Add changelog and rename examples

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

@@ -0,0 +1,138 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+from typing import List
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import LLMMessagesFrame, TranscriptionMessage, TranscriptionUpdateFrame
+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.transcript_processor import TranscriptProcessor
+from pipecat.services.anthropic import AnthropicLLMService
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+class TranscriptHandler:
+    """Simple handler to demonstrate transcript processing.
+
+    Maintains a list of conversation messages and logs them with timestamps.
+    """
+
+    def __init__(self):
+        self.messages: List[TranscriptionMessage] = []
+
+    async def on_transcript_update(
+        self, processor: TranscriptProcessor, frame: TranscriptionUpdateFrame
+    ):
+        """Handle new transcript messages.
+
+        Args:
+            processor: The TranscriptProcessor that emitted the update
+            frame: TranscriptionUpdateFrame containing new messages
+        """
+        self.messages.extend(frame.messages)
+
+        # Log the new messages
+        logger.info("New transcript messages:")
+        for msg in frame.messages:
+            timestamp = f"[{msg.timestamp}] " if msg.timestamp else ""
+            logger.info(f"{timestamp}{msg.role}: {msg.content}")
+
+        # # Log the full transcript
+        # logger.info("Full transcript:")
+        # for msg in self.messages:
+        #     timestamp = f"[{msg.timestamp}] " if msg.timestamp else ""
+        #     logger.info(f"{timestamp}{msg.role}: {msg.content}")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
+
+        llm = AnthropicLLMService(
+            api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-5-sonnet-20241022"
+        )
+
+        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.",
+            },
+            {"role": "user", "content": "Say hello."},
+        ]
+
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        # Create transcript processor and handler
+        transcript_processor = TranscriptProcessor()
+        transcript_handler = TranscriptHandler()
+
+        # Register event handler for transcript updates
+        @transcript_processor.event_handler("on_transcript_update")
+        async def on_transcript_update(processor, frame):
+            await transcript_handler.on_transcript_update(processor, frame)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                context_aggregator.user(),  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+                transcript_processor,  # Process transcripts
+            ]
+        )
+
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
+
+        @transport.event_handler("on_first_participant_joined")
+        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)])
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())