feat: add html parse api url parsing

llm_web_kit/api/dependencies.py

@@ -31,6 +31,7 @@ class Settings(BaseSettings):
     # 模型配置
     model_path: Optional[str] = None
     max_content_length: int = 10 * 1024 * 1024  # 10MB
+    crawl_url: str = "http://10.140.0.94:9500/crawl"
 
     # 缓存配置
     cache_ttl: int = 3600  # 1小时

llm_web_kit/api/requirements.txt

@@ -3,6 +3,7 @@ aiohttp>=3.9.0
 
 # FastAPI 相关依赖
 fastapi>=0.104.0
+httpx>=0.27.0
 pydantic>=2.0.0
 pydantic-settings>=2.0.0
 

llm_web_kit/api/services/html_service.py

@@ -5,9 +5,11 @@
 
 from typing import Any, Dict, Optional
 
-from llm_web_kit.simple import extract_content_from_main_html
+import httpx
 
-from ..dependencies import get_inference_service, get_logger, get_settings
+from llm_web_kit.api.dependencies import (get_inference_service, get_logger,
+                                          get_settings)
+from llm_web_kit.simple import extract_content_from_main_html
 
 logger = get_logger(__name__)
 settings = get_settings()
@@ -37,8 +39,26 @@ async def parse_html(
     ) -> Dict[str, Any]:
         """解析 HTML 内容."""
         try:
+            if not html_content and url:
+                logger.info(f'HTML 内容为空，尝试从 URL 爬取: {url}')
+                try:
+                    async with httpx.AsyncClient() as client:
+                        response = await client.post(settings.crawl_url, json={'url': url}, timeout=60)
+                        response.raise_for_status()
+                        data = response.json()
+                        html_content = data.get('html')
+                        if not html_content:
+                            raise ValueError('爬取成功，但未返回 HTML 内容')
+                        logger.info(f'URL 爬取成功，内容长度: {len(html_content)}')
+                except httpx.RequestError as exc:
+                    logger.error(f"调用爬虫服务失败: {exc}")
+                    raise ValueError(f"无法从 URL 爬取内容: {exc}")
+                except Exception as e:
+                    logger.error(f'爬取或解析爬取内容失败: {e}')
+                    raise ValueError(f'处理爬取内容时发生错误: {e}')
+
             if not html_content:
-                raise ValueError('必须提供 HTML 内容')
+                raise ValueError('必须提供 HTML 内容或有效的 URL')
 
             # 延迟导入，避免模块导入期异常导致服务类不可用
             try:
@@ -83,3 +103,21 @@ async def _parse_with_model(self, html_content: str, options: Optional[Dict[str,
         if self._inference_service is None:
             self._inference_service = get_inference_service()
         return await self._inference_service.inference(html_content, options or {})
+
+
+if __name__ == '__main__':
+    import asyncio
+
+    # 重新导入以确保加载最新的代码，绕过缓存问题
+    from llm_web_kit.api.dependencies import get_settings
+    settings = get_settings()
+
+    async def main():
+        async with httpx.AsyncClient() as client:
+            response = await client.post(settings.crawl_url, json={'url': 'https://aws.amazon.com/what-is/retrieval-augmented-generation/'}, timeout=60)
+            response.raise_for_status()
+            data = response.json()
+            html_content = data.get('html')
+            print(html_content)
+
+    asyncio.run(main())

llm_web_kit/main_html_parser/parser/layout_batch_parser.py

@@ -73,22 +73,25 @@ def parse(self, pre_data: PreDataJson) -> PreDataJson:
         content, body = self.process(html_source, template_dict_html)
 
         # 相似度计算
-        if pre_data.get(PreDataJsonKey.TYPICAL_MAIN_HTML, None):
-            template_main_html = pre_data[PreDataJsonKey.TYPICAL_MAIN_HTML]
-            if pre_data.get(PreDataJsonKey.SIMILARITY_LAYER, None):
-                layer = pre_data[PreDataJsonKey.SIMILARITY_LAYER]
-            else:
-                layer = self.__get_max_width_layer(template_data)
-            feature1 = get_feature(template_main_html)
-            feature2 = get_feature(body)
-            sim = None
-            if feature1 is not None and feature2 is not None:
-                sim = similarity(feature1, feature2, layer_n=layer)
-                pre_data[PreDataJsonKey.MAIN_HTML_SIM] = sim
-            if sim is None or sim < SIMILARITY_THRESHOLD:
-                pre_data[PreDataJsonKey.MAIN_HTML_SUCCESS] = False
-            else:
-                pre_data[PreDataJsonKey.MAIN_HTML_SUCCESS] = True
+        try:
+            if pre_data.get(PreDataJsonKey.TYPICAL_MAIN_HTML, None):
+                template_main_html = pre_data[PreDataJsonKey.TYPICAL_MAIN_HTML]
+                if pre_data.get(PreDataJsonKey.SIMILARITY_LAYER, None):
+                    layer = pre_data[PreDataJsonKey.SIMILARITY_LAYER]
+                else:
+                    layer = self.__get_max_width_layer(template_data)
+                feature1 = get_feature(template_main_html)
+                feature2 = get_feature(body)
+                sim = None
+                if feature1 is not None and feature2 is not None:
+                    sim = similarity(feature1, feature2, layer_n=layer)
+                    pre_data[PreDataJsonKey.MAIN_HTML_SIM] = sim
+                if sim is None or sim < SIMILARITY_THRESHOLD:
+                    pre_data[PreDataJsonKey.MAIN_HTML_SUCCESS] = False
+                else:
+                    pre_data[PreDataJsonKey.MAIN_HTML_SUCCESS] = True
+        except Exception:
+            pre_data[PreDataJsonKey.MAIN_HTML_SUCCESS] = None
 
         # 结果返回
         pre_data[PreDataJsonKey.MAIN_HTML] = content