Principle:Cohere ai Cohere python Batch Embedding Jobs
| Metadata |
|---|
| Cohere Python SDK |
| Cohere Embed Jobs |
| NLP, Embeddings, Batch_Processing |
| 2026-02-15 14:00 GMT |
Overview
An asynchronous batch processing pattern for embedding large pre-uploaded datasets on the server side.
Description
Batch Embedding Jobs provide server-side embedding for large datasets that have been pre-uploaded to Cohere's dataset storage. Unlike the synchronous embed() method (which batches client-side), embed jobs run asynchronously on Cohere's infrastructure. You upload a dataset, create an embed job referencing it, then poll for completion using the wait utility. This is suitable for embedding millions of documents where client-side processing would be impractical.
Usage
Use batch embedding jobs for large-scale offline embedding tasks. First upload a dataset via datasets.create(), wait for validation, then create an embed job. Poll with the wait utility until the job completes and retrieve results from the output dataset.
Theoretical Basis
Asynchronous job processing follows the submit-poll-retrieve pattern common in distributed systems. The client submits work, periodically checks status, and retrieves results when complete. This decouples processing time from client connection lifetime, enabling long-running computations.