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3 Commits
hush/openA
...
hush/googl
| Author | SHA1 | Date | |
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c6d759e043 | ||
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9643296e29 | ||
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c83c5b5a34 |
@@ -34,6 +34,7 @@ from pipecat.processors.aggregators.openai_llm_context import (
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from pipecat.processors.frame_processor import FrameProcessor
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from pipecat.services.cartesia.tts import CartesiaTTSService
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from pipecat.services.google.llm import GoogleLLMContext, GoogleLLMService
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from pipecat.services.openai.llm import OpenAILLMService
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from pipecat.transports.base_transport import TransportParams
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from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
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from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
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@@ -286,16 +287,18 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
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voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
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)
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conversation_llm = GoogleLLMService(
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name="Conversation",
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model="gemini-2.0-flash-001",
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# model="gemini-exp-1121",
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api_key=os.getenv("GOOGLE_API_KEY"),
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# we can give the GoogleLLMService a system instruction to use directly
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# in the GenerativeModel constructor. Let's do that rather than put
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# our system message in the messages list.
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system_instruction=conversation_system_message,
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)
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# conversation_llm = GoogleLLMService(
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# name="Conversation",
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# model="gemini-2.0-flash-001",
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# # model="gemini-exp-1121",
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# api_key=os.getenv("GOOGLE_API_KEY"),
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# # we can give the GoogleLLMService a system instruction to use directly
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# # in the GenerativeModel constructor. Let's do that rather than put
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# # our system message in the messages list.
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# system_instruction=conversation_system_message,
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# )
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conversation_llm = OpenAILLMService(name="Conversation", api_key=os.getenv("OPENAI_API_KEY"))
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input_transcription_llm = GoogleLLMService(
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name="Transcription",
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@@ -1,274 +0,0 @@
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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 abc import ABC, abstractmethod
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from dataclasses import field
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from typing import List, Literal, Optional
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import httpx
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from agents import Agent, Runner
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from dotenv import load_dotenv
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from loguru import logger
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from openai import AsyncStream, BaseModel
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from openai.types.chat import ChatCompletionChunk, ChatCompletionMessageParam
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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 (
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Frame,
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LLMFullResponseEndFrame,
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LLMFullResponseStartFrame,
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LLMMessagesFrame,
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LLMTextFrame,
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LLMUpdateSettingsFrame,
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VisionImageRawFrame,
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)
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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 (
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LLMAssistantAggregatorParams,
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LLMUserAggregatorParams,
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)
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from pipecat.processors.aggregators.openai_llm_context import (
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OpenAILLMContext,
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OpenAILLMContextFrame,
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)
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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from pipecat.services.ai_service import AIService
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from pipecat.services.cartesia.tts import CartesiaTTSService
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from pipecat.services.deepgram.stt import DeepgramSTTService
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from pipecat.services.openai.base_llm import BaseOpenAILLMService
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from pipecat.services.openai.llm import (
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OpenAIAssistantContextAggregator,
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OpenAIContextAggregatorPair,
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OpenAILLMService,
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OpenAIUserContextAggregator,
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)
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from pipecat.transports.base_transport import TransportParams
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from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
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from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
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load_dotenv(override=True)
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class LlmMessage(BaseModel):
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# ...
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role: Literal["system", "user", "assistant", "tool"]
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content: Optional[str]
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class AgentResponse(BaseModel):
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content: str
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msgs: list[LlmMessage] = field(default_factory=list)
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class BackendBase(ABC):
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@abstractmethod
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async def get_resp(self, messages: list[LlmMessage], extra_params) -> AgentResponse:
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raise NotImplementedError("The method get_resp is not implemented.")
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class ChoiceDelta(BaseModel):
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content: Optional[str] = None
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"""The contents of the chunk message."""
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class Choice(BaseModel):
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delta: ChoiceDelta
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"""The contents of the chunk message."""
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index: int
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"""The index of the choice in the list of choices."""
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class CustomLLMService(BaseOpenAILLMService):
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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self._client = Agent(
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name="Assistant agent",
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instructions="Respond with haikus.",
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# tools=[get_weather],
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)
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def create_client(
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self,
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api_key=None,
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base_url=None,
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organization=None,
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project=None,
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default_headers=None,
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**kwargs,
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):
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return Agent(
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name="Assistant agent",
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instructions="Respond with haikus.",
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# tools=[get_weather],
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)
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def create_context_aggregator(
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self,
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context: OpenAILLMContext,
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*,
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user_params: LLMUserAggregatorParams = LLMUserAggregatorParams(),
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assistant_params: LLMAssistantAggregatorParams = LLMAssistantAggregatorParams(),
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) -> OpenAIContextAggregatorPair:
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"""Create an instance of OpenAIContextAggregatorPair.
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from an
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OpenAILLMContext. Constructor keyword arguments for both the user and
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assistant aggregators can be provided.
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Args:
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context (OpenAILLMContext): The LLM context.
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user_params (LLMUserAggregatorParams, optional): User aggregator parameters.
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assistant_params (LLMAssistantAggregatorParams, optional): User aggregator parameters.
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Returns:
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OpenAIContextAggregatorPair: A pair of context aggregators, one for
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the user and one for the assistant, encapsulated in an
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OpenAIContextAggregatorPair.
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"""
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context.set_llm_adapter(self.get_llm_adapter())
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user = OpenAIUserContextAggregator(context, params=user_params)
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assistant = OpenAIAssistantContextAggregator(context, params=assistant_params)
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return OpenAIContextAggregatorPair(_user=user, _assistant=assistant)
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async def _process_context(self, context: OpenAILLMContext):
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functions_list = []
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arguments_list = []
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tool_id_list = []
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func_idx = 0
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function_name = ""
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arguments = ""
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tool_call_id = ""
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await self.start_ttfb_metrics()
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result = Runner.run_streamed(
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# context=context,
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starting_agent=self._client,
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input=context.messages, # messages
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# ---
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# no func tool
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# input="give me a 2 sentences about life",
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)
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logger.info(f"get_chat_completions: {result}")
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if result is None:
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logger.error("Runner.run_streamed returned None")
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return
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async for event in result.stream_events():
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if event.type == "raw_response_event":
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if event.data.type == "response.output_text.delta":
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await self.push_frame(LLMTextFrame(event.data.delta))
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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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context = None
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if isinstance(frame, OpenAILLMContextFrame):
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context: OpenAILLMContext = frame.context
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elif isinstance(frame, LLMMessagesFrame):
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context = OpenAILLMContext.from_messages(frame.messages)
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else:
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await self.push_frame(frame, direction)
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if context:
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try:
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await self.push_frame(LLMFullResponseStartFrame())
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await self.start_processing_metrics()
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await self._process_context(context)
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except httpx.TimeoutException:
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await self._call_event_handler("on_completion_timeout")
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finally:
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await self.stop_processing_metrics()
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await self.push_frame(LLMFullResponseEndFrame())
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async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
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logger.info(f"Starting bot")
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transport = SmallWebRTCTransport(
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webrtc_connection=webrtc_connection,
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params=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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stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
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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 = CustomLLMService(model="gpt-4.1", 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 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=messages)
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context_aggregator = llm.create_context_aggregator(context)
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pipeline = Pipeline(
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[
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transport.input(), # Transport user input
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stt,
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context_aggregator.user(), # User responses
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llm, # LLM
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tts, # TTS
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transport.output(), # Transport bot output
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context_aggregator.assistant(), # Assistant spoken responses
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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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allow_interruptions=True,
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enable_metrics=True,
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enable_usage_metrics=True,
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report_only_initial_ttfb=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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# messages.append({"role": "system", "content": "Please introduce yourself to the user."})
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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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@transport.event_handler("on_client_closed")
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async def on_client_closed(transport, client):
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logger.info(f"Client closed connection")
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await task.cancel()
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runner = PipelineRunner(handle_sigint=False)
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await runner.run(task)
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if __name__ == "__main__":
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from run import main
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main()
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@@ -1,122 +0,0 @@
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import asyncio
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import logging
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import os
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from datetime import datetime
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from agents import (
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Agent,
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FunctionTool,
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HandoffOutputItem,
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ItemHelpers,
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MessageOutputItem,
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RunContextWrapper,
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Runner,
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ToolCallItem,
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ToolCallOutputItem,
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function_tool,
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set_default_openai_api,
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set_default_openai_client,
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set_tracing_disabled,
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trace,
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)
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from httpx import get
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@function_tool
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async def get_weather(location: str) -> str:
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"""Fetch the weather for today.
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Args:
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location: The location to fetch the weather for.
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"""
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return f"{location} is sunny"
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system_prompt = """
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you are a helpful assistant for a real estate brokerage AI assistant.
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"""
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bot = Agent(
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name="Assistant agent",
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instructions=system_prompt,
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# tools=[get_weather],
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)
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async def main():
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# res = await Runner.run(
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# starting_agent=bot,
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# input="What is the weather today?",
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# )
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# print(res)
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result = Runner.run_streamed(
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starting_agent=bot,
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# ---
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# with func tool
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input="Tell a joke about pirates.",
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# ---
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# no func tool
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# input="give me a 2 sentences about life",
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)
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final = []
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async for event in result.stream_events():
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# We'll ignore the raw responses event deltas
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name = getattr(event, "name", None)
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# print(f"Event: {event.type} - name {name}")
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# print(event)
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# continue
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if event.type == "raw_response_event":
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if event.data.type == "response.output_text.delta":
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final += event.data.delta
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print(f"raw resp: {event}")
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# When the agent updates, print that
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elif event.type == "agent_updated_stream_event":
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print(f"Agent updated: {event.new_agent.name}")
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continue
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# When items are generated, print them
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elif event.type == "run_item_stream_event":
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if event.item.type == "tool_call_item":
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print("-- Tool was called")
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elif event.item.type == "tool_call_output_item":
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print(f"-- Tool output: {event.item.output}")
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elif event.item.type == "message_output_item":
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print(f"-- Message output:\n {ItemHelpers.text_message_output(event.item)}")
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else:
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print(f"-- Unknown item type: {event.item.type}")
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pass # Ignore other event types
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else:
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print(f"-- Unknown out item type: {event.item.type}")
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print(f"----------------------")
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print(f"FinalFinalFinal: {''.join(final)}")
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if __name__ == "__main__":
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asyncio.run(main())
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# no func tool:
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#
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# Event: agent_updated_stream_event - name None
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# Event: raw_response_event - name None
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# ...
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# Event: raw_response_event - name None
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# Event: run_item_stream_event - name message_output_created
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# with func tool:
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#
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# Event: agent_updated_stream_event - name None
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# Event: raw_response_event - name None
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# ...
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# Event: raw_response_event - name None
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# Event: run_item_stream_event - name tool_called
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# Event: run_item_stream_event - name tool_output
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# Event: raw_response_event - name None
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# ...
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# Event: raw_response_event - name None
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# Event: run_item_stream_event - name message_output_created
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@@ -1 +1 @@
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from .aws import AWSNovaSonicLLMService
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from .aws import AWSNovaSonicLLMService, Params
|
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|
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Reference in New Issue
Block a user