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49 lines
2.0 KiB
49 lines
2.0 KiB
2 months ago
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import aiohttp
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from typing import Annotated
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from livekit.agents import llm
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from datetime import datetime
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# Define the path to the log file
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LOG_FILE_PATH = 'assistant_functions.txt'
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def log_message(message: str):
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"""Append a message to the log file with a timestamp."""
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timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
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with open(LOG_FILE_PATH, 'a') as log_file:
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log_file.write(f"{timestamp} - {message}\n")
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# first define a class that inherits from llm.FunctionContext
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class AssistantFnc(llm.FunctionContext):
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# the llm.ai_callable decorator marks this function as a tool available to the LLM
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# by default, it'll use the docstring as the function's description
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@llm.ai_callable()
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async def get_weather(
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self,
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# by using the Annotated type, arg description and type are available to the LLM
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location: Annotated[
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str, llm.TypeInfo(description="The location to get the weather for")
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],
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) -> str:
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"""Called when the user asks about the weather. This function will return the weather for the given location."""
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log_message(f"getting weather for {location}")
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url = f"https://wttr.in/{location}?format=%C+%t"
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async with aiohttp.ClientSession() as session:
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async with session.get(url) as response:
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log_message(f"response: {response}")
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if response.status == 200:
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weather_data = await response.text()
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content: str = f"The weather in {location} is {weather_data}."
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log_message(f"content: {content}")
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# response from the function call is returned to the LLM
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# as a tool response. The LLM's response will include this data
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return content
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else:
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log_message(f"Failed to get weather data, status code: {response.status}")
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return f"Failed to get weather data, status code: {response.status}"
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