Google

The Google provider gives you access to Gemini models, Google’s multimodal AI models with strong performance across various tasks.

Setup

Set your API key as an environment variable:

export GOOGLE_API_KEY="..."

Usage

from ai_query import generate_text
from ai_query.providers import google

result = await generate_text(
    model=google("gemini-2.5-flash"),
    prompt="Summarize the key features of Python."
)

Available Models

Model	Description
`gemini-3-pro`	Gemini 3 Pro - most capable
`gemini-3-flash`	Gemini 3 Flash - fast and powerful
`gemini-2.5-pro`	Gemini 2.5 Pro - advanced reasoning
`gemini-2.5-flash`	Gemini 2.5 Flash - balanced performance
`gemini-2.5-flash-lite`	Gemini 2.5 Flash-Lite - lightweight
`gemma-3-27b-it`	Gemma 3 (27B) - open weights

Provider Options

Customize Google-specific parameters:

result = await generate_text(
    model=google("gemini-2.0-flash"),
    prompt="Write a story.",
    provider_options={
        "google": {
            "temperature": 0.7,
            "top_p": 0.95,
            "top_k": 40,
            "max_output_tokens": 2048,
            "presence_penalty": 0.0,
            "frequency_penalty": 0.0,
            "seed": 42
        }
    }
)

Available Options

Option	Type	Default	Description
`temperature`	`float`	`1.0`	Randomness (0-2)
`max_output_tokens`	`int`	Model default	Maximum output tokens
`top_p`	`float`	`0.95`	Nucleus sampling
`top_k`	`int`	`40`	Top-k sampling
`candidate_count`	`int`	`1`	Number of responses
`stop_sequences`	`list[str]`	`[]`	Stop generation at these sequences
`presence_penalty`	`float`	`0.0`	Penalize repeated tokens
`frequency_penalty`	`float`	`0.0`	Penalize frequent tokens
`seed`	`int`	-	Random seed for reproducibility
`response_mime_type`	`str`	-	Output format (e.g., `"application/json"`)
`response_schema`	`dict`	-	JSON schema for structured output

Thinking Config (Reasoning Models)

For normalized reasoning-depth control, use reasoning={"budget": ...}:

result = await generate_text(
    model=google("gemini-2.5-pro"),
    prompt="Solve this carefully.",
    reasoning={"budget": 1024},
)

For provider-specific Gemini thinking options, use raw provider_options:

result = await generate_text(
    model=google("gemini-3-flash"),
    prompt="Solve this complex math problem...",
    provider_options={
        "google": {
            "thinking_config": {
                "include_thoughts": True,
                "thinking_budget": 1024
            }
        }
    }
)

If you want Gemini thought text to arrive as reasoning events during streaming, include_thoughts must be enabled. See Thinking for the streaming callback model.

Safety Settings

Configure content safety thresholds. You can use either dict or list format:

# Dict format (shorthand)
result = await generate_text(
    model=google("gemini-2.0-flash"),
    prompt="Write a thriller story.",
    provider_options={
        "google": {
            "safety_settings": {
                "HARM_CATEGORY_VIOLENCE": "BLOCK_ONLY_HIGH",
                "HARM_CATEGORY_HATE_SPEECH": "BLOCK_MEDIUM_AND_ABOVE"
            }
        }
    }
)

# List format (official API format)
result = await generate_text(
    model=google("gemini-2.0-flash"),
    prompt="Write a thriller story.",
    provider_options={
        "google": {
            "safety_settings": [
                {"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_LOW_AND_ABOVE"},
                {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
                {"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_ONLY_HIGH"},
                {"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_NONE"}
            ]
        }
    }
)

Available thresholds: BLOCK_NONE, BLOCK_ONLY_HIGH, BLOCK_MEDIUM_AND_ABOVE, BLOCK_LOW_AND_ABOVE

Tool Calling

Gemini models support tool calling:

from ai_query import generate_text
from ai_query.providers import google, tool, Field

@tool(description="Get current stock price")
async def get_stock_price(
    symbol: str = Field(description="Stock ticker symbol")
) -> str:
    # Simulated stock data
    prices = {"GOOGL": 175.50, "AAPL": 150.25}
    price = prices.get(symbol.upper(), 0)
    return f"${price:.2f}"

result = await generate_text(
    model=google("gemini-2.0-flash"),
    prompt="What's the stock price of Google?",
    tools={"get_stock_price": get_stock_price}
)

Streaming

from ai_query import stream_text
from ai_query.providers import google

result = stream_text(
    model=google("gemini-2.0-flash"),
    prompt="Explain how neural networks learn."
)

async for chunk in result.text_stream:
    print(chunk, end="", flush=True)

Overview

Native Providers

OpenAI-Compatible

Cloud Providers

Setup

Usage

Available Models

Provider Options

Available Options

Thinking Config (Reasoning Models)

Safety Settings

Tool Calling

Streaming

​Setup

​Usage

​Available Models

​Provider Options

​Available Options

​Thinking Config (Reasoning Models)

​Safety Settings

​Tool Calling

​Streaming

Setup

Usage

Available Models

Provider Options

Available Options

Thinking Config (Reasoning Models)

Safety Settings

Tool Calling

Streaming