Function calling (#175)
* added function calling code back * removed old llm_context file * added integration testing for openai * added function calling example * added function callbacks * added function start callback * fixup * fixup * added different return type support for function calling * intake example working * added frame loggers * cleanup * fixup * Update openai.py * removed function call frame types * fixup * re-added example * renumbered wake phrase * fixup for autopep8 * remove unused imports
This commit is contained in:
chadbailey59
@@ -56,10 +56,11 @@ async def main(room_url: str, token):
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
model="gpt-4-turbo-preview")
|
||||
model="gpt-4o")
|
||||
|
||||
fl_in = FrameLogger("Inner")
|
||||
fl_out = FrameLogger("Outer")
|
||||
fl = FrameLogger("!!! after LLM", "red")
|
||||
fltts = FrameLogger("@@@ out of tts", "green")
|
||||
flend = FrameLogger("### out of the end", "magenta")
|
||||
|
||||
messages = [
|
||||
{
|
||||
@@ -71,14 +72,15 @@ async def main(room_url: str, token):
|
||||
tma_out = LLMAssistantResponseAggregator(messages)
|
||||
|
||||
pipeline = Pipeline([
|
||||
fl_in,
|
||||
transport.input(),
|
||||
tma_in,
|
||||
llm,
|
||||
fl_out,
|
||||
fl,
|
||||
tts,
|
||||
fltts,
|
||||
transport.output(),
|
||||
tma_out
|
||||
tma_out,
|
||||
flend
|
||||
])
|
||||
|
||||
task = PipelineTask(pipeline)
|
||||
|
||||
@@ -15,14 +15,15 @@ from pipecat.frames.frames import ImageRawFrame, Frame, SystemFrame, TextFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import (
|
||||
LLMAssistantContextAggregator,
|
||||
LLMUserContextAggregator,
|
||||
from pipecat.processors.aggregators.llm_response import (
|
||||
LLMAssistantResponseAggregator,
|
||||
LLMUserResponseAggregator,
|
||||
)
|
||||
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||||
from pipecat.services.openai import OpenAILLMService
|
||||
from pipecat.services.elevenlabs import ElevenLabsTTSService
|
||||
from pipecat.transports.services.daily import DailyTransport
|
||||
from pipecat.vad.silero import SileroVADAnalyzer
|
||||
|
||||
from pipecat.transports.services.daily import DailyParams
|
||||
from runner import configure
|
||||
@@ -66,7 +67,9 @@ async def main(room_url: str, token):
|
||||
audio_out_enabled=True,
|
||||
camera_out_width=1024,
|
||||
camera_out_height=1024,
|
||||
transcription_enabled=True
|
||||
transcription_enabled=True,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer()
|
||||
)
|
||||
)
|
||||
|
||||
@@ -87,8 +90,8 @@ async def main(room_url: str, token):
|
||||
},
|
||||
]
|
||||
|
||||
tma_in = LLMUserContextAggregator(messages)
|
||||
tma_out = LLMAssistantContextAggregator(messages)
|
||||
tma_in = LLMUserResponseAggregator(messages)
|
||||
tma_out = LLMAssistantResponseAggregator(messages)
|
||||
|
||||
image_sync_aggregator = ImageSyncAggregator(
|
||||
os.path.join(os.path.dirname(__file__), "assets", "speaking.png"),
|
||||
|
||||
@@ -1,156 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import asyncio
|
||||
import aiohttp
|
||||
import os
|
||||
import random
|
||||
import sys
|
||||
|
||||
from PIL import Image
|
||||
|
||||
from pipecat.frames.frames import Frame, ImageRawFrame, SpriteFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import (
|
||||
LLMUserContextAggregator,
|
||||
LLMAssistantContextAggregator,
|
||||
)
|
||||
from pipecat.processors.filters.wake_check_filter import WakeCheckFilter
|
||||
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||||
from pipecat.services.openai import OpenAILLMService
|
||||
from pipecat.services.elevenlabs import ElevenLabsTTSService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
from runner import configure
|
||||
|
||||
from loguru import logger
|
||||
|
||||
from dotenv import load_dotenv
|
||||
load_dotenv(override=True)
|
||||
|
||||
logger.remove(0)
|
||||
logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
|
||||
sprites = {}
|
||||
image_files = [
|
||||
"sc-default.png",
|
||||
"sc-talk.png",
|
||||
"sc-listen-1.png",
|
||||
"sc-think-1.png",
|
||||
"sc-think-2.png",
|
||||
"sc-think-3.png",
|
||||
"sc-think-4.png",
|
||||
]
|
||||
|
||||
script_dir = os.path.dirname(__file__)
|
||||
|
||||
for file in image_files:
|
||||
# Build the full path to the image file
|
||||
full_path = os.path.join(script_dir, "assets", file)
|
||||
# Get the filename without the extension to use as the dictionary key
|
||||
filename = os.path.splitext(os.path.basename(full_path))[0]
|
||||
# Open the image and convert it to bytes
|
||||
with Image.open(full_path) as img:
|
||||
sprites[file] = ImageRawFrame(image=img.tobytes(), size=img.size, format=img.format)
|
||||
|
||||
# When the bot isn't talking, show a static image of the cat listening
|
||||
quiet_frame = sprites["sc-listen-1.png"]
|
||||
|
||||
# When the bot is talking, build an animation from two sprites
|
||||
talking_list = [sprites["sc-default.png"], sprites["sc-talk.png"]]
|
||||
talking = [random.choice(talking_list) for x in range(30)]
|
||||
talking_frame = SpriteFrame(talking)
|
||||
|
||||
# TODO: Support "thinking" as soon as we get a valid transcript, while LLM
|
||||
# is processing
|
||||
thinking_list = [
|
||||
sprites["sc-think-1.png"],
|
||||
sprites["sc-think-2.png"],
|
||||
sprites["sc-think-3.png"],
|
||||
sprites["sc-think-4.png"],
|
||||
]
|
||||
thinking_frame = SpriteFrame(thinking_list)
|
||||
|
||||
|
||||
class ImageSyncAggregator(FrameProcessor):
|
||||
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
await self.push_frame(talking_frame)
|
||||
await self.push_frame(frame)
|
||||
await self.push_frame(quiet_frame)
|
||||
|
||||
|
||||
async def main(room_url: str, token):
|
||||
async with aiohttp.ClientSession() as session:
|
||||
transport = DailyTransport(
|
||||
room_url,
|
||||
token,
|
||||
"Santa Cat",
|
||||
DailyParams(
|
||||
audio_out_enabled=True,
|
||||
camera_out_enabled=True,
|
||||
camera_out_width=720,
|
||||
camera_out_height=1280,
|
||||
camera_out_framerate=10,
|
||||
transcription_enabled=True
|
||||
)
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
model="gpt-4-turbo-preview")
|
||||
|
||||
tts = ElevenLabsTTSService(
|
||||
aiohttp_session=session,
|
||||
api_key=os.getenv("ELEVENLABS_API_KEY"),
|
||||
voice_id="jBpfuIE2acCO8z3wKNLl",
|
||||
)
|
||||
isa = ImageSyncAggregator()
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are Santa Cat, a cat that lives in Santa's workshop at the North Pole. You should be clever, and a bit sarcastic. You should also tell jokes every once in a while. Your responses should only be a few sentences long.",
|
||||
},
|
||||
]
|
||||
|
||||
tma_in = LLMUserContextAggregator(messages)
|
||||
tma_out = LLMAssistantContextAggregator(messages)
|
||||
wcf = WakeCheckFilter(["Santa Cat", "Santa"])
|
||||
|
||||
pipeline = Pipeline([
|
||||
transport.input(), # Transport user input
|
||||
isa, # Cat talking/quiet images
|
||||
wcf, # Filter out speech not directed at Santa Cat
|
||||
tma_in, # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
tma_out # Santa Cat spoken responses
|
||||
])
|
||||
|
||||
@transport.event_handler("on_first_participant_joined")
|
||||
async def on_first_participant_joined(transport, participant):
|
||||
# Send some greeting at the beginning.
|
||||
await tts.say("Hi! If you want to talk to me, just say 'hey Santa Cat'.")
|
||||
transport.capture_participant_transcription(participant["id"])
|
||||
|
||||
async def starting_image():
|
||||
await transport.send_image(quiet_frame)
|
||||
|
||||
runner = PipelineRunner()
|
||||
|
||||
task = PipelineTask(pipeline)
|
||||
|
||||
await asyncio.gather(runner.run(task), starting_image())
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
(url, token) = configure()
|
||||
asyncio.run(main(url, token))
|
||||
@@ -19,15 +19,16 @@ from pipecat.frames.frames import (
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import (
|
||||
LLMUserContextAggregator,
|
||||
LLMAssistantContextAggregator,
|
||||
from pipecat.processors.aggregators.llm_response import (
|
||||
LLMUserResponseAggregator,
|
||||
LLMAssistantResponseAggregator,
|
||||
)
|
||||
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||||
from pipecat.processors.logger import FrameLogger
|
||||
from pipecat.services.elevenlabs import ElevenLabsTTSService
|
||||
from pipecat.services.openai import OpenAILLMService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
from pipecat.vad.silero import SileroVADAnalyzer
|
||||
|
||||
from runner import configure
|
||||
|
||||
@@ -84,7 +85,12 @@ async def main(room_url: str, token):
|
||||
room_url,
|
||||
token,
|
||||
"Respond bot",
|
||||
DailyParams(audio_out_enabled=True, transcription_enabled=True)
|
||||
DailyParams(
|
||||
audio_out_enabled=True,
|
||||
transcription_enabled=True,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer()
|
||||
)
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
@@ -104,8 +110,8 @@ async def main(room_url: str, token):
|
||||
},
|
||||
]
|
||||
|
||||
tma_in = LLMUserContextAggregator(messages)
|
||||
tma_out = LLMAssistantContextAggregator(messages)
|
||||
tma_in = LLMUserResponseAggregator(messages)
|
||||
tma_out = LLMAssistantResponseAggregator(messages)
|
||||
out_sound = OutboundSoundEffectWrapper()
|
||||
in_sound = InboundSoundEffectWrapper()
|
||||
fl = FrameLogger("LLM Out")
|
||||
|
||||
145
examples/foundational/14-function-calling.py
Normal file
145
examples/foundational/14-function-calling.py
Normal file
@@ -0,0 +1,145 @@
|
||||
#
|
||||
# Copyright (c) 2024, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import asyncio
|
||||
import aiohttp
|
||||
import os
|
||||
import json
|
||||
import sys
|
||||
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineTask
|
||||
from pipecat.processors.aggregators.llm_response import (
|
||||
LLMAssistantContextAggregator,
|
||||
LLMUserContextAggregator,
|
||||
)
|
||||
from pipecat.services.openai import OpenAILLMContext
|
||||
from pipecat.processors.logger import FrameLogger
|
||||
from pipecat.services.elevenlabs import ElevenLabsTTSService
|
||||
from pipecat.services.openai import OpenAILLMService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
from pipecat.vad.silero import SileroVADAnalyzer
|
||||
from openai.types.chat import (
|
||||
ChatCompletionToolParam,
|
||||
)
|
||||
from pipecat.frames.frames import (
|
||||
TextFrame
|
||||
)
|
||||
|
||||
from runner import configure
|
||||
|
||||
from loguru import logger
|
||||
|
||||
from dotenv import load_dotenv
|
||||
load_dotenv(override=True)
|
||||
|
||||
logger.remove(0)
|
||||
logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
|
||||
async def start_fetch_weather(llm):
|
||||
await llm.push_frame(TextFrame("Let me think."))
|
||||
|
||||
|
||||
async def fetch_weather_from_api(llm, args):
|
||||
return ({"conditions": "nice", "temperature": "75"})
|
||||
|
||||
|
||||
async def main(room_url: str, token):
|
||||
async with aiohttp.ClientSession() as session:
|
||||
transport = DailyTransport(
|
||||
room_url,
|
||||
token,
|
||||
"Respond bot",
|
||||
DailyParams(
|
||||
audio_out_enabled=True,
|
||||
transcription_enabled=True,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer()
|
||||
)
|
||||
)
|
||||
|
||||
tts = ElevenLabsTTSService(
|
||||
aiohttp_session=session,
|
||||
api_key=os.getenv("ELEVENLABS_API_KEY"),
|
||||
voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
model="gpt-4-turbo-preview")
|
||||
llm.register_function(
|
||||
"get_current_weather",
|
||||
fetch_weather_from_api,
|
||||
start_callback=start_fetch_weather)
|
||||
|
||||
fl_in = FrameLogger("Inner")
|
||||
fl_out = FrameLogger("Outer")
|
||||
|
||||
tools = [
|
||||
ChatCompletionToolParam(
|
||||
type="function",
|
||||
function={
|
||||
"name": "get_current_weather",
|
||||
"description": "Get the current weather",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"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"],
|
||||
},
|
||||
})]
|
||||
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)
|
||||
tma_in = LLMUserContextAggregator(context)
|
||||
tma_out = LLMAssistantContextAggregator(context)
|
||||
pipeline = Pipeline([
|
||||
fl_in,
|
||||
transport.input(),
|
||||
tma_in,
|
||||
llm,
|
||||
fl_out,
|
||||
tts,
|
||||
transport.output(),
|
||||
tma_out
|
||||
])
|
||||
|
||||
task = PipelineTask(pipeline)
|
||||
|
||||
@ transport.event_handler("on_first_participant_joined")
|
||||
async def on_first_participant_joined(transport, participant):
|
||||
transport.capture_participant_transcription(participant["id"])
|
||||
# Kick off the conversation.
|
||||
await tts.say("Hi! Ask me about the weather in San Francisco.")
|
||||
|
||||
runner = PipelineRunner()
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
(url, token) = configure()
|
||||
asyncio.run(main(url, token))
|
||||
16
examples/patient-intake/Dockerfile
Normal file
16
examples/patient-intake/Dockerfile
Normal file
@@ -0,0 +1,16 @@
|
||||
FROM python:3.10-bullseye
|
||||
|
||||
RUN mkdir /app
|
||||
RUN mkdir /app/assets
|
||||
RUN mkdir /app/utils
|
||||
COPY *.py /app/
|
||||
COPY requirements.txt /app/
|
||||
copy assets/* /app/assets/
|
||||
copy utils/* /app/utils/
|
||||
|
||||
WORKDIR /app
|
||||
RUN pip3 install -r requirements.txt
|
||||
|
||||
EXPOSE 7860
|
||||
|
||||
CMD ["python3", "server.py"]
|
||||
37
examples/patient-intake/README.md
Normal file
37
examples/patient-intake/README.md
Normal file
@@ -0,0 +1,37 @@
|
||||
# Simple Chatbot
|
||||
|
||||
<img src="image.png" width="420px">
|
||||
|
||||
This app connects you to a chatbot powered by GPT-4, complete with animations generated by Stable Video Diffusion.
|
||||
|
||||
See a video of it in action: https://x.com/kwindla/status/1778628911817183509
|
||||
|
||||
And a quick video walkthrough of the code: https://www.loom.com/share/13df1967161f4d24ade054e7f8753416
|
||||
|
||||
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/start` 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
|
||||
```
|
||||
BIN
examples/patient-intake/assets/clack-short-quiet.wav
Normal file
BIN
examples/patient-intake/assets/clack-short-quiet.wav
Normal file
Binary file not shown.
BIN
examples/patient-intake/assets/clack-short.wav
Normal file
BIN
examples/patient-intake/assets/clack-short.wav
Normal file
Binary file not shown.
BIN
examples/patient-intake/assets/clack.wav
Normal file
BIN
examples/patient-intake/assets/clack.wav
Normal file
Binary file not shown.
BIN
examples/patient-intake/assets/ding.wav
Normal file
BIN
examples/patient-intake/assets/ding.wav
Normal file
Binary file not shown.
BIN
examples/patient-intake/assets/ding2.wav
Normal file
BIN
examples/patient-intake/assets/ding2.wav
Normal file
Binary file not shown.
BIN
examples/patient-intake/assets/ding3.wav
Normal file
BIN
examples/patient-intake/assets/ding3.wav
Normal file
Binary file not shown.
359
examples/patient-intake/bot.py
Normal file
359
examples/patient-intake/bot.py
Normal file
@@ -0,0 +1,359 @@
|
||||
import asyncio
|
||||
import aiohttp
|
||||
import copy
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
import sys
|
||||
import wave
|
||||
from typing import List
|
||||
|
||||
from openai._types import NotGiven, NOT_GIVEN
|
||||
|
||||
from openai.types.chat import (
|
||||
ChatCompletionToolParam,
|
||||
)
|
||||
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineTask
|
||||
from pipecat.processors.aggregators.llm_response import LLMUserContextAggregator, LLMAssistantContextAggregator
|
||||
from pipecat.processors.logger import FrameLogger
|
||||
from pipecat.frames.frames import (
|
||||
Frame,
|
||||
LLMMessagesFrame,
|
||||
AudioRawFrame,
|
||||
)
|
||||
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||||
from pipecat.services.elevenlabs import ElevenLabsTTSService
|
||||
from pipecat.services.openai import OpenAILLMService
|
||||
from pipecat.services.ai_services import AIService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTranscriptionSettings, DailyTransport
|
||||
from pipecat.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.services.openai import OpenAILLMContext, OpenAILLMContextFrame
|
||||
|
||||
from runner import configure
|
||||
|
||||
from loguru import logger
|
||||
|
||||
from dotenv import load_dotenv
|
||||
load_dotenv(override=True)
|
||||
|
||||
logger.remove(0)
|
||||
logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
sounds = {}
|
||||
sound_files = [
|
||||
"clack-short.wav",
|
||||
"clack.wav",
|
||||
"clack-short-quiet.wav",
|
||||
"ding.wav",
|
||||
"ding2.wav",
|
||||
]
|
||||
|
||||
script_dir = os.path.dirname(__file__)
|
||||
|
||||
for file in sound_files:
|
||||
# Build the full path to the sound file
|
||||
full_path = os.path.join(script_dir, "assets", file)
|
||||
# Get the filename without the extension to use as the dictionary key
|
||||
filename = os.path.splitext(os.path.basename(full_path))[0]
|
||||
# Open the sound and convert it to bytes
|
||||
with wave.open(full_path) as audio_file:
|
||||
sounds[file] = AudioRawFrame(audio_file.readframes(-1),
|
||||
audio_file.getframerate(), audio_file.getnchannels())
|
||||
|
||||
|
||||
class IntakeProcessor:
|
||||
def __init__(
|
||||
self,
|
||||
context: OpenAILLMContext,
|
||||
llm: AIService,
|
||||
tools: List[ChatCompletionToolParam] | NotGiven = NOT_GIVEN,
|
||||
*args,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(*args, **kwargs)
|
||||
self._context: OpenAILLMContext = context
|
||||
self._llm = llm
|
||||
print(f"Initializing context from IntakeProcessor")
|
||||
self._context.add_message({"role": "system", "content": "You are Jessica, an agent for a company called Tri-County Health Services. Your job is to collect important information from the user before their doctor visit. You're talking to Chad Bailey. You should address the user by their first name and be polite and professional. You're not a medical professional, so you shouldn't provide any advice. Keep your responses short. Your job is to collect information to give to a doctor. Don't make assumptions about what values to plug into functions. Ask for clarification if a user response is ambiguous. Start by introducing yourself. Then, ask the user to confirm their identity by telling you their birthday, including the year. When they answer with their birthday, call the verify_birthday function."})
|
||||
self._context.set_tools([
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "verify_birthday",
|
||||
"description": "Use this function to verify the user has provided their correct birthday.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"birthday": {
|
||||
"type": "string",
|
||||
"description": "The user's birthdate, including the year. The user can provide it in any format, but convert it to YYYY-MM-DD format to call this function.",
|
||||
}},
|
||||
},
|
||||
},
|
||||
}])
|
||||
# Create an allowlist of functions that the LLM can call
|
||||
self._functions = [
|
||||
"verify_birthday",
|
||||
"list_prescriptions",
|
||||
"list_allergies",
|
||||
"list_conditions",
|
||||
"list_visit_reasons",
|
||||
]
|
||||
|
||||
async def verify_birthday(self, llm, args):
|
||||
if args["birthday"] == "1983-01-01":
|
||||
self._context.set_tools(
|
||||
[
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "list_prescriptions",
|
||||
"description": "Once the user has provided a list of their prescription medications, call this function.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"prescriptions": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"medication": {
|
||||
"type": "string",
|
||||
"description": "The medication's name",
|
||||
},
|
||||
"dosage": {
|
||||
"type": "string",
|
||||
"description": "The prescription's dosage",
|
||||
},
|
||||
},
|
||||
},
|
||||
}},
|
||||
},
|
||||
},
|
||||
}])
|
||||
# It's a bit weird to push this to the LLM, but it gets it into the pipeline
|
||||
await llm.push_frame(sounds["ding2.wav"], FrameDirection.DOWNSTREAM)
|
||||
# We don't need the function call in the context, so just return a new
|
||||
# system message and let the framework re-prompt
|
||||
return [{"role": "system", "content": "Next, thank the user for confirming their identity, then ask the user to list their current prescriptions. Each prescription needs to have a medication name and a dosage. Do not call the list_prescriptions function with any unknown dosages."}]
|
||||
else:
|
||||
# The user provided an incorrect birthday; ask them to try again
|
||||
return [{"role": "system", "content": "The user provided an incorrect birthday. Ask them for their birthday again. When they answer, call the verify_birthday function."}]
|
||||
|
||||
async def start_prescriptions(self, llm):
|
||||
print(f"!!! doing start prescriptions")
|
||||
# Move on to allergies
|
||||
self._context.set_tools(
|
||||
[
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "list_allergies",
|
||||
"description": "Once the user has provided a list of their allergies, call this function.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"allergies": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"name": {
|
||||
"type": "string",
|
||||
"description": "What the user is allergic to",
|
||||
}},
|
||||
},
|
||||
}},
|
||||
},
|
||||
},
|
||||
}])
|
||||
self._context.add_message(
|
||||
{
|
||||
"role": "system",
|
||||
"content": "Next, ask the user if they have any allergies. Once they have listed their allergies or confirmed they don't have any, call the list_allergies function."})
|
||||
print(f"!!! about to await llm process frame in start prescrpitions")
|
||||
await llm.process_frame(OpenAILLMContextFrame(self._context), FrameDirection.DOWNSTREAM)
|
||||
print(f"!!! past await process frame in start prescriptions")
|
||||
|
||||
async def start_allergies(self, llm):
|
||||
print("!!! doing start allergies")
|
||||
# Move on to conditions
|
||||
self._context.set_tools(
|
||||
[
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "list_conditions",
|
||||
"description": "Once the user has provided a list of their medical conditions, call this function.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"conditions": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"name": {
|
||||
"type": "string",
|
||||
"description": "The user's medical condition",
|
||||
}},
|
||||
},
|
||||
}},
|
||||
},
|
||||
},
|
||||
},
|
||||
])
|
||||
self._context.add_message(
|
||||
{
|
||||
"role": "system",
|
||||
"content": "Now ask the user if they have any medical conditions the doctor should know about. Once they've answered the question, call the list_conditions function."})
|
||||
await llm.process_frame(OpenAILLMContextFrame(self._context), FrameDirection.DOWNSTREAM)
|
||||
|
||||
async def start_conditions(self, llm):
|
||||
print("!!! doing start conditions")
|
||||
# Move on to visit reasons
|
||||
self._context.set_tools(
|
||||
[
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "list_visit_reasons",
|
||||
"description": "Once the user has provided a list of the reasons they are visiting a doctor today, call this function.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"visit_reasons": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"name": {
|
||||
"type": "string",
|
||||
"description": "The user's reason for visiting the doctor",
|
||||
}},
|
||||
},
|
||||
}},
|
||||
},
|
||||
},
|
||||
}])
|
||||
self._context.add_message(
|
||||
{"role": "system", "content": "Finally, ask the user the reason for their doctor visit today. Once they answer, call the list_visit_reasons function."})
|
||||
await llm.process_frame(OpenAILLMContextFrame(self._context), FrameDirection.DOWNSTREAM)
|
||||
pass
|
||||
|
||||
async def start_visit_reasons(self, llm):
|
||||
print("!!! doing start visit reasons")
|
||||
# move to finish call
|
||||
self._context.set_tools([])
|
||||
self._context.add_message({"role": "system",
|
||||
"content": "Now, thank the user and end the conversation."})
|
||||
await llm.process_frame(OpenAILLMContextFrame(self._context), FrameDirection.DOWNSTREAM)
|
||||
pass
|
||||
|
||||
async def save_data(self, llm, args):
|
||||
logger.info(f"!!! Saving data: {args}")
|
||||
# Since this is supposed to be "async", returning None from the callback
|
||||
# will prevent adding anything to context or re-prompting
|
||||
return None
|
||||
|
||||
|
||||
async def main(room_url: str, token):
|
||||
async with aiohttp.ClientSession() as session:
|
||||
transport = DailyTransport(
|
||||
room_url,
|
||||
token,
|
||||
"Chatbot",
|
||||
DailyParams(
|
||||
audio_out_enabled=True,
|
||||
camera_out_enabled=True,
|
||||
camera_out_width=1024,
|
||||
camera_out_height=576,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
transcription_enabled=True,
|
||||
#
|
||||
# Spanish
|
||||
#
|
||||
# transcription_settings=DailyTranscriptionSettings(
|
||||
# language="es",
|
||||
# tier="nova",
|
||||
# model="2-general"
|
||||
# )
|
||||
)
|
||||
)
|
||||
|
||||
tts = ElevenLabsTTSService(
|
||||
aiohttp_session=session,
|
||||
api_key=os.getenv("ELEVENLABS_API_KEY"),
|
||||
#
|
||||
# English
|
||||
#
|
||||
voice_id="pNInz6obpgDQGcFmaJgB",
|
||||
|
||||
#
|
||||
# Spanish
|
||||
#
|
||||
# model="eleven_multilingual_v2",
|
||||
# voice_id="gD1IexrzCvsXPHUuT0s3",
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
model="gpt-4o")
|
||||
|
||||
messages = []
|
||||
context = OpenAILLMContext(
|
||||
messages=messages,
|
||||
)
|
||||
user_context = LLMUserContextAggregator(context)
|
||||
assistant_context = LLMAssistantContextAggregator(context)
|
||||
# checklist = ChecklistProcessor(context, llm)
|
||||
intake = IntakeProcessor(context, llm)
|
||||
llm.register_function("verify_birthday", intake.verify_birthday)
|
||||
llm.register_function(
|
||||
"list_prescriptions",
|
||||
intake.save_data,
|
||||
start_callback=intake.start_prescriptions)
|
||||
llm.register_function(
|
||||
"list_allergies",
|
||||
intake.save_data,
|
||||
start_callback=intake.start_allergies)
|
||||
llm.register_function(
|
||||
"list_conditions",
|
||||
intake.save_data,
|
||||
start_callback=intake.start_conditions)
|
||||
llm.register_function(
|
||||
"list_visit_reasons",
|
||||
intake.save_data,
|
||||
start_callback=intake.start_visit_reasons)
|
||||
fl = FrameLogger("LLM Output")
|
||||
|
||||
pipeline = Pipeline([
|
||||
transport.input(),
|
||||
user_context,
|
||||
llm,
|
||||
fl,
|
||||
tts,
|
||||
transport.output(),
|
||||
assistant_context,
|
||||
])
|
||||
|
||||
task = PipelineTask(pipeline, allow_interruptions=False)
|
||||
|
||||
@transport.event_handler("on_first_participant_joined")
|
||||
async def on_first_participant_joined(transport, participant):
|
||||
transport.capture_participant_transcription(participant["id"])
|
||||
print(f"Context is: {context}")
|
||||
await task.queue_frames([OpenAILLMContextFrame(context)])
|
||||
|
||||
runner = PipelineRunner()
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
(url, token) = configure()
|
||||
asyncio.run(main(url, token))
|
||||
4
examples/patient-intake/env.example
Normal file
4
examples/patient-intake/env.example
Normal file
@@ -0,0 +1,4 @@
|
||||
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...
|
||||
BIN
examples/patient-intake/image.png
Normal file
BIN
examples/patient-intake/image.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 733 KiB |
5
examples/patient-intake/requirements.txt
Normal file
5
examples/patient-intake/requirements.txt
Normal file
@@ -0,0 +1,5 @@
|
||||
python-dotenv
|
||||
requests
|
||||
fastapi[all]
|
||||
uvicorn
|
||||
pipecat-ai[daily,openai,silero]
|
||||
58
examples/patient-intake/runner.py
Normal file
58
examples/patient-intake/runner.py
Normal file
@@ -0,0 +1,58 @@
|
||||
import argparse
|
||||
import os
|
||||
import time
|
||||
import urllib
|
||||
import requests
|
||||
|
||||
|
||||
def configure():
|
||||
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.")
|
||||
|
||||
# Create a meeting token for the given room with an expiration 1 hour in
|
||||
# the future.
|
||||
room_name: str = urllib.parse.urlparse(url).path[1:]
|
||||
expiration: float = time.time() + 60 * 60
|
||||
|
||||
res: requests.Response = requests.post(
|
||||
f"https://api.daily.co/v1/meeting-tokens",
|
||||
headers={
|
||||
"Authorization": f"Bearer {key}"},
|
||||
json={
|
||||
"properties": {
|
||||
"room_name": room_name,
|
||||
"is_owner": True,
|
||||
"exp": expiration}},
|
||||
)
|
||||
|
||||
if res.status_code != 200:
|
||||
raise Exception(
|
||||
f"Failed to create meeting token: {res.status_code} {res.text}")
|
||||
|
||||
token: str = res.json()["token"]
|
||||
|
||||
return (url, token)
|
||||
124
examples/patient-intake/server.py
Normal file
124
examples/patient-intake/server.py
Normal file
@@ -0,0 +1,124 @@
|
||||
import os
|
||||
import argparse
|
||||
import subprocess
|
||||
import atexit
|
||||
|
||||
from fastapi import FastAPI, Request, HTTPException
|
||||
from fastapi.middleware.cors import CORSMiddleware
|
||||
from fastapi.responses import JSONResponse, RedirectResponse
|
||||
|
||||
from utils.daily_helpers import create_room as _create_room, get_token
|
||||
|
||||
MAX_BOTS_PER_ROOM = 1
|
||||
|
||||
# Bot sub-process dict for status reporting and concurrency control
|
||||
bot_procs = {}
|
||||
|
||||
|
||||
def cleanup():
|
||||
# Clean up function, just to be extra safe
|
||||
for proc in bot_procs.values():
|
||||
proc.terminate()
|
||||
proc.wait()
|
||||
|
||||
|
||||
atexit.register(cleanup)
|
||||
|
||||
|
||||
app = FastAPI()
|
||||
|
||||
app.add_middleware(
|
||||
CORSMiddleware,
|
||||
allow_origins=["*"],
|
||||
allow_credentials=True,
|
||||
allow_methods=["*"],
|
||||
allow_headers=["*"],
|
||||
)
|
||||
|
||||
|
||||
@app.get("/start")
|
||||
async def start_agent(request: Request):
|
||||
print(f"!!! Creating room")
|
||||
room_url, room_name = _create_room()
|
||||
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 = 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()
|
||||
print(f"to join a test room, visit http://localhost:{config.port}/start")
|
||||
uvicorn.run(
|
||||
"server:app",
|
||||
host=config.host,
|
||||
port=config.port,
|
||||
reload=config.reload,
|
||||
)
|
||||
109
examples/patient-intake/utils/daily_helpers.py
Normal file
109
examples/patient-intake/utils/daily_helpers.py
Normal file
@@ -0,0 +1,109 @@
|
||||
|
||||
import urllib.parse
|
||||
import os
|
||||
import time
|
||||
import urllib
|
||||
import requests
|
||||
|
||||
from dotenv import load_dotenv
|
||||
load_dotenv()
|
||||
|
||||
|
||||
daily_api_path = os.getenv("DAILY_API_URL") or "api.daily.co/v1"
|
||||
daily_api_key = os.getenv("DAILY_API_KEY")
|
||||
|
||||
|
||||
def create_room() -> tuple[str, str]:
|
||||
"""
|
||||
Helper function to create a Daily room.
|
||||
# See: https://docs.daily.co/reference/rest-api/rooms
|
||||
|
||||
Returns:
|
||||
tuple: A tuple containing the room URL and room name.
|
||||
|
||||
Raises:
|
||||
Exception: If the request to create the room fails or if the response does not contain the room URL or room name.
|
||||
"""
|
||||
room_props = {
|
||||
"exp": time.time() + 60 * 60, # 1 hour
|
||||
"enable_chat": True,
|
||||
"enable_emoji_reactions": True,
|
||||
"eject_at_room_exp": True,
|
||||
"enable_prejoin_ui": False, # Important for the bot to be able to join headlessly
|
||||
}
|
||||
res = requests.post(
|
||||
f"https://{daily_api_path}/rooms",
|
||||
headers={"Authorization": f"Bearer {daily_api_key}"},
|
||||
json={
|
||||
"properties": room_props
|
||||
},
|
||||
)
|
||||
if res.status_code != 200:
|
||||
raise Exception(f"Unable to create room: {res.text}")
|
||||
|
||||
data = res.json()
|
||||
room_url: str = data.get("url")
|
||||
room_name: str = data.get("name")
|
||||
if room_url is None or room_name is None:
|
||||
raise Exception("Missing room URL or room name in response")
|
||||
|
||||
return room_url, room_name
|
||||
|
||||
|
||||
def get_name_from_url(room_url: str) -> str:
|
||||
"""
|
||||
Extracts the name from a given room URL.
|
||||
|
||||
Args:
|
||||
room_url (str): The URL of the room.
|
||||
|
||||
Returns:
|
||||
str: The extracted name from the room URL.
|
||||
"""
|
||||
return urllib.parse.urlparse(room_url).path[1:]
|
||||
|
||||
|
||||
def get_token(room_url: str) -> str:
|
||||
"""
|
||||
Retrieves a meeting token for the specified Daily room URL.
|
||||
# See: https://docs.daily.co/reference/rest-api/meeting-tokens
|
||||
|
||||
Args:
|
||||
room_url (str): The URL of the Daily room.
|
||||
|
||||
Returns:
|
||||
str: The meeting token.
|
||||
|
||||
Raises:
|
||||
Exception: If no room URL is specified or if no Daily API key is specified.
|
||||
Exception: If there is an error creating the meeting token.
|
||||
"""
|
||||
if not room_url:
|
||||
raise Exception(
|
||||
"No Daily room specified. You must specify a Daily room in order a token to be generated.")
|
||||
|
||||
if not daily_api_key:
|
||||
raise Exception(
|
||||
"No Daily API key specified. set DAILY_API_KEY in your environment to specify a Daily API key, available from https://dashboard.daily.co/developers.")
|
||||
|
||||
expiration: float = time.time() + 60 * 60
|
||||
room_name = get_name_from_url(room_url)
|
||||
|
||||
res: requests.Response = requests.post(
|
||||
f"https://{daily_api_path}/meeting-tokens",
|
||||
headers={
|
||||
"Authorization": f"Bearer {daily_api_key}"},
|
||||
json={
|
||||
"properties": {
|
||||
"room_name": room_name,
|
||||
"is_owner": True, # Owner tokens required for transcription
|
||||
"exp": expiration}},
|
||||
)
|
||||
|
||||
if res.status_code != 200:
|
||||
raise Exception(
|
||||
f"Failed to create meeting token: {res.status_code} {res.text}")
|
||||
|
||||
token: str = res.json()["token"]
|
||||
|
||||
return token
|
||||
Reference in New Issue
Block a user