Google Gemini API

--- name: Google Gemini API description: Use this skill when you need to integrate Google's Gemini AI models for text generation, multimodal AI tasks, or building AI-powered applications. Essential for developers working with generative AI, content creation, code generation, or image analysis. metadata: author: skynet version: 1.0.0 category: dev --- # Google Gemini API ## Installation and Setup ### Install the SDK ```bash # Python pip install google-generativeai # Node.js npm install @google/generative-ai # cURL (no installation needed) curl --version ``` ### API Key Setup ```bash # Set environment variable export GOOGLE_API_KEY="your-api-key-here" # Or create .env file echo "GOOGLE_API_KEY=your-api-key-here" > .env ``` ## Quick Start Examples ### Python Basic Text Generation ```python import google.generativeai as genai import os genai.configure(api_key=os.environ["GOOGLE_API_KEY"]) model = genai.GenerativeModel('gemini-pro') response = model.generate_content("Write a haiku about coding") print(response.text) ``` ### Node.js Basic Implementation ```javascript import { GoogleGenerativeAI } from "@google/generative-ai"; const genAI = new GoogleGenerativeAI(process.env.GOOGLE_API_KEY); const model = genAI.getGenerativeModel({ model: "gemini-pro" }); const result = await model.generateContent("Explain quantum computing"); console.log(result.response.text()); ``` ### cURL API Call ```bash curl -X POST \ -H "Content-Type: application/json" \ -H "x-goog-api-key: ${GOOGLE_API_KEY}" \ -d '{ "contents": [{ "parts": [{"text": "Write a Python function to sort a list"}] }] }' \ "https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateContent" ``` ## Model Selection Decision Tree ``` Need to process images/video? ├─ Yes: Use gemini-pro-vision │ └─ High quality images → gemini-1.5-pro │ └─ General use → gemini-pro-vision └─ No: Text only ├─ Complex reasoning needed → gemini-1.5-pro ├─ General use → gemini-pro └─ Fast/simple tasks → gemini-pro ``` ## Common Workflows ### Multimodal Image Analysis ```python import PIL.Image import google.generativeai as genai genai.configure(api_key=os.environ["GOOGLE_API_KEY"]) model = genai.GenerativeModel('gemini-pro-vision') image = PIL.Image.open('image.jpg') response = model.generate_content([ "What's in this image? Describe it in detail.", image ]) print(response.text) ``` ### Streaming Responses ```python model = genai.GenerativeModel('gemini-pro') response = model.generate_content( "Write a long story about space exploration", stream=True ) for chunk in response: print(chunk.text, end='') ``` ### Safety Settings Configuration ```python safety_settings = [ { "category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_MEDIUM_AND_ABOVE" }, { "category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_MEDIUM_AND_ABOVE" } ] model = genai.GenerativeModel( 'gemini-pro', safety_settings=safety_settings ) ``` ### Generation Configuration ```python generation_config = genai.types.GenerationConfig( candidate_count=1, max_output_tokens=1000, temperature=0.7, top_p=0.8, top_k=40 ) response = model.generate_content( "Write creative marketing copy", generation_config=generation_config ) ``` ## Advanced Features ### Chat Conversations ```python model = genai.GenerativeModel('gemini-pro') chat = model.start_chat(history=[]) response = chat.send_message("Hello, I'm working on a Python project") print(response.text) response = chat.send_message("Can you help me with error handling?") print(response.text) ``` ### Batch Processing ```python prompts = [ "Summarize the benefits of renewable energy", "Explain machine learning in simple terms", "Write a product description for a smartwatch" ] responses = [] for prompt in prompts: response = model.generate_content(prompt) responses.append(response.text) ``` ### Function Calling (Tool Use) ```python def get_weather(location: str) -> str: return f"Weather in {location}: Sunny, 72°F" tools = [get_weather] model = genai.GenerativeModel('gemini-pro', tools=tools) response = model.generate_content( "What's the weather like in New York?" ) ``` ## Error Handling and Troubleshooting ### Common Errors and Solutions **API Key Error:** ``` google.api_core.exceptions.Unauthenticated: 401 API key not valid ``` **Fix:** ```bash # Verify API key is set echo $GOOGLE_API_KEY # Regenerate key in Google AI Studio # Ensure key has proper permissions ``` **Rate Limit Error:** ``` google.api_core.exceptions.ResourceExhausted: 429 Quota exceeded ``` **Fix:** ```python import time from google.api_core import retry @retry.Retry(predicate=retry.if_exception_type(Exception)) def generate_with_retry(): return model.generate_content(prompt) ``` **Content Filtering:** ``` google.generativeai.types.BlockedPromptException: Prompt was blocked ``` **Fix:** ```python # Adjust safety settings or rephrase prompt safety_settings = [ { "category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_ONLY_HIGH" } ] ``` ### Validation Checks ```python def validate_response(response): if not response.text: if response.prompt_feedback.block_reason: print(f"Blocked: {response.prompt_feedback.block_reason}") return False return True response = model.generate_content("Your prompt here") if validate_response(response): print(response.text) ``` ## Performance Optimization ### Response Caching ```python import functools @functools.lru_cache(maxsize=100) def cached_generate(prompt): return model.generate_content(prompt).text # Use for repeated similar prompts result = cached_generate("Explain photosynthesis") ``` ### Async Operations (Node.js) ```javascript async function batchGenerate(prompts) { const promises = prompts.map(prompt => model.generateContent(prompt) ); const results = await Promise.all(promises); return results.map(result => result.response.text()); } ``` ## Monitoring and Logging ### Usage Tracking ```python import logging logging.basicConfig(level=logging.INFO) def tracked_generate(prompt): start_time = time.time() try: response = model.generate_content(prompt) duration = time.time() - start_time logging.info(f"Generated {len(response.text)} chars in {duration:.2f}s") return response.text except Exception as e: logging.error(f"Generation failed: {e}") raise ``` ## Best Practices 1. **Prompt Engineering**: Be specific and provide context 2. **Error Handling**: Always wrap API calls in try-catch blocks 3. **Rate Limiting**: Implement exponential backoff for retries 4. **Security**: Never hardcode API keys in source code 5. **Content Filtering**: Review safety settings for your use case 6. **Cost Management**: Monitor token usage and set limits