多智能体RAG（检索和生成）应用程序AutoGen构建全面指南！

llm_config = {    "config_list": [{"model": "gpt-3.5-turbo", "api_key": os.environ["OPENAI_API_KEY"]}],    }
def termination_msg(x):    return isinstance(x, dict) and "TERMINATE" == str(x.get("content", ""))[-9:].upper()

boss = autogen.UserProxyAgent(    name="Boss",    is_termination_msg=termination_msg,    human_input_mode="NEVER",    code_execution_config=False,  # we don't want to execute code in this case.    default_auto_reply="Reply `TERMINATE` if the task is done.",    description="The boss who ask questions and give tasks.",)
boss_aid = SingleStoreRetrieveUserProxyAgent(    name="Boss_Assistant",    is_termination_msg=termination_msg,    human_input_mode="NEVER",    max_consecutive_auto_reply=3,    retrieve_config={        "task": "code",    },    code_execution_config=False,  # we don't want to execute code in this case.    description="Assistant who has extra content retrieval power for solving difficult problems.",    singlestore_db=singlestore_db)
coder = autogen.AssistantAgent(    name="Senior_Python_Engineer",    is_termination_msg=termination_msg,    system_message="You are a senior python engineer, you provide python code to answer questions. Reply `TERMINATE` in the end when everything is done.",    llm_config=llm_config,    description="Senior Python Engineer who can write code to solve problems and answer questions.",)
pm = autogen.AssistantAgent(    name="Product_Manager",    is_termination_msg=termination_msg,    system_message="You are a product manager. Reply `TERMINATE` in the end when everything is done.",    llm_config=llm_config,    description="Product Manager who can design and plan the project.",)
reviewer = autogen.AssistantAgent(    name="Code_Reviewer",    is_termination_msg=termination_msg,    system_message="You are a code reviewer. Reply `TERMINATE` in the end when everything is done.",    llm_config=llm_config,    description="Code Reviewer who can review the code.",)
PROBLEM = "How to use spark for parallel training in FLAML? Give me sample code."

def _reset_agents():    boss.reset()    boss_aid.reset()    coder.reset()    pm.reset()    reviewer.reset()

def rag_chat():    _reset_agents()    groupchat = autogen.GroupChat(        agents=[boss_aid, pm, coder, reviewer], messages=[], max_round=12, speaker_selection_method="round_robin"    )    manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)
    # Start chatting with boss_aid as this is the user proxy agent.    boss_aid.initiate_chat(        manager,        problem=PROBLEM,        n_results=3,    )

def norag_chat():    _reset_agents()    groupchat = autogen.GroupChat(        agents=[boss, pm, coder, reviewer],        messages=[],        max_round=12,        speaker_selection_method="auto",        allow_repeat_speaker=False,    )    manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)
    # Start chatting with the boss as this is the user proxy agent.    boss.initiate_chat(        manager,        message=PROBLEM,    )

def call_rag_chat():    _reset_agents()
    # In this case, we will have multiple user proxy agents and we don't initiate the chat    # with RAG user proxy agent.    # In order to use RAG user proxy agent, we need to wrap RAG agents in a function and call    # it from other agents.    def retrieve_content(        message: Annotated[            str,            "Refined message which keeps the original meaning and can be used to retrieve content for code generation and question answering.",        ],        n_results: Annotated[int, "number of results"] = 3,    ) -> str:        boss_aid.n_results = n_results  # Set the number of results to be retrieved.        # Check if we need to update the context.        update_context_case1, update_context_case2 = boss_aid._check_update_context(message)        if (update_context_case1 or update_context_case2) and boss_aid.update_context:            boss_aid.problem = message if not hasattr(boss_aid, "problem") else boss_aid.problem            _, ret_msg = boss_aid._generate_retrieve_user_reply(message)        else:            ret_msg = boss_aid.generate_init_message(message, n_results=n_results)        return ret_msg if ret_msg else message
    boss_aid.human_input_mode = "NEVER"  # Disable human input for boss_aid since it only retrieves content.
    for caller in [pm, coder, reviewer]:        d_retrieve_content = caller.register_for_llm(            description="retrieve content for code generation and question answering.", api_style="function"        )(retrieve_content)
    for executor in [boss, pm]:        executor.register_for_execution()(d_retrieve_content)
    groupchat = autogen.GroupChat(        agents=[boss, pm, coder, reviewer],        messages=[],        max_round=12,        speaker_selection_method="round_robin",        allow_repeat_speaker=False,    )
    manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)
    # Start chatting with the boss as this is the user proxy agent.    boss.initiate_chat(        manager,        message=PROBLEM,    )