58a17c7b1b
Remove `examples/` from the `pyrightconfig.json` ignore list and fix
the resulting type errors across all example files. Common fixes:
- Required API keys: `os.getenv("X")` -> `os.environ["X"]` so the
return type is `str` rather than `str | None`, and misconfiguration
fails fast.
- Narrow `LLMContextMessage` union members with `isinstance(..., dict)`
before dict-style access.
- `assert isinstance(params.llm, ...)` before calling service-specific
methods that aren't on the base `LLMService`.
- Guard optional frame fields (e.g. `LLMSearchResponseFrame.search_result`)
before use.
134 lines
4.3 KiB
Python
134 lines
4.3 KiB
Python
#
|
|
# Copyright (c) 2024-2026, Daily
|
|
#
|
|
# SPDX-License-Identifier: BSD 2-Clause License
|
|
#
|
|
|
|
|
|
import os
|
|
|
|
from dotenv import load_dotenv
|
|
from loguru import logger
|
|
|
|
from pipecat.frames.frames import LLMRunFrame
|
|
from pipecat.pipeline.pipeline import Pipeline
|
|
from pipecat.pipeline.runner import PipelineRunner
|
|
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
|
from pipecat.processors.aggregators.llm_context import LLMContext
|
|
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
|
from pipecat.runner.types import RunnerArguments
|
|
from pipecat.runner.utils import create_transport
|
|
from pipecat.services.google.gemini_live.llm import GeminiLiveLLMService
|
|
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
|
from pipecat.transports.daily.transport import DailyParams
|
|
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
|
|
|
load_dotenv(override=True)
|
|
|
|
|
|
# Function handlers for the LLM
|
|
search_tool = {"google_search": {}}
|
|
tools = [search_tool]
|
|
|
|
system_instruction = """
|
|
You are an expert at providing the most recent news from any place. Your responses will be converted to audio, so avoid using special characters or overly complex formatting.
|
|
|
|
Always use the google search API to retrieve the latest news. You must also use it to check which day is today.
|
|
|
|
You can:
|
|
- Use the Google search API to check the current date.
|
|
- Provide the most recent and relevant news from any place by using the google search API.
|
|
- Answer any questions the user may have, ensuring your responses are accurate and concise.
|
|
|
|
Start each interaction by asking the user about which place they would like to know the information.
|
|
"""
|
|
|
|
|
|
# We use lambdas to defer transport parameter creation until the transport
|
|
# type is selected at runtime.
|
|
transport_params = {
|
|
"daily": lambda: DailyParams(
|
|
audio_in_enabled=True,
|
|
audio_out_enabled=True,
|
|
),
|
|
"twilio": lambda: FastAPIWebsocketParams(
|
|
audio_in_enabled=True,
|
|
audio_out_enabled=True,
|
|
),
|
|
"webrtc": lambda: TransportParams(
|
|
audio_in_enabled=True,
|
|
audio_out_enabled=True,
|
|
),
|
|
}
|
|
|
|
|
|
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
|
logger.info(f"Starting bot")
|
|
|
|
# Initialize the Gemini Multimodal Live model
|
|
llm = GeminiLiveLLMService(
|
|
api_key=os.environ["GOOGLE_API_KEY"],
|
|
settings=GeminiLiveLLMService.Settings(
|
|
voice="Puck", # Aoede, Charon, Fenrir, Kore, Puck
|
|
system_instruction=system_instruction,
|
|
),
|
|
tools=tools,
|
|
)
|
|
|
|
context = LLMContext(
|
|
[
|
|
{
|
|
"role": "developer",
|
|
"content": "Start by greeting the user warmly, introducing yourself, and mentioning the current day. Be friendly and engaging to set a positive tone for the interaction.",
|
|
}
|
|
],
|
|
)
|
|
# Server-side VAD is enabled by default; no local VAD is added.
|
|
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(context)
|
|
|
|
pipeline = Pipeline(
|
|
[
|
|
transport.input(), # Transport user input
|
|
user_aggregator, # User responses
|
|
llm, # LLM
|
|
transport.output(), # Transport bot output
|
|
assistant_aggregator, # Assistant spoken responses
|
|
]
|
|
)
|
|
|
|
task = PipelineTask(
|
|
pipeline,
|
|
params=PipelineParams(
|
|
enable_metrics=True,
|
|
enable_usage_metrics=True,
|
|
),
|
|
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
|
)
|
|
|
|
@transport.event_handler("on_client_connected")
|
|
async def on_client_connected(transport, client):
|
|
logger.info(f"Client connected")
|
|
# Kick off the conversation.
|
|
await task.queue_frames([LLMRunFrame()])
|
|
|
|
@transport.event_handler("on_client_disconnected")
|
|
async def on_client_disconnected(transport, client):
|
|
logger.info(f"Client disconnected")
|
|
await task.cancel()
|
|
|
|
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
|
|
|
await runner.run(task)
|
|
|
|
|
|
async def bot(runner_args: RunnerArguments):
|
|
"""Main bot entry point compatible with Pipecat Cloud."""
|
|
transport = await create_transport(runner_args, transport_params)
|
|
await run_bot(transport, runner_args)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
from pipecat.runner.run import main
|
|
|
|
main()
|