Merge pull request #190 from TomTom101/TomTom101/langchain
Langchain service
This commit is contained in:
Aleix Conchillo Flaqué
130
examples/foundational/07b-interruptible-langchain.py
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130
examples/foundational/07b-interruptible-langchain.py
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#
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# Copyright (c) 2024, 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 asyncio
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import os
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import sys
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import aiohttp
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from dotenv import load_dotenv
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from loguru import logger
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from runner import configure
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from pipecat.frames.frames import LLMMessagesFrame
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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_response import (
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LLMAssistantResponseAggregator, LLMUserResponseAggregator)
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from pipecat.services.elevenlabs import ElevenLabsTTSService
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from pipecat.services.langchain import LangchainProcessor
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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from pipecat.vad.silero import SileroVADAnalyzer
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load_dotenv(override=True)
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try:
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from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
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from langchain_community.chat_message_histories import ChatMessageHistory
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from langchain_core.chat_history import BaseChatMessageHistory
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from langchain_core.runnables.history import RunnableWithMessageHistory
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from langchain_openai import ChatOpenAI
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except ModuleNotFoundError as e:
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logger.exception(
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"In order to run this example you need to `pip install pipecat-ai[langchain] langchain-community langchain-openai. Also, be sure to set `OPENAI_API_KEY` in the environment variable."
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)
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raise Exception(f"Missing module: {e}")
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logger.remove(0)
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logger.add(sys.stderr, level="DEBUG")
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message_store = {}
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def get_session_history(session_id: str) -> BaseChatMessageHistory:
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if session_id not in message_store:
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message_store[session_id] = ChatMessageHistory()
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return message_store[session_id]
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async def main(room_url: str, token):
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async with aiohttp.ClientSession() as session:
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transport = DailyTransport(
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room_url,
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token,
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"Respond bot",
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DailyParams(
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audio_out_enabled=True,
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transcription_enabled=True,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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),
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)
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tts = ElevenLabsTTSService(
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aiohttp_session=session,
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api_key=os.getenv("ELEVENLABS_API_KEY"),
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voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
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)
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prompt = ChatPromptTemplate.from_messages(
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[
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("system",
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"Be nice and helpful. Answer very briefly and without special characters like `#` or `*`. "
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"Your response will be synthesized to voice and those characters will create unnatural sounds.",
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),
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MessagesPlaceholder("chat_history"),
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("human", "{input}"),
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])
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chain = prompt | ChatOpenAI(model="gpt-4o", temperature=0.7)
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history_chain = RunnableWithMessageHistory(
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chain,
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get_session_history,
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history_messages_key="chat_history",
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input_messages_key="input")
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lc = LangchainProcessor(history_chain)
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tma_in = LLMUserResponseAggregator()
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tma_out = LLMAssistantResponseAggregator()
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pipeline = Pipeline(
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[
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transport.input(), # Transport user input
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tma_in, # User responses
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lc, # Langchain
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tts, # TTS
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transport.output(), # Transport bot output
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tma_out, # Assistant spoken responses
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]
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)
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task = PipelineTask(pipeline, allow_interruptions=True)
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@transport.event_handler("on_first_participant_joined")
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async def on_first_participant_joined(transport, participant):
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transport.capture_participant_transcription(participant["id"])
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lc.set_participant_id(participant["id"])
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# Kick off the conversation.
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# the `LLMMessagesFrame` will be picked up by the LangchainProcessor using
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# only the content of the last message to inject it in the prompt defined
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# above. So no role is required here.
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messages = [(
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{
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"content": "Please briefly introduce yourself to the user."
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}
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)]
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await task.queue_frames([LLMMessagesFrame(messages)])
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runner = PipelineRunner()
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await runner.run(task)
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if __name__ == "__main__":
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(url, token) = configure()
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asyncio.run(main(url, token))
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@@ -41,6 +41,7 @@ examples = [ "python-dotenv~=1.0.0", "flask~=3.0.3", "flask_cors~=4.0.1" ]
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fal = [ "fal-client~=0.4.0" ]
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google = [ "google-generativeai~=0.5.3" ]
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fireworks = [ "openai~=1.26.0" ]
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langchain = [ "langchain~=0.2.1" ]
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local = [ "pyaudio~=0.2.0" ]
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moondream = [ "einops~=0.8.0", "timm~=0.9.16", "transformers~=4.40.2" ]
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openai = [ "openai~=1.26.0" ]
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79
src/pipecat/services/langchain.py
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79
src/pipecat/services/langchain.py
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import sys
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from typing import Union
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from loguru import logger
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from pipecat.frames.frames import (Frame, LLMFullResponseEndFrame,
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LLMFullResponseStartFrame, LLMMessagesFrame,
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LLMResponseEndFrame, LLMResponseStartFrame,
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TextFrame)
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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try:
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from langchain_core.messages import AIMessageChunk
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from langchain_core.runnables import Runnable
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except ModuleNotFoundError as e:
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logger.exception(
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"In order to use Langchain, you need to `pip install pipecat-ai[langchain]`. "
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)
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raise Exception(f"Missing module: {e}")
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class LangchainProcessor(FrameProcessor):
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def __init__(self, chain: Runnable, transcript_key: str = "input"):
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super().__init__()
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self._chain = chain
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self._transcript_key = transcript_key
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self._participant_id: str | None = None
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def set_participant_id(self, participant_id: str):
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self._participant_id = participant_id
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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if isinstance(frame, LLMMessagesFrame):
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# Messages are accumulated by the `LLMUserResponseAggregator` in a list of messages.
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# The last one by the human is the one we want to send to the LLM.
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logger.debug(f"Got transcription frame {frame}")
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text: str = frame.messages[-1]["content"]
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await self._ainvoke(text.strip())
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else:
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await self.push_frame(frame)
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async def _invoke(self, text: str):
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response = await self._chain.ainvoke(
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{self._transcript_key: text},
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config={"configurable": {"session_id": self._participant_id}},
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)
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await self.push_frame(LLMFullResponseStartFrame())
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await self.push_frame(TextFrame(response))
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await self.push_frame(LLMFullResponseEndFrame())
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@staticmethod
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def __get_token_value(text: Union[str, AIMessageChunk]) -> str | None:
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match text:
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case str():
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return text
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case AIMessageChunk():
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return text.content
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case _:
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return None
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async def _ainvoke(self, text: str):
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logger.debug(f"Invoking chain with {text}")
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await self.push_frame(LLMFullResponseStartFrame())
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try:
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async for token in self._chain.astream(
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{self._transcript_key: text},
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config={"configurable": {"session_id": self._participant_id}},
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):
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await self.push_frame(LLMResponseStartFrame())
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await self.push_frame(TextFrame(self.__get_token_value(token)))
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await self.push_frame(LLMResponseEndFrame())
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except GeneratorExit:
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logger.warning("Generator was closed prematurely")
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raise # Re-raise to ensure proper generator closure
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except Exception as e:
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logger.error(f"An unknown error occurred: {e}")
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raise
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await self.push_frame(LLMFullResponseEndFrame())
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86
tests/test_langchain.py
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86
tests/test_langchain.py
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import unittest
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from langchain.prompts import ChatPromptTemplate
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from langchain_core.language_models import FakeStreamingListLLM
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from pipecat.frames.frames import (LLMFullResponseEndFrame,
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LLMFullResponseStartFrame, StopTaskFrame,
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TextFrame, TranscriptionFrame,
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UserStartedSpeakingFrame,
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UserStoppedSpeakingFrame)
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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_response import (
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LLMAssistantResponseAggregator, LLMUserResponseAggregator)
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from pipecat.processors.frame_processor import FrameProcessor
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from pipecat.services.langchain import LangchainProcessor
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class TestLangchain(unittest.IsolatedAsyncioTestCase):
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class MockProcessor(FrameProcessor):
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def __init__(self, name):
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self.name = name
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self.token: list[str] = []
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# Start collecting tokens when we see the start frame
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self.start_collecting = False
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def __str__(self):
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return self.name
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async def process_frame(self, frame, direction):
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if isinstance(frame, LLMFullResponseStartFrame):
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self.start_collecting = True
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elif isinstance(frame, TextFrame) and self.start_collecting:
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self.token.append(frame.text)
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elif isinstance(frame, LLMFullResponseEndFrame):
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self.start_collecting = False
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await self.push_frame(frame, direction)
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def setUp(self):
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self.expected_response = "Hello dear human"
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self.fake_llm = FakeStreamingListLLM(responses=[self.expected_response])
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self.mock_proc = self.MockProcessor("token_collector")
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async def test_langchain(self):
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messages = [("system", "Say hello to {name}"), ("human", "{input}")]
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prompt = ChatPromptTemplate.from_messages(messages).partial(name="Thomas")
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chain = prompt | self.fake_llm
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proc = LangchainProcessor(chain=chain)
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tma_in = LLMUserResponseAggregator(messages)
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tma_out = LLMAssistantResponseAggregator(messages)
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pipeline = Pipeline(
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[
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tma_in,
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proc,
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self.mock_proc,
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tma_out,
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]
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)
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task = PipelineTask(pipeline)
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await task.queue_frames(
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[
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UserStartedSpeakingFrame(),
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TranscriptionFrame(text="Hi World", user_id="user", timestamp="now"),
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UserStoppedSpeakingFrame(),
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StopTaskFrame(),
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]
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)
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runner = PipelineRunner()
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await runner.run(task)
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self.assertEqual("".join(self.mock_proc.token), self.expected_response)
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# TODO: Address this issue
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# This next one would fail with:
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# AssertionError: ' H e l l o d e a r h u m a n' != 'Hello dear human'
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# self.assertEqual(tma_out.messages[-1]["content"], self.expected_response)
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if __name__ == "__main__":
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unittest.main()
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