Add a conversation flow processor
This commit is contained in:
Mark Backman
186
examples/foundational/25-conversation-flow.py
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186
examples/foundational/25-conversation-flow.py
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#
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# Copyright (c) 2024, 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 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.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.aggregators.openai_llm_context import OpenAILLMContext
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from pipecat.processors.conversation_flow import ConversationFlowProcessor
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from pipecat.services.deepgram import DeepgramSTTService, DeepgramTTSService
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from pipecat.services.openai import 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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# Define our conversation flow
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flow_config = {
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"initial_node": "start",
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"nodes": {
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"start": {
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"message": {
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"role": "assistant",
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"content": "You are starting a conversation. Ask the user if they'd like to hear a joke or get weather information.",
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},
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"functions": [
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{
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"name": "tell_joke",
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"description": "User wants to hear a joke",
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"parameters": {"type": "object", "properties": {}},
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},
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{
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"name": "get_weather",
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"description": "User wants weather information",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The location to get weather for",
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}
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},
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"required": ["location"],
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},
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},
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],
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},
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"tell_joke": {
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"message": {
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"role": "assistant",
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"content": "Tell a funny, clean joke and then ask if they'd like to hear another joke or get weather information.",
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},
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"functions": [
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{
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"name": "tell_joke",
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"description": "User wants another joke",
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"parameters": {"type": "object", "properties": {}},
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},
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{
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"name": "get_weather",
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"description": "User wants weather information",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The location to get weather for",
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}
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},
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"required": ["location"],
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},
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},
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],
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"actions": [{"type": "tts.say", "text": "Let me think of a good one..."}],
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},
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"get_weather": {
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"message": {
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"role": "assistant",
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"content": "Provide the weather information and ask if they'd like to hear a joke or check another location's weather.",
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},
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"functions": [
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{
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"name": "tell_joke",
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"description": "User wants to hear a joke",
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"parameters": {"type": "object", "properties": {}},
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},
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{
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"name": "get_weather",
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"description": "User wants weather for another location",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The location to get weather for",
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}
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},
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"required": ["location"],
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},
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},
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],
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"actions": [
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{"type": "tts.say", "text": "Let me check that weather information for you..."}
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],
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},
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},
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}
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async def main():
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async with aiohttp.ClientSession() as session:
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(room_url, _) = await configure(session)
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transport = DailyTransport(
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room_url,
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None,
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"Respond bot",
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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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vad_audio_passthrough=True,
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),
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)
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stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
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tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4")
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# Initialize conversation flow processor
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flow_processor = ConversationFlowProcessor(flow_config)
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# Create initial context
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messages = [
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{
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"role": "system",
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"content": "You are a helpful assistant in a WebRTC call. Your responses will be converted to audio so avoid special characters. Always use the available functions to progress the conversation.",
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}
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]
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context = OpenAILLMContext(messages)
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context_aggregator = llm.create_context_aggregator(context)
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pipeline = Pipeline(
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[
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transport.input(), # Transport user input
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stt, # STT
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context_aggregator.user(), # User responses
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flow_processor, # Conversation flow management
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llm, # LLM
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tts, # TTS
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transport.output(), # Transport bot output
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context_aggregator.assistant(), # Assistant spoken responses
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]
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)
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task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
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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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# Initialize the flow processor
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await flow_processor.initialize(messages)
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# Kick off the conversation
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await task.queue_frames([context_aggregator.user().get_context_frame()])
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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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3
src/pipecat/processors/conversation_flow/__init__.py
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3
src/pipecat/processors/conversation_flow/__init__.py
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from .processor import ConversationFlowProcessor
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__all__ = ["ConversationFlowProcessor"]
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69
src/pipecat/processors/conversation_flow/flow.py
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69
src/pipecat/processors/conversation_flow/flow.py
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#
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# Copyright (c) 2024, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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from dataclasses import dataclass
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from typing import Dict, List, Optional, Set
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from loguru import logger
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@dataclass
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class NodeConfig:
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"""Configuration for a single node in the conversation flow"""
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message: dict
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functions: List[dict]
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actions: Optional[List[dict]] = None
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class ConversationFlow:
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"""Manages the state and transitions of the conversation flow"""
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def __init__(self, flow_config: dict):
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self.nodes: Dict[str, NodeConfig] = {}
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self.current_node: str = flow_config["initial_node"]
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self._load_config(flow_config)
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def _load_config(self, config: dict):
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"""Load and validate the flow configuration"""
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if "initial_node" not in config:
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raise ValueError("Flow config must specify 'initial_node'")
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if "nodes" not in config:
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raise ValueError("Flow config must specify 'nodes'")
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for node_id, node_config in config["nodes"].items():
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self.nodes[node_id] = NodeConfig(
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message=node_config["message"],
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functions=node_config["functions"],
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actions=node_config.get("actions"),
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)
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def get_current_message(self) -> dict:
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"""Get the message for the current node"""
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return self.nodes[self.current_node].message
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def get_current_functions(self) -> List[dict]:
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"""Get the available functions for the current node"""
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return self.nodes[self.current_node].functions
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def get_current_actions(self) -> Optional[List[dict]]:
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"""Get the actions for the current node"""
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return self.nodes[self.current_node].actions
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def get_available_function_names(self) -> Set[str]:
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"""Get the names of available functions for the current node"""
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return {f["name"] for f in self.nodes[self.current_node].functions}
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def transition(self, function_name: str) -> Optional[str]:
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"""Attempt to transition based on function call"""
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available_functions = self.get_available_function_names()
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if function_name in available_functions:
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if function_name in self.nodes:
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self.current_node = function_name
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return self.current_node
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else:
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logger.warning(f"Function {function_name} is available but no matching node exists")
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return None
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83
src/pipecat/processors/conversation_flow/processor.py
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83
src/pipecat/processors/conversation_flow/processor.py
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#
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# Copyright (c) 2024, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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from typing import List, Optional
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from loguru import logger
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from pipecat.frames.frames import (
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Frame,
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FunctionCallResultFrame,
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LLMMessagesAppendFrame,
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LLMMessagesUpdateFrame,
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LLMSetToolsFrame,
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TTSSpeakFrame,
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)
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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from .flow import ConversationFlow
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class ConversationFlowProcessor(FrameProcessor):
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"""Processor that manages conversation flow based on function calls"""
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def __init__(self, flow_config: dict):
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super().__init__()
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self.flow = ConversationFlow(flow_config)
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self.initialized = False
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async def initialize(self, initial_messages: List[dict]):
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"""Initialize the conversation with starting messages and functions"""
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if not self.initialized:
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# Combine initial messages with the first node's message
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# TODO: Not sure if this is needed
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messages = initial_messages + [self.flow.get_current_message()]
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await self.push_frame(LLMMessagesUpdateFrame(messages=initial_messages))
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await self.push_frame(LLMSetToolsFrame(tools=self.flow.get_current_functions()))
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self.initialized = True
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logger.info(f"Initialized conversation flow at node: {self.flow.current_node}")
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else:
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logger.warning("Attempted to initialize ConversationFlowProcessor multiple times")
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async def _execute_actions(self, actions: Optional[List[dict]]) -> None:
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"""Execute actions specified for the current node"""
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if not actions:
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return
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for action in actions:
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if action["type"] == "tts.say":
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await self.push_frame(TTSSpeakFrame(text=action["text"]))
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else:
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logger.warning(f"Unknown action type: {action['type']}")
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async def process_frame(self, frame: Frame, direction: FrameDirection) -> None:
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"""Process incoming frames and manage state transitions"""
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if not self.initialized:
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logger.warning("ConversationFlowProcessor received frames before initialization")
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return
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if isinstance(frame, FunctionCallResultFrame):
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available_functions = self.flow.get_available_function_names()
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if frame.function_name in available_functions:
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new_node = self.flow.transition(frame.function_name)
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if new_node:
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# Execute any entry actions for the new node
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await self._execute_actions(self.flow.get_current_actions())
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# Update the LLM context with the new node's message
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await self.push_frame(
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LLMMessagesAppendFrame(messages=[self.flow.get_current_message()])
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)
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# Update available functions for this node
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await self.push_frame(LLMSetToolsFrame(tools=self.flow.get_current_functions()))
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logger.info(f"Transitioned to node: {new_node}")
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else:
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logger.warning(
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f"Received function call '{frame.function_name}' not in available functions: {available_functions}"
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)
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