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Add call transfer to bot_daily

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This commit is contained in:
James Hush
2025-01-23 15:54:53 +08:00
parent fc6aa6eae8
commit c2fef9584b
2 changed files with 261 additions and 2 deletions
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80
examples/dialin-chatbot/bot_daily.py
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@@ -1,3 +1,8 @@
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import argparse
import asyncio
import os
@@ -5,6 +10,7 @@ import sys
from dotenv import load_dotenv
from loguru import logger
from openai.types.chat import ChatCompletionToolParam
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import EndFrame
@@ -12,6 +18,8 @@ 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.ai_services import LLMService
from pipecat.services.deepgram import DeepgramSTTService
from pipecat.services.elevenlabs import ElevenLabsTTSService
from pipecat.services.openai import OpenAILLMService
from pipecat.transports.services.daily import DailyDialinSettings, DailyParams, DailyTransport
@@ -55,14 +63,44 @@ async def main(room_url: str, token: str, callId: str, callDomain: str):
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
content = f"""
You are a delivery service customer support specialist supporting customers with their orders.
Begin with: "Hello, this is Hailey from customer support. What can I help you with today?"
"""
messages = [
{
"role": "system",
"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 saying 'Oh, hello! Who dares dial me at this hour?!'.",
"content": content,
},
]
context = OpenAILLMContext(messages)
tools = [
ChatCompletionToolParam(
type="function",
function={
"name": "transfer_call",
"description": "Transfer the call to a person. This function is used to connect the call to a real person. Examples of real people are: managers, supervisors, or other customer support specialists. Any person is okay as long as they are not a bot.",
"parameters": {
"type": "object",
"properties": {
"call_id": {
"type": "string",
"description": "This is always {callId}.",
},
"summary": {
"type": "string",
"description": """
Provide a concise summary in 3-5 sentences. Highlight any important details or unusual aspects of the conversation.
""",
},
},
},
},
)
]
context = OpenAILLMContext(messages, tools)
context_aggregator = llm.create_context_aggregator(context)
pipeline = Pipeline(
@@ -87,6 +125,44 @@ async def main(room_url: str, token: str, callId: str, callDomain: str):
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
@transport.event_handler("on_dialin_ready")
async def on_dialin_ready(_, sip_endpoint):
logger.info(f"on_dialin_ready: {sip_endpoint}")
@transport.event_handler("on_dialin_connected")
async def on_dialin_connected(transport, event):
logger.info(f"on_dialin_connected: {event}")
sip_session_id = event["sessionId"]
async def transfer_call(
function_name, tool_call_id, args, llm: LLMService, context, result_callback
):
logger.debug(f"transfer_call: {function_name} {tool_call_id} {args}")
# sip_url = "sip:your_user_name@sip.linphone.org"
sip_url = (
f"sip:your_username@dailyco.sip.twilio.com?x-daily_id={room_url.split('/')[-1]}"
)
try:
await transport.sip_refer(
settings={
"sessionId": sip_session_id,
"toEndPoint": sip_url,
}
)
except Exception as e:
logger.error(f"An error occurred during SIP refer: {e}")
await result_callback({"transfer_call": False})
await result_callback({"transfer_call": True})
llm.register_function(
function_name="transfer_call",
callback=transfer_call,
)
runner = PipelineRunner()
await runner.run(task)

183
examples/foundational/32-call-transfer.py Normal file
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@@ -0,0 +1,183 @@
#
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import asyncio
import os
import sys
from loguru import logger
from openai.types.chat import ChatCompletionToolParam
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import EndFrame, LLMMessagesFrame
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.ai_services import LLMService
from pipecat.services.cartesia import CartesiaTTSService
from pipecat.services.deepgram import DeepgramSTTService
from pipecat.services.openai import OpenAILLMService
from pipecat.transports.services.daily import DailyDialinSettings, DailyParams, DailyTransport
logger.remove(0)
logger.add(sys.stderr, level="DEBUG")
async def main(room_url: str, token: str, call_id: str, call_domain: str):
"""Main entrypoint for the voice bot process.
:param room_url: The Daily.co room URL
:param token: The Daily.co token
:param callId: The call ID from Daily.co
:param callDomain: The domain associated with the call
"""
diallin_settings = DailyDialinSettings(call_id=call_id, call_domain=call_domain)
transport = DailyTransport(
room_url,
token,
"Chatbot",
DailyParams(
api_url="https://api.daily.co/v1/",
api_key=os.getenv("DAILY_API_KEY", ""),
dialin_settings=diallin_settings,
audio_in_enabled=True,
audio_out_enabled=True,
camera_out_enabled=False,
vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
transcription_enabled=True,
),
)
cartesia_params = CartesiaTTSService.InputParams(
speed=-0.1,
emotion=["positivity:high", "curiosity"],
language="en",
)
tts = CartesiaTTSService(
api_key=os.getenv("CARTESIA_API_KEY", ""),
# Use Helpful Woman voice by default
voice_id="156fb8d2-335b-4950-9cb3-a2d33befec77",
params=cartesia_params,
)
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
tools = [
ChatCompletionToolParam(
type="function",
function={
"name": "transfer_call",
"description": "Transfer the call to a person. This function is used to connect the call to a real person. Examples of real people are: managers, supervisors, or other customer support specialists. Any person is okay as long as they are not a bot.",
"parameters": {
"type": "object",
"properties": {
"call_id": {
"type": "string",
"description": "This is always {call_id}.",
},
"summary": {
"type": "string",
"description": """
Provide a concise summary in 3-5 sentences. Highlight any important details or unusual aspects of the conversation.
""",
},
},
},
},
)
]
content = f"""
You are a delivery service customer support specialist supporting customers with their orders.
Begin with: "Hello, this is Hailey from customer support. What can I help you with today?"
"""
messages = [
{
"role": "system",
"content": content,
},
]
context = OpenAILLMContext(messages, tools)
context_aggregator = llm.create_context_aggregator(context)
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
pipeline = Pipeline(
[
transport.input(),
stt, # STT
context_aggregator.user(),
llm,
tts,
transport.output(),
context_aggregator.assistant(),
]
)
task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
@transport.event_handler("on_first_participant_joined")
async def on_first_participant_joined(_, participant):
logger.info(f"on_first_participant_joined: {participant}")
# await transport.capture_participant_transcription(participant["id"]) Might not need this
await task.queue_frames([LLMMessagesFrame(messages)])
@transport.event_handler("on_participant_left")
async def on_participant_left(_, participant, reason):
logger.info(f"on_participant_left: {participant} {reason}")
await task.queue_frame(EndFrame())
@transport.event_handler("on_dialin_ready")
async def on_dialin_ready(_, sip_endpoint):
logger.info(f"on_dialin_ready: {sip_endpoint}")
@transport.event_handler("on_dialin_connected")
async def on_dialin_connected(transport, event):
logger.info(f"on_dialin_connected: {event}")
sip_session_id = event["sessionId"]
async def transfer_call(
function_name, tool_call_id, args, llm: LLMService, context, result_callback
):
logger.debug(f"transfer_call: {function_name} {tool_call_id} {args}")
# sip_url = "sip:your_user_name@sip.linphone.org"
sip_url = (
f"sip:your_username@dailyco.sip.twilio.com?x-daily_id={room_url.split('/')[-1]}"
)
try:
await transport.sip_refer(
settings={
"sessionId": sip_session_id,
"toEndPoint": sip_url,
}
)
except Exception as e:
logger.error(f"An error occurred during SIP refer: {e}")
await result_callback({"transfer_call": False})
await result_callback({"transfer_call": True})
llm.register_function(
function_name="transfer_call",
callback=transfer_call,
)
runner = PipelineRunner()
await runner.run(task)
if __name__ == "__main__":
asyncio.run(main())