Endpoints
Create embeddings
Create vectors from text. Use the same model and dimensions for documents and queries.
For the workflow and examples, read the guide.
POST
/v1/embeddingsAuthorization
bearerAuth AuthorizationBearer <token>
Your Triton AI Developer API key.
In: header
Request Body
application/json
Response Body
application/json
import osfrom openai import OpenAIclient = OpenAI( api_key=os.environ["TRITONAI_API_KEY"], base_url="https://tritonai-api.ucsd.edu/v1",)response = client.embeddings.create( model="api-tgpt-embeddings", input=[ "Information retrieval maps text to vectors.", "Course catalogs contain titles and descriptions.", ], dimensions=1024,)for item in response.data: print(item.index, len(item.embedding))