Embeddings

POST /v1/embeddings

Convert text into vector representations for semantic search, clustering, and recommendations using OpenAI, Cohere, and Google embedding models.

Request

POST https://api.chuizi.ai/v1/embeddings

Authentication

Authorization: Bearer ck-your-api-key

Parameters

Parameter	Type	Required	Default	Description
`model`	string	Yes	—	Model name, e.g. `openai/text-embedding-3-small`
`input`	string/array	Yes	—	Text to encode. Single string or array of strings (max 2048 items)

Available Models

Model	Dimensions	Max Tokens
`openai/text-embedding-3-small`	1536	8191
`openai/text-embedding-3-large`	3072	8191
`cohere/embed-v4`	1024	512
`google/gemini-embedding-001`	768	2048

Request Example

config.json

json

{
  "model": "openai/text-embedding-3-small",
  "input": ["How to learn machine learning", "Machine learning beginner tutorial"],
  "encoding_format": "float"
}

Response

config.json

json

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0023064255, -0.009327292, 0.015797347, "..."]
    },
    {
      "object": "embedding",
      "index": 1,
      "embedding": [-0.004843265, 0.012576489, -0.008321547, "..."]
    }
  ],
  "model": "openai/text-embedding-3-small",
  "usage": {
    "prompt_tokens": 14,
    "total_tokens": 14
  }
}

Code Examples

terminal

bash

curl -X POST https://api.chuizi.ai/v1/embeddings \
  -H "Authorization: Bearer ck-your-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/text-embedding-3-small",
    "input": "Hello world"
  }'

Next Steps

Rerank API — improve retrieval quality by reranking embedding results
Choose a Model — compare embedding model dimensions and pricing
Billing Model — understand how embedding requests are billed