261 lines
8.4 KiB
Python
261 lines
8.4 KiB
Python
#
|
||
# Copyright (c) 2024–2025, Daily
|
||
#
|
||
# SPDX-License-Identifier: BSD 2-Clause License
|
||
#
|
||
|
||
import argparse
|
||
import os
|
||
from abc import ABC, abstractmethod
|
||
from dataclasses import field
|
||
from typing import List, Literal, Optional
|
||
|
||
import httpx
|
||
from agents import Agent, Runner
|
||
from dotenv import load_dotenv
|
||
from loguru import logger
|
||
from openai import AsyncStream, BaseModel
|
||
from openai.types.chat import ChatCompletionChunk, ChatCompletionMessageParam
|
||
|
||
from pipecat.adapters.schemas.tools_schema import ToolsSchema
|
||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||
from pipecat.frames.frames import (
|
||
Frame,
|
||
LLMFullResponseEndFrame,
|
||
LLMFullResponseStartFrame,
|
||
LLMMessagesFrame,
|
||
LLMTextFrame,
|
||
LLMUpdateSettingsFrame,
|
||
)
|
||
from pipecat.pipeline.pipeline import Pipeline
|
||
from pipecat.pipeline.runner import PipelineRunner
|
||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||
from pipecat.processors.aggregators.llm_response import (
|
||
LLMAssistantAggregatorParams,
|
||
LLMUserAggregatorParams,
|
||
)
|
||
from pipecat.processors.aggregators.openai_llm_context import (
|
||
OpenAILLMContext,
|
||
OpenAILLMContextFrame,
|
||
)
|
||
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||
from pipecat.services.ai_service import AIService
|
||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||
from pipecat.services.openai.base_llm import BaseOpenAILLMService
|
||
from pipecat.services.openai.llm import (
|
||
OpenAIAssistantContextAggregator,
|
||
OpenAIContextAggregatorPair,
|
||
OpenAILLMService,
|
||
OpenAIUserContextAggregator,
|
||
)
|
||
from pipecat.transports.base_transport import TransportParams
|
||
from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
|
||
from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
|
||
|
||
load_dotenv(override=True)
|
||
|
||
|
||
class LlmMessage(BaseModel):
|
||
# ...
|
||
role: Literal["system", "user", "assistant", "tool"]
|
||
content: Optional[str]
|
||
|
||
|
||
class AgentResponse(BaseModel):
|
||
content: str
|
||
msgs: list[LlmMessage] = field(default_factory=list)
|
||
|
||
|
||
class BackendBase(ABC):
|
||
@abstractmethod
|
||
async def get_resp(self, messages: list[LlmMessage], extra_params) -> AgentResponse:
|
||
raise NotImplementedError("The method get_resp is not implemented.")
|
||
|
||
|
||
class ChoiceDelta(BaseModel):
|
||
content: Optional[str] = None
|
||
"""The contents of the chunk message."""
|
||
|
||
|
||
class Choice(BaseModel):
|
||
delta: ChoiceDelta
|
||
"""The contents of the chunk message."""
|
||
|
||
index: int
|
||
"""The index of the choice in the list of choices."""
|
||
|
||
|
||
class CustomLLMService(BaseOpenAILLMService):
|
||
def __init__(self, **kwargs):
|
||
super().__init__(**kwargs)
|
||
self._client = Agent(
|
||
name="Assistant agent",
|
||
instructions="Respond with haikus.",
|
||
# tools=[get_weather],
|
||
)
|
||
|
||
def create_context_aggregator(
|
||
self,
|
||
context: OpenAILLMContext,
|
||
*,
|
||
user_params: LLMUserAggregatorParams = LLMUserAggregatorParams(),
|
||
assistant_params: LLMAssistantAggregatorParams = LLMAssistantAggregatorParams(),
|
||
) -> OpenAIContextAggregatorPair:
|
||
"""Create an instance of OpenAIContextAggregatorPair.
|
||
|
||
from an
|
||
OpenAILLMContext. Constructor keyword arguments for both the user and
|
||
assistant aggregators can be provided.
|
||
|
||
Args:
|
||
context (OpenAILLMContext): The LLM context.
|
||
user_params (LLMUserAggregatorParams, optional): User aggregator parameters.
|
||
assistant_params (LLMAssistantAggregatorParams, optional): User aggregator parameters.
|
||
|
||
Returns:
|
||
OpenAIContextAggregatorPair: A pair of context aggregators, one for
|
||
the user and one for the assistant, encapsulated in an
|
||
OpenAIContextAggregatorPair.
|
||
|
||
"""
|
||
context.set_llm_adapter(self.get_llm_adapter())
|
||
user = OpenAIUserContextAggregator(context, params=user_params)
|
||
assistant = OpenAIAssistantContextAggregator(context, params=assistant_params)
|
||
return OpenAIContextAggregatorPair(_user=user, _assistant=assistant)
|
||
|
||
def create_client(
|
||
self,
|
||
api_key=None,
|
||
base_url=None,
|
||
organization=None,
|
||
project=None,
|
||
default_headers=None,
|
||
**kwargs,
|
||
):
|
||
return Agent(
|
||
name="Assistant agent",
|
||
instructions="Respond with haikus.",
|
||
# tools=[get_weather],
|
||
)
|
||
|
||
async def get_chat_completions(
|
||
self, context: OpenAILLMContext, messages: List[ChatCompletionMessageParam]
|
||
) -> AsyncStream[ChatCompletionChunk]:
|
||
# self._client.tools = context.tools
|
||
logger.info(f"get_chat_completions: {self._client}")
|
||
|
||
result = Runner.run_streamed(
|
||
# context=context,
|
||
starting_agent=self._client,
|
||
input="Tell a joke about pirates.", # messages
|
||
# ---
|
||
# no func tool
|
||
# input="give me a 2 sentences about life",
|
||
)
|
||
|
||
logger.info(f"get_chat_completions: {result}")
|
||
|
||
if result is None:
|
||
logger.error("Runner.run_streamed returned None")
|
||
return
|
||
|
||
async for event in result.stream_events():
|
||
# if not event.type == "raw_response_event":
|
||
# break
|
||
if event.type == "raw_response_event":
|
||
if event.data.type == "response.output_text.delta":
|
||
delta = ChoiceDelta(content=event.data.delta)
|
||
choice = Choice(delta=delta, index=event.data.output_index)
|
||
|
||
converted_message = ChatCompletionChunk(
|
||
id=event.data.item_id,
|
||
choices=[choice],
|
||
)
|
||
return converted_message
|
||
else:
|
||
break
|
||
|
||
# chunks = await self._client.chat.completions.create(**params)
|
||
# return chunks
|
||
|
||
|
||
async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
|
||
logger.info(f"Starting bot")
|
||
|
||
transport = SmallWebRTCTransport(
|
||
webrtc_connection=webrtc_connection,
|
||
params=TransportParams(
|
||
audio_in_enabled=True,
|
||
audio_out_enabled=True,
|
||
vad_analyzer=SileroVADAnalyzer(),
|
||
),
|
||
)
|
||
|
||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||
|
||
tts = CartesiaTTSService(
|
||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||
)
|
||
|
||
llm = CustomLLMService(model="gpt-4.1", api_key=os.getenv("OPENAI_API_KEY"))
|
||
|
||
messages = [
|
||
{
|
||
"role": "system",
|
||
"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.",
|
||
},
|
||
]
|
||
|
||
context = OpenAILLMContext(messages=messages)
|
||
context_aggregator = llm.create_context_aggregator(context)
|
||
|
||
pipeline = Pipeline(
|
||
[
|
||
transport.input(), # Transport user input
|
||
stt,
|
||
context_aggregator.user(), # User responses
|
||
llm, # LLM
|
||
tts, # TTS
|
||
transport.output(), # Transport bot output
|
||
context_aggregator.assistant(), # Assistant spoken responses
|
||
]
|
||
)
|
||
|
||
task = PipelineTask(
|
||
pipeline,
|
||
params=PipelineParams(
|
||
allow_interruptions=True,
|
||
enable_metrics=True,
|
||
enable_usage_metrics=True,
|
||
report_only_initial_ttfb=True,
|
||
),
|
||
)
|
||
|
||
@transport.event_handler("on_client_connected")
|
||
async def on_client_connected(transport, client):
|
||
logger.info(f"Client connected")
|
||
# Kick off the conversation.
|
||
# messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||
# await task.queue_frames([context_aggregator.user().get_context_frame()])
|
||
|
||
@transport.event_handler("on_client_disconnected")
|
||
async def on_client_disconnected(transport, client):
|
||
logger.info(f"Client disconnected")
|
||
|
||
@transport.event_handler("on_client_closed")
|
||
async def on_client_closed(transport, client):
|
||
logger.info(f"Client closed connection")
|
||
await task.cancel()
|
||
|
||
runner = PipelineRunner(handle_sigint=False)
|
||
|
||
await runner.run(task)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
from run import main
|
||
|
||
main()
|