Principle:Googleapis Python genai Batch Processing
| Knowledge Sources | |
|---|---|
| Domains | Batch_Processing, Generative_AI |
| Last Updated | 2026-02-15 14:00 GMT |
Overview
Design pattern for processing large volumes of generative AI requests as offline batch jobs rather than individual synchronous API calls.
Description
Batch Processing is a computational pattern where multiple independent requests are grouped into a single job submitted for asynchronous processing. In the context of generative AI, this enables cost-efficient processing of large datasets (thousands to millions of requests) without requiring real-time response latency. The system accepts a data source (file, database, or inline requests), processes them using a specified model, and writes results to a configured destination.
Usage
Use this principle when processing datasets too large for individual API calls, when real-time latency is not required, or when cost optimization through batch pricing is desired. Typical scenarios include dataset annotation, large-scale content generation, and batch embedding computation.
Theoretical Basis
The batch processing pattern follows the Producer-Consumer model:
# Pseudo-code for batch processing lifecycle
job = submit_batch_job(model, source, destination)
while job.state != COMPLETED:
job = poll_job_status(job.name)
wait(backoff_interval)
results = read_results(destination)
Key properties:
- Atomicity: The entire batch succeeds or fails as a unit
- Asynchronous: Submissions return immediately; results are polled
- Idempotent source: Input data is read-only during processing