feat: 在依赖组中添加 ruff 依赖，并优化代码中的类型提示和异常处理，提升代码可读性和稳定性

pyproject.toml

@@ -56,6 +56,7 @@ lint.ignore = ["F401"]  # 忽略的规则
 
 [dependency-groups]
 dev = [
+    "ruff>=0.12.1",
     "vllm>=0.8.5.post1",
 ]
 

server/routers/graph_router.py

@@ -35,19 +35,19 @@ async def get_subgraph(
     """
     try:
         logger.info(f"获取子图数据 - db_id: {db_id}, node_label: {node_label}, max_depth: {max_depth}, max_nodes: {max_nodes}")
-        
+
         # 获取 LightRAG 实例
         rag_instance = await knowledge_base._get_lightrag_instance(db_id)
         if not rag_instance:
             raise HTTPException(status_code=404, detail=f"数据库 {db_id} 不存在")
-        
+
         # 使用 LightRAG 的原生 get_knowledge_graph 方法
         knowledge_graph = await rag_instance.get_knowledge_graph(
             node_label=node_label,
             max_depth=max_depth,
             max_nodes=max_nodes
         )
-        
+
         # 将 LightRAG 的 KnowledgeGraph 格式转换为前端需要的格式
         nodes = []
         for node in knowledge_graph.nodes:
@@ -57,7 +57,7 @@ async def get_subgraph(
                 "entity_type": node.properties.get("entity_type", "unknown"),
                 "properties": node.properties
             })
-        
+
         edges = []
         for edge in knowledge_graph.edges:
             edges.append({
@@ -67,7 +67,7 @@ async def get_subgraph(
                 "type": edge.type,
                 "properties": edge.properties
             })
-        
+
         result = {
             "success": True,
             "data": {
@@ -78,10 +78,10 @@ async def get_subgraph(
                 "total_edges": len(edges)
             }
         }
-        
+
         logger.info(f"成功获取子图 - 节点数: {len(nodes)}, 边数: {len(edges)}")
         return result
-        
+
     except Exception as e:
         logger.error(f"获取子图数据失败: {e}")
         logger.error(f"Traceback: {traceback.format_exc()}")
@@ -104,22 +104,22 @@ async def get_graph_labels(
     """
     try:
         logger.info(f"获取图谱标签 - db_id: {db_id}")
-        
+
         # 获取 LightRAG 实例
         rag_instance = await knowledge_base._get_lightrag_instance(db_id)
         if not rag_instance:
             raise HTTPException(status_code=404, detail=f"数据库 {db_id} 不存在")
-        
+
         # 使用 LightRAG 的原生方法获取所有标签
         labels = await rag_instance.get_graph_labels()
-        
+
         return {
             "success": True,
             "data": {
                 "labels": labels
             }
         }
-        
+
     except Exception as e:
         logger.error(f"获取图谱标签失败: {e}")
         logger.error(f"Traceback: {traceback.format_exc()}")
@@ -142,7 +142,7 @@ async def get_available_databases(
             "success": True,
             "data": databases
         }
-        
+
     except Exception as e:
         logger.error(f"获取数据库列表失败: {e}")
         raise HTTPException(status_code=500, detail=f"获取数据库列表失败: {str(e)}")
@@ -154,8 +154,8 @@ async def get_graph_nodes_legacy(
     db_id: str = Query(..., description="数据库ID"),
     limit: int = Query(500, description="最大节点数量", ge=1, le=2000),
     offset: int = Query(0, description="偏移量", ge=0),
-    entity_type: Optional[str] = Query(None, description="实体类型筛选"),
+    entity_type: str | None = Query(None, description="实体类型筛选"),
-    search: Optional[str] = Query(None, description="搜索关键词"),
+    search: str | None = Query(None, description="搜索关键词"),
     current_user: User = Depends(get_admin_user)
 ):
     """
@@ -173,7 +173,7 @@ async def get_graph_nodes_legacy(
                 "total": 0
             }
         }
-        
+
     except Exception as e:
         logger.error(f"获取图节点数据失败: {e}")
         raise HTTPException(status_code=500, detail=f"获取图节点数据失败: {str(e)}")
@@ -184,7 +184,7 @@ async def get_graph_edges_legacy(
     db_id: str = Query(..., description="数据库ID"),
     limit: int = Query(500, description="最大边数量", ge=1, le=2000),
     offset: int = Query(0, description="偏移量", ge=0),
-    min_weight: Optional[float] = Query(None, description="最小权重筛选"),
+    min_weight: float | None = Query(None, description="最小权重筛选"),
     current_user: User = Depends(get_admin_user)
 ):
     """
@@ -202,7 +202,7 @@ async def get_graph_edges_legacy(
                 "total": 0
             }
         }
-        
+
     except Exception as e:
         logger.error(f"获取图边数据失败: {e}")
         raise HTTPException(status_code=500, detail=f"获取图边数据失败: {str(e)}")
@@ -218,30 +218,30 @@ async def get_graph_stats(
     """
     try:
         logger.info(f"获取图谱统计信息 - db_id: {db_id}")
-        
+
         # 获取 LightRAG 实例
         rag_instance = await knowledge_base._get_lightrag_instance(db_id)
         if not rag_instance:
             raise HTTPException(status_code=404, detail=f"数据库 {db_id} 不存在")
-        
+
         # 通过获取全图来统计节点和边的数量
         knowledge_graph = await rag_instance.get_knowledge_graph(
             node_label="*",
             max_depth=1,
             max_nodes=10000  # 设置较大值以获取完整统计
         )
-        
+
         # 统计实体类型分布
         entity_types = {}
         for node in knowledge_graph.nodes:
             entity_type = node.properties.get("entity_type", "unknown")
             entity_types[entity_type] = entity_types.get(entity_type, 0) + 1
-        
+
         entity_types_list = [
-            {"type": k, "count": v} 
+            {"type": k, "count": v}
             for k, v in sorted(entity_types.items(), key=lambda x: x[1], reverse=True)
         ]
-        
+
         return {
             "success": True,
             "data": {
@@ -251,8 +251,8 @@ async def get_graph_stats(
                 "is_truncated": knowledge_graph.is_truncated
             }
         }
-        
+
     except Exception as e:
         logger.error(f"获取图谱统计信息失败: {e}")
         logger.error(f"Traceback: {traceback.format_exc()}")
-        raise HTTPException(status_code=500, detail=f"获取图谱统计信息失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"获取图谱统计信息失败: {str(e)}")

src/agents/chatbot/configuration.py

@@ -29,11 +29,7 @@ class ChatbotConfiguration(Configuration):
         metadata={
             "name": "智能体模型",
             "configurable": True,
-            "options": [
+            "options": [],
-                "zhipu/glm-4-plus",
-                "siliconflow/Qwen/Qwen2.5-72B-Instruct",
-                "siliconflow/Qwen/Qwen2.5-7B-Instruct",
-            ],
             "description": "智能体的驱动模型"
         },
     )

src/agents/utils.py

@@ -1,29 +1,73 @@
 from datetime import datetime, timezone, UTC
 import asyncio
+import os
+import traceback
 
-from src.models import select_model
+from src import config
+from src.utils import logger, get_docker_safe_url
+from src.models import get_custom_model
 from src.agents.registry import BaseAgent
 from langchain_core.language_models import BaseChatModel
 from langchain_core.runnables import RunnableConfig
 from langchain_core.messages import AIMessageChunk, ToolMessage
+from pydantic import SecretStr
 
 
 
 
 def load_chat_model(fully_specified_name: str, **kwargs) -> BaseChatModel:
-    """Load a chat model from a fully specified name.
+    """
-
+    Load a chat model from a fully specified name.
-    Args:
-        fully_specified_name (str): String in the format 'provider/model'.
-        **kwargs: Additional parameters to pass to the model.
     """
     provider, model = fully_specified_name.split("/", maxsplit=1)
-    model_instance = select_model(model_name=model, model_provider=provider)
 
-    return model_instance.chat_open_ai
+    if provider == "custom":
+        from langchain_openai import ChatOpenAI
+        model_info = get_custom_model(model)
+        api_key = model_info.get("api_key") or "custom_model"
+        base_url = get_docker_safe_url(model_info["api_base"])
+        model_name = model_info.get("name") or "custom_model"
+        return ChatOpenAI(
+            model=model_name,
+            api_key=SecretStr(api_key),
+            base_url=base_url,
+        )
+
+    model_info = config.model_names.get(provider, {})
+    api_key = os.getenv(model_info["env"][0], model_info["env"][0])
+    base_url = get_docker_safe_url(model_info["base_url"])
+
+    if provider in ["deepseek", "dashscope"]:
+        from langchain_deepseek import ChatDeepSeek
+        return ChatDeepSeek(
+            model=model,
+            api_key=SecretStr(api_key),
+            base_url=base_url,
+            api_base=base_url,
+        )
+
+    elif provider == "together":
+        from langchain_together import ChatTogether
+        return ChatTogether(
+            model=model,
+            api_key=SecretStr(api_key),
+            base_url=base_url,
+        )
+
+    else:
+        try:  # 其他模型，默认使用OpenAIBase, like openai, zhipuai
+            from langchain_openai import ChatOpenAI
+            return ChatOpenAI(
+                model=model,
+                api_key=SecretStr(api_key),
+                base_url=base_url,
+            )
+        except Exception as e:
+            raise ValueError(f"Model provider {provider} load failed, {e} \n {traceback.format_exc()}")
 
 
-async def agent_cli(agent: BaseAgent, config: RunnableConfig = None):
+
+async def agent_cli(agent: BaseAgent, config: RunnableConfig | None = None):
     config = config or {}
     if "configurable" not in config:
         config["configurable"] = {}

src/config/__init__.py

@@ -59,9 +59,6 @@ class Config(SimpleConfig):
 
         self.add_item("embed_model", default="siliconflow/BAAI/bge-m3", des="Embedding 模型", choices=list(self.embed_model_names.keys()))
         self.add_item("reranker", default="siliconflow/BAAI/bge-reranker-v2-m3", des="Re-Ranker 模型", choices=list(self.reranker_names.keys()))  # noqa: E501
-        self.add_item("model_local_paths", default={}, des="本地模型路径")
-        self.add_item("use_rewrite_query", default="on", des="重写查询", choices=["off", "on", "hyde"])
-        self.add_item("device", default="cuda", des="运行本地模型的设备", choices=["cpu", "cuda"])
         ### <<< 默认配置结束
 
         self.load()
@@ -120,7 +117,6 @@ class Config(SimpleConfig):
         """
         处理配置
         """
-        model_provider_info = self.model_names.get(self.model_provider, {})
         self.model_dir = os.environ.get("MODEL_DIR", "")
 
         if self.model_dir:
@@ -130,26 +126,6 @@ class Config(SimpleConfig):
                 logger.warning(f"Warning: The model directory （{self.model_dir}） does not exist. If not configured, please ignore it. If configured, please check if the configuration is correct;"
                                "For example, the mapping in the docker-compose file")
 
-
-        # 检查模型提供商是否存在
-        if self.model_provider != "custom":
-            if self.model_name not in model_provider_info["models"]:
-                logger.warning(f"Model name {self.model_name} not in {self.model_provider}, using default model name")
-                self.model_name = model_provider_info["default"]
-
-            default_model_name = model_provider_info["default"]
-            self.model_name = self.get("model_name") or default_model_name
-        else:
-            self.model_name = self.get("model_name")
-            if self.model_name not in [item["custom_id"] for item in self.get("custom_models", [])]:
-                logger.warning(f"Model name {self.model_name} not in custom models, using default model name")
-                if self.get("custom_models", []):
-                    self.model_name = self.get("custom_models", [])[0]["custom_id"]
-                else:
-                    self.model_name = self._config_items["model_name"]["default"]
-                    self.model_provider = self._config_items["model_provider"]["default"]
-                    logger.error(f"No custom models found, using default model {self.model_name} from {self.model_provider}")
-
         # 检查模型提供商的环境变量
         conds = {}
         self.model_provider_status = {}
@@ -158,12 +134,6 @@ class Config(SimpleConfig):
             conds_bool = [bool(os.getenv(_k)) for _k in conds[provider]]
             self.model_provider_status[provider] = all(conds_bool)
 
-        # 检查web_search的环境变量
-        # if self.enable_web_search and not os.getenv("TAVILY_API_KEY"):
-        #     logger.warning("TAVILY_API_KEY not set, web search will be disabled")
-        #     self.enable_web_search = False
-
-        # 2025.04.08 修改为不手动配置，只要配置了TAVILY_API_KEY，就默认开启web_search
         if os.getenv("TAVILY_API_KEY"):
             self.enable_web_search = True
 

src/core/graphbase.py

@@ -3,7 +3,6 @@ import json
 import warnings
 import traceback
 
-import torch
 from neo4j import GraphDatabase as GD
 
 from src import config

src/core/lightrag_based_kb.py

@@ -5,7 +5,7 @@ import traceback
 import shutil
 import asyncio
 from pathlib import Path
-from typing import Dict, List, Any, Optional
+from typing import Optional
 from datetime import datetime
 
 from lightrag import LightRAG, QueryParam
@@ -26,11 +26,11 @@ class LightRagBasedKB:
 
     def __init__(self) -> None:
         # 存储 LightRAG 实例映射 {db_id: LightRAG}
-        self.instances: Dict[str, LightRAG] = {}
+        self.instances: dict[str, LightRAG] = {}
         # 数据库元信息存储 {db_id: metadata}
-        self.databases_meta: Dict[str, dict] = {}
+        self.databases_meta: dict[str, dict] = {}
         # 文件信息存储 {file_id: file_info}
-        self.files_meta: Dict[str, dict] = {}
+        self.files_meta: dict[str, dict] = {}
         # 工作目录
         self.work_dir = os.path.join(config.save_dir, "lightrag_data")
         os.makedirs(self.work_dir, exist_ok=True)
@@ -45,7 +45,7 @@ class LightRagBasedKB:
         meta_file = os.path.join(self.work_dir, "metadata.json")
         if os.path.exists(meta_file):
             try:
-                with open(meta_file, 'r', encoding='utf-8') as f:
+                with open(meta_file, encoding='utf-8') as f:
                     data = json.load(f)
                     self.databases_meta = data.get("databases", {})
                     self.files_meta = data.get("files", {})
@@ -69,7 +69,7 @@ class LightRagBasedKB:
         except Exception as e:
             logger.error(f"Failed to save metadata: {e}")
 
-    async def _get_lightrag_instance(self, db_id: str) -> Optional[LightRAG]:
+    async def _get_lightrag_instance(self, db_id: str) -> LightRAG | None:
         """获取或创建 LightRAG 实例"""
         logger.info(f"Getting or creating LightRAG instance for {db_id}")
 
@@ -170,13 +170,13 @@ class LightRagBasedKB:
 
         elif file_ext in ['.txt', '.md']:
             # 直接读取文本文件
-            with open(file_path_obj, 'r', encoding='utf-8') as f:
+            with open(file_path_obj, encoding='utf-8') as f:
                 content = f.read()
             return f"# {file_path_obj.name}\n\n{content}"
 
         elif file_ext in ['.doc', '.docx']:
             # 处理 Word 文档
-            
+
             from docx import Document  # type: ignore
             doc = Document(file_path_obj)
             text = '\n'.join([para.text for para in doc.paragraphs])
@@ -416,7 +416,7 @@ class LightRagBasedKB:
             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()
+                all_chunks = await rag.text_chunks.get_all() # type: ignore
 
                 # 筛选属于该文档的 chunks
                 doc_chunks = []
@@ -506,4 +506,4 @@ class LightRagBasedKB:
                 "description": meta["description"],
                 "retriever": make_retriever(db_id),
             }
-        return retrievers
+        return retrievers

src/models/__init__.py

@@ -5,19 +5,14 @@ from src import config
 from src.utils.logging_config import logger
 from src.models.chat_model import OpenAIBase
 
-def select_model(model_provider=None, model_name=None):
+def select_model(model_provider, model_name=None):
     """根据模型提供者选择模型"""
-    model_provider = model_provider or config.model_provider
+    assert model_provider is not None, "Model provider not specified"
     model_info = config.model_names.get(model_provider, {})
-    model_name = model_name or config.model_name or model_info.get("default", "")
+    model_name = model_name or model_info.get("default", "")
-
 
     logger.info(f"Selecting model from `{model_provider}` with `{model_name}`")
 
-
-    if model_provider is None:
-        raise ValueError("Model provider not specified, please modify `model_provider` in `src/config/base.yaml`")
-
     if model_provider == "qianfan":
         from src.models.chat_model import Qianfan
         return Qianfan(model_name)
@@ -26,33 +21,6 @@ def select_model(model_provider=None, model_name=None):
         from src.models.chat_model import OpenModel
         return OpenModel(model_name)
 
-    if model_provider == "deepseek" or model_provider == "dashscope":
-        from langchain_deepseek import ChatDeepSeek
-        return OpenAIBase(
-            api_key=os.getenv(model_info["env"][0]),
-            base_url=model_info["base_url"],
-            model_name=model_name,
-            chat_open_ai=ChatDeepSeek(
-                model=model_name,
-                api_key=os.getenv(model_info["env"][0]),
-                base_url=model_info["base_url"],
-                api_base=model_info["base_url"],
-            )
-        )
-
-    if model_provider == "together":
-        from langchain_together import ChatTogether
-        return OpenAIBase(
-            api_key=os.getenv(model_info["env"][0]),
-            base_url=model_info["base_url"],
-            model_name=model_name,
-            chat_open_ai=ChatTogether(
-                model=model_name,
-                api_key=os.getenv(model_info["env"][0]),
-                base_url=model_info["base_url"],
-            )
-        )
-
     if model_provider == "custom":
         model_info = get_custom_model(model_name)
 
@@ -69,8 +37,7 @@ 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"

src/models/chat_model.py

@@ -5,15 +5,12 @@ from src.utils import logger, get_docker_safe_url
 from langchain_openai import ChatOpenAI
 
 class OpenAIBase:
-    def __init__(self, api_key, base_url, model_name, chat_open_ai=None, **kwargs):
+    def __init__(self, api_key, base_url, model_name, **kwargs):
         self.api_key = api_key
         self.base_url = base_url
         self.client = OpenAI(api_key=api_key, base_url=base_url)
         self.model_name = model_name
         self.info = kwargs
-        self.chat_open_ai = chat_open_ai or ChatOpenAI(model=model_name,
-                                       api_key=api_key,
-                                       base_url=base_url)
 
     def predict(self, message, stream=False):
         if isinstance(message, str):

src/models/embedding.py

@@ -13,19 +13,18 @@ from src.utils import hashstr, logger, get_docker_safe_url
 class BaseEmbeddingModel:
     embed_state = {}
 
+    def __init__(self, model_id):
+        self.model_id = model_id
+        self.info = config.embed_model_names[model_id]
+        self.model = self.info["name"]
+        self.dimension = self.info.get("dimension", None)
+        self.url = get_docker_safe_url(self.info["base_url"])
+        self.api_key = os.getenv(self.info["api_key"], self.info["api_key"])
+
     @abstractmethod
     def predict(self, message):
         raise NotImplementedError("Subclasses must implement this method")
 
-    def get_dimension(self):
-        if hasattr(self, "dimension"):
-            return self.dimension
-
-        if hasattr(self, "embed_model_fullname"):
-            return config.embed_model_names[self.embed_model_fullname].get("dimension", None)
-
-        return config.embed_model_names[self.model].get("dimension", None)
-
     def encode(self, message):
         return self.predict(message)
 
@@ -66,77 +65,14 @@ class BaseEmbeddingModel:
 
         return data
 
-class LocalEmbeddingModel(BaseEmbeddingModel):
-    def __init__(self, **kwargs):
-        info = config.embed_model_names[config.embed_model]
-
-        self.model = config.model_local_paths.get(info["name"], info.get("local_path"))
-        self.model = self.model or info["name"]
-        self.dimension = info["dimension"]
-        self.embed_model_fullname = config.embed_model
-
-        if os.getenv("MODEL_DIR"):
-            if os.path.exists(_path := os.path.join(os.getenv("MODEL_DIR"), self.model)):
-                self.model = _path
-            else:
-                logger.warning(f"Local model `{info['name']}` not found in `{self.model}`, using `{info['name']}`")
-
-        logger.info(f"Loading local model `{info['name']}` from `{self.model}` with device `{config.device}`")
-        logger.debug("如果没配置任何路径的话，正常情况下会自动从 Huggingface 下载模型，如果遇到下载失败，可以尝试使用 HF_MIRROR 环境变量；"
-                    f"如果还是不行，建议手动下载到某个文件夹，比如  {os.getenv('MODEL_DIR', '/models')}/BAAI/bge-m3 目录下；")
-
-        self.model = HuggingFaceEmbeddings(
-            model_name=self.model,
-            model_kwargs={'device': config.device},
-            encode_kwargs={
-                'normalize_embeddings': True,
-                'prompt_name': info.get("query_instruction", None),
-            },
-        )
-
-        logger.info(f"Embedding model {info['name']} loaded, {self.model=}")
-
-    def predict(self, message):
-        return self.model.embed_documents(message)
-
-    async def aencode(self, message):
-        return await self.model.aembed_documents(message)
-
-    def encode_queries(self, queries):
-        logger.warning("Huggingface Model 不支持批量 encode queries，因此使用训练实现")
-        data = []
-        for q in queries:
-            data.append(self.predict(q))
-
-        return data
-
-
-class ZhipuEmbedding(BaseEmbeddingModel):
-
-    def __init__(self) -> None:
-        self.config = config
-        self.model = config.embed_model_names[config.embed_model]["name"]
-        self.dimension = config.embed_model_names[config.embed_model]["dimension"]
-        self.client = ZhipuAI(api_key=os.getenv("ZHIPUAI_API_KEY"))
-        self.embed_model_fullname = config.embed_model
-
-    def predict(self, message):
-        response = self.client.embeddings.create(
-            model=self.model,
-            input=message,
-        )
-        data = [a.embedding for a in response.data]
-        return data
-
-
 class OllamaEmbedding(BaseEmbeddingModel):
-    def __init__(self) -> None:
+    """
-        self.info = config.embed_model_names[config.embed_model]
+    Ollama Embedding Model
-        self.model = self.info["name"]
+    """
-        self.url = self.info.get("base_url", "http://localhost:11434/api/embed")
+
-        self.url = get_docker_safe_url(self.url)
+    def __init__(self, model_id) -> None:
-        self.dimension = self.info.get("dimension", None)
+        super().__init__(model_id)
-        self.embed_model_fullname = config.embed_model
+        self.url = self.url or get_docker_safe_url("http://localhost:11434/api/embed")
 
     def predict(self, message: list[str] | str):
         if isinstance(message, str):
@@ -154,14 +90,8 @@ class OllamaEmbedding(BaseEmbeddingModel):
 
 class OtherEmbedding(BaseEmbeddingModel):
 
-    def __init__(self) -> None:
+    def __init__(self, model_id) -> None:
-        self.info = config.embed_model_names[config.embed_model]
+        super().__init__(model_id)
-        self.embed_model_fullname = config.embed_model
-        self.dimension = self.info.get("dimension", None)
-        self.model = self.info["name"]
-        self.api_key = os.getenv(self.info["api_key"], self.info["api_key"])
-        self.url = get_docker_safe_url(self.info["base_url"])
-        assert self.url and self.model, f"URL and model are required. Cur embed model: {config.embed_model}"
         self.headers = {
             "Authorization": f"Bearer {self.api_key}",
             "Content-Type": "application/json"
@@ -181,26 +111,18 @@ class OtherEmbedding(BaseEmbeddingModel):
             "input": message,
         }
 
-def get_embedding_model():
+def get_embedding_model(model_id):
-    provider, model_name = config.embed_model.split('/', 1)
+    provider, model_name = model_id.split('/', 1) if model_id else ("", "")
     support_embed_models = config.embed_model_names.keys()
-    assert config.embed_model in support_embed_models, f"Unsupported embed model: {config.embed_model}, only support {support_embed_models}"
+    assert model_id in support_embed_models, f"Unsupported embed model: {model_id}, only support {support_embed_models}"
-    logger.debug(f"Loading embedding model {config.embed_model}")
+    logger.debug(f"Loading embedding model {model_id}")
     if provider == "local":
-        logger.warning("[DEPRECATED] Local embedding model will be removed in v0.2, please use other embedding models")
+        raise ValueError("Local embedding model is not supported, please use other embedding models")
-        model = LocalEmbeddingModel()
-
-    elif provider == "zhipu":
-        model = ZhipuEmbedding()
 
     elif provider == "ollama":
-        model = OllamaEmbedding()
+        model = OllamaEmbedding(model_id)
 
     else:
-        model = OtherEmbedding()
+        model = OtherEmbedding(model_id)
 
     return model
-
-def handle_local_model(paths, model_name, default_path):
-    model_path = paths.get(model_name, default_path)
-    return model_path

src/models/rerank_model.py

@@ -2,34 +2,18 @@ import os
 import json
 import requests
 import numpy as np
-from FlagEmbedding import FlagReranker
 
 from src import config
 from src.utils import logger, get_docker_safe_url
 
-
-class LocalReranker(FlagReranker):
-    def __init__(self, **kwargs):
-        model_info = config.reranker_names[config.reranker]
-        model_name_or_path = config.model_local_paths.get(model_info["name"], model_info.get("local_path"))
-        model_name_or_path = model_name_or_path or model_info["name"]
-        logger.info(f"Loading Reranker model {config.reranker} from {model_name_or_path}")
-
-        super().__init__(model_name_or_path, use_fp16=True, device=config.device, **kwargs)
-        logger.info(f"Reranker model {config.reranker} loaded")
-
-
 def sigmoid(x):
     return 1 / (1 + np.exp(-x))
 
-class OnlineRerank:
+class OnlineReranker:
-    def __init__(self, **kwargs):
+    def __init__(self, model_name, api_key, base_url, **kwargs):
-        model_info = config.reranker_names[config.reranker]
+        self.url = get_docker_safe_url(base_url)
-        self.url = get_docker_safe_url(model_info["base_url"])
+        self.model = model_name
-        self.model = model_info["name"]
+        self.api_key = api_key
-
-        api_key = os.getenv(model_info["api_key"], model_info["api_key"])
-        assert api_key, f"{model_info['name']} api_key is required"
         self.headers = {
             "Authorization": f"Bearer {api_key}",
             "Content-Type": "application/json"
@@ -59,15 +43,17 @@ class OnlineRerank:
             "max_chunks_per_doc": max_length,
         }
 
-def get_reranker():
+def get_reranker(model_id, **kwargs):
     support_rerankers = config.reranker_names.keys()
-    assert config.reranker in support_rerankers, f"Unsupported Reranker: {config.reranker}, only support {support_rerankers}"
+    assert model_id in support_rerankers, f"Unsupported Reranker: {model_id}, only support {support_rerankers}"
-    provider, model_name = config.reranker.split('/', 1)
-    if provider == "local":
-        logger.warning("[DEPRECATED] Local reranker will be removed in v0.2, please use other reranker")
-        return LocalReranker()
-    elif provider == "siliconflow":
-        return OnlineRerank()
-    else:
-        raise ValueError(f"Unsupported Reranker: {config.reranker}, only support {config.reranker_names.keys()}")
 
+    model_info = config.reranker_names[model_id]
+    base_url = model_info["base_url"]
+    api_key = os.getenv(model_info["api_key"], model_info["api_key"])
+    assert api_key, f"{model_info['name']} api_key is required"
+    return OnlineReranker(
+        model_name=model_info["name"],
+        api_key=api_key,
+        base_url=base_url,
+        **kwargs
+    )

src/static/models.yaml

@@ -6,12 +6,11 @@
 #
 #####################################################
 
-
-
 MODEL_NAMES:
   openai:
     name: OpenAI
     url: https://platform.openai.com/docs/models
+    base_url: https://api.openai.com/v1
     default: gpt-3.5-turbo
     env:
       - OPENAI_API_KEY
@@ -20,21 +19,23 @@ MODEL_NAMES:
       - gpt-4o
       - gpt-4o-mini
       - gpt-3.5-turbo
+
   deepseek:
     name: DeepSeek
     url: https://platform.deepseek.com/api-docs/zh-cn/pricing
-    default: deepseek-chat
     base_url: https://api.deepseek.com/v1
+    default: deepseek-chat
     env:
       - DEEPSEEK_API_KEY
     models:
       - deepseek-chat
       - deepseek-reasoner
+
   zhipu:
     name: 智谱AI (Zhipu)
     url: https://open.bigmodel.cn/dev/api
-    default: glm-4-flash
     base_url: https://open.bigmodel.cn/api/paas/v4/
+    default: glm-4-flash
     env:
       - ZHIPUAI_API_KEY
     models:
@@ -43,17 +44,19 @@ MODEL_NAMES:
       - glm-4-air
       - glm-4-flash
       - glm-z1-air
+
   siliconflow:
     name: SiliconFlow
     url: https://cloud.siliconflow.cn/models
-    default: Qwen/Qwen2.5-7B-Instruct
     base_url: https://api.siliconflow.cn/v1
+    default: Qwen/Qwen3-8B
     env:
       - SILICONFLOW_API_KEY
     models:
       - Pro/deepseek-ai/DeepSeek-R1
       - Pro/deepseek-ai/DeepSeek-V3
       - Qwen/QwQ-32B
+      - Qwen/Qwen3-8B
 
   together.ai:
     name: Together.ai
@@ -71,8 +74,8 @@ MODEL_NAMES:
   dashscope:
     name: 阿里百炼 (DashScope)
     url: https://bailian.console.aliyun.com/?switchAgent=10226727&productCode=p_efm#/model-market
-    default: qwen3-235b-a22b
     base_url: https://dashscope.aliyuncs.com/compatible-mode/v1
+    default: qwen3-235b-a22b
     env:
       - DASHSCOPE_API_KEY
     models:
@@ -83,8 +86,8 @@ MODEL_NAMES:
   ark:
     name: 豆包（Ark）
     url: https://console.volcengine.com/ark/region:ark+cn-beijing/model
-    default: doubao-1-5-pro-32k-250115
     base_url: https://ark.cn-beijing.volces.com/api/v3
+    default: doubao-1-5-pro-32k-250115
     env:
       - ARK_API_KEY
     models:

web/src/components/ModelProvidersComponent.vue

@@ -15,9 +15,8 @@
                 okText="确认删除"
                 cancelText="取消"
                 ok-type="danger"
-                :disabled="configStore.config.model_name == item.name"
               >
-                <a-button type="text" :disabled="configStore.config.model_name == item.name" @click.stop><DeleteOutlined /></a-button>
+                <a-button type="text" @click.stop><DeleteOutlined /></a-button>
               </a-popconfirm>
               <a-button type="text" @click.stop="prepareToEditCustomModel(item)"><EditOutlined /></a-button>
             </div>

web/src/views/DataBaseInfoView.vue

@@ -15,7 +15,6 @@
       </a-button>
     </template>
   </HeaderComponent>
-  <!-- <a-alert v-if="configStore.config.embed_model &&database.embed_model != configStore.config.embed_model" message="向量模型不匹配，请重新选择" type="warning" style="margin: 10px 20px;" /> -->
 
   <!-- 添加编辑对话框 -->
   <a-modal v-model:open="editModalVisible" title="编辑知识库信息">
@@ -282,12 +281,12 @@
                     <a-switch v-model:checked="meta.only_need_context" />
                   </div>
                   <div class="params-item">
-                    <p>只使用上下文：</p>
+                    <p>只使用提示：</p>
                     <a-switch v-model:checked="meta.only_need_prompt" />
                   </div>
                   <div class="params-item">
                     <p>筛选 TopK：</p>
-                    <a-input-number size="small" v-model:value="meta.top_k" :min="1" :max="meta.maxQueryCount" />
+                    <a-input-number size="small" v-model:value="meta.top_k" :min="1" :max="100" />
                   </div>
                 </div>
                 <div class="params-group">
@@ -349,7 +348,6 @@ import {
   CloseCircleFilled,
   ClockCircleFilled,
   DeleteOutlined,
-  CloudUploadOutlined,
   SearchOutlined,
   LoadingOutlined,
   FileOutlined,
@@ -357,7 +355,6 @@ import {
   EditOutlined,
   PlusOutlined,
   SettingOutlined,
-  ApartmentOutlined,
 } from '@ant-design/icons-vue'
 import HeaderComponent from '@/components/HeaderComponent.vue';
 import KnowledgeGraphViewer from '@/components/KnowledgeGraphViewer.vue';
@@ -376,7 +373,6 @@ const selectedFile = ref(null);
 // 查询测试
 const queryText = ref('');
 const queryResult = ref(null)
-const filteredResults = ref([])
 const configStore = useConfigStore()
 
 const state = reactive({
@@ -431,49 +427,7 @@ const pagination = ref({
   onShowSizeChange: (current, pageSize) => pagination.value.pageSize = pageSize,
 })
 
-const filterQueryResults = () => {
-  if (!queryResult.value || !queryResult.value.all_results) {
-    return;
-  }
-
-  let results = toRaw(queryResult.value.all_results);
-  console.log("results", results);
-
-  if (meta.filter) {
-    results = results.filter(r => r.distance >= meta.distanceThreshold);
-    console.log("before", results);
-
-    // 根据排序方式决定排序逻辑
-    if (configStore.config.enable_reranker) {
-      // 先过滤掉低于阈值的结果
-      results = results.filter(r => r.rerank_score >= meta.rerankThreshold);
-
-      // 根据选择的排序方式进行排序
-      if (meta.sortBy === 'rerank_score') {
-        results = results.sort((a, b) => b.rerank_score - a.rerank_score);
-      } else {
-        // 按距离排序 (数值越大表示越相似)
-        results = results.sort((a, b) => b.distance - a.distance);
-      }
-    } else {
-      // 没有启用重排序时，默认按距离排序
-      results = results.sort((a, b) => b.distance - a.distance);
-    }
-
-    console.log("after", results);
-
-    results = results.slice(0, meta.topK);
-  }
-
-  filteredResults.value = results;
-}
-
 const onQuery = () => {
-  // if (database.value.embed_model != configStore.config.embed_model) {
-  //   message.error('向量模型不匹配，请重新选择')
-  //   return
-  // }
-
   console.log(queryText.value)
   state.searchLoading = true
   if (!queryText.value.trim()) {
@@ -491,7 +445,6 @@ const onQuery = () => {
     .then(data => {
       console.log(data)
       queryResult.value = data
-      filterQueryResults()
     })
     .catch(error => {
       console.error(error)
@@ -665,7 +618,7 @@ const getDatabaseInfo = () => {
 const deleteFile = (fileId) => {
   state.lock = true
   console.debug("deleteFile", databaseId.value, fileId)
-  knowledgeBaseApi.deleteFile(databaseId.value, fileId)
+  return knowledgeBaseApi.deleteFile(databaseId.value, fileId)
     .then(data => {
       console.log(data)
       message.success(data.message || '删除成功')
@@ -674,6 +627,7 @@ const deleteFile = (fileId) => {
     .catch(error => {
       console.error(error)
       message.error(error.message || '删除失败')
+      throw error
     })
     .finally(() => {
       state.lock = false
@@ -802,27 +756,7 @@ const addFiles = (items, contentType = 'file') => {
   });
 };
 
-// "生成分块" - 修改后的逻辑（文件）
-const handleFiles = () => {
-  console.log(fileList.value);
-  const files = fileList.value.filter(file => file.status === 'done').map(file => file.response.file_path);
-  console.log(files);
-  addFiles(files, 'file');
-}
 
-// "生成分块" - 修改后的逻辑（URL）
-const handleUrls = () => {
-  const urls = urlList.value.split('\n')
-    .map(url => url.trim())
-    .filter(url => url.length > 0 && (url.startsWith('http://') || url.startsWith('https://')));
-
-  if (urls.length === 0) {
-    message.error('请输入有效的网页链接（必须以http://或https://开头）');
-    return;
-  }
-
-  addFiles(urls, 'url');
-};
 
 const columns = [
   // { title: '文件ID', dataIndex: 'file_id', key: 'file_id' },
@@ -843,10 +777,6 @@ watch(() => route.params.database_id, (newId) => {
   }
 );
 
-// 检测到 meta 变化时重新查询
-watch(() => meta, () => {
-  filterQueryResults()
-}, { deep: true })
 
 // 添加更多示例查询
 const queryExamples = ref([
@@ -875,9 +805,20 @@ const urlList = ref('');
 
 const chunkData = () => {
   if (uploadMode.value === 'file') {
-    handleFiles();
+    const files = fileList.value.filter(file => file.status === 'done').map(file => file.response.file_path);
+    console.log(files);
+    addFiles(files, 'file');
   } else if (uploadMode.value === 'url') {
-    handleUrls();
+    const urls = urlList.value.split('\n')
+      .map(url => url.trim())
+      .filter(url => url.length > 0 && (url.startsWith('http://') || url.startsWith('https://')));
+
+    if (urls.length === 0) {
+      message.error('请输入有效的网页链接（必须以http://或https://开头）');
+      return;
+    }
+
+    addFiles(urls, 'url');
   }
 }
 
@@ -1494,59 +1435,9 @@ const toggleAutoRefresh = (checked) => {
   }
 }
 
-.chunk-preview {
-  margin-top: 20px;
 
-  .preview-header {
-    display: flex;
-    justify-content: space-between;
-    align-items: center;
-    margin-bottom: 16px;
 
-    h3 {
-      margin: 0;
-      color: var(--main-color);
-      font-size: 18px;
-    }
-  }
 
-  .result-cards {
-    display: grid;
-    grid-template-columns: repeat(auto-fill, minmax(600px, 1fr));
-    gap: 12px;
-    margin-top: 10px;
-  }
-
-  .chunk {
-    background-color: var(--main-light-5);
-    border: 1px solid var(--main-light-3);
-    border-radius: 8px;
-    padding: 16px;
-    word-wrap: break-word;
-    word-break: break-all;
-    transition: all 0.2s ease;
-
-    &:hover {
-      background-color: var(--main-light-4);
-      border-color: var(--main-light-2);
-      box-shadow: 0 2px 6px rgba(0, 0, 0, 0.06);
-    }
-
-    p {
-      margin: 0;
-      line-height: 1.6;
-
-      strong {
-        color: var(--main-color);
-        margin-right: 6px;
-      }
-    }
-  }
-}
-
-.url-input {
-  margin-bottom: 20px;
-}