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Generate embedding vectors with Floodgate’s compatible embeddings operation. Use the accepted model ID and a routing group that exposes this exact API in Model Square.

Submit

Send POST /v1/embeddings with your API key. input accepts one string or an array of strings:
The response is synchronous. No task polling or video download is needed.
This sample shows one input and a shortened vector. Read vectors from data[].embedding and match each data[].index to its input. Read token usage separately from the vectors.

Parameters and limits

Current compatible metadata leaves input-count limits, input-token limits, dimensions and encoding choices undocumented. Do not borrow another model’s limits or assume a default encoding. Native Gemini embedding operations and reranking do not have reviewed contracts in this reference. There is no chat or embedding fallback for /v1/rerank. Embeddings return vectors, not relevance scores.

Errors and billing

Check required fields and published limits on 400, the API key on 401, group access and funds on 403, and rate limits on 429. Embedding usage reports prompt_tokens and total_tokens. Pricing depends on the selected model/group; see Model Square. The response’s indexes and usage apply to the submitted inputs.