Embeddings

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Request

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Headers

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Body Parameters

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Example

curl https://api.consus.io/v1/embeddings \
  -H "x-api-key: $CONSUS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "titan-embed-text-v2:il5",
    "input": ["first document chunk", "second document chunk"]
  }'

from openai import OpenAI

client = OpenAI(base_url="https://api.consus.io/v1", api_key=CONSUS_API_KEY)
result = client.embeddings.create(
    model="titan-embed-text-v2:il5",
    input=["first document chunk", "second document chunk"],
)
vectors = [d.embedding for d in result.data]

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Response

{
  "object": "list",
  "data": [
    { "object": "embedding", "index": 0, "embedding": [0.0123, -0.0456, ...] },
    { "object": "embedding", "index": 1, "embedding": [0.0789, 0.0012, ...] }
  ],
  "model": "titan-embed-text-v2:il5",
  "usage": { "prompt_tokens": 9, "total_tokens": 9 }
}

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Models

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Behavior and Limits

• Batching. Up to 512 strings per request; larger batches are rejected with a 400 rather than truncated, so split them client-side. Each string in input is embedded as its own upstream call; a request with many strings takes correspondingly longer. Large ingestion jobs may see 429 responses when the upstream per-minute quota is reached — standard client retry with backoff handles this.
• Over-length input returns 400; text is never silently truncated.
• Billing is per input token at the model’s rate. There are no output tokens.
• Nothing is stored. Inputs and vectors are returned to you and discarded, the same zero-persistence posture as every other endpoint.
• Embedding models are rejected on /v1/chat/completions, /v1/messages, and /v1/responses, and chat models are rejected here, each with a 400 that names the correct endpoint.
• There is no Anthropic-SDK equivalent of this endpoint; use an OpenAI-compatible client for embeddings even if the rest of your application uses /v1/messages.

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Errors