refactor(lightrag): 优化Python类型提示以及代码稳健型

src/core/lightrag_based_kb.py

@@ -120,21 +120,23 @@ class LightRagBasedKB:
 
     def _get_llm_func(self, llm_info: dict):
         """获取 LLM 函数"""
-        llm_info = llm_info | {
+        # llm_info = llm_info | {
-            "model_name": "qwen3-1.7b",
+        #     "model_name": "qwen3-1.7b",
-            "provider": "dashscope"
+        #     "provider": "custom"
-        }
+        # }
-        provider_info = config.model_names[llm_info.get("provider")]
+        # provider_info = config.model_names[llm_info.get("provider")]
-        api_key = os.getenv(provider_info.get("env")[0] or "OPENAI_API_KEY") or "no_api_key"
+        # api_key = os.getenv(provider_info.get("env")[0] or "OPENAI_API_KEY") or "no_api_key"
-        base_url = get_docker_safe_url(provider_info.get("base_url", "http://localhost:8081/v1"))
+        # base_url = get_docker_safe_url(provider_info.get("base_url", "http://localhost:8081/v1"))
+        from src.models import get_custom_model
+        llm_info = get_custom_model("qwen3:32b-RFnC")
         async def llm_model_func(prompt, system_prompt=None, history_messages=[], **kwargs):
             return await openai_complete_if_cache(
-                llm_info.get("model_name"),
+                llm_info.get("name", "qwen3-1.7b"),
                 prompt,
                 system_prompt=system_prompt,
                 history_messages=history_messages,
-                api_key=api_key,
+                api_key=llm_info.get("api_key"),
-                base_url=base_url,
+                base_url=get_docker_safe_url(llm_info.get("api_base")),
                 extra_body={"enable_thinking": False},
                 **kwargs,
             )
@@ -155,42 +157,43 @@ class LightRagBasedKB:
             ),
         )
 
-    async def _process_file_to_markdown(self, file_path: str, params=None) -> str:
+    async def _process_file_to_markdown(self, file_path: str, params: dict | None = None) -> str:
         """将不同类型的文件转换为 markdown 格式"""
-        file_path = Path(file_path)
+        file_path_obj = Path(file_path)
-        file_ext = file_path.suffix.lower()
+        file_ext = file_path_obj.suffix.lower()
 
         if file_ext == '.pdf':
             # 使用 OCR 处理 PDF
             from src.core.indexing import parse_pdf_async
-            text = await parse_pdf_async(str(file_path), params=params)
+            text = await parse_pdf_async(str(file_path_obj), params=params)
-            return f"Using OCR to process {file_path.name}\n\n{text}"
+            return f"Using OCR to process {file_path_obj.name}\n\n{text}"
 
         elif file_ext in ['.txt', '.md']:
             # 直接读取文本文件
-            with open(file_path, 'r', encoding='utf-8') as f:
+            with open(file_path_obj, 'r', encoding='utf-8') as f:
                 content = f.read()
-            return f"# {file_path.name}\n\n{content}"
+            return f"# {file_path_obj.name}\n\n{content}"
 
         elif file_ext in ['.doc', '.docx']:
             # 处理 Word 文档
-            from docx import Document
+            
-            doc = Document(file_path)
+            from docx import Document  # type: ignore
+            doc = Document(file_path_obj)
             text = '\n'.join([para.text for para in doc.paragraphs])
-            return f"# {file_path.name}\n\n{text}"
+            return f"# {file_path_obj.name}\n\n{text}"
 
         elif file_ext in ['.jpg', '.jpeg', '.png', '.bmp']:
             # 使用 OCR 处理图片
-            text = ocr.process_image(str(file_path))
+            text = ocr.process_image(str(file_path_obj))
-            return f"# {file_path.name}\n\n{text}"
+            return f"# {file_path_obj.name}\n\n{text}"
 
         else:
             # 尝试作为文本文件读取
-            import textract
+            import textract  # type: ignore
-            text = textract.process(file_path)
+            text = textract.process(file_path_obj)
-            return f"# {file_path.name}\n\n{text}"
+            return f"# {file_path_obj.name}\n\n{text}"
 
-    async def _process_url_to_markdown(self, url: str, params=None) -> str:
+    async def _process_url_to_markdown(self, url: str, params: dict | None = None) -> str:
         """将 URL 转换为 markdown 格式"""
         import requests
         from bs4 import BeautifulSoup
@@ -231,7 +234,7 @@ class LightRagBasedKB:
 
         return {"databases": databases}
 
-    def create_database(self, database_name, description, embed_info: dict = None, **kwargs):
+    def create_database(self, database_name, description, embed_info: dict | None = None, **kwargs):
         """创建数据库 - data_router.py 使用"""
         db_id = f"kb_{hashstr(database_name, with_salt=True)}"
 
@@ -285,7 +288,7 @@ class LightRagBasedKB:
 
         return {"message": "删除成功"}
 
-    async def add_content(self, db_id, items, params=None):
+    async def add_content(self, db_id, items, params: dict | None = None):
         """通用的内容添加方法 - 支持文件和URL"""
         if db_id not in self.databases_meta:
             raise ValueError(f"Database {db_id} not found")
@@ -294,7 +297,7 @@ class LightRagBasedKB:
         if not rag:
             raise ValueError(f"Failed to get LightRAG instance for {db_id}")
 
-        content_type = params.get('content_type', 'file')
+        content_type = params.get('content_type', 'file') if params else 'file'
 
         processed_items_info = []
 
@@ -412,6 +415,7 @@ class LightRagBasedKB:
         if rag:
             try:
                 # 获取文档的所有 chunks
+                assert hasattr(rag.text_chunks, 'get_all'), "text_chunks does not have get_all method"
                 all_chunks = await rag.text_chunks.get_all()
 
                 # 筛选属于该文档的 chunks

src/models/__init__.py

@@ -54,9 +54,7 @@ def select_model(model_provider=None, model_name=None):
         )
 
     if model_provider == "custom":
-        model_info = next((x for x in config.custom_models if x["custom_id"] == model_name), None)
+        model_info = get_custom_model(model_name)
-        if model_info is None:
-            raise ValueError(f"Model {model_name} not found in custom models")
 
         from src.models.chat_model import CustomModel
         return CustomModel(model_info)
@@ -71,3 +69,12 @@ def select_model(model_provider=None, model_name=None):
         return model
     except Exception as e:
         raise ValueError(f"Model provider {model_provider} load failed, {e} \n {traceback.format_exc()}")
+    
+    
+def get_custom_model(model_id):
+    """return model_info"""
+    assert config.custom_models is not None, "custom_models is not set"
+    modle_info = next((x for x in config.custom_models if x["custom_id"] == model_id), None)
+    if modle_info is None:
+        raise ValueError(f"Model {model_id} not found in custom models")
+    return modle_info