368 lines
15 KiB
Python
368 lines
15 KiB
Python
#
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# Copyright (c) 2024–2025, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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import asyncio
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import os
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import sys
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import wave
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import aiohttp
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from dotenv import load_dotenv
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from loguru import logger
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from runner import configure
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import OutputAudioRawFrame
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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from pipecat.processors.frame_processor import FrameDirection
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from pipecat.processors.logger import FrameLogger
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from pipecat.services.cartesia import CartesiaTTSService
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from pipecat.services.openai import OpenAILLMContext, OpenAILLMContextFrame, OpenAILLMService
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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load_dotenv(override=True)
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logger.remove(0)
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logger.add(sys.stderr, level="DEBUG")
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sounds = {}
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sound_files = [
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"clack-short.wav",
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"clack.wav",
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"clack-short-quiet.wav",
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"ding.wav",
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"ding2.wav",
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]
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script_dir = os.path.dirname(__file__)
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for file in sound_files:
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# Build the full path to the sound file
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full_path = os.path.join(script_dir, "assets", file)
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# Get the filename without the extension to use as the dictionary key
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filename = os.path.splitext(os.path.basename(full_path))[0]
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# Open the sound and convert it to bytes
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with wave.open(full_path) as audio_file:
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sounds[file] = OutputAudioRawFrame(
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audio_file.readframes(-1), audio_file.getframerate(), audio_file.getnchannels()
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)
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class IntakeProcessor:
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def __init__(self, context: OpenAILLMContext):
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print(f"Initializing context from IntakeProcessor")
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context.add_message(
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{
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"role": "system",
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"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.",
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}
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)
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context.set_tools(
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[
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{
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"type": "function",
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"function": {
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"name": "verify_birthday",
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"description": "Use this function to verify the user has provided their correct birthday.",
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"parameters": {
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"type": "object",
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"properties": {
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"birthday": {
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"type": "string",
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"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.",
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}
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},
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},
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},
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}
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]
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)
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async def verify_birthday(
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self, function_name, tool_call_id, args, llm, context, result_callback
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):
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if args["birthday"] == "1983-01-01":
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context.set_tools(
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[
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{
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"type": "function",
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"function": {
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"name": "list_prescriptions",
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"description": "Once the user has provided a list of their prescription medications, call this function.",
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"parameters": {
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"type": "object",
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"properties": {
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"prescriptions": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"medication": {
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"type": "string",
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"description": "The medication's name",
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},
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"dosage": {
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"type": "string",
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"description": "The prescription's dosage",
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},
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},
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},
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}
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},
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},
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},
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}
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]
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)
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# It's a bit weird to push this to the LLM, but it gets it into the pipeline
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# await llm.push_frame(sounds["ding2.wav"], FrameDirection.DOWNSTREAM)
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# We don't need the function call in the context, so just return a new
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# system message and let the framework re-prompt
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await result_callback(
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[
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{
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"role": "system",
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"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.",
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}
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]
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)
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else:
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# The user provided an incorrect birthday; ask them to try again
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await result_callback(
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[
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{
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"role": "system",
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"content": "The user provided an incorrect birthday. Ask them for their birthday again. When they answer, call the verify_birthday function.",
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}
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]
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)
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async def list_prescriptions(
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self, function_name, tool_call_id, args, llm, context, result_callback
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):
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print(f"!!! doing start prescriptions")
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# Move on to allergies
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context.set_tools(
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[
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{
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"type": "function",
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"function": {
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"name": "list_allergies",
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"description": "Once the user has provided a list of their allergies, call this function.",
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"parameters": {
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"type": "object",
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"properties": {
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"allergies": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"name": {
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"type": "string",
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"description": "What the user is allergic to",
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}
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},
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},
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}
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},
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},
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},
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}
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]
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)
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context.add_message(
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{
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"role": "system",
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"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.",
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}
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)
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print(f"!!! about to await llm process frame in start prescrpitions")
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await llm.queue_frame(OpenAILLMContextFrame(context), FrameDirection.DOWNSTREAM)
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print(f"!!! past await process frame in start prescriptions")
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await self.save_data(args, result_callback)
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async def list_allergies(
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self, function_name, tool_call_id, args, llm, context, result_callback
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):
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print("!!! doing list allergies")
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# Move on to conditions
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context.set_tools(
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[
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{
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"type": "function",
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"function": {
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"name": "list_conditions",
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"description": "Once the user has provided a list of their medical conditions, call this function.",
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"parameters": {
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"type": "object",
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"properties": {
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"conditions": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"name": {
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"type": "string",
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"description": "The user's medical condition",
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}
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},
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},
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}
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},
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},
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},
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},
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]
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)
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context.add_message(
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{
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"role": "system",
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"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.",
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}
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)
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await llm.queue_frame(OpenAILLMContextFrame(context), FrameDirection.DOWNSTREAM)
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await self.save_data(args, result_callback)
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async def list_conditions(
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self, function_name, tool_call_id, args, llm, context, result_callback
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):
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print("!!! doing start conditions")
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# Move on to visit reasons
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context.set_tools(
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[
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{
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"type": "function",
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"function": {
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"name": "list_visit_reasons",
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"description": "Once the user has provided a list of the reasons they are visiting a doctor today, call this function.",
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"parameters": {
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"type": "object",
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"properties": {
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"visit_reasons": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"name": {
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"type": "string",
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"description": "The user's reason for visiting the doctor",
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}
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},
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},
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}
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},
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},
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},
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}
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]
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)
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context.add_message(
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{
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"role": "system",
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"content": "Finally, ask the user the reason for their doctor visit today. Once they answer, call the list_visit_reasons function.",
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}
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)
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await llm.queue_frame(OpenAILLMContextFrame(context), FrameDirection.DOWNSTREAM)
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await self.save_data(args, result_callback)
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async def list_visit_reasons(
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self, function_name, tool_call_id, args, llm, context, result_callback
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):
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print("!!! doing start visit reasons")
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# move to finish call
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context.set_tools([])
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context.add_message(
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{"role": "system", "content": "Now, thank the user and end the conversation."}
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)
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await llm.queue_frame(OpenAILLMContextFrame(context), FrameDirection.DOWNSTREAM)
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await self.save_data(args, result_callback)
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async def save_data(self, args, result_callback):
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logger.info(f"!!! Saving data: {args}")
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# Since this is supposed to be "async", returning None from the callback
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# will prevent adding anything to context or re-prompting
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await result_callback(None)
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async def main():
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async with aiohttp.ClientSession() as session:
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(room_url, token) = await configure(session)
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transport = DailyTransport(
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room_url,
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token,
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"Chatbot",
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DailyParams(
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audio_out_enabled=True,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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transcription_enabled=True,
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#
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# Spanish
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#
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# transcription_settings=DailyTranscriptionSettings(
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# language="es",
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# tier="nova",
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# model="2-general"
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# )
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),
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)
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
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)
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# tts = CartesiaTTSService(
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# api_key=os.getenv("CARTESIA_API_KEY"),
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# voice_id="846d6cb0-2301-48b6-9683-48f5618ea2f6", # Spanish-speaking Lady
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# )
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
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messages = []
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context = OpenAILLMContext(messages=messages)
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context_aggregator = llm.create_context_aggregator(context)
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intake = IntakeProcessor(context)
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llm.register_function("verify_birthday", intake.verify_birthday)
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llm.register_function("list_prescriptions", intake.list_prescriptions)
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llm.register_function("list_allergies", intake.list_allergies)
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llm.register_function("list_conditions", intake.list_conditions)
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llm.register_function("list_visit_reasons", intake.list_visit_reasons)
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fl = FrameLogger("LLM Output")
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pipeline = Pipeline(
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[
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transport.input(), # Transport input
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context_aggregator.user(), # User responses
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llm, # LLM
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fl, # Frame logger
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tts, # TTS
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transport.output(), # Transport output
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context_aggregator.assistant(), # Assistant responses
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]
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)
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task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=False))
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@transport.event_handler("on_first_participant_joined")
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async def on_first_participant_joined(transport, participant):
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await transport.capture_participant_transcription(participant["id"])
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print(f"Context is: {context}")
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await task.queue_frames([OpenAILLMContextFrame(context)])
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runner = PipelineRunner()
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await runner.run(task)
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if __name__ == "__main__":
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asyncio.run(main())
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