Example
Add create context aggregator
This commit is contained in:
James Hush
@@ -5,20 +5,47 @@
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#
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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 Literal, Optional
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from agents import Agent
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import httpx
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from dotenv import load_dotenv
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from loguru import logger
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from openai import BaseModel
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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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)
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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.openai_llm_context import OpenAILLMContext
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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.llm import OpenAILLMService
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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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@@ -26,6 +53,100 @@ 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 CompassLLMService(AIService):
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def __init__(self, backend: BackendBase):
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super().__init__()
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self.backend = backend
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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 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_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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elif isinstance(frame, LLMUpdateSettingsFrame):
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await self._update_settings(frame.settings)
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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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msgs = []
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for contmsg in context.messages:
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msgs.append(
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LlmMessage(
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role=contmsg["role"],
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content=contmsg["content"],
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)
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)
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resp = await self.backend.get_resp(
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msgs,
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{
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"conversation_id": "fake_conversation_id",
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"user_id": "fake_user_id",
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},
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)
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context.add_messages(resp.msgs)
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await self.push_frame(LLMTextFrame(resp.content))
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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):
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logger.info(f"Starting bot")
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@@ -55,22 +176,8 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
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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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agent = Agent(
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name="Math Tutor",
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instructions="You provide help with math problems. Explain your reasoning at each step and include examples",
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# output_type="probably something here?",
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# handoffs=[]
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)
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# You can also do this to pass a context...
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# agent = Agent[OpenAILLMContext]
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# Not sure how useful this is, but it seems like a good idea to have
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math_tool = agent.as_tool(
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tool_name="math_tool",
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tool_description="Call this tool whenever someone asks for help with math problems. You will be able to ask follow up questions to clarify the problem.",
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)
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tools = ToolsSchema(standard_tools=[math_tool])
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context = OpenAILLMContext(messages=messages, tools=tools)
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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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