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#
# Copyright (c) 2024, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import base64
from pipecat.frames.frames import (
Frame,
TextFrame,
VisionImageRawFrame,
LLMMessagesFrame,
LLMFullResponseStartFrame,
LLMResponseStartFrame,
LLMResponseEndFrame,
LLMFullResponseEndFrame
)
from pipecat.processors.frame_processor import FrameDirection
from pipecat.services.ai_services import LLMService
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext, OpenAILLMContextFrame
from loguru import logger
try:
from anthropic import AsyncAnthropic
except ModuleNotFoundError as e:
logger.error(f"Exception: {e}")
logger.error(
"In order to use Anthropic, you need to `pip install pipecat-ai[anthropic]`. Also, set `ANTHROPIC_API_KEY` environment variable.")
raise Exception(f"Missing module: {e}")
class AnthropicLLMService(LLMService):
"""This class implements inference with Anthropic's AI models
This service translates internally from OpenAILLMContext to the messages format
expected by the Anthropic Python SDK. We are using the OpenAILLMContext as a lingua
franca for all LLM services, so that it is easy to switch between different LLMs.
"""
def __init__(
self,
*,
api_key: str,
model: str = "claude-3-opus-20240229",
max_tokens: int = 1024):
super().__init__()
self._client = AsyncAnthropic(api_key=api_key)
self._model = model
self._max_tokens = max_tokens
def can_generate_metrics(self) -> bool:
return True
def _get_messages_from_openai_context(
self, context: OpenAILLMContext):
openai_messages = context.get_messages()
anthropic_messages = []
for message in openai_messages:
role = message["role"]
text = message["content"]
if role == "system":
role = "user"
if message.get("mime_type") == "image/jpeg":
# vision frame
encoded_image = base64.b64encode(message["data"].getvalue()).decode("utf-8")
anthropic_messages.append({
"role": role,
"content": [{
"type": "image",
"source": {
"type": "base64",
"media_type": message.get("mime_type"),
"data": encoded_image,
}
}, {
"type": "text",
"text": text
}]
})
else:
# Text frame. Anthropic needs the roles to alternate. This will
# cause an issue with interruptions. So, if we detect we are the
# ones asking again it probably means we were interrupted.
if role == "user" and len(anthropic_messages) > 1:
last_message = anthropic_messages[-1]
if last_message["role"] == "user":
anthropic_messages = anthropic_messages[:-1]
content = last_message["content"]
anthropic_messages.append(
{"role": "user", "content": f"Sorry, I just asked you about [{content}] but now I would like to know [{text}]."})
else:
anthropic_messages.append({"role": role, "content": text})
else:
anthropic_messages.append({"role": role, "content": text})
return anthropic_messages
async def _process_context(self, context: OpenAILLMContext):
await self.push_frame(LLMFullResponseStartFrame())
try:
logger.debug(f"Generating chat: {context.get_messages_json()}")
messages = self._get_messages_from_openai_context(context)
await self.start_ttfb_metrics()
response = await self._client.messages.create(
messages=messages,
model=self._model,
max_tokens=self._max_tokens,
stream=True)
await self.stop_ttfb_metrics()
async for event in response:
# logger.debug(f"Anthropic LLM event: {event}")
if (event.type == "content_block_delta"):
await self.push_frame(LLMResponseStartFrame())
await self.push_frame(TextFrame(event.delta.text))
await self.push_frame(LLMResponseEndFrame())
except Exception as e:
logger.exception(f"{self} exception: {e}")
finally:
await self.push_frame(LLMFullResponseEndFrame())
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
context = None
if isinstance(frame, OpenAILLMContextFrame):
context: OpenAILLMContext = frame.context
elif isinstance(frame, LLMMessagesFrame):
context = OpenAILLMContext.from_messages(frame.messages)
elif isinstance(frame, VisionImageRawFrame):
context = OpenAILLMContext.from_image_frame(frame)
else:
await self.push_frame(frame, direction)
if context:
await self._process_context(context)
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