Embeddings

Convert text into vector representations for semantic search, clustering, classification, and other use cases.

Endpoint


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

Request Parameters

Parameter	Type	Required	Description
`model`	string	✅	Embedding model, e.g. `openai/text-embedding-3-small`
`input`	string \| string[]	✅	Text to embed (single or batch)
`encoding_format`	string	—	Output format: `float` (default) or `base64`
`dimensions`	number	—	Output vector dimensions (supported by some models)

Request Examples

cURL

Terminal


curl https://api.ofox.ai/v1/embeddings \
  -H "Authorization: Bearer $OFOX_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/text-embedding-3-small",
    "input": "OfoxAI is an LLM Gateway"
  }'

Python

embedding.py


from openai import OpenAI
 
client = OpenAI(
    base_url="https://api.ofox.ai/v1",
    api_key="<your OFOXAI_API_KEY>"
)
 
response = client.embeddings.create(
    model="openai/text-embedding-3-small",
    input="OfoxAI is an LLM Gateway"
)
 
embedding = response.data[0].embedding
print(f"Vector dimensions: {len(embedding)}")

TypeScript

embedding.ts


import OpenAI from 'openai'
 
const client = new OpenAI({
  baseURL: 'https://api.ofox.ai/v1',
  apiKey: '<your OFOXAI_API_KEY>'
})
 
const response = await client.embeddings.create({
  model: 'openai/text-embedding-3-small',
  input: 'OfoxAI is an LLM Gateway'
})
 
console.log(`Vector dimensions: ${response.data[0].embedding.length}`)

Response Format


{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0023, -0.0091, 0.0156, ...]
    }
  ],
  "model": "openai/text-embedding-3-small",
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  }
}

Available Models

Model	Dimensions	Description
`openai/text-embedding-3-small`	1536	Cost-effective
`openai/text-embedding-3-large`	3072	Highest accuracy
`bailian/text-embedding-v4`	1024	Optimized for Chinese, Qwen embedding model