使用 Gemini Embedding 模型生成文本向量
curl -X POST "$BASE_URL/v1/embeddings" \ -H "Authorization: Bearer $TOKEN" \ -H "Content-Type: application/json" \ -d '{ "model": "text-embedding-004", "input": "文本内容" }'
{ "object": "list", "data": [{"embedding": [0.123, -0.456, ...], "index": 0}], "model": "text-embedding-004", "usage": {"prompt_tokens": 4, "total_tokens": 4} }
curl -X POST "$BASE_URL/v1/embeddings" \ -H "Authorization: Bearer $TOKEN" \ -H "Content-Type: application/json" \ -d '{ "model": "text-embedding-004", "input": ["第一段文本", "第二段文本", "第三段文本"] }'
text-embedding-004
gemini-embedding-001
from openai import OpenAI client = OpenAI( api_key="your-token", base_url="https://models.kapon.cloud/v1" ) response = client.embeddings.create( model="text-embedding-004", input="文本内容" ) print(response.data[0].embedding[:5])
import OpenAI from 'openai'; const client = new OpenAI({ apiKey: 'your-token', baseURL: 'https://models.kapon.cloud/v1' }); const response = await client.embeddings.create({ model: 'text-embedding-004', input: '文本内容' });