增强对非OpenAI模型的支持

import autogen
from autogen import AssistantAgent, UserProxyAgent

config_list = autogen.config_list_from_json(
    "OAI_CONFIG_LIST"
)

user_proxy =  UserProxyAgent(
    name="User_proxy",
    code_execution_config={
        "last_n_messages": 2,
        "work_dir": "groupchat",
        "use_docker": False, # Please set use_docker = True if docker is available to run the generated code. Using docker is safer than running the generated code directly.
    },
    human_input_mode="ALWAYS",
    is_termination_msg=lambda msg: not msg["content"]
)

assistant = AssistantAgent(
    name="assistant",
    llm_config = {"config_list": config_list}
)

user_proxy.initiate_chat(assistant, message="Write python code to print Hello World!")

import os
import autogen
import json
from typing import Literal
from typing_extensions import Annotated

# Anthropic configuration, using api_type='anthropic'
anthropic_llm_config = {
    "config_list":
    [
        {
            "api_type": "anthropic",
            "model": "claude-3-5-sonnet-20240620",
            "api_key": os.getenv("ANTHROPIC_API_KEY"),
            "cache_seed": None
        }
    ]
}

# Our functionbot, who will be assigned two functions and
# given directions to use them.
functionbot = autogen.AssistantAgent(
    name="functionbot",
    system_message="For currency exchange tasks, only use "
    "the functions you have been provided with. Do not "
    "reply with helpful tips. Once you've recommended functions "
    "reply with 'TERMINATE'.",
    is_termination_msg=lambda x: x.get("content", "") and (x.get("content", "").rstrip().endswith("TERMINATE") or x.get("content", "") == ""),
    llm_config=anthropic_llm_config,
)

# Our user proxy agent, who will be used to manage the customer
# request and conversation with the functionbot, terminating
# when we have the information we need.
user_proxy = autogen.UserProxyAgent(
    name="user_proxy",
    system_message="You are a travel agent that provides "
        "specific information to your customers. Get the "
        "information you need and provide a great summary "
        "so your customer can have a great trip. If you "
        "have the information you need, simply reply with "
        "'TERMINATE'.",
    is_termination_msg=lambda x: x.get("content", "") and (x.get("content", "").rstrip().endswith("TERMINATE") or x.get("content", "") == ""),
    human_input_mode="NEVER",
    max_consecutive_auto_reply=10,
)

CurrencySymbol = Literal["USD", "EUR"]

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}")

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"})

@user_proxy.register_for_execution()
@functionbot.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"{quote_amount} {quote_currency}"

@user_proxy.register_for_execution()
@functionbot.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(
    functionbot,
    message="My customer wants to travel to New York and "
        "they need to exchange 830 EUR to USD. Can you please "
        "provide them with a summary of the weather and "
        "exchanged currently in USD?",
    summary_method="reflection_with_llm",
    summary_args={
        "summary_prompt": """Summarize the conversation by
        providing an email response with the travel information
        for the customer addressed as 'Dear Customer'. Do not
        provide any additional conversation or apologise,
        just provide the relevant information and the email."""
    },
)

print(f"Here's the LLM summary of the conversation:\n\n{res.summary['content']}")