Code review changes

examples/foundational/07a-interruptible-anthropic.py

@@ -7,7 +7,6 @@
 import asyncio
 import os
 import sys
-from typing import List
 
 import aiohttp
 from dotenv import load_dotenv
@@ -15,13 +14,12 @@ from loguru import logger
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import Frame, LLMMessagesFrame
+from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.processors.frame_processor import FrameDirection
-from pipecat.services.anthropic import AnthropicLLMContext, AnthropicLLMService
+from pipecat.services.anthropic import AnthropicLLMService
 from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
@@ -31,28 +29,6 @@ logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-class TestAnthropicLLMService(AnthropicLLMService):
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        if isinstance(frame, LLMMessagesFrame):
-            logger.info("Original OpenAI format messages:")
-            logger.info(frame.messages)
-
-            # Convert to Anthropic format
-            context = AnthropicLLMContext.from_messages(frame.messages)
-            logger.info("Converted to Anthropic format:")
-            logger.info(context.messages)
-
-            # Convert back to OpenAI format
-            openai_messages = []
-            for msg in context.messages:
-                converted = context.to_standard_messages(msg)
-                openai_messages.extend(converted)
-            logger.info("Converted back to OpenAI format:")
-            logger.info(openai_messages)
-
-        await super().process_frame(frame, direction)
-
-
 async def main():
     async with aiohttp.ClientSession() as session:
         (room_url, token) = await configure(session)
@@ -74,24 +50,18 @@ async def main():
             voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
-        llm = TestAnthropicLLMService(
+        llm = AnthropicLLMService(
             api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-opus-20240229"
         )
 
-        # Test messages including various formats
+        # todo: think more about how to handle system prompts in a more general way. OpenAI,
+        # Google, and Anthropic all have slightly different approaches to providing a system
+        # prompt.
         messages = [
             {
                 "role": "system",
                 "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.",
             },
-            {
-                "role": "assistant",
-                "content": [
-                    {"type": "text", "text": "Hello! How can I help you today?"},
-                    {"type": "text", "text": "I'm ready to assist."},
-                ],
-            },
-            {"role": "user", "content": "Hi there!"},
         ]
 
         context = OpenAILLMContext(messages)

examples/foundational/28a-transcription-processor-openai.py

@@ -127,7 +127,7 @@ async def main():
         async def on_first_participant_joined(transport, participant):
             await transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
-            await task.queue_frames([LLMMessagesFrame(messages)])
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
         runner = PipelineRunner()
 

examples/foundational/28b-transcript-processor-anthropic.py

@@ -127,7 +127,7 @@ async def main():
         async def on_first_participant_joined(transport, participant):
             await transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
-            await task.queue_frames([LLMMessagesFrame(messages)])
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
         runner = PipelineRunner()