Merge pull request #167 from pipecat-ai/khk-bump-anthropic
add new response frame types and vision support for anthropic
This commit is contained in:
Aleix Conchillo Flaqué
@@ -9,6 +9,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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### Added
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- Added vision support to Anthropic service.
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- Added `WakeCheckFilter` which allows you to pass information downstream only
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if you say a certain phrase/word.
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@@ -19,6 +21,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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### Fixed
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- Fixed Anthropic service to use new frame types.
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- Fixed an issue in `LLMUserResponseAggregator` and `UserResponseAggregator`
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that would cause frames after a brief pause to not be pushed to the LLM.
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95
examples/foundational/07a-interruptible-anthropic.py
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95
examples/foundational/07a-interruptible-anthropic.py
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@@ -0,0 +1,95 @@
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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 aiohttp
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import os
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import sys
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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.anthropic import AnthropicLLMService
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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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from runner import configure
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from loguru import logger
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from dotenv import load_dotenv
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load_dotenv(override=True)
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logger.remove(0)
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logger.add(sys.stderr, level="DEBUG")
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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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llm = AnthropicLLMService(
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api_key=os.getenv("ANTHROPIC_API_KEY"),
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model="claude-3-opus-20240229")
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# todo: think more about how to handle system prompts in a more general way. OpenAI,
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# Google, and Anthropic all have slightly different approaches to providing a system
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# prompt.
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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, helpful, and brief way. Say hello.",
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},
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]
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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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transport.input(), # Transport user input
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tma_in, # 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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tma_out # Assistant spoken responses
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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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# Kick off the conversation.
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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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112
examples/foundational/12c-describe-video-anthropic.py
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112
examples/foundational/12c-describe-video-anthropic.py
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@@ -0,0 +1,112 @@
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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 aiohttp
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import os
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import sys
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from pipecat.frames.frames import Frame, TextFrame, UserImageRequestFrame
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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.user_response import UserResponseAggregator
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from pipecat.processors.aggregators.vision_image_frame import VisionImageFrameAggregator
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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from pipecat.services.elevenlabs import ElevenLabsTTSService
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from pipecat.services.anthropic import AnthropicLLMService
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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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from runner import configure
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from loguru import logger
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from dotenv import load_dotenv
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load_dotenv(override=True)
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logger.remove(0)
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logger.add(sys.stderr, level="DEBUG")
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class UserImageRequester(FrameProcessor):
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def __init__(self, participant_id: str | None = None):
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super().__init__()
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self._participant_id = participant_id
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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 self._participant_id and isinstance(frame, TextFrame):
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await self.push_frame(UserImageRequestFrame(self._participant_id), FrameDirection.UPSTREAM)
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await self.push_frame(frame, direction)
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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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"Describe participant video",
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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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user_response = UserResponseAggregator()
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image_requester = UserImageRequester()
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vision_aggregator = VisionImageFrameAggregator()
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anthropic = AnthropicLLMService(
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api_key=os.getenv("ANTHROPIC_API_KEY"),
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model="claude-3-sonnet-20240229"
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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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@transport.event_handler("on_first_participant_joined")
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async def on_first_participant_joined(transport, participant):
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await tts.say("Hi there! Feel free to ask me what I see.")
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transport.capture_participant_video(participant["id"], framerate=0)
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transport.capture_participant_transcription(participant["id"])
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image_requester.set_participant_id(participant["id"])
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pipeline = Pipeline([
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transport.input(),
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user_response,
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image_requester,
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vision_aggregator,
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anthropic,
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tts,
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transport.output()
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])
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task = PipelineTask(pipeline)
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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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@@ -4,9 +4,24 @@
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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from pipecat.frames.frames import Frame, LLMMessagesFrame, TextFrame
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import os
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import asyncio
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import time
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import base64
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from pipecat.frames.frames import (
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Frame,
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TextFrame,
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VisionImageRawFrame,
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LLMMessagesFrame,
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LLMFullResponseStartFrame,
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LLMResponseStartFrame,
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LLMResponseEndFrame,
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LLMFullResponseEndFrame
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)
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from pipecat.processors.frame_processor import FrameDirection
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from pipecat.services.ai_services import LLMService
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from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext, OpenAILLMContextFrame
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from loguru import logger
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@@ -20,6 +35,12 @@ except ModuleNotFoundError as e:
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class AnthropicLLMService(LLMService):
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"""This class implements inference with Anthropic's AI models
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This service translates internally from OpenAILLMContext to the messages format
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expected by the Anthropic Python SDK. We are using the OpenAILLMContext as a lingua
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franca for all LLM services, so that it is easy to switch between different LLMs.
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"""
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def __init__(
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self,
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@@ -27,11 +48,85 @@ class AnthropicLLMService(LLMService):
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model="claude-3-opus-20240229",
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max_tokens=1024):
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super().__init__()
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self.client = AsyncAnthropic(api_key=api_key)
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self.model = model
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self.max_tokens = max_tokens
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self._client = AsyncAnthropic(api_key=api_key)
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self._model = model
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self._max_tokens = max_tokens
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def _get_messages_from_openai_context(
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self, context: OpenAILLMContext):
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openai_messages = context.get_messages()
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anthropic_messages = []
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for message in openai_messages:
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role = message["role"]
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text = message["content"]
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if role == "system":
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role = "user"
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if message.get("mime_type") == "image/jpeg":
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# vision frame
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encoded_image = base64.b64encode(message["data"].getvalue()).decode("utf-8")
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anthropic_messages.append({
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"role": role,
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"content": [{
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"type": "image",
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"source": {
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"type": "base64",
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"media_type": message.get("mime_type"),
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"data": encoded_image,
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}
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}, {
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"type": "text",
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"text": text
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}]
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})
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else:
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# text frame
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anthropic_messages.append({"role": role, "content": content})
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return anthropic_messages
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async def _process_context(self, context: OpenAILLMContext):
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await self.push_frame(LLMFullResponseStartFrame())
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try:
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logger.debug(f"Generating chat: {context.get_messages_json()}")
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messages = self._get_messages_from_openai_context(context)
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start_time = time.time()
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response = await self._client.messages.create(
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messages=messages,
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model=self._model,
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max_tokens=self._max_tokens,
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stream=True)
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logger.debug(f"Anthropic LLM TTFB: {time.time() - start_time}")
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async for event in response:
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# logger.debug(f"Anthropic LLM event: {event}")
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if (event.type == "content_block_delta"):
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await self.push_frame(LLMResponseStartFrame())
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await self.push_frame(TextFrame(event.delta.text))
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await self.push_frame(LLMResponseEndFrame())
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except Exception as e:
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logger.error(f"Exception: {e}")
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finally:
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await self.push_frame(LLMFullResponseEndFrame())
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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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, VisionImageRawFrame):
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context = OpenAILLMContext.from_image_frame(frame)
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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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await self._process_context(context)
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async def x_process_frame(self, frame: Frame, direction: FrameDirection):
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if isinstance(frame, LLMMessagesFrame):
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stream = await self.client.messages.create(
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max_tokens=self.max_tokens,
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