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

IRON LAW: Use Examples First, Never Guess API

READ EXAMPLES BEFORE WRITING ANY CODE. NO EXCEPTIONS.

The Rule

User asks for batch API work
    ↓
MANDATORY: Read examples/batch_processor.py or examples/icon_batch_vision.py
    ↓
Copy the pattern exactly
    ↓
DO NOT guess parameter names
DO NOT try wrapper types
DO NOT improvise API calls

Why This Matters

The Batch API has non-obvious requirements that will fail silently:

1. Metadata must be flat primitives - Nested objects cause cryptic errors
2. Parameter is dest= not destination= - Wrong name → TypeError
3. Config is plain dict - Not a wrapper type
4. Examples are authoritative - Working code beats assumptions

Rationale: Previous agents wasted hours debugging API errors that the examples would have prevented. The patterns in examples/ are battle-tested production code.

Rationalization Table - STOP If You Catch Yourself Thinking:

Red Flags - STOP If You Catch Yourself Thinking:

• "Let me try destination= instead of dest=" → You're about to cause a TypeError. Read examples.
• "I'll create a CreateBatchJobConfig object" → You're instantiating a type instead of using a plain dict. Stop.
• "I'll nest metadata like a normal API" → You'll trigger BigQuery type errors. Flatten your data.
• "This should work like other Google APIs" → Your assumption is wrong; this API is different.
• "I'll figure out the JSONL format" → You'll waste time. Copy from examples instead.

MANDATORY Checklist Before ANY Batch API Code

• Read examples/batch_processor.py OR examples/icon_batch_vision.py
• Identify which example matches the use case (Standard API vs Vertex AI)
• Copy the example's API call pattern exactly
• Copy the example's JSONL structure exactly
• Copy the example's metadata structure exactly
• Adapt for specific needs only after copying base pattern

Enforcement: Writing batch API code without reading examples first violates this IRON LAW and will result in preventable errors.

Prerequisites

Install gcloud SDK

# macOS: Install Google Cloud SDK via Homebrew
brew install google-cloud-sdk

# Linux: Install Google Cloud SDK from official sources
curl https://sdk.cloud.google.com | bash

Authentication Setup

# Authenticate with Google Cloud Platform
gcloud auth login

# Set up Application Default Credentials for Python libraries
gcloud auth application-default login

# Enable Vertex AI API in your project
gcloud services enable aiplatform.googleapis.com

Why both auth methods?

• gcloud auth login: For gsutil and gcloud CLI commands
• gcloud auth application-default login: For google-generativeai Python library
• CRITICAL: Vertex AI requires ADC (step 2), not just API key

Create GCS Bucket

# Create bucket in us-central1 (required region)
gsutil mb -l us-central1 gs://your-batch-bucket

# Verify bucket location is us-central1
gsutil ls -L -b gs://your-batch-bucket | grep "Location"

See references/gcs-setup.md for complete setup guide.

Quick Start

Standard Gemini API (API Key)

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"
)

Vertex AI (Recommended)

import google.generativeai as genai

# Use Vertex AI with ADC
client = genai.Client(
    vertexai=True,
    project="your-project-id",
    location="us-central1"
)

# Submit batch job
job = client.batches.create(
    model="gemini-2.5-flash-lite",
    src="gs://bucket/requests.jsonl",
    dest="gs://bucket/outputs/"
)

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)

IRON LAW: Metadata and API Call Structure

YOU MUST USE FLAT PRIMITIVES FOR METADATA. YOU MUST USE SIMPLE STRINGS FOR API PARAMETERS.

Rule 1: Metadata Structure

CORRECT ✓
"metadata": {
    "request_id": "icon_123",        # String
    "file_name": "copy.svg",         # String
    "file_size": 1024                # Integer
}

WRONG ✗
"metadata": {
    "request_id": "icon_123",
    "file_info": {                   # ← NESTED OBJECT FAILS!
        "name": "copy.svg",
        "size": 1024
    }
}

WORKAROUND (if complex data needed)
"metadata": {
    "request_id": "icon_123",
    "file_info": json.dumps({"name": "copy.svg", "size": 1024})  # JSON string OK
}

Why: Vertex AI stores metadata in BigQuery-compatible format. BigQuery doesn't support nested types. Violation causes: "metadata" in the specified input data is of unsupported type.

Rule 2: API Call Structure

CORRECT ✓
job = client.batches.create(
    model="gemini-2.5-flash-lite",
    src="gs://bucket/input.jsonl",        # Just a string
    dest="gs://bucket/output/",           # Just a string
    config={"display_name": "my-job"}     # Just a dict
)

WRONG ✗
job = client.batches.create(
    model="gemini-2.5-flash-lite",
    src="gs://bucket/input.jsonl",
    destination="gs://bucket/output/",    # ← PARAMETER DOESN'T EXIST!
)

WRONG ✗
job = client.batches.create(
    model="gemini-2.5-flash-lite",
    src="gs://bucket/input.jsonl",
    config=types.CreateBatchJobConfig(    # ← DON'T INSTANTIATE TYPES!
        dest="gs://bucket/output/"
    )
)

Why: The SDK uses simple types. Parameter is dest= (not destination). Config is a plain dict (not a type instance). The SDK converts internally.

Rationalization Table - STOP If You Catch Yourself Thinking:

Pre-Submission Validation

# Add this check BEFORE submitting batch job
def validate_metadata(metadata: dict):
    """Ensure metadata contains only primitive types."""
    for key, value in metadata.items():
        if isinstance(value, (dict, list)):
            raise ValueError(
                f"Metadata '{key}' is {type(value).__name__}. "
                f"Only primitives (str, int, float, bool) allowed. "
                f"Use json.dumps() for complex data."
            )
        if not isinstance(value, (str, int, float, bool, type(None))):
            raise ValueError(f"Unsupported type for '{key}': {type(value)}")

# Validate all requests before submission:
for request in batch_requests:
    validate_metadata(request["metadata"])

Enforcement: Jobs will fail if metadata contains nested objects. There is no workaround for this requirement.

Key Gotchas

Top 2 mistakes (bolded above):

1. Using nested objects in metadata instead of flat primitives
2. Guessing parameter names instead of using dest=

See references/gotchas.md for detailed solutions (now with Gotchas 10 & 11).

Rate Limits

Recommended Models

Additional Resources

References

• references/gcs-setup.md - NEW: Complete GCS and Vertex AI setup guide
• references/gotchas.md - 9 critical production gotchas (updated auth section)
• references/best-practices.md - Idempotent IDs, state tracking, validation
• references/troubleshooting.md - Common errors and debugging
• references/vertex-ai.md - Enterprise alternative with comparison
• references/cli-reference.md - gsutil and gcloud commands

Examples

• examples/icon_batch_vision.py - NEW: Batch vision analysis with Vertex AI
• examples/batch_processor.py - Complete GeminiBatchProcessor class
• examples/pipeline_template.py - Customizable pipeline template

Scripts

• scripts/validate_jsonl.py - Validate JSONL before submission
• scripts/test_single.py - Test single request before batch

External Documentation

• Gemini Batch API Guide
• Google Cloud Storage
• Vertex AI Batch Prediction

Date Awareness

Pattern from oh-my-opencode: Gemini API and documentation evolve rapidly.

Current date: Use datetime.now() for:

• API version checking
• Model availability ("gemini-2.5-flash-lite available as of Dec 2024")
• Documentation freshness validation

For API features or model names with uncertainty, verify against current date and check latest Gemini API documentation.