Google’s Gemini Cookbook is your ultimate hands-on guide to mastering the Gemini API. This official collection of Jupyter notebooks and quickstarts transforms beginners into production-ready developers by providing structured, copy-paste-ready examples for text generation, embeddings, function calling, streaming, multimodal inputs, and structured outputs.

Whether you’re building chatbots, RAG systems, or multimodal apps, the Cookbook equips you with battle-tested patterns used by Google’s AI engineers.

What is the Gemini Cookbook?

The Gemini Cookbook is an official GitHub repository (google-gemini/cookbook) maintained by Google, featuring 50+ Jupyter notebooks organized into quickstarts and examples. It covers every major Gemini API capability with complete, runnable code.

Key structure:

cookbook/
├── quickstarts/     # Foundational 5-minute tutorials
├── examples/        # Advanced real-world patterns
└── README.md        # Navigation + API keys setup

Why it’s essential:

  • Production-ready code: Direct from Google engineers, optimized for scale
  • Multimodal focus: Images, audio, video + text in single APIs
  • Battle-tested patterns: Function calling, grounding, safety settings
  • Zero setup: Colab-ready notebooks with one-click API key integration

Quickstart: Get Your API Key (2 Minutes)

  1. Visit ai.google.dev → Get API key
  2. Open any Cookbook notebook in Colab
  3. Paste your key: import google.generativeai as genai; genai.configure(api_key="YOUR_KEY")
import google.generativeai as genai
genai.configure(api_key="your_api_key_here")
model = genai.GenerativeModel('gemini-1.5-flash')

1. Text Generation: Your First Prompt

Python SDK (from Cookbook quickstarts):

model = genai.GenerativeModel('gemini-1.5-flash')
response = model.generate_content("Write a haiku about machine learning")
print(response.text)

REST API equivalent:

curl https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:generateContent?key=$API_KEY \
-H 'Content-Type: application/json' \
-d '{
  "contents": [{    "parts":[{      "text": "Write a haiku about machine learning"
    }]
  }]
}'

Pro prompting tip: Always specify format - “Respond as JSON with keys: title, summary, keywords”

2. Embeddings: Power Your RAG Pipeline

Gemini embeddings (gemini-embedding-001) generate 3072D vectors optimized for retrieval, clustering, and semantic search.[1]

Batch embeddings (process 1000s efficiently):

result = genai.embed_content(
    model="models/embedding-001",
    content=["What is AI?", "Machine learning basics"],
    task_type="RETRIEVAL_DOCUMENT"
)
embeddings = [e['embedding'] for e in result['embedding']]

REST:

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-001:embedContent?key=$API_KEY" \
-H 'Content-Type: application/json' \
-d '{
  "content": {"parts":[{"text": "What is AI?"}]},
  "taskType": "RETRIEVAL_DOCUMENT"
}'

Best practice: Use task_type="RETRIEVAL_QUERY" for search queries, "RETRIEVAL_DOCUMENT" for docs.[1]

3. Function Calling: Build Agentic Workflows

Gemini excels at structured function calling - define tools, Gemini decides when/how to call them.

Cookbook example (weather agent):

def get_weather(city):
    return f"Weather in {city}: 72°F, sunny"

model = genai.GenerativeModel(
    model_name="gemini-1.5-flash",
    tools='get_weather'
)

response = model.generate_content("What's the weather in San Francisco?")
# Gemini automatically calls get_weather("San Francisco")

Full REST implementation:

{
  "contents": [{"parts": [{"text": "Weather in NYC?"}]}],
  "tools": [{    "functionDeclarations": [{      "name": "get_weather",
      "description": "Get current weather",
      "parameters": {
        "type": "object",
        "properties": {"city": {"type": "string"}}
      }
    }]
  }]
}

Pitfall: Always include required: ["city"] in parameters schema.

4. Streaming: Real-Time Chat UX

Stream responses token-by-token for chat-like responsiveness:

response = model.generate_content("Tell a story", stream=True)
for chunk in response:
    print(chunk.text, end='', flush=True)

REST streaming:

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:streamGenerateContent?key=$API_KEY" \
-H 'Content-Type: application/json' \
-d '{"contents":[{"parts":[{"text":"Write a story"}]}]}'

5. Multimodal Magic: Images + Audio + Video

Gemini 1.5 processes 1M+ tokens across modalities natively.

Image analysis:

img = genai.upload_file("chart.png")
response = model.generate_content(["Analyze this sales chart", img])

Audio transcription + reasoning:

audio = genai.upload_file("meeting.mp3")
response = model.generate_content([    "Transcribe this meeting and extract action items:", audio
])

Video understanding:

video = genai.upload_file("demo.mp4")
response = model.generate_content([    "Summarize key moments in 30s:", video
])

REST multimodal:

{
  "contents": [{    "parts": [      {"text": "What's in this image?"},
      {
        "inline_data": {
          "mime_type": "image/jpeg",
          "data": "base64_encoded_image"
        }
      }
    ]
  }]
}

6. Structured Outputs: JSON Guaranteed

Force JSON responses with response_mime_type="application/json":

model = genai.GenerativeModel(
    'gemini-1.5-flash',
    generation_config={
        "response_mime_type": "application/json",
        "response_schema": {
            "type": "object",
            "properties": {
                "title": {"type": "string"},
                "summary": {"type": "string"}
            }
        }
    }
)
response = model.generate_content("Analyze this article")
import json
result = json.loads(response.text)

System Instructions: Consistent AI Personas

Define behavior once, reuse everywhere:

model = genai.GenerativeModel(
    'gemini-1.5-flash',
    system_instruction="""
    You are a helpful coding assistant. Always respond with:
    1. Working code
    2. Explanation
    3. Tests
    Format as markdown.
    """
)

Common Pitfalls & Pro Tips

❌ Pitfalls

  • Rate limits: 60 RPM free tier → Use batching + exponential backoff
  • Context overflow: 1M tokens ≠ free lunch → Chunk strategically
  • No safety settings: Enable block_nsfw/harassment for production
  • Missing error handling: Always wrap in try/catch

✅ Best Practices

# Production template
model = genai.GenerativeModel(
    'gemini-1.5-pro',
    system_instruction=SYSTEM_PROMPT,
    generation_config={
        'temperature': 0.1,  # Deterministic for tools
        'top_p': 0.95,
        'max_output_tokens': 2048
    },
    safety_settings={
        'HARM_CATEGORY_HARASSMENT': 'BLOCK_MEDIUM_AND_ABOVE'
    }
)

Scaling tips:

  • Async batching: Process 1000s via client.batch_embed_contents()
  • Vertex AI: Enterprise → Use for higher quotas + fine-tuning
  • Caching: Redis for embeddings, deterministic prompts
  • Monitoring: Track usageMetadata for cost optimization

Building Your First Production App: RAG Chatbot

Complete example combining everything:

import google.generativeai as genai
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np

# 1. Embed your docs
docs = ["Gemini is multimodal", "API supports function calling"]
doc_embeds = genai.embed_content(model="embedding-001", content=docs)

# 2. Query embedding + similarity search
query = "Does Gemini do tools?"
query_embed = genai.embed_content(model="embedding-001", content=[query])
scores = cosine_similarity([query_embed['embedding']], doc_embeds)

# 3. Rerank + context
top_docs = [docs[np.argmax(scores)]]

# 4. Multimodal RAG query
model = genai.GenerativeModel('gemini-1.5-flash')
response = model.generate_content([    f"Context: {top_docs}\n\nUser: {query}"
])

Top 10 Authoritative Gemini Cookbook Resources

  1. Official Gemini Cookbook GitHub - 50+ notebooks, quickstarts, examples
  2. Google AI Developers Cookbook - Web version + latest updates
  3. Gemini TS Cookbook - TypeScript quickstarts
  4. Get Started Notebook - 2-minute setup
  5. System Instructions Guide - Persona engineering
  6. Function Calling Example - Tool integration
  7. Info Extraction Patterns - Structured parsing
  8. Audio Processing Quickstart - Speech-to-insights
  9. Vertex AI Cookbook - Enterprise deployment
  10. Official Gemini API Docs - Complete reference

Next Steps: From Tutorial to Production

  1. Fork the Cookbook → Customize notebooks for your use case
  2. Build 3 apps: Chatbot, RAG search, multimodal analyzer
  3. Deploy to Vertex AI → Scale + compliance
  4. Join community: Google AI Dev Discord + GitHub issues

The Gemini Cookbook isn’t just documentation—it’s a production engineering playbook. Start with “Get Started.ipynb” today and ship your first Gemini app by EOD.

Ready to build? Open this notebook in Colab now.