add 13 and 14 type foundational examples for sambanova iontegration
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jhpiedrahitao
109
examples/foundational/13g-sambanova-transcription.py
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109
examples/foundational/13g-sambanova-transcription.py
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#
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# Copyright (c) 2024–2025, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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import argparse
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import time
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import os
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from dotenv import load_dotenv
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from loguru import logger
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.audio.vad.vad_analyzer import VADParams
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from pipecat.frames.frames import Frame, TranscriptionFrame, UserStoppedSpeakingFrame
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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from pipecat.services.sambanova.stt import SambaNovaSTTService
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
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from pipecat.transports.services.daily import DailyParams
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load_dotenv(override=True)
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STOP_SECS = 2.0
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class TranscriptionLogger(FrameProcessor):
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"""Measures transcription latency.
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Uses the (intentionally) long STOP_SECS parameter to give the transcription time to finish,
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then outputs the timing between when the VAD first classified audio input as not-speech and
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the delivery of the last transcription frame.
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"""
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def __init__(self):
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super().__init__()
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self._last_transcription_time = time.time()
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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await super().process_frame(frame, direction)
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if isinstance(frame, UserStoppedSpeakingFrame):
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logger.debug(
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f"Transcription latency: {(STOP_SECS - (time.time() - self._last_transcription_time)):.2f}"
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)
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if isinstance(frame, TranscriptionFrame):
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self._last_transcription_time = time.time()
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# We store functions so objects (e.g. SileroVADAnalyzer) don't get
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# instantiated. The function will be called when the desired transport gets
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# selected.
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transport_params = {
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"daily": lambda: DailyParams(
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audio_in_enabled=True,
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vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=STOP_SECS)),
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),
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"twilio": lambda: FastAPIWebsocketParams(
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audio_in_enabled=True,
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vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=STOP_SECS)),
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),
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"webrtc": lambda: TransportParams(
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audio_in_enabled=True,
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vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=STOP_SECS)),
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),
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}
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async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
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logger.info(f"Starting bot")
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stt = SambaNovaSTTService(
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model='Whisper-Large-v3',
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api_key=os.getenv('SAMBANOVA_API_KEY'),
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)
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tl = TranscriptionLogger()
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pipeline = Pipeline([transport.input(), stt, tl])
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task = PipelineTask(
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pipeline,
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params=PipelineParams(
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enable_metrics=True,
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enable_usage_metrics=True,
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),
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)
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@transport.event_handler("on_client_disconnected")
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async def on_client_disconnected(transport, client):
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logger.info(f"Client disconnected")
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await task.cancel()
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runner = PipelineRunner(handle_sigint=handle_sigint)
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await runner.run(task)
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if __name__ == "__main__":
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from pipecat.examples.run import main
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main(run_example, transport_params=transport_params)
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152
examples/foundational/14s-function-calling-sambanova.py
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152
examples/foundational/14s-function-calling-sambanova.py
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#
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# Copyright (c) 2024–2025, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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import argparse
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import os
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from dotenv import load_dotenv
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from loguru import logger
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from pipecat.adapters.schemas.function_schema import FunctionSchema
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from pipecat.adapters.schemas.tools_schema import ToolsSchema
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import TTSSpeakFrame
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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from pipecat.processors.aggregators.llm_response import LLMUserAggregatorParams
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from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
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from pipecat.services.cartesia.tts import CartesiaTTSService
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from pipecat.services.sambanova.llm import SambaNovaLLMService
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from pipecat.services.sambanova.stt import SambaNovaSTTService
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from pipecat.services.llm_service import FunctionCallParams
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
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from pipecat.transports.services.daily import DailyParams
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load_dotenv(override=True)
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async def fetch_weather_from_api(params: FunctionCallParams):
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await params.result_callback({"conditions": "nice", "temperature": "75"})
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# We store functions so objects (e.g. SileroVADAnalyzer) don't get
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# instantiated. The function will be called when the desired transport gets
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# selected.
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transport_params = {
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"daily": lambda: DailyParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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),
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"twilio": lambda: FastAPIWebsocketParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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),
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"webrtc": lambda: TransportParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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),
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}
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async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
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logger.info(f"Starting bot")
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stt = SambaNovaSTTService(
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model='Whisper-Large-v3',
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api_key=os.getenv('SAMBANOVA_API_KEY'),
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)
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
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)
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llm = SambaNovaLLMService(
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api_key=os.getenv('SAMBANOVA_API_KEY'),
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model='Llama-4-Maverick-17B-128E-Instruct',
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)
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# You can also register a function_name of None to get all functions
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# sent to the same callback with an additional function_name parameter.
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llm.register_function("get_current_weather", fetch_weather_from_api)
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@llm.event_handler("on_function_calls_started")
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async def on_function_calls_started(service, function_calls):
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await tts.queue_frame(TTSSpeakFrame("Let me check on that."))
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weather_function = FunctionSchema(
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name="get_current_weather",
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description="Get the current weather",
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properties={
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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"format": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"],
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"description": "The temperature unit to use. Infer this from the user's location.",
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},
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},
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required=["location"],
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)
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tools = ToolsSchema(standard_tools=[weather_function])
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messages = [
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{
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"role": "system",
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"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
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},
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]
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context = OpenAILLMContext(messages, tools)
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context_aggregator = llm.create_context_aggregator(
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context, user_params=LLMUserAggregatorParams(aggregation_timeout=0.05)
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)
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pipeline = Pipeline(
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[
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transport.input(),
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stt,
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context_aggregator.user(),
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llm,
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tts,
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transport.output(),
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context_aggregator.assistant(),
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]
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)
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task = PipelineTask(
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pipeline,
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params=PipelineParams(
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enable_metrics=True,
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enable_usage_metrics=True,
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),
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)
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@transport.event_handler("on_client_connected")
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async def on_client_connected(transport, client):
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logger.info(f"Client connected")
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# Kick off the conversation.
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await task.queue_frames([context_aggregator.user().get_context_frame()])
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@transport.event_handler("on_client_disconnected")
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async def on_client_disconnected(transport, client):
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logger.info(f"Client disconnected")
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await task.cancel()
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runner = PipelineRunner(handle_sigint=handle_sigint)
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await runner.run(task)
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if __name__ == "__main__":
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from pipecat.examples.run import main
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main(run_example, transport_params=transport_params)
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