Text Embeddings

Convert text into numerical vector representations for semantic search, clustering, and recommendations.

Create embeddings

POST https://ciyuanx.io/v1/embeddings

cURL

curl https://ciyuanx.io/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "text-embedding-3-small",
    "input": "Your text here"
  }'

Python

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://ciyuanx.io/v1",
)

response = client.embeddings.create(
    model="text-embedding-3-small",
    input="Your text here",
)

embedding = response.data[0].embedding
print(f"Embedding dimension: {len(embedding)}")

TypeScript

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "YOUR_API_KEY",
  baseURL: "https://ciyuanx.io/v1",
});

const response = await client.embeddings.create({
  model: "text-embedding-3-small",
  input: "Your text here",
});

console.log(`Embedding dimension: ${response.data[0].embedding.length}`);

Request parameters

Parameter	Type	Required	Description
model	string	Yes	The embedding model ID to use
input	string or array	Yes	The text to embed, as a single string or an array of strings
dimensions	integer	No	Number of dimensions for the output embedding (only text-embedding-3 and newer models)
encoding_format	string	No	Return format, float (default) or base64

Response

The response returns the embedding vector inside the data array:

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0023, -0.0091, 0.0145, "..."]
    }
  ],
  "model": "text-embedding-3-small",
  "usage": {
    "prompt_tokens": 5,
    "total_tokens": 5
  }
}

Best practices

Use embeddings efficiently

• Pass an array of strings to input to embed in bulk and reduce the number of requests
• Cache embeddings for frequently used text to avoid recomputation
• Use cosine similarity to compare vectors when measuring relatedness

Tip

Use embeddings for semantic search, document similarity, and recommendation systems.