wow agent day10 Zigent实现教程编写智能体

Reference：Datawhale wow agent day10
本节课我们将通过 Zigent 框架实现一个教程编写智能体，其主要功能是输入教程主题，然后自动生成完整的教程内容。 设计思路：
先通过 LLM 大模型生成教程的目录，再对目录按照二级标题进行分块，对于每块目录按照标题生成详细内容，最后再将标题和内容进行拼接。分块的设计解决了 LLM 大模型长文本的限制问题。

定义生成教程的目录 Action 类

定义 WriteDirectoryAction 类，继承自 BaseAction。该类的主要功能是生成一个教程的目录结构。具体来说，它通过调用大语言模型（LLM）来根据给定的主题和语言生成一个符合特定格式的目录。

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class WriteDirectoryAction(BaseAction):
"""Generate tutorial directory structure action"""
def __init__(self) -> None:
action_name = "WriteDirectory"
action_desc = "Generate tutorial directory structure"
params_doc = {
"topic": "(Type: string): The tutorial topic name",
"language": "(Type: string): Output language (default: 'Chinese')"
}
super().__init__(action_name, action_desc, params_doc)

def __call__(self, **kwargs):
topic = kwargs.get("topic", "")
language = kwargs.get("language", "Chinese")

directory_prompt = f"""
请为主题"{topic}"生成教程目录结构,要求:
1. 输出语言必须是{language}
2. 严格按照以下字典格式输出: {{"title": "xxx", "directory": [{{"章节1": ["小节1", "小节2"]}}, {{"章节2": ["小节3", "小节4"]}}]}}
3. 目录层次要合理,包含主目录和子目录
4. 每个目录标题要有实际意义
5. 不要有多余的空格或换行
"""

# 调用 LLM 生成目录
directory_data = llm.run(directory_prompt)
try:
directory_data = json.loads(directory_data)
except:
directory_data = {"title": topic, "directory": []}

return {
"topic": topic,
"language": language,
"directory_data": directory_data
}

定义生成教程内容的 Action 类

WriteContentAction 类用于生成教程内容。它的 call 方法接收标题、章节、语言和目录数据，并构建一个内容提示，最后调用 LLM 生成相应的内容。

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class WriteContentAction(BaseAction):
"""Generate tutorial content action"""
def __init__(self) -> None:
action_name = "WriteContent"
action_desc = "Generate detailed tutorial content based on directory structure"
params_doc = {
"title": "(Type: string): The section title",
"chapter": "(Type: string): The chapter title",
"directory_data": "(Type: dict): The complete directory structure",
"language": "(Type: string): Output language (default: 'Chinese')"
}
super().__init__(action_name, action_desc, params_doc)

def __call__(self, **kwargs):
title = kwargs.get("title", "")
chapter = kwargs.get("chapter", "")
language = kwargs.get("language", "Chinese")
directory_data = kwargs.get("directory_data", {})

content_prompt = f"""
请为教程章节生成详细内容:
教程标题: {directory_data.get('title', '')}
章节: {chapter}
小节: {title}

要求:
1. 内容要详细且准确
2. 如果需要代码示例,请按标准规范提供
3. 使用 Markdown 格式
4. 输出语言必须是{language}
5. 内容长度适中,通常在500-1000字之间
"""

# 调用 LLM 生成内容
content = llm.run(content_prompt)
return content

定义教程编写智能体

定义 TutorialAssistant 类，继承自 BaseAgent，用于生成教程内容。其主要功能包括：初始化目录和内容生成的动作（WriteDirectoryAction 和 WriteContentAction）、_generate_tutorial 方法根据目录数据生成完整的教程内容包括目录和每个章节的详细内容、_add_tutorial_example 方法为助手添加一个示例任务并展示如何生成一个 Python 教程的目录和内容。最终调用 call 方法处理生成教程的任务。它从任务中提取主题，生成目录结构，然后生成完整的教程内容，并将结果保存到本地。

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class TutorialAssistant(BaseAgent):
"""Tutorial generation assistant that manages directory and content creation"""
def __init__(
self,
llm: LLM,
language: str = "Chinese"
):
name = "TutorialAssistant"
role = """You are a professional tutorial writer. You can create well-structured,
comprehensive tutorials on various topics. You excel at organizing content logically
and explaining complex concepts clearly."""

super().__init__(
name=name,
role=role,
llm=llm,
)

self.language = language
self.directory_action = WriteDirectoryAction()
self.content_action = WriteContentAction()

# Add example for the tutorial assistant
self._add_tutorial_example()

def _generate_tutorial(self, directory_data: Dict) -> str:
"""Generate complete tutorial content based on directory structure"""
full_content = []
title = directory_data["title"]
full_content.append(f"# {title}\n")

# Generate table of contents
full_content.append("## 目录\n")
for idx, chapter in enumerate(directory_data["directory"], 1):
for chapter_title, sections in chapter.items():
full_content.append(f"{idx}. {chapter_title}")
for section_idx, section in enumerate(sections, 1):
full_content.append(f" {idx}.{section_idx}. {section}")
full_content.append("\n---\n")

# Generate content for each section
for chapter in directory_data["directory"]:
for chapter_title, sections in chapter.items():
for section in sections:
content = self.content_action(
title=section,
chapter=chapter_title,
directory_data=directory_data,
language=self.language
)
full_content.append(content)
full_content.append("\n---\n")

return "\n".join(full_content)

def __call__(self, task: TaskPackage):
"""Process the tutorial generation task"""
# Extract topic from task
topic = task.instruction.split("Create a ")[-1].split(" tutorial")[0]
if not topic:
topic = task.instruction

# Generate directory structure
directory_result = self.directory_action(
topic=topic,
language=self.language
)

print(directory_result)

# Generate complete tutorial
tutorial_content = self._generate_tutorial(directory_result["directory_data"])

# Save the result
task.answer = tutorial_content
task.completion = "completed"

return task

def _add_tutorial_example(self):
"""Add an illustration example for the tutorial assistant"""
exp_task = "Create a Python tutorial for beginners"
exp_task_pack = TaskPackage(instruction=exp_task)
topic = "Python基础教程"

act_1 = AgentAct(
name=ThinkAct.action_name,
params={INNER_ACT_KEY: """First, I'll create a directory structure for the Python tutorial,
then generate detailed content for each section."""}
)
obs_1 = "OK. I'll start with the directory structure."

act_2 = AgentAct(
name=self.directory_action.action_name,
params={
"topic": topic,
"language": self.language
}
)
obs_2 = """{"title": "Python基础教程", "directory": [
{"第一章：Python介绍": ["1.1 什么是Python", "1.2 环境搭建"]},
{"第二章：基础语法": ["2.1 变量和数据类型", "2.2 控制流"]}
]}"""

act_3 = AgentAct(
name=self.content_action.action_name,
params={
"title": "什么是Python",
"chapter": "第一章：Python介绍",
"directory_data": json.loads(obs_2),
"language": self.language
}
)
obs_3 = """# 第一章：Python介绍\n## 什么是Python\n\nPython是一种高级编程语言..."""

act_4 = AgentAct(
name=FinishAct.action_name,
params={INNER_ACT_KEY: "Tutorial structure and content generated successfully."}
)
obs_4 = "Tutorial generation task completed successfully."

exp_act_obs = [(act_1, obs_1), (act_2, obs_2), (act_3, obs_3), (act_4, obs_4)]

self.prompt_gen.add_example(
task=exp_task_pack,
action_chain=exp_act_obs
)

交互式操作调用教程编写智能体

在主程序中，创建 TutorialAssistant 实例并调用其 call 方法，实现交互式生成教程的功能。用户可以输入要创建的教程主题，然后调用 TutorialAssistant 生成相应的教程内容，并将结果保存到本地文件。

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if __name__ == "__main__":
assistant = TutorialAssistant(llm=llm)

# 交互式生成教程
FLAG_CONTINUE = True
while FLAG_CONTINUE:
input_text = input("What tutorial would you like to create?\n")
task = TaskPackage(instruction=input_text)
result = assistant(task)
print("\nGenerated Tutorial:\n")
print(result.answer)

# 创建输出目录
output_dir = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
os.makedirs(output_dir, exist_ok=True)

# 保存文件
output_file = os.path.join(output_dir, f"{input_text}.md")
with open(output_file, 'w', encoding='utf-8') as f:
f.write(result.answer)
if input("\nDo you want to create another tutorial? (y/n): ").lower() != "y":
FLAG_CONTINUE = False

测试结果

生成基础拓扑学教程：

太长了就不完全放上来了。