能不能实现一些复杂意图理解，例如要提取 “我早上8点半左右起床，每天起床帮我拉开窗帘让阳光叫醒我，然后呢放点音乐，偷悦下心情，还得把洗手间的灯打开，我得去洗漱“，这段文本中的时间，设备，动作

[liubin3702]

功能描述 / Feature Description 用简洁明了的语言描述所需的功能 / Describe the desired feature in a clear and concise manner.

解决的问题 / Problem Solved 解释此功能如何解决现有问题或改进项目 / Explain how this feature solves existing problems or improves the project.

实现建议 / Implementation Suggestions 如果可能，请提供关于如何实现此功能的建议 / If possible, provide suggestions on how to implement this feature.

替代方案 / Alternative Solutions 描述您考虑过的替代方案 / Describe alternative solutions you have considered.

其他信息 / Additional Information 添加与功能请求相关的任何其他信息 / Add any other information related to the feature request.

[dosubot[bot]]

🤖

嗨，@liubin3702！很高兴再次见到你。希望你一切都好！

Langchain-Chatchat 可以实现复杂意图理解，例如从提供的文本中提取时间，设备和动作。StructuredGLM3ChatAgent 类以及其 output_parser（StructuredChatOutputParserWithRetries）被设计用来解析和理解对话的结构化输出。这个解析器可以被扩展或利用来从给定的文本中提取特定的信息，如时间，设备和动作。

StructuredChatOutputParserWithRetries 类继承自 AgentOutputParser，它展示了将结构化文本解析为可操作 JSON 的能力，这可以包括从输入文本中提取的各种类型的信息。解析逻辑在文本中寻找特定的模式或令牌，然后将相关信息提取到结构化格式（action_json）中。

对于给定的文本，"我早上8点半左右起床，每天起床帮我拉开窗帘让阳光叫醒我，然后呢放点音乐，偷悦下心情，还得把洗手间的灯打开，我得去洗漱"，可以开发一个自定义工具或扩展现有的解析器来：

1. 识别并提取时间（"早上8点半"）。
2. 识别设备或交互对象（"窗帘"，"音乐"，"洗手间的灯"）。
3. 确定与这些设备相关的动作（"拉开"，"放"，"打开"）。

这将涉及定义模式或使用自然语言理解（NLU）技术来识别文本中的这些元素。StructuredChatOutputParserWithRetries 的 parse 方法可以被适应来包含这个逻辑，利用 Langchain-Chatchat 框架的结构化输出能力。

考虑到 StructuredGLM3ChatAgent 及其组件的模块化和可扩展设计，实现如上所述的复杂意图理解在这个框架内是可行的。

参考源码：

• server/agent/custom_agent/ChatGLM3Agent.py

Sources

• server/agent/custom_agent/ChatGLM3Agent.py

About Dosu

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Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[liubin3702]

你提取的结果正确，基于这个框架，具体需要什么类型的数据训练呢

[liubin3702]

我想离线部署并实现这个功能，具体要怎么做呢@dosu-bot

🤖

嗨，@liubin3702！很高兴再次见到你。希望你一切都好！

Langchain-Chatchat 可以实现复杂意图理解，例如从提供的文本中提取时间，设备和动作。StructuredGLM3ChatAgent 类以及其 output_parser（StructuredChatOutputParserWithRetries）被设计用来解析和理解对话的结构化输出。这个解析器可以被扩展或利用来从给定的文本中提取特定的信息，如时间，设备和动作。

StructuredChatOutputParserWithRetries 类继承自 AgentOutputParser，它展示了将结构化文本解析为可操作 JSON 的能力，这可以包括从输入文本中提取的各种类型的信息。解析逻辑在文本中寻找特定的模式或令牌，然后将相关信息提取到结构化格式（action_json）中。

对于给定的文本，"我早上8点半左右起床，每天起床帮我拉开窗帘让阳光叫醒我，然后呢放点音乐，偷悦下心情，还得把洗手间的灯打开，我得去洗漱"，可以开发一个自定义工具或扩展现有的解析器来：

1. 识别并提取时间（"早上8点半"）。
2. 识别设备或交互对象（"窗帘"，"音乐"，"洗手间的灯"）。
3. 确定与这些设备相关的动作（"拉开"，"放"，"打开"）。

这将涉及定义模式或使用自然语言理解（NLU）技术来识别文本中的这些元素。StructuredChatOutputParserWithRetries 的 parse 方法可以被适应来包含这个逻辑，利用 Langchain-Chatchat 框架的结构化输出能力。

考虑到 StructuredGLM3ChatAgent 及其组件的模块化和可扩展设计，实现如上所述的复杂意图理解在这个框架内是可行的。

参考源码：

• server/agent/custom_agent/ChatGLM3Agent.py

Sources

• server/agent/custom_agent/ChatGLM3Agent.py

About Dosu

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot. 你提取的结果正确，如果我想离线部署，具体要怎么做

[dosubot[bot]]

🤖

Based on your requirements, you can indeed extend the StructuredChatOutputParserWithRetries class to implement complex intent understanding. Here's a step-by-step guide on how you can achieve this:

1. Extend the StructuredChatOutputParserWithRetries Class: Create a new class that inherits from StructuredChatOutputParserWithRetries. In this new class, you will override the parse method to include your custom logic for extracting complex intents.

class CustomIntentOutputParser(StructuredChatOutputParserWithRetries):
    def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
        # Your custom logic goes here

2. Implement Intent Extraction Logic: In the overridden parse method, you can implement your logic to extract time, devices, and actions from the given text. This could involve using regular expressions, natural language processing libraries, or custom parsing logic.

3. Deploy Offline: To deploy this functionality offline, you need to ensure that all dependencies (e.g., NLP libraries) are available in the offline environment. You may need to package your application and its dependencies into a format that can be easily deployed offline (e.g., a Docker container).

Here's an example of how you might extend the StructuredChatOutputParserWithRetries class to include custom intent extraction logic:

import re
import json
from langchain.agents.agent import AgentAction, AgentFinish
from langchain.schema import OutputParserException

class CustomIntentOutputParser(StructuredChatOutputParserWithRetries):
    def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
        # Custom logic to extract time, devices, and actions
        time_pattern = r'\b\d{1,2}:\d{2}\b'
        device_pattern = r'\b(device1|device2|device3)\b'
        action_pattern = r'\b(turn on|turn off|adjust)\b'

        time_matches = re.findall(time_pattern, text)
        device_matches = re.findall(device_pattern, text, re.IGNORECASE)
        action_matches = re.findall(action_pattern, text, re.IGNORECASE)

        # Construct the action JSON with extracted information
        action_json = {
            "time": time_matches,
            "device": device_matches,
            "action": action_matches
        }

        action_str = f"""
Action:

{json.dumps(action_json, ensure_ascii=False)}

        try:
            if self.output_fixing_parser is not None:
                parsed_obj: Union[
                    AgentAction, AgentFinish
                ] = self.output_fixing_parser.parse(action_str)
            else:
                parsed_obj = self.base_parser.parse(action_str)
            return parsed_obj
        except Exception as e:
            raise OutputParserException(f"Could not parse LLM output: {text}") from e

To use this custom parser in your agent, you would instantiate it and set it as the output_parser for your StructuredGLM3ChatAgent instance:

custom_parser = CustomIntentOutputParser()
agent_obj = StructuredGLM3ChatAgent.from_llm_and_tools(
    llm=llm,
    tools=tools,
    prompt=prompt,
    output_parser=custom_parser,  # Use the custom parser
    **agent_kwargs
)

This example demonstrates how to extend the StructuredChatOutputParserWithRetries class to include custom logic for intent extraction. You'll need to adapt the regex patterns and parsing logic based on the specific requirements of your application.

Please note that the above code is just a basic example and might not work as expected for complex sentences or different languages. You might need to use more advanced NLP techniques or libraries for accurate intent extraction.

For offline deployment, you can package your application and its dependencies into a Docker container or a similar format that can be easily deployed offline. Make sure all the dependencies (including NLP libraries if you're using any) are included in the package.

I hope this helps! If you have any more questions or need further clarification, feel free to ask.

Sources

• server/agent/custom_agent/ChatGLM3Agent.py

About Dosu

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[zRzRzRzRzRzRzR]

那直接用lc重新写就行了，这个难度的理解本地模型基本做不到