In part 1, we chose a text embedding model and saved the model with BentoML. Now, let’s create a BentoML Service which has the serving logic. Create a file service.py. import bentoml import torch from bentoml.io import JSON, NumpyNdarray from pydantic import BaseModel from typing import Any,

Developers often rely on the OpenAI embedding API to generate embeddings from data, enabling the construction of LLM apps or the implementation of semantic search. The following code snippet demonstrates how text can be converted into vectors using the OpenAI embedding API, returning the processed token length: import openai openai.