pip install grafi

from grafi.common.topics.topic import Topic
from grafi.common.topics.human_request_topic import human_request_topic
from grafi.common.topics.output_topic import agent_output_topic
from grafi.nodes.llm_node import LLMNode
from grafi.nodes.llm_function_call_node import LLMFunctionCallNode
from grafi.commands.llm_response_command import LLMResponseCommand
from grafi.commands.function_calling_command import FunctionCallingCommand
from grafi.tools.openai_tool import OpenAITool
from grafi.common.models.message import Message
from grafi.common.decorators.llm_function import llm_function
from grafi.common.models.execution_context import ExecutionContext
import json
import uuid

# 定義主題
user_info_extract_topic = Topic(name="user_info_extract_topic")
hitl_call_topic = Topic(
    name="hitl_call_topic",
    cnotallow=lambda msgs: msgs[-1].tool_calls[0].function.name != "register_client",
)
register_user_topic = Topic(
    name="register_user_topic",
    cnotallow=lambda msgs: msgs[-1].tool_calls[0].function.name == "register_client",
)
register_user_respond_topic = Topic(name="register_user_respond")

# 定義用戶信息提取節點
user_info_extract_node = (
    LLMNode.Builder()
    .name("UserInfoExtractNode")
    .subscribe(agent_input_topic)
    .command(
        LLMResponseCommand.Builder()
        .llm(
            OpenAITool.Builder()
            .name("UserInfoExtractLLM")
            .api_key("YOUR_OPENAI_API_KEY")
            .model("gpt-3.5-turbo")
            .system_message("Extract user's full name and email from the input.")
            .build()
        )
        .build()
    )
    .publish_to(user_info_extract_topic)
    .build()
)

# 定義動作節點
action_node = (
    LLMNode.Builder()
    .name("ActionNode")
    .subscribe(user_info_extract_topic)
    .command(
        LLMResponseCommand.Builder()
        .llm(
            OpenAITool.Builder()
            .name("ActionLLM")
            .api_key("YOUR_OPENAI_API_KEY")
            .model("gpt-3.5-turbo")
            .system_message("Decide the next action based on the extracted information.")
            .build()
        )
        .build()
    )
    .publish_to(hitl_call_topic)
    .publish_to(register_user_topic)
    .build()
)

# 定義人工干預節點
human_request_function_call_node = (
    LLMFunctionCallNode.Builder()
    .name("HumanRequestNode")
    .subscribe(hitl_call_topic)
    .command(
        FunctionCallingCommand.Builder()
        .function_tool(ClientInfo())
        .build()
    )
    .publish_to(human_request_topic)
    .build()
)

# 定義注冊用戶節點
register_user_node = (
    LLMFunctionCallNode.Builder()
    .name("RegisterUserNode")
    .subscribe(register_user_topic)
    .command(
        FunctionCallingCommand.Builder()
        .function_tool(RegisterClient())
        .build()
    )
    .publish_to(register_user_respond_topic)
    .build()
)

# 定義響應用戶節點
user_reply_node = (
    LLMNode.Builder()
    .name("UserReplyNode")
    .subscribe(register_user_respond_topic)
    .command(
        LLMResponseCommand.Builder()
        .llm(
            OpenAITool.Builder()
            .name("UserReplyLLM")
            .api_key("YOUR_OPENAI_API_KEY")
            .model("gpt-3.5-turbo")
            .system_message("Generate a response to the user based on the registration result.")
            .build()
        )
        .build()
    )
    .publish_to(agent_output_topic)
    .build()
)

def test_kyc_assistant():
    execution_context = ExecutionContext(
        conversation_id="conversation_id",
        execution_id=uuid.uuid4().hex,
        assistant_request_id=uuid.uuid4().hex,
    )

    # 初始化助手
    assistant = (
        KycAssistant.Builder()
        .name("KycAssistant")
        .api_key("YOUR_OPENAI_API_KEY")
        .user_info_extract_system_message(user_info_extract_system_message)
        .action_llm_system_message(
            "Select the most appropriate tool based on the request."
        )
        .summary_llm_system_message(
            "Response to user with result of registering. You must include 'registered' in the response if succeed."
        )
        .hitl_request(ClientInfo())
        .register_request(RegisterClient())
        .build()
    )

    whileTrue:
        # 獲取用戶輸入
        user_input = input("User: ")
        input_data = [Message(role="user", cnotallow=user_input)]

        # 執行助手
        output = assistant.execute(execution_context, input_data)

        # 處理輸出
        responses = []
        for message in output:
            try:
                content_json = json.loads(message.content)
                responses.append(content_json["question_description"])
            except json.JSONDecodeError:
                responses.append(message.content)

        respond_to_user = " and ".join(responses)
        print("Assistant:", respond_to_user)

        # 如果注冊成功，結束循環
        if"registered"in output[0].content:
            break

if __name__ == "__main__":
    test_kyc_assistant()

User: Hi, I'd like to sign up for your gym. Could you help me with the process?
Assistant: Please provide your full name and email address to sign up for the gym.
User: My name is John Doe, and my email is john.doe@example.com
Assistant: Congratulations, John! You are now registered at our gym. If you have any questions or need assistance, feel free to ask!