249 lines
8.6 KiB
Python
249 lines
8.6 KiB
Python
#
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# Copyright (c) 2024-2026, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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"""Grok Realtime persistent context example.
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This example demonstrates how to save and load conversation history with
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Grok's Realtime Voice Agent API. It allows:
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- Saving the current conversation to a JSON file
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- Loading a previous conversation from disk
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- Listing all saved conversation files
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This is useful for building voice agents that remember past conversations.
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"""
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import asyncio
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import glob
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import json
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import os
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from datetime import datetime
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from dotenv import load_dotenv
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from loguru import logger
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from pipecat.adapters.schemas.function_schema import FunctionSchema
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from pipecat.adapters.schemas.tools_schema import ToolsSchema
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from pipecat.frames.frames import LLMRunFrame
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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.llm_context import LLMContext
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from pipecat.processors.aggregators.llm_response_universal import (
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LLMContextAggregatorPair,
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)
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from pipecat.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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from pipecat.services.llm_service import FunctionCallParams
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from pipecat.services.xai.realtime.events import SessionProperties, TurnDetection
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from pipecat.services.xai.realtime.llm import GrokRealtimeLLMService
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.daily.transport import DailyParams
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from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
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load_dotenv(override=True)
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BASE_FILENAME = "/tmp/pipecat_grok_conversation_"
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async def fetch_weather_from_api(params: FunctionCallParams):
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"""Mock weather function for demonstration."""
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temperature = 75 if params.arguments["format"] == "fahrenheit" else 24
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await params.result_callback(
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{
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"conditions": "nice",
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"temperature": temperature,
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"format": params.arguments["format"],
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"timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
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}
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)
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async def get_saved_conversation_filenames(params: FunctionCallParams):
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"""Get a list of saved conversation history files."""
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full_pattern = f"{BASE_FILENAME}*.json"
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matching_files = glob.glob(full_pattern)
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logger.debug(f"matching files: {matching_files}")
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await params.result_callback({"filenames": matching_files})
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async def save_conversation(params: FunctionCallParams):
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"""Save the current conversation to a JSON file."""
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timestamp = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
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filename = f"{BASE_FILENAME}{timestamp}.json"
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logger.debug(
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f"writing conversation to {filename}\n{json.dumps(params.context.get_messages(), indent=4)}"
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)
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try:
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with open(filename, "w") as file:
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messages = params.context.get_messages()
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# Remove the last message (the save instruction)
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messages.pop()
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json.dump(messages, file, indent=2)
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await params.result_callback({"success": True})
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except Exception as e:
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await params.result_callback({"success": False, "error": str(e)})
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async def load_conversation(params: FunctionCallParams):
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"""Load a conversation history from a JSON file."""
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async def _reset():
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filename = params.arguments["filename"]
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logger.debug(f"loading conversation from {filename}")
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try:
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with open(filename, "r") as file:
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params.context.set_messages(json.load(file))
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await params.llm.reset_conversation()
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# Manually create a response since we've reset the conversation
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await params.llm._create_response()
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except Exception as e:
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await params.result_callback({"success": False, "error": str(e)})
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asyncio.create_task(_reset())
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# Define the tools schema
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tools = ToolsSchema(
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standard_tools=[
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FunctionSchema(
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name="get_current_weather",
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description="Get the current weather",
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properties={
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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"format": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"],
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"description": "The temperature unit to use. Infer this from the users location.",
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},
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},
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required=["location", "format"],
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),
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FunctionSchema(
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name="save_conversation",
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description="Save the current conversation. Use this function to persist the current conversation to external storage.",
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properties={},
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required=[],
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),
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FunctionSchema(
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name="get_saved_conversation_filenames",
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description="Get a list of saved conversation histories. Returns a list of filenames. Each filename includes a date and timestamp.",
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properties={},
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required=[],
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),
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FunctionSchema(
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name="load_conversation",
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description="Load a conversation history. Use this function to load a conversation history into the current session.",
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properties={
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"filename": {
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"type": "string",
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"description": "The filename of the conversation history to load.",
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}
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},
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required=["filename"],
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),
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]
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)
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# Transport configuration - no local VAD needed since Grok has server-side VAD
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transport_params = {
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"daily": lambda: DailyParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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"twilio": lambda: FastAPIWebsocketParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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"webrtc": lambda: TransportParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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}
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async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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logger.info("Starting Grok Realtime persistent context bot")
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session_properties = SessionProperties(
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voice="Ara",
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turn_detection=TurnDetection(type="server_vad"),
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instructions="""You are a helpful and friendly AI assistant powered by Grok.
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Your voice and personality should be warm and engaging, with a lively and playful tone.
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You are participating in a voice conversation. Keep your responses concise, short, and to the point
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unless specifically asked to elaborate on a topic.
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You have access to tools for:
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- Getting weather information
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- Saving the current conversation to disk
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- Loading previous conversations from disk
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- Listing saved conversation files
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When the user asks to save or load a conversation, use the appropriate tool.
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Remember, your responses should be short - just one or two sentences usually.""",
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)
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llm = GrokRealtimeLLMService(
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api_key=os.getenv("XAI_API_KEY"),
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session_properties=session_properties,
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)
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# Register function handlers
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llm.register_function("get_current_weather", fetch_weather_from_api)
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llm.register_function("save_conversation", save_conversation)
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llm.register_function("get_saved_conversation_filenames", get_saved_conversation_filenames)
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llm.register_function("load_conversation", load_conversation)
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context = LLMContext([{"role": "developer", "content": "Say hello!"}], tools)
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user_aggregator, assistant_aggregator = LLMContextAggregatorPair(context)
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pipeline = Pipeline(
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[
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transport.input(),
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user_aggregator,
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llm,
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transport.output(),
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assistant_aggregator,
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]
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)
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task = PipelineTask(
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pipeline,
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params=PipelineParams(enable_metrics=True, enable_usage_metrics=True),
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idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
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)
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@transport.event_handler("on_client_connected")
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async def on_client_connected(transport, client):
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logger.info("Client connected")
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await task.queue_frames([LLMRunFrame()])
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@transport.event_handler("on_client_disconnected")
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async def on_client_disconnected(transport, client):
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logger.info("Client disconnected")
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await task.cancel()
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runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
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await runner.run(task)
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async def bot(runner_args: RunnerArguments):
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"""Main bot entry point compatible with Pipecat Cloud."""
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transport = await create_transport(runner_args, transport_params)
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await run_bot(transport, runner_args)
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if __name__ == "__main__":
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from pipecat.runner.run import main
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main()
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