate with Cloudflare Vectorize for edge-deployed vector search. Generate embeddings via Gemini's API, store them in Vectorize, and query at the edge for low-latency retrieval. Supports both SDK and REST usage patterns, enabling real-time semantic search across distributed infrastructure.

Split documents into chunks with overlap to preserve semantic context across boundaries. Generate embeddings for each chunk, then index them in your vector store. During retrieval, overlapping chunks ensure related passages cluster together, improving answer quality when the RAG system reconstructs context from retrieved fragments.