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hush/realt
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hush/callT
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b29ac3c7a8 | ||
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5222488fb5 | ||
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c2fef9584b |
@@ -37,7 +37,16 @@ Run `bot_runner.py` to handle incoming HTTP requests:
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Then target the following URL:
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Then target the following URL:
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`POST /daily_start_bot`
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```bash
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curl -X POST 'http://localhost:7860/daily_start_bot' \
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-H 'Content-Type: application/json' \
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-d '{
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"callId": "callId-from-call",
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"callDomain": "callDomain-from-call"
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}'
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```
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Use [this guide](https://docs.pipecat.ai/guides/telephony/daily-webrtc) to connect a phone number purchased from Daily to the bot.
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For more configuration options, please consult Daily's API documentation.
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For more configuration options, please consult Daily's API documentation.
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@@ -82,4 +91,4 @@ If you're using Twilio as a number vendor:
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## Need to do something more advanced?
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## Need to do something more advanced?
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This demo covers the basics of bot telephony. If you want to know more about working with PSTN / SIP, please ping us on [Discord](https://discord.gg/pipecat).
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This demo covers the basics of bot telephony. If you want to know more about working with PSTN / SIP, please ping us on [Discord](https://discord.gg/pipecat).
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@@ -1,3 +1,8 @@
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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 argparse
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import argparse
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import asyncio
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import asyncio
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import os
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import os
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@@ -5,13 +10,16 @@ import sys
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from dotenv import load_dotenv
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from dotenv import load_dotenv
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from loguru import logger
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from loguru import logger
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from openai.types.chat import ChatCompletionToolParam
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import EndFrame
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from pipecat.frames.frames import EndFrame, TextFrame
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from pipecat.pipeline.pipeline import Pipeline
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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.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
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from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
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from pipecat.services.ai_services import LLMService
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from pipecat.services.deepgram import DeepgramSTTService
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from pipecat.services.elevenlabs import ElevenLabsTTSService
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from pipecat.services.elevenlabs import ElevenLabsTTSService
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from pipecat.services.openai import OpenAILLMService
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from pipecat.services.openai import OpenAILLMService
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from pipecat.transports.services.daily import DailyDialinSettings, DailyParams, DailyTransport
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from pipecat.transports.services.daily import DailyDialinSettings, DailyParams, DailyTransport
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@@ -55,16 +63,62 @@ async def main(room_url: str, token: str, callId: str, callDomain: str):
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
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content = f"""
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You are a delivery service customer support specialist supporting customers with their orders.
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Begin with: "Hello, this is Hailey from customer support. What can I help you with today?"
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"""
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messages = [
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messages = [
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{
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{
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"role": "system",
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"role": "system",
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"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?!'.",
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"content": content,
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},
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},
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]
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]
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context = OpenAILLMContext(messages)
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tools = [
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ChatCompletionToolParam(
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type="function",
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function={
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"name": "transfer_call",
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"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.",
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"parameters": {
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"type": "object",
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"properties": {
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"call_id": {
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"type": "string",
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"description": "This is always {callId}.",
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},
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"summary": {
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"type": "string",
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"description": """
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Provide a concise summary in 3-5 sentences. Highlight any important details or unusual aspects of the conversation.
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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 = OpenAILLMContext(messages, tools)
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context_aggregator = llm.create_context_aggregator(context)
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context_aggregator = llm.create_context_aggregator(context)
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async def default_transfer_call(
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function_name, tool_call_id, args, llm: LLMService, context, result_callback
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):
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logger.debug(f"default_transfer_call: {function_name} {tool_call_id} {args}")
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await result_callback(
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{
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"transfer_call": False,
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"reason": "To transfer call calls, please dial in to the room using a phone or a SIP client.",
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}
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)
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llm.register_function(
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function_name="transfer_call",
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callback=default_transfer_call,
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)
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pipeline = Pipeline(
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pipeline = Pipeline(
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[
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[
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transport.input(),
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transport.input(),
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@@ -87,6 +141,44 @@ async def main(room_url: str, token: str, callId: str, callDomain: str):
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async def on_participant_left(transport, participant, reason):
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async def on_participant_left(transport, participant, reason):
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await task.queue_frame(EndFrame())
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await task.queue_frame(EndFrame())
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@transport.event_handler("on_dialin_ready")
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async def on_dialin_ready(_, sip_endpoint):
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logger.info(f"on_dialin_ready: {sip_endpoint}")
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@transport.event_handler("on_dialin_connected")
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async def on_dialin_connected(transport, event):
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logger.info(f"on_dialin_connected: {event}")
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sip_session_id = event["sessionId"]
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async def transfer_call(
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function_name, tool_call_id, args, llm: LLMService, context, result_callback
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):
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logger.debug(f"transfer_call: {function_name} {tool_call_id} {args}")
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# sip_url = "sip:your_user_name@sip.linphone.org"
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sip_url = (
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f"sip:your_username@dailyco.sip.twilio.com?x-daily_id={room_url.split('/')[-1]}"
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)
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try:
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await transport.sip_refer(
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settings={
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"sessionId": sip_session_id,
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"toEndPoint": sip_url,
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}
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)
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except Exception as e:
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logger.error(f"An error occurred during SIP refer: {e}")
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await result_callback({"transfer_call": False})
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await result_callback({"transfer_call": True})
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llm.register_function(
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function_name="transfer_call",
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callback=transfer_call,
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)
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runner = PipelineRunner()
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runner = PipelineRunner()
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await runner.run(task)
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await runner.run(task)
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