AutoGen主打的是多智能体，对话和写代码，但是教程方面没有langchain丰富，我这里抛砖引玉提供一个autogen接入开源function calling模型的教程，我这里使用的开源repo是：https://github.com/SalesforceAIResearch/xLAM
开源模型是：https://huggingface.co/Salesforce/xLAM-7b-fc-r

1b的模型效果有点差，推荐使用7b的模型。首先使用vllm运行：

vllm serve Salesforce/xLAM-8x7b-r --host 0.0.0.0 --port 8000 --tensor-parallel-size 4

然后autogen代码示例：

import re
import json
import random
import time from typing import Literal
from pydantic import BaseModel, Field
from typing_extensions import Annotated
import autogen
from autogen.cache import Cache
from openai.types.completion import Completion
import openai
from xLAM.client import xLAMChatCompletion, xLAMConfig
from openai.types.chat import ChatCompletion, ChatCompletionMessageToolCall
from openai.types.chat.chat_completion import ChatCompletionMessage, Choice
from openai.types.completion_usage import CompletionUsagelocal_llm_config={"config_list": [{"model": "/<your_path>/xLAM-7b-fc-r", # Same as in vLLM command"api_key": "NotRequired", # Not needed"model_client_cls": "CustomModelClient","base_url": "http://localhost:8000/v1",  # Your vLLM URL, with '/v1' added"price": [0, 0],}],"cache_seed": None # Turns off caching, useful for testing different models
}TOOL_ENABLED = Trueclass CustomModelClient:def __init__(self, config, **kwargs):print(f"CustomModelClient config: {config}")gen_config_params = config.get("params", {})self.max_length = gen_config_params.get("max_length", 256)print(f"Loaded model {config['model']}")config = xLAMConfig(base_url=config["base_url"], model=config['model'])self.llm = xLAMChatCompletion.from_config(config)def create(self, params):if params.get("stream", False) and "messages" in params:raise NotImplementedError("Local models do not support streaming.")else:if "tools" in params:tools=[item['function'] for item in params["tools"]]response = self.llm.completion(params["messages"], tools=tools)if len(response['choices'][0]['message']['tool_calls'])>0:finish_reason='tool_calls'tool_results = response['choices'][0]['message']['tool_calls']if isinstance(tool_results, list) and isinstance(tool_results[0], list):tool_results = tool_results[0]tool_calls = []try:for tool_call in tool_results:tool_calls.append(ChatCompletionMessageToolCall(id=str(random.randint(0,2500)),function={"name": tool_call['name'], "arguments": json.dumps(tool_call["arguments"])},type="function"))except Exception as e:print("Tool parse error: {tool_results}")tool_calls=Nonefinish_reason='stop'else:finish_reason='stop'tool_calls = Nonemessage  = ChatCompletionMessage(role="assistant",content=response['choices'][0]['message']['content'],function_call=None,tool_calls=tool_calls,)choices = [Choice(finish_reason=finish_reason, index=0, message=message)]response_oai = ChatCompletion(id=str(random.randint(0,25000)),model=params["model"],created=int(time.time()),object="chat.completion",choices=choices,usage=CompletionUsage(prompt_tokens=0,completion_tokens=0,total_tokens=0),cost=0.0,)return response_oaidef message_retrieval(self, response):"""Retrieve the messages from the response."""choices = response.choicesif isinstance(response, Completion):return [choice.text for choice in choices] if TOOL_ENABLED:return [  # type: ignore [return-value](choice.message  # type: ignore [union-attr]if choice.message.function_call is not None or choice.message.tool_calls is not None  # type: ignore [union-attr]else choice.message.content)  # type: ignore [union-attr]for choice in choices]else:return [  # type: ignore [return-value]choice.message if choice.message.function_call is not None else choice.message.content  # type: ignore [union-attr]for choice in choices]def cost(self, response) -> float:"""Calculate the cost of the response."""response.cost = 0return 0@staticmethoddef get_usage(response):# returns a dict of prompt_tokens, completion_tokens, total_tokens, cost, model# if usage needs to be tracked, else Nonereturn {"prompt_tokens": response.usage.prompt_tokens if response.usage is not None else 0,"completion_tokens": response.usage.completion_tokens if response.usage is not None else 0,"total_tokens": (response.usage.prompt_tokens + response.usage.completion_tokens if response.usage is not None else 0),"cost": response.cost if hasattr(response, "cost") else 0,"model": response.model,}chatbot = autogen.AssistantAgent(name="chatbot",system_message="For currency exchange tasks, only use the functions you have been provided with. Reply TERMINATE when the task is done.",llm_config=local_llm_config,
)# create a UserProxyAgent instance named "user_proxy"
user_proxy = autogen.UserProxyAgent(name="user_proxy",is_termination_msg=lambda x: x.get("content", "") and x.get("content", "").rstrip().endswith("TERMINATE"),human_input_mode="NEVER",max_consecutive_auto_reply=5,
)CurrencySymbol = Literal["USD", "EUR"]def exchange_rate(base_currency: CurrencySymbol, quote_currency: CurrencySymbol) -> float:if base_currency == quote_currency:return 1.0elif base_currency == "USD" and quote_currency == "EUR":return 1 / 1.1elif base_currency == "EUR" and quote_currency == "USD":return 1.1else:raise ValueError(f"Unknown currencies {base_currency}, {quote_currency}")@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_amountreturn f"{quote_amount} {quote_currency}"print(chatbot.llm_config["tools"])
chatbot.register_model_client(model_client_cls=CustomModelClient)
query = "How much is 123.45 USD in EUR?"
# query = "What's the weather like in New York in fahrenheit?"
res = user_proxy.initiate_chat(chatbot, message=query,max_round=5,)
print("Chat history:", res.chat_history)

运行示例结果：

user_proxy (to chatbot):How much is 123.45 USD in EUR?--------------------------------------------------------------------------------
chatbot (to user_proxy):***** Suggested tool call (507): currency_calculator *****
Arguments:
{"base_amount": 123.45, "base_currency": "USD", "quote_currency": "EUR"}
**********************************************************-------------------------------------------------------------------------------->>>>>>>> EXECUTING FUNCTION currency_calculator...
user_proxy (to chatbot):user_proxy (to chatbot):***** Response from calling tool (507) *****
112.22727272727272 EUR
********************************************--------------------------------------------------------------------------------
chatbot (to user_proxy):The currency calculator returned 112.23 EUR.--------------------------------------------------------------------------------
user_proxy (to chatbot):