基于LangGraph的智能文章生成Agent架构设计思路 原创

+-------------------+
|   应用接口层       |
|  (API Gateway)    |
+-------------------+
         |
+-------------------+
|   工作流引擎       |
|  (LangGraph Core) |
+-------------------+
         |
+-------------------+
| 功能组件层         |
| - 数据采集        |
| - 内容生成        |
| - 质量审核        |
| - 发布适配        |
+-------------------+
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from langgraph.graph import StateGraph


class ArticleState:
    topics: list
    titles: list
    outlines: dict
    contents: str
    media: dict


workflow = StateGraph(ArticleState)


# 定义状态节点
workflow.add_node("collect", data_collection)
workflow.add_node("generate", content_generation)
workflow.add_node("verify", quality_verification)


# 构建条件转移逻辑
workflow.add_conditional_edges(
    "verify",
    lambda s: "generate" if s.need_revision else "publish"
)
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class DataCollector:
    def __init__(self):
        self.adapters = {
            'wechat': WeChatAdapter(),
            'zhihu': ZhihuAdapter()
        }


    async def fetch(self, platform):
        return await self.adapters[platform].get_hot_topics()


class WeChatAdapter:
    async def get_hot_topics(self):
        # 实现微信特定数据解析逻辑
        return processed_data
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title_prompt = """
基于以下热点话题生成候选标题：
{topics}


要求：
- 包含数字和表情符号
- 长度不超过25字
- 使用疑问句式结构"""
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Root
├─ 现状分析
├─ 核心论点
│   ├─ 数据支撑
│   └─ 案例佐证
└─ 结论展望
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class ContentExpander:
    def __init__(self, retriever):
        self.retriever = retriever
        
    def expand(self, outline):
        context = self.retriever.query(outline['keywords'])
        return self._merge_content(outline, context)
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def validate_image(text, image):
    inputs = processor(text=text, images=image, return_tensors="pt")
    return model(**inputs).logits_per_image
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class CitationGenerator:
    def generate(self, claims):
        return [self._find_source(c) for c in claims]
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class ComplianceChecker:
    def check(self, text):
        return all([
            self._sensitive_words_check(text),
            self._copyright_check(text),
            self._platform_rules_check(text)
        ])
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class StateManager:
    def save_checkpoint(self, state):
        # 序列化存储状态快照
        pass
        
    def load_checkpoint(self, run_id):
        # 恢复执行状态
        pass
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ERROR_HANDLERS = {
    'retry': lambda e: logger.warning(f"Retrying: {e}"),
    'fallback': lambda e: switch_alternative_method(),
    'critical': lambda e: abort_workflow()
}
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class Component(ABC):
    @abstractmethod
    def execute(self, state):
        pass
        
    @property
    def version(self):
        return "1.0"
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