Cerebras

pip install "pipecat-ai[cerebras]"

from pipecat.services.cerebras import CerebrasLLMService
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.task import PipelineParams, PipelineTask

# Configure service
llm = CerebrasLLMService(
    api_key="your-cerebras-api-key",
    model="llama-3.3-70b"
)

# 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 LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way.

You have one function available:

1. get_current_weather is used to get current weather information.

Infer whether to use Fahrenheit or Celsius automatically based on the location, unless the user specifies a preference. Start by asking me for my location. Then, use 'get_current_weather' to give me a forecast. Respond to what the user said in a creative and helpful way.""",
        },
    ],
    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,
    ),
)