gemini-batch

name	gemini-batch
description	This skill should be used when the user asks to "use Gemini Batch API", "process documents at scale", "submit a batch job", "upload files to Gemini", or needs large-scale LLM processing. Includes production gotchas and best practices.

Gemini Batch API Skill

Large-scale asynchronous document processing using Google's Gemini models.

When to Use

• Process thousands of documents with the same prompt
• Cost-effective bulk extraction (50% cheaper than synchronous API)
• Jobs that can tolerate 24-hour completion windows

Quick Start

from examples.batch_processor import GeminiBatchProcessor

processor = GeminiBatchProcessor(
    bucket_name="my-batch-bucket",  # Must be in us-central1
    model="gemini-2.0-flash-lite"
)

results = processor.run_pipeline(
    input_dir="./documents",
    prompt="Extract as JSON: {title, date, summary}",
    output_dir="./results"
)

Core Workflow

1. Upload files to GCS bucket (us-central1 region required)
2. Create JSONL request file with document URIs and prompts
3. Submit batch job via genai.batches.create()
4. Poll for completion (jobs expire after 24 hours)
5. Download and parse results from output URI
6. Handle failures gracefully (partial failures are common)

Key Gotchas

Issue