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Merge branch 'main' into simple_chatbot-react-native

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Filipi Fuchter
2025-05-08 09:03:16 -03:00
parent 56ca7360ae 7280e390d9
commit 277e2d7fc0
66 changed files with 4078 additions and 1130 deletions
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161
examples/canonical-metrics/.gitignore vendored
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@@ -1,161 +0,0 @@
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recordings/
# C extensions
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# Distribution / packaging
.Python
build/
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*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
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# Unit test / coverage reports
htmlcov/
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.coverage.*
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nosetests.xml
coverage.xml
*.cover
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cover/
# Translations
*.mo
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*.log
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# PyBuilder
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# intended to run in multiple environments; otherwise, check them in:
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runpod.toml

10
examples/canonical-metrics/Dockerfile
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@@ -1,10 +0,0 @@
FROM python:3.10-bullseye
RUN mkdir /app
COPY *.py /app/
COPY requirements.txt /app/
WORKDIR /app
RUN pip3 install -r requirements.txt
EXPOSE 7860
CMD ["python3", "server.py"]

66
examples/canonical-metrics/README.md
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@@ -1,66 +0,0 @@
# Chatbot with canonical-metrics
This project implements a chatbot using a pipeline architecture that integrates audio processing, transcription, and a language model for conversational interactions. The chatbot operates within a daily communication environment, utilizing various services for text-to-speech and language model responses.
## Features
- **Audio Input and Output**: Captures microphone input and plays back audio responses.
- **Voice Activity Detection**: Utilizes Silero VAD to manage audio input intelligently.
- **Text-to-Speech**: Integrates ElevenLabs TTS service to convert text responses into audio.
- **Language Model Interaction**: Uses OpenAI's GPT-4 model to generate responses based on user input.
- **Transcription Services**: Captures and transcribes participant speech for analytics.
- **Metrics Collection**: Sends audio data for analysis via Canonical Metrics Service.
## Requirements
- Python 3.10+
- `python-dotenv`
- Additional libraries from the `pipecat` package.
## Setup
1. Clone the repository.
2. Install the required packages.
3. Set up environment variables for API keys:
- `OPENAI_API_KEY`
- `ELEVENLABS_API_KEY`
- `CANONICAL_API_KEY`
- `CANONICAL_API_URL`
4. Run the script.
## Usage
The chatbot introduces itself and engages in conversations, providing brief and creative responses. Designed for flexibility, it can support multiple languages with appropriate configuration.
## Events
- Participants joining or leaving the call are handled dynamically, adjusting the chatbot's behavior accordingly.
ℹ️ The first time, things might take extra time to get started since VAD (Voice Activity Detection) model needs to be downloaded.
## Get started
```python
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
cp env.example .env # and add your credentials
```
## Run the server
```bash
python server.py
```
Then, visit `http://localhost:7860/` in your browser to start a chatbot session.
## Build and test the Docker image
```
docker build -t chatbot .
docker run --env-file .env -p 7860:7860 chatbot
```

146
examples/canonical-metrics/bot.py
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@@ -1,146 +0,0 @@
#
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import asyncio
import os
import sys
import uuid
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 EndFrame
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.audio.audio_buffer_processor import AudioBufferProcessor
from pipecat.services.canonical.metrics import CanonicalMetricsService
from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport
load_dotenv(override=True)
logger.remove(0)
logger.add(sys.stderr, level="DEBUG")
async def main():
async with aiohttp.ClientSession() as session:
(room_url, token) = await configure(session)
transport = DailyTransport(
room_url,
token,
"Chatbot",
DailyParams(
audio_out_enabled=True,
audio_in_enabled=True,
video_out_enabled=False,
vad_analyzer=SileroVADAnalyzer(),
transcription_enabled=True,
#
# Spanish
#
# transcription_settings=DailyTranscriptionSettings(
# language="es",
# tier="nova",
# model="2-general"
# )
),
)
tts = ElevenLabsTTSService(
api_key=os.getenv("ELEVENLABS_API_KEY"),
#
# English
#
voice_id="cgSgspJ2msm6clMCkdW9",
#
# Spanish
#
# model="eleven_multilingual_v2",
# voice_id="gD1IexrzCvsXPHUuT0s3",
)
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
messages = [
{
"role": "system",
#
# English
#
"content": "You are Chatbot, a friendly, helpful robot. 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, but keep your responses brief. Start by introducing yourself. Keep all your responses to 12 words or fewer.",
#
# Spanish
#
# "content": "Eres Chatbot, un amigable y útil robot. Tu objetivo es demostrar tus capacidades de una manera breve. Tus respuestas se convertiran a audio así que nunca no debes incluir caracteres especiales. Contesta a lo que el usuario pregunte de una manera creativa, útil y breve. Empieza por presentarte a ti mismo.",
},
]
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
"""
CanonicalMetrics uses AudioBufferProcessor under the hood to buffer the audio. On
call completion, CanonicalMetrics will send the audio buffer to Canonical for
analysis. Visit https://voice.canonical.chat to learn more.
"""
audio_buffer_processor = AudioBufferProcessor(num_channels=2)
canonical = CanonicalMetricsService(
audio_buffer_processor=audio_buffer_processor,
aiohttp_session=session,
api_key=os.getenv("CANONICAL_API_KEY"),
call_id=str(uuid.uuid4()),
assistant="pipecat-chatbot",
assistant_speaks_first=True,
context=context,
)
pipeline = Pipeline(
[
transport.input(), # microphone
context_aggregator.user(),
llm,
tts,
transport.output(),
canonical, # uploads audio buffer to Canonical AI for metrics
audio_buffer_processor, # captures audio into a buffer
context_aggregator.assistant(),
]
)
task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
@transport.event_handler("on_first_participant_joined")
async def on_first_participant_joined(transport, participant):
await audio_buffer_processor.start_recording()
await transport.capture_participant_transcription(participant["id"])
await task.queue_frames([context_aggregator.user().get_context_frame()])
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
print(f"Participant left: {participant}")
await task.cancel()
@transport.event_handler("on_call_state_updated")
async def on_call_state_updated(transport, state):
if state == "left":
# Here we don't want to cancel, we just want to finish sending
# whatever is queued, so we use an EndFrame().
await task.queue_frame(EndFrame())
runner = PipelineRunner()
await runner.run(task)
if __name__ == "__main__":
asyncio.run(main())

6
examples/canonical-metrics/env.example
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@@ -1,6 +0,0 @@
DAILY_SAMPLE_ROOM_URL=https://yourdomain.daily.co/yourroom # (for joining the bot to the same room repeatedly for local dev)
DAILY_API_KEY=7df...
OPENAI_API_KEY=sk-PL...
ELEVENLABS_API_KEY=aeb...
CANONICAL_API_KEY=can...
CANONICAL_API_URL=

5
examples/canonical-metrics/requirements.txt
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@@ -1,5 +0,0 @@
python-dotenv
fastapi[all]
uvicorn
pipecat-ai[daily,openai,silero,elevenlabs,canonical]

55
examples/canonical-metrics/runner.py
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@@ -1,55 +0,0 @@
#
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import argparse
import os
import aiohttp
from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper
async def configure(aiohttp_session: aiohttp.ClientSession):
parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample")
parser.add_argument(
"-u", "--url", type=str, required=False, help="URL of the Daily room to join"
)
parser.add_argument(
"-k",
"--apikey",
type=str,
required=False,
help="Daily API Key (needed to create an owner token for the room)",
)
args, unknown = parser.parse_known_args()
url = args.url or os.getenv("DAILY_SAMPLE_ROOM_URL")
key = args.apikey or os.getenv("DAILY_API_KEY")
if not url:
raise Exception(
"No Daily room specified. use the -u/--url option from the command line, or set DAILY_SAMPLE_ROOM_URL in your environment to specify a Daily room URL."
)
if not key:
raise Exception(
"No Daily API key specified. use the -k/--apikey option from the command line, or set DAILY_API_KEY in your environment to specify a Daily API key, available from https://dashboard.daily.co/developers."
)
daily_rest_helper = DailyRESTHelper(
daily_api_key=key,
daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
aiohttp_session=aiohttp_session,
)
# Create a meeting token for the given room with an expiration 1 hour in
# the future.
expiry_time: float = 60 * 60
token = await daily_rest_helper.get_token(url, expiry_time)
return (url, token)

139
examples/canonical-metrics/server.py
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@@ -1,139 +0,0 @@
#
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import argparse
import os
import subprocess
from contextlib import asynccontextmanager
import aiohttp
from dotenv import load_dotenv
from fastapi import FastAPI, HTTPException, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse, RedirectResponse
from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper, DailyRoomParams
MAX_BOTS_PER_ROOM = 1
# Bot sub-process dict for status reporting and concurrency control
bot_procs = {}
daily_helpers = {}
load_dotenv(override=True)
def cleanup():
# Clean up function, just to be extra safe
for entry in bot_procs.values():
proc = entry[0]
proc.terminate()
proc.wait()
@asynccontextmanager
async def lifespan(app: FastAPI):
aiohttp_session = aiohttp.ClientSession()
daily_helpers["rest"] = DailyRESTHelper(
daily_api_key=os.getenv("DAILY_API_KEY", ""),
daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
aiohttp_session=aiohttp_session,
)
yield
await aiohttp_session.close()
cleanup()
app = FastAPI(lifespan=lifespan)
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
@app.get("/")
async def start_agent(request: Request):
print(f"!!! Creating room")
room = await daily_helpers["rest"].create_room(DailyRoomParams())
print(f"!!! Room URL: {room.url}")
# Ensure the room property is present
if not room.url:
raise HTTPException(
status_code=500,
detail="Missing 'room' property in request data. Cannot start agent without a target room!",
)
# Check if there is already an existing process running in this room
num_bots_in_room = sum(
1 for proc in bot_procs.values() if proc[1] == room.url and proc[0].poll() is None
)
if num_bots_in_room >= MAX_BOTS_PER_ROOM:
raise HTTPException(status_code=500, detail=f"Max bot limited reach for room: {room.url}")
# Get the token for the room
token = await daily_helpers["rest"].get_token(room.url)
if not token:
raise HTTPException(status_code=500, detail=f"Failed to get token for room: {room.url}")
# Spawn a new agent, and join the user session
# Note: this is mostly for demonstration purposes (refer to 'deployment' in README)
try:
proc = subprocess.Popen(
[f"python3 -m bot -u {room.url} -t {token}"],
shell=True,
bufsize=1,
cwd=os.path.dirname(os.path.abspath(__file__)),
)
bot_procs[proc.pid] = (proc, room.url)
except Exception as e:
raise HTTPException(status_code=500, detail=f"Failed to start subprocess: {e}")
return RedirectResponse(room.url)
@app.get("/status/{pid}")
def get_status(pid: int):
# Look up the subprocess
proc = bot_procs.get(pid)
# If the subprocess doesn't exist, return an error
if not proc:
raise HTTPException(status_code=404, detail=f"Bot with process id: {pid} not found")
# Check the status of the subprocess
if proc[0].poll() is None:
status = "running"
else:
status = "finished"
return JSONResponse({"bot_id": pid, "status": status})
if __name__ == "__main__":
import uvicorn
default_host = os.getenv("HOST", "0.0.0.0")
default_port = int(os.getenv("FAST_API_PORT", "7860"))
parser = argparse.ArgumentParser(description="Daily Storyteller FastAPI server")
parser.add_argument("--host", type=str, default=default_host, help="Host address")
parser.add_argument("--port", type=int, default=default_port, help="Port number")
parser.add_argument("--reload", action="store_true", help="Reload code on change")
config = parser.parse_args()
uvicorn.run(
"server:app",
host=config.host,
port=config.port,
reload=config.reload,
)

2
examples/foundational/07c-interruptible-deepgram-vad.py
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@@ -47,7 +47,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
live_options=LiveOptions(vad_events=True, utterance_end_ms="1000"),
)
tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-2-andromeda-en")
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))

2
examples/foundational/07c-interruptible-deepgram.py
View File

@@ -39,7 +39,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-2-andromeda-en")
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))

27
examples/foundational/07m-interruptible-polly.py → examples/foundational/07m-interruptible-aws.py
View File

@@ -5,7 +5,6 @@
#
import argparse
import os
from dotenv import load_dotenv
from loguru import logger
@@ -15,9 +14,9 @@ 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.services.aws.tts import PollyTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.services.aws.llm import AWSBedrockLLMService
from pipecat.services.aws.stt import AWSTranscribeSTTService
from pipecat.services.aws.tts import AWSPollyTTSService
from pipecat.transports.base_transport import TransportParams
from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
@@ -37,17 +36,19 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
),
)
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
stt = AWSTranscribeSTTService()
tts = PollyTTSService(
api_key=os.getenv("AWS_SECRET_ACCESS_KEY"),
aws_access_key_id=os.getenv("AWS_ACCESS_KEY_ID"),
region=os.getenv("AWS_REGION"),
voice_id="Amy",
params=PollyTTSService.InputParams(engine="neural", language="en-GB", rate="1.05"),
tts = AWSPollyTTSService(
region="us-west-2", # only specific regions support generative TTS
voice_id="Joanna",
params=AWSPollyTTSService.InputParams(engine="generative", rate="1.1"),
)
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
llm = AWSBedrockLLMService(
aws_region="us-west-2",
model="us.anthropic.claude-3-5-haiku-20241022-v1:0",
params=AWSBedrockLLMService.InputParams(temperature=0.8, latency="optimized"),
)
messages = [
{
@@ -85,7 +86,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
messages.append({"role": "user", "content": "Please introduce yourself to the user."})
await task.queue_frames([context_aggregator.user().get_context_frame()])
@transport.event_handler("on_client_disconnected")

139
examples/foundational/14r-function-calling-aws.py Normal file
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@@ -0,0 +1,139 @@
#
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import argparse
import os
from dotenv import load_dotenv
from loguru import logger
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
from pipecat.audio.vad.silero import SileroVADAnalyzer
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.services.aws.llm import AWSBedrockLLMService
from pipecat.services.aws.stt import AWSTranscribeSTTService
from pipecat.services.aws.tts import AWSPollyTTSService
from pipecat.services.llm_service import FunctionCallParams
from pipecat.transports.base_transport import TransportParams
from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
async def fetch_weather_from_api(params: FunctionCallParams):
await params.result_callback({"conditions": "nice", "temperature": "75"})
async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
logger.info(f"Starting bot")
transport = SmallWebRTCTransport(
webrtc_connection=webrtc_connection,
params=TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
),
)
stt = AWSTranscribeSTTService()
tts = AWSPollyTTSService(
region="us-west-2", # only specific regions support generative TTS
voice_id="Joanna",
params=AWSPollyTTSService.InputParams(engine="generative", rate="1.1"),
)
llm = AWSBedrockLLMService(
aws_region="us-west-2",
model="us.anthropic.claude-3-5-haiku-20241022-v1:0",
params=AWSBedrockLLMService.InputParams(temperature=0.8, latency="optimized"),
)
# You can also register a function_name of None to get all functions
# sent to the same callback with an additional function_name parameter.
llm.register_function("get_current_weather", fetch_weather_from_api)
weather_function = FunctionSchema(
name="get_current_weather",
description="Get the current weather",
properties={
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
},
"format": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "The temperature unit to use. Infer this from the user's location.",
},
},
required=["location", "format"],
)
tools = ToolsSchema(standard_tools=[weather_function])
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 = OpenAILLMContext(messages, tools)
context_aggregator = llm.create_context_aggregator(context)
pipeline = Pipeline(
[
transport.input(),
stt,
context_aggregator.user(),
llm,
tts,
transport.output(),
context_aggregator.assistant(),
]
)
task = PipelineTask(
pipeline,
params=PipelineParams(
allow_interruptions=True,
enable_metrics=True,
enable_usage_metrics=True,
report_only_initial_ttfb=True,
),
)
@transport.event_handler("on_client_connected")
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
messages.append({"role": "user", "content": "Please introduce yourself to the user."})
await task.queue_frames([context_aggregator.user().get_context_frame()])
@transport.event_handler("on_client_disconnected")
async def on_client_disconnected(transport, client):
logger.info(f"Client disconnected")
@transport.event_handler("on_client_closed")
async def on_client_closed(transport, client):
logger.info(f"Client closed connection")
await task.cancel()
runner = PipelineRunner(handle_sigint=False)
await runner.run(task)
if __name__ == "__main__":
from run import main
main()

267
examples/foundational/20e-persistent-context-aws-nova-sonic.py Normal file
View File

@@ -0,0 +1,267 @@
#
# Copyright (c) 2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import argparse
import asyncio
import glob
import json
import os
from datetime import datetime
from dotenv import load_dotenv
from loguru import logger
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.audio.vad.vad_analyzer import VADParams
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.services.aws_nova_sonic.aws import AWSNovaSonicLLMService
from pipecat.services.llm_service import FunctionCallParams
from pipecat.transports.base_transport import TransportParams
from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
BASE_FILENAME = "/tmp/pipecat_conversation_"
async def fetch_weather_from_api(params: FunctionCallParams):
temperature = 75 if params.arguments["format"] == "fahrenheit" else 24
await params.result_callback(
{
"conditions": "nice",
"temperature": temperature,
"format": params.arguments["format"],
"timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
}
)
async def get_saved_conversation_filenames(params: FunctionCallParams):
# Construct the full pattern including the BASE_FILENAME
full_pattern = f"{BASE_FILENAME}*.json"
# Use glob to find all matching files
matching_files = glob.glob(full_pattern)
logger.debug(f"matching files: {matching_files}")
await params.result_callback({"filenames": matching_files})
# async def get_saved_conversation_filenames(
# function_name, tool_call_id, args, llm, context, result_callback
# ):
# pattern = re.compile(re.escape(BASE_FILENAME) + "\\d{8}_\\d{6}\\.json$")
# matching_files = []
# for filename in os.listdir("."):
# if pattern.match(filename):
# matching_files.append(filename)
# await result_callback({"filenames": matching_files})
async def save_conversation(params: FunctionCallParams):
timestamp = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
filename = f"{BASE_FILENAME}{timestamp}.json"
try:
with open(filename, "w") as file:
messages = params.context.get_messages_for_persistent_storage()
# remove the last few messages. in reverse order, they are:
# - the in progress save tool call
# - the invocation of the save tool call
# - the user ask to save (which may encompass one or more messages)
# the simplest thing to do is to pop messages until the last one is an assistant
# response
while messages and not (
messages[-1].get("role") == "assistant" and "content" in messages[-1]
):
messages.pop()
if messages: # we never expect this to be empty
logger.debug(
f"writing conversation to {filename}\n{json.dumps(messages, indent=4)}"
)
json.dump(messages, file, indent=2)
await params.result_callback({"success": True})
except Exception as e:
await params.result_callback({"success": False, "error": str(e)})
async def load_conversation(params: FunctionCallParams):
async def _reset():
filename = params.arguments["filename"]
logger.debug(f"loading conversation from {filename}")
try:
with open(filename, "r") as file:
messages = json.load(file)
messages.append(
{
"role": "user",
"content": f"{AWSNovaSonicLLMService.AWAIT_TRIGGER_ASSISTANT_RESPONSE_INSTRUCTION}",
}
)
params.context.set_messages(messages)
await params.llm.reset_conversation()
await params.llm.trigger_assistant_response()
except Exception as e:
await params.result_callback({"success": False, "error": str(e)})
asyncio.create_task(_reset())
get_current_weather_tool = FunctionSchema(
name="get_current_weather",
description="Get the current weather",
properties={
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
},
"format": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "The temperature unit to use. Infer this from the user's location.",
},
},
required=["location", "format"],
)
save_conversation_tool = FunctionSchema(
name="save_conversation",
description="Save the current conversation. Use this function to persist the current conversation to external storage.",
properties={},
required=[],
)
get_saved_conversation_filenames_tool = FunctionSchema(
name="get_saved_conversation_filenames",
description="Get a list of saved conversation histories. Returns a list of filenames. Each filename includes a date and timestamp. Each file is conversation history that can be loaded into this session.",
properties={},
required=[],
)
load_conversation_tool = FunctionSchema(
name="load_conversation",
description="Load a conversation history. Use this function to load a conversation history into the current session.",
properties={
"filename": {
"type": "string",
"description": "The filename of the conversation history to load.",
}
},
required=["filename"],
)
tools = ToolsSchema(
standard_tools=[
get_current_weather_tool,
save_conversation_tool,
get_saved_conversation_filenames_tool,
load_conversation_tool,
]
)
async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
logger.info(f"Starting bot")
transport = SmallWebRTCTransport(
webrtc_connection=webrtc_connection,
params=TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.8)),
),
)
# Specify initial system instruction.
# HACK: note that, for now, we need to inject a special bit of text into this instruction to
# allow the first assistant response to be programmatically triggered (which happens in the
# on_client_connected handler, below)
system_instruction = (
"You are a friendly assistant. The user and you will engage in a spoken dialog exchanging "
"the transcripts of a natural real-time conversation. Keep your responses short, generally "
"two or three sentences for chatty scenarios. "
f"{AWSNovaSonicLLMService.AWAIT_TRIGGER_ASSISTANT_RESPONSE_INSTRUCTION}"
)
llm = AWSNovaSonicLLMService(
secret_access_key=os.getenv("AWS_SECRET_ACCESS_KEY"),
access_key_id=os.getenv("AWS_ACCESS_KEY_ID"),
region=os.getenv("AWS_REGION"), # as of 2025-05-06, us-east-1 is the only supported region
voice_id="tiffany", # matthew, tiffany, amy
# you could choose to pass instruction here rather than via context
# system_instruction=system_instruction,
# you could choose to pass tools here rather than via context
# tools=tools
)
llm.register_function("get_current_weather", fetch_weather_from_api)
llm.register_function("save_conversation", save_conversation)
llm.register_function("get_saved_conversation_filenames", get_saved_conversation_filenames)
llm.register_function("load_conversation", load_conversation)
context = OpenAILLMContext(
messages=[
{"role": "system", "content": f"{system_instruction}"},
],
tools=tools,
)
context_aggregator = llm.create_context_aggregator(context)
pipeline = Pipeline(
[
transport.input(), # Transport user input
context_aggregator.user(),
llm, # LLM
transport.output(), # Transport bot output
context_aggregator.assistant(),
]
)
task = PipelineTask(
pipeline,
params=PipelineParams(
allow_interruptions=True,
enable_metrics=True,
enable_usage_metrics=True,
report_only_initial_ttfb=True,
),
)
@transport.event_handler("on_client_connected")
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
await task.queue_frames([context_aggregator.user().get_context_frame()])
# HACK: for now, we need this special way of triggering the first assistant response in AWS
# Nova Sonic. Note that this trigger requires a special corresponding bit of text in the
# system instruction. In the future, simply queueing the context frame should be sufficient.
await llm.trigger_assistant_response()
@transport.event_handler("on_client_disconnected")
async def on_client_disconnected(transport, client):
logger.info(f"Client disconnected")
@transport.event_handler("on_client_closed")
async def on_client_closed(transport, client):
logger.info(f"Client closed connection")
await task.cancel()
runner = PipelineRunner(handle_sigint=False)
await runner.run(task)
if __name__ == "__main__":
from run import main
main()

46
examples/foundational/30-observer.py
View File

@@ -14,19 +14,26 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import (
BotStartedSpeakingFrame,
BotStoppedSpeakingFrame,
Frame,
EndFrame,
StartInterruptionFrame,
TTSTextFrame,
UserStartedSpeakingFrame,
)
from pipecat.observers.base_observer import BaseObserver
from pipecat.observers.base_observer import BaseObserver, FramePushed
from pipecat.observers.loggers.debug_log_observer import DebugLogObserver, FrameEndpoint
from pipecat.observers.loggers.llm_log_observer import LLMLogObserver
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, FrameProcessor
from pipecat.processors.aggregators.openai_llm_context import (
OpenAILLMContext,
)
from pipecat.processors.frame_processor import FrameDirection
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.base_input import BaseInputTransport
from pipecat.transports.base_output import BaseOutputTransport
from pipecat.transports.base_transport import TransportParams
from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
@@ -34,7 +41,7 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
class DebugObserver(BaseObserver):
class CustomObserver(BaseObserver):
"""Observer to log interruptions and bot speaking events to the console.
Logs all frame instances of:
@@ -46,21 +53,20 @@ class DebugObserver(BaseObserver):
Log format: [EVENT TYPE]: [source processor] → [destination processor] at [timestamp]s
"""
async def on_push_frame(
self,
src: FrameProcessor,
dst: FrameProcessor,
frame: Frame,
direction: FrameDirection,
timestamp: int,
):
async def on_push_frame(self, data: FramePushed):
src = data.source
dst = data.destination
frame = data.frame
direction = data.direction
timestamp = data.timestamp
# Convert timestamp to seconds for readability
time_sec = timestamp / 1_000_000_000
# Create direction arrow
arrow = "→" if direction == FrameDirection.DOWNSTREAM else "←"
if isinstance(frame, StartInterruptionFrame):
if isinstance(frame, StartInterruptionFrame) and isinstance(src, BaseOutputTransport):
logger.info(f"⚡ INTERRUPTION START: {src} {arrow} {dst} at {time_sec:.2f}s")
elif isinstance(frame, BotStartedSpeakingFrame):
logger.info(f"🤖 BOT START SPEAKING: {src} {arrow} {dst} at {time_sec:.2f}s")
@@ -119,7 +125,17 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
enable_usage_metrics=True,
report_only_initial_ttfb=True,
),
observers=[DebugObserver(), LLMLogObserver()],
observers=[
CustomObserver(),
LLMLogObserver(),
DebugLogObserver(
frame_types={
TTSTextFrame: (BaseOutputTransport, FrameEndpoint.DESTINATION),
UserStartedSpeakingFrame: (BaseInputTransport, FrameEndpoint.SOURCE),
EndFrame: None,
}
),
],
)
@transport.event_handler("on_client_connected")

173
examples/foundational/39-aws-nova-sonic.py Normal file
View File

@@ -0,0 +1,173 @@
#
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import argparse
import os
from datetime import datetime
from dotenv import load_dotenv
from loguru import logger
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.audio.vad.vad_analyzer import VADParams
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.services.aws_nova_sonic import AWSNovaSonicLLMService
from pipecat.services.llm_service import FunctionCallParams
from pipecat.transports.base_transport import TransportParams
from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
# Load environment variables
load_dotenv(override=True)
async def fetch_weather_from_api(params: FunctionCallParams):
temperature = 75 if params.arguments["format"] == "fahrenheit" else 24
await params.result_callback(
{
"conditions": "nice",
"temperature": temperature,
"format": params.arguments["format"],
"timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
}
)
weather_function = FunctionSchema(
name="get_current_weather",
description="Get the current weather",
properties={
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
},
"format": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "The temperature unit to use. Infer this from the users location.",
},
},
required=["location", "format"],
)
# Create tools schema
tools = ToolsSchema(standard_tools=[weather_function])
async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
logger.info(f"Starting bot")
# Initialize the SmallWebRTCTransport with the connection
transport = SmallWebRTCTransport(
webrtc_connection=webrtc_connection,
params=TransportParams(
audio_in_enabled=True,
audio_in_sample_rate=16000,
audio_out_enabled=True,
camera_in_enabled=False,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.8)),
),
)
# Specify initial system instruction.
# HACK: note that, for now, we need to inject a special bit of text into this instruction to
# allow the first assistant response to be programmatically triggered (which happens in the
# on_client_connected handler, below)
system_instruction = (
"You are a friendly assistant. The user and you will engage in a spoken dialog exchanging "
"the transcripts of a natural real-time conversation. Keep your responses short, generally "
"two or three sentences for chatty scenarios. "
f"{AWSNovaSonicLLMService.AWAIT_TRIGGER_ASSISTANT_RESPONSE_INSTRUCTION}"
)
# Create the AWS Nova Sonic LLM service
llm = AWSNovaSonicLLMService(
secret_access_key=os.getenv("AWS_SECRET_ACCESS_KEY"),
access_key_id=os.getenv("AWS_ACCESS_KEY_ID"),
region=os.getenv("AWS_REGION"), # as of 2025-05-06, us-east-1 is the only supported region
voice_id="tiffany", # matthew, tiffany, amy
# you could choose to pass instruction here rather than via context
# system_instruction=system_instruction
# you could choose to pass tools here rather than via context
# tools=tools
)
# Register function for function calls
# you can either register a single function for all function calls, or specific functions
# llm.register_function(None, fetch_weather_from_api)
llm.register_function("get_current_weather", fetch_weather_from_api)
# Set up context and context management.
# AWSNovaSonicService will adapt OpenAI LLM context objects with standard message format to
# what's expected by Nova Sonic.
context = OpenAILLMContext(
messages=[
{"role": "system", "content": f"{system_instruction}"},
{
"role": "user",
"content": "Tell me a fun fact!",
},
],
tools=tools,
)
context_aggregator = llm.create_context_aggregator(context)
# Build the pipeline
pipeline = Pipeline(
[
transport.input(),
context_aggregator.user(),
llm,
transport.output(),
context_aggregator.assistant(),
]
)
# Configure the pipeline task
task = PipelineTask(
pipeline,
params=PipelineParams(
allow_interruptions=True,
enable_metrics=True,
enable_usage_metrics=True,
),
)
# Handle client connection event
@transport.event_handler("on_client_connected")
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
await task.queue_frames([context_aggregator.user().get_context_frame()])
# HACK: for now, we need this special way of triggering the first assistant response in AWS
# Nova Sonic. Note that this trigger requires a special corresponding bit of text in the
# system instruction. In the future, simply queueing the context frame should be sufficient.
await llm.trigger_assistant_response()
# Handle client disconnection events
@transport.event_handler("on_client_disconnected")
async def on_client_disconnected(transport, client):
logger.info(f"Client disconnected")
@transport.event_handler("on_client_closed")
async def on_client_closed(transport, client):
logger.info(f"Client closed connection")
await task.cancel()
# Run the pipeline
runner = PipelineRunner(handle_sigint=False)
await runner.run(task)
if __name__ == "__main__":
from run import main
main()

4
examples/moondream-chatbot/server.py
View File

@@ -10,12 +10,16 @@ import subprocess
from contextlib import asynccontextmanager
import aiohttp
from dotenv import load_dotenv
from fastapi import FastAPI, HTTPException, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse, RedirectResponse
from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper, DailyRoomParams
# Load environment variables from .env file
load_dotenv(override=True)
MAX_BOTS_PER_ROOM = 1
# Bot sub-process dict for status reporting and concurrency control

4
examples/patient-intake/server.py
View File

@@ -10,12 +10,16 @@ import subprocess
from contextlib import asynccontextmanager
import aiohttp
from dotenv import load_dotenv
from fastapi import FastAPI, HTTPException, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse, RedirectResponse
from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper, DailyRoomParams
# Load environment variables from .env file
load_dotenv(override=True)
MAX_BOTS_PER_ROOM = 1
# Bot sub-process dict for status reporting and concurrency control