Invoking Gemini

Delegate tasks to Google's Gemini models when they offer advantages over Claude.

When to Use Gemini

Structured outputs:

• JSON Schema validation with property ordering guarantees
• Pydantic model compliance
• Strict schema adherence (enum values, required fields)

Cost optimization:

• Parallel batch processing (Gemini Flash is lightweight)
• High-volume simple tasks
• Budget-constrained operations

Google ecosystem:

• Integration with Google services
• Vertex AI workflows
• Google-specific APIs

Multi-modal tasks:

• Image analysis with JSON output
• Video processing
• Audio transcription with structure

Available Models

gemini-2.0-flash-exp (Recommended):

• Fast, cost-effective
• Native JSON Schema support
• Good for structured outputs

gemini-1.5-pro:

• More capable reasoning
• Better for complex tasks
• Higher cost

gemini-1.5-flash:

• Balanced speed/quality
• Good for most tasks

See references/models.md for full model details.

Setup

Prerequisites:

1. Install google-generativeai:

   pip install google-generativeai --break-system-packages
   

2. Configure API key via api-credentials skill:

   # Create /home/claude/config.json
   {
     "google_api_key": "AIzaSy..."
   }
   

Basic Usage

Import the client:

import sys
sys.path.append('/mnt/skills/invoking-gemini/scripts')
from gemini_client import invoke_gemini

# Simple prompt
response = invoke_gemini(
    prompt="Explain quantum computing in 3 bullet points",
    model="gemini-2.0-flash-exp"
)
print(response)

Structured Output

Use Pydantic models for guaranteed JSON Schema compliance:

from pydantic import BaseModel, Field
from gemini_client import invoke_with_structured_output

class BookAnalysis(BaseModel):
    title: str
    genre: str = Field(description="Primary genre")
    key_themes: list[str] = Field(max_length=5)
    rating: int = Field(ge=1, le=5)

result = invoke_with_structured_output(
    prompt="Analyze the book '1984' by George Orwell",
    pydantic_model=BookAnalysis
)

# result is a BookAnalysis instance
print(result.title)  # "1984"
print(result.genre)  # "Dystopian Fiction"

Advantages over Claude:

• Guaranteed property ordering in JSON
• Strict enum enforcement
• Native schema validation (no prompt engineering)
• Lower cost for simple extractions

Parallel Invocation

Process multiple prompts concurrently:

from gemini_client import invoke_parallel

prompts = [
    "Summarize the plot of Hamlet",
    "Summarize the plot of Macbeth",
    "Summarize the plot of Othello"
]

results = invoke_parallel(
    prompts=prompts,
    model="gemini-2.0-flash-exp"
)

for prompt, result in zip(prompts, results):
    print(f"Q: {prompt[:30]}...")
    print(f"A: {result[:100]}...\n")

Use cases:

• Batch classification tasks
• Data labeling
• Multiple independent analyses
• A/B testing prompts

Error Handling

The client handles common errors:

from gemini_client import invoke_gemini

response = invoke_gemini(
    prompt="Your prompt here",
    model="gemini-2.0-flash-exp"
)

if response is None:
    print("Error: API call failed")
    # Check /home/claude/config.json for valid google_api_key

Common issues:

• Missing API key → Returns None, prints error
• Invalid model → Raises ValueError
• Rate limit → Automatically retries with backoff
• Network error → Returns None after retries

Advanced Features

Custom Generation Config

response = invoke_gemini(
    prompt="Write a haiku",
    model="gemini-2.0-flash-exp",
    temperature=0.9,
    max_output_tokens=100,
    top_p=0.95
)

Multi-modal Input

# Image analysis with structured output
from pydantic import BaseModel

class ImageDescription(BaseModel):
    objects: list[str]
    scene: str
    colors: list[str]

result = invoke_with_structured_output(
    prompt="Describe this image",
    pydantic_model=ImageDescription,
    image_path="/mnt/user-data/uploads/photo.jpg"
)

See references/advanced.md for more patterns.

Comparison: Gemini vs Claude

Use Gemini when:

• Structured output is primary goal
• Cost is a constraint
• Property ordering matters
• Batch processing many simple tasks

Use Claude when:

• Complex reasoning required
• Long context needed (200K tokens)
• Code generation quality matters
• Nuanced instruction following

Use both:

• Claude for planning/reasoning
• Gemini for structured extraction
• Parallel workflows with different strengths

Token Efficiency Pattern

Gemini Flash is cost-effective for sub-tasks:

# Claude (you) plans the approach
# Gemini executes structured extractions

data_points = []
for file in uploaded_files:
    # Gemini extracts structured data
    result = invoke_with_structured_output(
        prompt=f"Extract contact info from {file}",
        pydantic_model=ContactInfo
    )
    data_points.append(result)

# Claude synthesizes results
# ... your analysis here ...

Limitations

Not suitable for:

• Tasks requiring deep reasoning
• Long context (>1M tokens)
• Complex code generation
• Subjective creative writing

Token limits:

• gemini-2.0-flash-exp: ~1M input tokens
• gemini-1.5-pro: ~2M input tokens

Rate limits:

• Vary by API tier
• Client handles automatic retry

Examples

See references/examples.md for:

• Data extraction from documents
• Batch classification
• Multi-modal analysis
• Hybrid Claude+Gemini workflows

Troubleshooting

"API key not configured":

# Create config file
cat > /home/claude/config.json << EOF
{
  "google_api_key": "AIzaSy..."
}
EOF

Import errors:

pip install google-generativeai --break-system-packages

Schema validation failures:

• Check Pydantic model definitions
• Ensure prompt is clear about expected structure
• Add examples to prompt if needed

Cost Comparison

Approximate pricing (as of 2024):

Gemini 2.0 Flash:

• Input: $0.15 / 1M tokens
• Output: $0.60 / 1M tokens

Claude Sonnet:

• Input: $3.00 / 1M tokens
• Output: $15.00 / 1M tokens

For 1000 simple extraction tasks (100 tokens each):

• Gemini Flash: ~$0.10
• Claude Sonnet: ~$2.00

Strategy: Use Claude for complex reasoning, Gemini for high-volume simple tasks.