272532a3ea
With all these examples updated, we no longer need dedicated examples illustrating `LLMContext`, so they're removed. Here’s where we *don’t* yet use `LLMContext` and associated machinery: - Realtime services: OpenAI Realtime, Gemini Live, and AWS Nova Sonic (support coming soon) - `GoogleLLMOpenAIBetaService` (it’s deprecated, so we didn’t bother adding support) - `LLMLogObserver` (support coming soon) - `GatedOpenAILLMContextAggregator` (support coming soon) - `LangchainProcessor` (support coming soon) - `Mem0MemoryService` (support coming soon) - Examples that use LLM-specific tools definitions as opposed to `ToolsSchema` (these will be updated soon) - Examples that rely `GoogleLLMContext.upgrade_to_google` (TBD what to do with these) Examples that use `LLMLogObserver`: - 30- Examples that use `GatedOpenAILLMContextAggregator`: - 22- Examples that use `LangchainProcessor`: - 07b- Examples that use `Mem0MemoryService`: - 37- Examples that need updating to use `ToolsSchema`: - 15- - 15a- - 20a- - 20c- - 20d- - 22b- - 22c- - 33- - 36- Examples that use `GoogleLLMContext.upgrade_to_google`: - 22d- - 25-
80 lines
2.5 KiB
Python
80 lines
2.5 KiB
Python
#
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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 os
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from dotenv import load_dotenv
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from loguru import logger
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from pipecat.frames.frames import EndFrame, LLMContextFrame
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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 PipelineTask
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from pipecat.processors.aggregators.llm_context import LLMContext
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from pipecat.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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from pipecat.services.cartesia.tts import CartesiaTTSService
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from pipecat.services.openai.llm import OpenAILLMService
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.daily.transport import DailyParams
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from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
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load_dotenv(override=True)
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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(audio_out_enabled=True),
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"twilio": lambda: FastAPIWebsocketParams(audio_out_enabled=True),
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"webrtc": lambda: TransportParams(audio_out_enabled=True),
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}
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async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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logger.info(f"Starting bot")
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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 = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
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messages = [
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{
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"role": "system",
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"content": "You are an LLM in a WebRTC session, and this is a 'hello world' demo. Say hello to the world.",
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}
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]
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task = PipelineTask(
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Pipeline([llm, tts, transport.output()]),
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idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
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)
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# Register an event handler so we can play the audio when the client joins
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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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await task.queue_frames([LLMContextFrame(LLMContext(messages)), EndFrame()])
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runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
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await runner.run(task)
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async def bot(runner_args: RunnerArguments):
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"""Main bot entry point compatible with Pipecat Cloud."""
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transport = await create_transport(runner_args, transport_params)
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await run_bot(transport, runner_args)
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if __name__ == "__main__":
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from pipecat.runner.run import main
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main()
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