example: 49-oi-instrumentation.py

examples/foundational/49-oi-instrumentation.py

@@ -0,0 +1,179 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+
+import os
+from datetime import datetime
+
+from arize.otel import register as register_arize
+from dotenv import load_dotenv
+from loguru import logger
+from phoenix.otel import register as register_phoenix
+from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
+from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.audio.vad.vad_analyzer import VADParams
+from pipecat.frames.frames import LLMRunFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import (
+    LLMContextAggregatorPair,
+)
+from pipecat.runner.types import RunnerArguments
+from pipecat.runner.utils import create_transport
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.stt import OpenAISTTService
+from pipecat.services.openai.tts import OpenAITTSService
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.daily.transport import DailyParams
+from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+
+from openinference.instrumentation.pipecat import PipecatInstrumentor
+
+load_dotenv(override=True)
+
+conversation_id = f"test-conversation-001_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
+debug_log_filename = os.path.join(os.getcwd(), f"pipecat_frames_{conversation_id}.log")
+print(f"_____49-oi-instrumentation.py * debug_log_filename: {debug_log_filename}")
+
+def setup_tracer_provider():
+    """
+    Setup the tracer provider.
+    """
+    project_name = os.getenv("ARIZE_PROJECT_NAME", "default")
+
+    ARIZE_SPACE_ID = os.getenv("ARIZE_SPACE_ID")
+    ARIZE_API_KEY = os.getenv("ARIZE_API_KEY")
+    if ARIZE_SPACE_ID and ARIZE_API_KEY:
+        return register_arize(
+            space_id=ARIZE_SPACE_ID,
+            api_key=ARIZE_API_KEY,
+            project_name=project_name,
+        )
+    else:
+        return register_phoenix(project_name="default")
+
+
+tracer_provider = setup_tracer_provider()
+PipecatInstrumentor().instrument(
+    tracer_provider=tracer_provider,
+    debug_log_filename=debug_log_filename,
+)
+
+transport_params = {
+    "daily": lambda: DailyParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
+        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+    ),
+    "twilio": lambda: FastAPIWebsocketParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
+        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
+        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+    ),
+}
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    logger.info("Starting bot")
+
+    ### STT ###
+    stt = OpenAISTTService(
+        api_key=os.getenv("OPENAI_API_KEY"),
+        model="gpt-4o-transcribe",
+        prompt="Expect normal helpful conversation.",
+    )
+    ### alternative stt - cartesia ###
+    # stt = CartesiaSTTService(api_key=os.getenv("CARTESIA_API_KEY"))
+
+    ### LLM ###
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+
+    ### TTS ###
+    tts = OpenAITTSService(
+        api_key=os.getenv("OPENAI_API_KEY"),
+        voice="ballad",
+        params=OpenAITTSService.InputParams(
+            instructions="Please speak clearly and at a moderate pace."
+        ),
+    )
+
+    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 and helpful way.",
+        }
+    ]
+
+    context = LLMContext(messages)
+    context_aggregator = LLMContextAggregatorPair(context)
+
+    ### PIPELINE ###
+    pipeline = Pipeline(
+        [
+            transport.input(),  # Transport user input
+            stt,
+            context_aggregator.user(),  # User responses
+            llm,  # LLM
+            tts,  # TTS
+            transport.output(),  # Transport bot output
+            context_aggregator.assistant(),  # Assistant spoken responses
+        ]
+    )
+
+    ### TASK ###
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        conversation_id=conversation_id,  # Use dynamic conversation ID for session tracking
+        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+    )
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info("Client connected")
+        # Kick off the conversation.
+        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+        await task.queue_frames([LLMRunFrame()])
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info("Client disconnected")
+        await task.cancel()
+
+    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+
+    await runner.run(task)
+
+
+async def bot(runner_args: RunnerArguments):
+    """Main bot entry point compatible with Pipecat Cloud."""
+    transport = await create_transport(runner_args, transport_params)
+    await run_bot(transport, runner_args)
+
+
+if __name__ == "__main__":
+    from pipecat.runner.run import main
+
+    main()

pyproject.toml

@@ -4,6 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "pipecat-ai"
+
 dynamic = ["version"]
 description = "An open source framework for voice (and multimodal) assistants"
 license = "BSD-2-Clause"
@@ -115,6 +116,20 @@ websocket = [ "pipecat-ai[websockets-base]", "fastapi>=0.115.6,<0.122.0" ]
 websockets-base = [ "websockets>=13.1,<16.0" ]
 whisper = [ "faster-whisper~=1.1.1" ]
 
+oiip = [
+    "arize-otel>=0.0.1",
+    "arize-phoenix>=0.0.1",
+    "openinference-instrumentation>=0.1.12",
+    "opentelemetry-api",
+    "opentelemetry-sdk",
+    "opentelemetry-semantic-conventions",
+    # Note: openinference-instrumentation-pipecat comes from [tool.uv.sources]
+    "openinference-instrumentation-pipecat"
+]
+
+[tool.uv.sources]
+openinference-instrumentation-pipecat = { path = "../../opensource/openinference/python/instrumentation/openinference-instrumentation-pipecat", editable = true }
+
 [dependency-groups]
 dev = [
     "build~=1.2.2",