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Overview

FireworksLLMService provides access to Fireworks AI’s language models through an OpenAI-compatible interface. It inherits from BaseOpenAILLMService and supports streaming responses, function calling, and context management.

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Installation

To use FireworksLLMService, install the required dependencies:

pip install pipecat-ai[fireworks]

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

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Configuration

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Constructor Parameters

api_key
str
required

Your Fireworks AI API key

model
str
default: "accounts/fireworks/models/firefunction-v1"

Model identifier

base_url
str
default: "https://api.fireworks.ai/inference/v1"

Fireworks AI API endpoint

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Input Parameters

Inherits all input parameters from BaseOpenAILLMService:

extra
Optional[Dict[str, Any]]

Additional parameters to pass to the model

frequency_penalty
Optional[float]

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

max_completion_tokens
Optional[int]

Maximum number of tokens in the completion. Must be greater than or equal to 1

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]

seed
Optional[int]

Random seed for deterministic generation. Must be greater than or equal to 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]

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Input Frames

OpenAILLMContextFrame
Frame

Contains OpenAI-specific conversation context

LLMMessagesFrame
Frame

Contains conversation messages

VisionImageRawFrame
Frame

Contains image for vision model processing

LLMUpdateSettingsFrame
Frame

Updates model settings

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Output Frames

TextFrame
Frame

Contains generated text chunks

FunctionCallInProgressFrame
Frame

Indicates start of function call

FunctionCallResultFrame
Frame

Contains function call results

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Context Management

The Fireworks service uses specialized context management to handle conversations and message formatting. It relies on the OpenAI base class for context management, which includes managing the conversation history, system prompts, tool calls, and converting between OpenAI and Fireworks message formats.

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OpenAILLMContext

The base context manager for OpenAI conversations:

context = OpenAILLMContext(
    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:

create_context_aggregator
static method

Creates user and assistant aggregators for handling message formatting.

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

Parameters

context
OpenAILLMContext
required

The context object containing conversation history and settings

assistant_expect_stripped_words
bool
default: "True"

Controls text preprocessing for assistant responses

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Usage Example


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

# 2. Create aggregators for message handling
aggregators = OpenAILLMService.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.

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Methods

See the LLM base class methods for additional functionality.

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Usage Example

from pipecat.services.fireworks import FireworksLLMService
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext

# Configure service
service = FireworksLLMService(
    api_key="your-fireworks-api-key",
    model="accounts/fireworks/models/firefunction-v1",
    params=FireworksLLMService.InputParams(
        temperature=0.7,
        max_tokens=1000
    )
)

# Create context
context = OpenAILLMContext(
    messages=[
        {"role": "system", "content": "You are a helpful assistant"},
        {"role": "user", "content": "What is machine learning?"}
    ]
)

# Use in pipeline
pipeline = Pipeline([
    context_manager,  # Manages conversation context
    service,          # Processes LLM requests
    text_handler      # Handles responses
])

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Function Calling

Supports OpenAI-compatible function calling with the firefunction-v1 model:

# Define tools
tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Get weather information",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {"type": "string"}
            }
        }
    }
}]

# Configure context with tools
context = OpenAILLMContext(
    messages=[],
    tools=tools
)

# Register function handler
@service.function("get_weather")
async def handle_weather(location: str):
    return {"temperature": 72, "condition": "sunny"}

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Available Models

Fireworks AI provides access to various models, including:

Model NameDescription
accounts/fireworks/models/firefunction-v1Optimized for function calling
accounts/fireworks/models/llama-v2-7bLlama 2 7B base model
accounts/fireworks/models/llama-v2-13bLlama 2 13B base model
accounts/fireworks/models/llama-v2-70bLlama 2 70B base model
accounts/fireworks/models/mixtral-8x7bMixtral 8x7B model

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Frame Flow

Inherits the BaseOpenAI LLM Service frame flow:

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Metrics Support

The service collects standard LLM metrics:

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

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Notes

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