Remove logs

examples/dialin-chatbot/README.md

@@ -37,7 +37,16 @@ Run `bot_runner.py` to handle incoming HTTP requests:
 
 Then target the following URL:
 
-`POST /daily_start_bot`
+```bash
+curl -X POST 'http://localhost:7860/daily_start_bot' \
+  -H 'Content-Type: application/json' \
+  -d '{
+      "callId": "callId-from-call",
+      "callDomain": "callDomain-from-call"
+  }'
+```
+
+Use [this guide](https://docs.pipecat.ai/guides/telephony/daily-webrtc) to connect a phone number purchased from Daily to the bot.
 
 For more configuration options, please consult Daily's API documentation.
 

examples/dialin-chatbot/bot_daily.py

@@ -1,3 +1,8 @@
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
 import argparse
 import asyncio
 import os
@@ -5,13 +10,16 @@ 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
+from pipecat.frames.frames import EndFrame, TextFrame
 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
@@ -55,16 +63,62 @@ 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)
 
+    async def default_transfer_call(
+        function_name, tool_call_id, args, llm: LLMService, context, result_callback
+    ):
+        logger.debug(f"default_transfer_call: {function_name} {tool_call_id} {args}")
+        await result_callback(
+            {
+                "transfer_call": False,
+                "reason": "To transfer call calls, please dial in to the room using a phone or a SIP client.",
+            }
+        )
+
+    llm.register_function(
+        function_name="transfer_call",
+        callback=default_transfer_call,
+    )
+
     pipeline = Pipeline(
         [
             transport.input(),
@@ -87,6 +141,44 @@ 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)