import json
import os
from typing import Literal
from typing_extensions import Annotated
import autogen
llm_config = autogen.LLMConfig(
model="deepseek-chat",
base_url="https://api.deepseek.com/v1",
api_key=os.environ.get("DEEPSEEK_API_KEY"),
api_type="deepseek",
tags=["deepseek"],
)
# Create the agent for tool calling
with llm_config:
chatbot = autogen.AssistantAgent(
name="chatbot",
system_message="""For currency exchange and weather forecasting tasks,
only use the functions you have been provided with.
Output 'HAVE FUN!' when an answer has been provided.""",
)
# Note that we have changed the termination string to be "HAVE FUN!"
user_proxy = autogen.UserProxyAgent(
name="user_proxy",
is_termination_msg=lambda x: x.get("content", "") and "HAVE FUN!" in x.get("content", ""),
human_input_mode="NEVER",
max_consecutive_auto_reply=1,
)
# Create the two functions, annotating them so that those descriptions can be passed through to the LLM.
# We associate them with the agents using `register_for_execution` for the user_proxy so it can execute the function and `register_for_llm` for the chatbot (powered by the LLM) so it can pass the function definitions to the LLM.
# Currency Exchange function
CurrencySymbol = Literal["USD", "EUR"]
# Define our function that we expect to call
def exchange_rate(base_currency: CurrencySymbol, quote_currency: CurrencySymbol) -> float:
if base_currency == quote_currency:
return 1.0
elif base_currency == "USD" and quote_currency == "EUR":
return 1 / 1.1
elif base_currency == "EUR" and quote_currency == "USD":
return 1.1
else:
raise ValueError(f"Unknown currencies {base_currency}, {quote_currency}")
# Register the function with the agent
@user_proxy.register_for_execution()
@chatbot.register_for_llm(description="Currency exchange calculator.")
def currency_calculator(
base_amount: Annotated[float, "Amount of currency in base_currency"],
base_currency: Annotated[CurrencySymbol, "Base currency"] = "USD",
quote_currency: Annotated[CurrencySymbol, "Quote currency"] = "EUR",
) -> str:
quote_amount = exchange_rate(base_currency, quote_currency) * base_amount
return f"{format(quote_amount, '.2f')} {quote_currency}"
# Weather function
# Example function to make available to model
def get_current_weather(location, unit="fahrenheit"):
"""Get the weather for some location"""
if "chicago" in location.lower():
return json.dumps({"location": "Chicago", "temperature": "13", "unit": unit})
elif "san francisco" in location.lower():
return json.dumps({"location": "San Francisco", "temperature": "55", "unit": unit})
elif "new york" in location.lower():
return json.dumps({"location": "New York", "temperature": "11", "unit": unit})
else:
return json.dumps({"location": location, "temperature": "unknown"})
# Register the function with the agent
@user_proxy.register_for_execution()
@chatbot.register_for_llm(description="Weather forecast for US cities.")
def weather_forecast(
location: Annotated[str, "City name"],
) -> str:
weather_details = get_current_weather(location=location)
weather = json.loads(weather_details)
return f"{weather['location']} will be {weather['temperature']} degrees {weather['unit']}"
# start the conversation
res = user_proxy.initiate_chat(
chatbot,
message="What's the weather in New York and can you tell me how much is 123.45 EUR in USD so I can spend it on my holiday? Throw a few holiday tips in as well.",
)
