Overview

GroqLLMService provides access to Groq’s language models through an OpenAI-compatible interface. It inherits from OpenAILLMService and supports streaming responses, function calling, and context management.

Installation

To use GroqLLMService, install the required dependencies:

pip install "pipecat-ai[groq]"

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

Configuration

Constructor Parameters

api_key
str
required

Your Groq API key

model
str
default:"llama-3.3-70b-versatile"

Model identifier

base_url
str
default:"https://api.groq.com/openai/v1"

Groq API endpoint

Input Parameters

Inherits OpenAI-compatible parameters:

frequency_penalty
Optional[float]

Reduces likelihood of repeating tokens based on their frequency. Range: [-2.0, 2.0]

max_tokens
Optional[int]

Maximum number of tokens to generate. Must be greater than or equal to 1

presence_penalty
Optional[float]

Reduces likelihood of repeating any tokens that have appeared. Range: [-2.0, 2.0]

temperature
Optional[float]

Controls randomness in the output. Range: [0.0, 2.0]

top_p
Optional[float]

Controls diversity via nucleus sampling. Range: [0.0, 1.0]

Usage Example

from pipecat.services.groq import GroqLLMService
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from openai.types.chat import ChatCompletionToolParam
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.task import PipelineParams, PipelineTask

# Configure service
llm = GroqLLMService(
    api_key="your-groq-api-key",
    model="llama-3.3-70b-versatile"
)

# Define weather function using standardized schema
weather_function = FunctionSchema(
    name="get_current_weather",
    description="Get the current weather",
    properties={
        "location": {
            "type": "string",
            "description": "The city and state, e.g. San Francisco, CA"
        },
        "format": {
            "type": "string",
            "enum": ["celsius", "fahrenheit"],
            "description": "The temperature unit to use"
        }
    },
    required=["location", "format"]
)

# Create tools schema
tools = ToolsSchema(standard_tools=[weather_function])

# Create context with system message and tools
context = OpenAILLMContext(
    messages=[
        {
            "role": "system",
            "content": "You are a helpful assistant in a voice conversation. Keep responses concise."
        }
    ],
    tools=tools
)

# Register function handlers
async def fetch_weather(function_name, tool_call_id, args, llm, context, result_callback):
    await result_callback({"conditions": "nice", "temperature": "75"})

llm.register_function("get_current_weather", fetch_weather)

# Create context aggregator for message handling
context_aggregator = llm.create_context_aggregator(context)

# Set up pipeline
pipeline = Pipeline([
    transport.input(),
    context_aggregator.user(),
    llm,
    tts,
    transport.output(),
    context_aggregator.assistant()
])

# Create and configure task
task = PipelineTask(
    pipeline,
    params=PipelineParams(
        allow_interruptions=True,
        enable_metrics=True,
        enable_usage_metrics=True,
    ),
)

Methods

See the LLM base class methods for additional functionality.

Function Calling

This service supports function calling (also known as tool calling) which allows the LLM to request information from external services and APIs. For example, you can enable your bot to:

  • Check current weather conditions
  • Query databases
  • Access external APIs
  • Perform custom actions

Function Calling Guide

Learn how to implement function calling with standardized schemas, register handlers, manage context properly, and control execution flow in your conversational AI applications.

Available Models

Model NameDescription
llama-3.3-70b-versatileLlama 3.3 70B versatile model
llama-3.2-90b-vision-previewLlama 3.2 90B vision model (Preview)
llama-3.2-11b-vision-previewLlama 3.2 11B vision model (Preview)
llama-3.1-8b-instantLlama 3.1 8B instant model
mixtral-8x7b-chatMixtral 8x7B chat model
gemma-7b-itGemma 7B instruction model

See Groq’s docs for a complete list of supported models.

Frame Flow

Inherits the OpenAI LLM Service frame flow:

Metrics Support

The service collects standard LLM metrics:

  • Token usage (prompt and completion)
  • Processing duration
  • Time to First Byte (TTFB)
  • Function call metrics

Notes

  • OpenAI-compatible interface
  • Supports streaming responses
  • Handles function calling
  • Manages conversation context
  • Includes token usage tracking
  • Thread-safe processing
  • Automatic error handling