httpkobarupayaithirdweb

QuickBuild

Drop-in OpenAI Embeddings endpoint: POST {input: string | string[], model, encoding_format: float|base64, dimensions} and get {object: "list", data: [{embedding, index}], model, usage}. Models (Cloudflare Workers AI): bge-m3 (1024-d, multilingual (100+ languages), 8192-token context); bge-large-en-v1.5 (1024-d, English); bge-base-en-v1.5 (768-d, English); bge-small-en-v1.5 (384-d, English, fastest); embeddinggemma-300m (768-d, multilingual (Google EmbeddingGemma)). Any other model name (e.g. text-embedding-3-small) maps to bge-m3; the actual model is in the response. `dimensions` truncates and re-normalizes. Up to 100 inputs per call, 8000 chars each, 60000 chars total, one fixed price per call. For RAG, semantic search, clustering and dedupe. Errors are never charged. GET ?input= also works; with no params it embeds a sample sentence.