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Overview

Google Gemini is Google’s most capable AI model family, offering multimodal capabilities including text, vision, audio, and code. Access Gemini through Portkey for advanced reasoning, long context understanding, and function calling. Base URL: https://generativelanguage.googleapis.com

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Supported Features

  • ✅ Chat Completions (including streaming)
  • ✅ Embeddings
  • ✅ Function Calling
  • ✅ Vision (Image and Video inputs)
  • ✅ Audio Understanding
  • ✅ Long Context (up to 2M tokens)
  • ✅ JSON Mode
  • ✅ System Instructions
  • ❌ Image Generation (use Vertex AI)
  • ❌ Fine-tuning (use Vertex AI)

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Quick Start

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Chat Completions

from portkey_ai import Portkey

client = Portkey(
    provider="google",
    api_key="***"  # Your Google AI Studio API key
)

response = client.chat.completions.create(
    model="gemini-2.0-flash-exp",
    messages=[
        {"role": "user", "content": "Explain how Gemini differs from other AI models"}
    ]
)

print(response.choices[0].message.content)

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Streaming

stream = client.chat.completions.create(
    model="gemini-2.0-flash-exp",
    messages=[{"role": "user", "content": "Write a poem about AI"}],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

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Available Models

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Gemini 2.0 (Latest)

ModelContext WindowDescriptionBest For
gemini-2.0-flash-exp1M tokensLatest experimental Gemini 2.0General purpose, fast
gemini-2.0-flash-thinking-exp32K tokensReasoning model (experimental)Complex problem solving

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Gemini 1.5

ModelContext WindowDescriptionBest For
gemini-1.5-pro2M tokensMost capable Gemini 1.5Complex tasks, long context
gemini-1.5-flash1M tokensFast, efficient modelHigh-throughput applications
gemini-1.5-flash-8b1M tokensSmallest, fastestCost-effective tasks

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Embeddings

ModelDimensionsDescription
text-embedding-004768Latest embedding model
text-multilingual-embedding-002768Multilingual support
Gemini models excel at:
  • Long context understanding (up to 2M tokens)
  • Multimodal reasoning (text, images, video, audio)
  • Code generation and analysis
  • Multilingual capabilities

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Configuration Options

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Getting Your API Key

  1. Go to Google AI Studio
  2. Click Get API Key
  3. Create or select a project
  4. Copy your API key
client = Portkey(
    provider="google",
    api_key="AIza***"  # Your Google AI Studio API key
)

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Advanced Features

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Vision (Image Understanding)

response = client.chat.completions.create(
    model="gemini-2.0-flash-exp",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "What's in this image?"},
            {
                "type": "image_url",
                "image_url": {
                    "url": "https://example.com/image.jpg"
                }
            }
        ]
    }]
)
Base64 images: 
import base64

with open("image.jpg", "rb") as f:
    image_data = base64.b64encode(f.read()).decode()

response = client.chat.completions.create(
    model="gemini-2.0-flash-exp",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Describe this image"},
            {
                "type": "image_url",
                "image_url": {"url": f"data:image/jpeg;base64,{image_data}"}
            }
        ]
    }]
)

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Function Calling

tools = [
    {
        "type": "function",
        "function": {
            "name": "search_web",
            "description": "Search the web for information",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {
                        "type": "string",
                        "description": "The search query"
                    }
                },
                "required": ["query"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="gemini-2.0-flash-exp",
    messages=[{"role": "user", "content": "Search for the latest AI news"}],
    tools=tools
)

if response.choices[0].message.tool_calls:
    tool_call = response.choices[0].message.tool_calls[0]
    print(f"Function: {tool_call.function.name}")
    print(f"Arguments: {tool_call.function.arguments}")

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System Instructions

response = client.chat.completions.create(
    model="gemini-2.0-flash-exp",
    messages=[
        {
            "role": "system",
            "content": "You are a helpful Python programming expert. Always provide working code examples."
        },
        {
            "role": "user",
            "content": "How do I read a JSON file?"
        }
    ]
)

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Long Context Processing

Gemini excels at processing very long documents: 
# Process a very long document (up to 2M tokens with Gemini 1.5 Pro)
long_document = """[Your very long document here - up to 2 million tokens]"""

response = client.chat.completions.create(
    model="gemini-1.5-pro",
    messages=[
        {"role": "user", "content": f"Summarize this document:\n\n{long_document}"}
    ]
)

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JSON Mode

response = client.chat.completions.create(
    model="gemini-2.0-flash-exp",
    messages=[{
        "role": "user",
        "content": "List 3 colors with their hex codes"
    }],
    response_format={"type": "json_object"}
)

import json
result = json.loads(response.choices[0].message.content)
print(result)

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Embeddings

response = client.embeddings.create(
    model="text-embedding-004",
    input="Gemini is Google's most capable AI model"
)

embedding = response.data[0].embedding
print(f"Embedding dimension: {len(embedding)}")
Batch embeddings: 
response = client.embeddings.create(
    model="text-embedding-004",
    input=[
        "First document to embed",
        "Second document to embed",
        "Third document to embed"
    ]
)

for i, item in enumerate(response.data):
    print(f"Document {i}: {len(item.embedding)} dimensions")

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Fallback Configuration

Fallback to GPT-4 if Gemini fails: 
config = {
    "strategy": {"mode": "fallback"},
    "targets": [
        {
            "provider": "google",
            "api_key": "AIza***",
            "override_params": {"model": "gemini-2.0-flash-exp"}
        },
        {
            "provider": "openai",
            "api_key": "sk-***",
            "override_params": {"model": "gpt-4o"}
        }
    ]
}

client = Portkey().with_options(config=config)

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Load Balancing

Balance between different Gemini models: 
config = {
    "strategy": {"mode": "loadbalance"},
    "targets": [
        {
            "provider": "google",
            "api_key": "AIza***",
            "override_params": {"model": "gemini-1.5-pro"},
            "weight": 0.3
        },
        {
            "provider": "google",
            "api_key": "AIza***",
            "override_params": {"model": "gemini-1.5-flash"},
            "weight": 0.7
        }
    ]
}

client = Portkey().with_options(config=config)

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Error Handling

from portkey_ai.exceptions import (
    RateLimitError,
    APIError,
    AuthenticationError
)

try:
    response = client.chat.completions.create(
        model="gemini-2.0-flash-exp",
        messages=[{"role": "user", "content": "Hello"}]
    )
except RateLimitError as e:
    print(f"Rate limit: {e}")
except AuthenticationError as e:
    print(f"Invalid API key: {e}")
except APIError as e:
    print(f"API error: {e}")

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Key Features

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Context Windows

ModelContext WindowNotes
gemini-1.5-pro2,097,152 tokensLargest available
gemini-1.5-flash1,048,576 tokensFast processing
gemini-2.0-flash-exp1,048,576 tokensLatest generation
gemini-2.0-flash-thinking-exp32,768 tokensReasoning focused

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Safety Settings

Gemini includes built-in safety filters. Responses may be blocked if content violates safety thresholds.

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Rate Limits

  • Free tier: 15 requests per minute
  • Pay-as-you-go: Higher limits based on usage

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Best Practices

  1. Use Flash for speed - Gemini Flash is significantly faster
  2. Leverage long context - Process entire documents in one request
  3. Multimodal inputs - Combine text, images, and more
  4. System instructions - Guide behavior with clear instructions
  5. Handle safety blocks - Implement fallbacks for blocked responses
  6. Use embeddings - text-embedding-004 for semantic search
  7. Stream responses - Better UX for long generations

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Gemini vs Vertex AI

FeatureGoogle AI (Gemini)Vertex AI
AccessGoogle AI Studio API keyGCP Service Account
PricingPay-per-requestEnterprise pricing
FeaturesCore featuresAdditional enterprise features
AuthenticationAPI keyOAuth 2.0, Service Accounts
Use CaseDevelopment, small appsProduction, enterprise
For enterprise deployments, consider using Google Vertex AI which offers additional features like fine-tuning, private endpoints, and SLA.

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Pricing

Gemini offers competitive pricing with a free tier:

Gemini Pricing

View detailed pricing for all Gemini models

Google Vertex AI

Enterprise Gemini through GCP

Function Calling

Advanced function calling

Vision Guide

Working with images

Fallbacks

Fallback configurations

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