Google Gemini

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Overview

GoogleLLMService provides integration with Google’s Gemini models, supporting streaming responses, function calling, and multimodal inputs. It includes specialized context handling for Google’s message format while maintaining compatibility with OpenAI-style contexts.

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Installation

To use GoogleLLMService, install the required dependencies:

pip install pipecat-ai[google]

You’ll also need to set up your Google API key as an environment variable: GOOGLE_API_KEY

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Configuration

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Constructor Parameters

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Input Parameters

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Input Frames

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Output Frames

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Context Management

The Google service uses specialized context management to handle conversations and message formatting. This includes managing the conversation history, system prompts, function calls, and converting between OpenAI and Google message formats.

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GoogleLLMContext

The base context manager for Google conversations:

context = GoogleLLMContext(
    messages=[],  # Conversation history
    tools=[],     # Available function calling tools
    system="You are a helpful assistant"  # System prompt
)

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Context Aggregators

Context aggregators handle message format conversion and management. The service provides a method to create paired aggregators:

@staticmethod
def create_context_aggregator(
    context: OpenAILLMContext,
    *,
    assistant_expect_stripped_words: bool = True
) -> GoogleContextAggregatorPair

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Usage Example

# 1. Create the context
context = GoogleLLMContext(
    messages=[],
    system="You are a helpful assistant"
)

# 2. Create aggregators for message handling
aggregators = GoogleLLMService.create_context_aggregator(context)

# 3. Access individual aggregators
user_aggregator = aggregators.user()      # Handles user message formatting
assistant_aggregator = aggregators.assistant()  # Handles assistant responses

# 4. Use in a pipeline
pipeline = Pipeline([
    user_aggregator,
    llm_service,
    assistant_aggregator
])

The context management system ensures proper message formatting and history tracking throughout the conversation while handling the conversion between OpenAI and Google message formats automatically.

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Methods

See the LLM base class methods for additional functionality.

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Usage Examples

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Basic Usage

# Configure service
llm_service = GoogleLLMService(
    api_key="your-api-key",
    model="gemini-1.5-flash-latest",
    params=GoogleLLMService.InputParams(
        temperature=0.7,
        max_tokens=1000
    )
)

# Create pipeline
pipeline = Pipeline([
    context_manager,
    llm_service,
    response_handler
])

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With Function Calling

# Configure function calling
context = GoogleLLMContext()
context.add_tool({
    "function_declarations": [{
        "name": "get_weather",
        "description": "Get weather information",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {"type": "string"}
            }
        }
    }]
})

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Frame Flow

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Metrics Support

The service collects various metrics:

• Token usage (prompt and completion)
• Processing time
• Time to first byte (TTFB)

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Notes

• Supports streaming responses
• Handles function calling
• Provides OpenAI compatibility
• Manages conversation context
• Supports vision inputs
• Includes metrics collection
• Thread-safe processing