OpenAI
text-embedding-3
High-performance text embedding model for semantic search and similarity calculation
Vector Dimensions3072
Model TypeText Embedding
Max Input8191 tokens
ๅฎไปทไธ่งๆ ผ
๐ฐ ๅฎไปท
ไปทๆ ผ$0.00013 / 1K tokens
โ๏ธ ่งๆ ผ
Vector Dimensions3072
Model TypeText Embedding
Max Input8191 tokens
OutputFloat vector
API ่ฐ็จ็คบไพ
Python
from openai import OpenAI
client = OpenAI(
base_url="https://api.xairouter.com/v1",
api_key="your-api-key"
)
response = client.embeddings.create(
model="text-embedding-3-large",
input="This is a text that needs to be vectorized",
encoding_format="float"
)
embedding = response.data[0].embedding
print(f"Vector dimension: {len(embedding)}")cURL
curl https://api.xairouter.com/v1/embeddings \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_API_KEY" \
-d '{
"model": "text-embedding-3-large",
"input": "This is a text that needs to be vectorized",
"encoding_format": "float"
}'