#Create embeddings

POST /v1/embeddings

Converts text into a vector representation. Vectors are needed for semantic search, clustering, duplicate detection, and finding similar CRM cards. The request and response format is compatible with the OpenAI API. There is no streaming.

Embeddings are supported only by models whose capabilities.embeddings field equals true in GET /v1/models.

#Request fields (body)

#Examples

#curl — personal key

curl -X POST https://vibecode.bitrix24.com/v1/embeddings \
  -H "X-Api-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "bitrix/embeddings",
    "input": "We want a CRM for 50 users"
  }'

#curl — OAuth application

curl -X POST https://vibecode.bitrix24.com/v1/embeddings \
  -H "X-Api-Key: YOUR_APP_KEY" \
  -H "Authorization: Bearer USER_SESSION_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "bitrix/embeddings",
    "input": "We want a CRM for 50 users"
  }'

#JavaScript — personal key

const res = await fetch('https://vibecode.bitrix24.com/v1/embeddings', {
  method: 'POST',
  headers: {
    'X-Api-Key': 'YOUR_API_KEY',
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    model: 'bitrix/embeddings',
    input: ['First text', 'Second text'],
  }),
})

const result = await res.json()
console.log(result.data.length)        // 2 — one vector per string
console.log(result.data[0].embedding)  // [0.0203, 0.0034, ...]

#JavaScript — OAuth application

const res = await fetch('https://vibecode.bitrix24.com/v1/embeddings', {
  method: 'POST',
  headers: {
    'X-Api-Key': 'YOUR_APP_KEY',
    'Authorization': 'Bearer USER_SESSION_TOKEN',
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    model: 'bitrix/embeddings',
    input: ['First text', 'Second text'],
  }),
})

const result = await res.json()

#Response fields

The response comes in raw OpenAI format, without the success and data wrapper.

#Response example

The first three vector values are shown. The full dimensionality depends on the model — for bitrix/embeddings it is 4096 values.

{
  "object": "list",
  "model": "bitrix/embeddings",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0203, 0.0034, -0.0156]
    }
  ],
  "usage": {
    "prompt_tokens": 12,
    "completion_tokens": 0,
    "total_tokens": 12
  }
}

#Error response example

501 embeddings_unsupported — the model does not support embeddings:

{
  "error": {
    "message": "Model \"bitrix/bitrixgpt-5.5\" does not support embeddings.",
    "type": "server_error",
    "code": "embeddings_unsupported"
  }
}

#Errors

The full list of common API errors — Errors.

#Known specifics

Error codes come in lowercase. Most error bodies use the raw OpenAI format { "error": { "message", "type", "code" } }, without a success field. Two cases arrive in the { "success": false, "error": { … } } envelope instead: quota and pacing rejections (402 ai_quota_exhausted, 429 ai_pacing_limited), and an unexpected server error 5xx, whose code is written in upper case. A handler must accept both envelopes.

The input array preserves order. The vector data[i] corresponds to the string input[i], and the data[].index field duplicates this position — you can use it to match the result after parallel processing.

The processing budget for a request is about 850 seconds. Once the budget runs out, 503 ai_provider_timeout is returned. This response deliberately carries no Retry-After header: repeating the same request would hit the same budget. Split the input array into smaller parts.

The top-up hint in a 402 ai_quota_exhausted response. Only in the reason: "wallet_empty" case may the response additionally carry a hint string and a topupUrl link to top up the balance. Both fields appear when enforced quota control and the top-up hint are enabled on the platform, so read them as optional. The hint field in this response is a string.

Usage is counted only by input tokens. The usage.completion_tokens field is always 0, so you pay only for the input. For the bitrix/embeddings model the input price is zero.

#See also

• Models list
• Create a chat completion
• Company AI quota
• AI Router