fix: 更新嵌入模型配置，修复相关信息获取逻辑

- 修改数据路由器中的嵌入模型信息获取方式，确保使用正确的配置项。
- 在 LightRagBasedKB 类中，增强 LLM 和嵌入函数的获取逻辑，支持动态配置。
- 更新嵌入模型和重排序模型的 URL 获取方式，统一为 base_url。
- 前端数据库视图中，调整嵌入模型选择的样式和信息展示，提升用户体验。

server/routers/data_router.py

@@ -29,7 +29,7 @@ async def create_database(
 ):
     logger.debug(f"Create database {database_name}")
     try:
-        embed_info = config.embed_model_info[embed_model_name]
+        embed_info = config.embed_model_names[embed_model_name]
         database_info = knowledge_base.create_database(
             database_name,
             description,

src/core/lightrag_based_kb.py

@@ -76,6 +76,11 @@ class LightRagBasedKB:
         if db_id not in self.databases_meta:
             return None
 
+        llm_info = self.databases_meta[db_id].get("llm_info", {})
+        embed_info = self.databases_meta[db_id].get("embed_info", {})
+        logger.info(f"LLM info: {llm_info}")
+        logger.info(f"Embed info: {embed_info}")
+
         # 创建 LightRAG 实例
         working_dir = os.path.join(self.work_dir, db_id)
         os.makedirs(working_dir, exist_ok=True)
@@ -84,8 +89,8 @@ class LightRagBasedKB:
             # 使用配置的 LLM 和 embedding 函数
             rag = LightRAG(
                 working_dir=working_dir,
-                llm_model_func=self._get_llm_func(),
+                llm_model_func=self._get_llm_func(llm_info),
-                embedding_func=self._get_embedding_func(),
+                embedding_func=self._get_embedding_func(embed_info),
                 vector_storage="MilvusVectorDBStorage",
                 kv_storage="JsonKVStorage",
                 graph_storage="PGGraphStorage",
@@ -110,30 +115,40 @@ class LightRagBasedKB:
         await rag.initialize_storages()
         await initialize_pipeline_status()
 
-    def _get_llm_func(self):
+    def _get_llm_func(self, llm_info: dict):
         """获取 LLM 函数"""
+        llm_info = llm_info | {
+            "model_name": "qwen3-1.7b",
+            "provider": "dashscope"
+        }
+        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"
+        base_url = get_docker_safe_url(provider_info.get("base_url", "http://localhost:8081/v1"))
         async def llm_model_func(prompt, system_prompt=None, history_messages=[], **kwargs):
             return await openai_complete_if_cache(
-                "qwen3:32b",
+                llm_info.get("model_name"),
                 prompt,
                 system_prompt=system_prompt,
                 history_messages=history_messages,
-                api_key="no_api_key",
+                api_key=api_key,
-                base_url="http://172.19.13.7:8080/v1",
+                base_url=base_url,
+                extra_body={"enable_thinking": False},
                 **kwargs,
             )
         return llm_model_func
 
-    def _get_embedding_func(self):
+    def _get_embedding_func(self, embed_info: dict):
         """获取 embedding 函数"""
+        api_key = os.getenv(embed_info.get("api_key", "OPENAI_API_KEY")) or "no_api_key"
+        base_url = embed_info.get("base_url", "http://localhost:8081/v1").replace("/embeddings", "")
         return EmbeddingFunc(
-            embedding_dim=1024,
+            embedding_dim=embed_info.get("dimension") or 1024,
             max_token_size=4096,
             func=lambda texts: openai_embed(
                 texts=texts,
-                model="Qwen3-Embedding-0.6B",
+                model=embed_info.get("model_name") or "Qwen3-Embedding-0.6B",
-                api_key="no_api_key",
+                api_key=api_key,
-                base_url=get_docker_safe_url("http://localhost:8081/v1")
+                base_url=get_docker_safe_url(base_url)
             ),
         )
 
@@ -248,6 +263,7 @@ class LightRagBasedKB:
 
     def delete_database(self, db_id):
         """删除数据库 - data_router.py 使用"""
+        # TODO 删除数据库时，需要删除文件记录，并删除 LightRAG 中的文件
         if db_id in self.databases_meta:
             # 删除相关文件记录
             files_to_delete = [fid for fid, finfo in self.files_meta.items()
@@ -377,6 +393,7 @@ class LightRagBasedKB:
 
     async def delete_file(self, db_id, file_id):
         """删除文件 - data_router.py 使用"""
+        # TODO 删除文件时，需要删除文件记录，并删除 LightRAG 中的文件
         rag = await self._get_lightrag_instance(db_id)
         if rag:
             try:

src/models/embedding.py

@@ -133,7 +133,7 @@ class OllamaEmbedding(BaseEmbeddingModel):
     def __init__(self) -> None:
         self.info = config.embed_model_names[config.embed_model]
         self.model = self.info["name"]
-        self.url = self.info.get("url", "http://localhost:11434/api/embed")
+        self.url = self.info.get("base_url", "http://localhost:11434/api/embed")
         self.url = get_docker_safe_url(self.url)
         self.dimension = self.info.get("dimension", None)
         self.embed_model_fullname = config.embed_model
@@ -160,7 +160,7 @@ class OtherEmbedding(BaseEmbeddingModel):
         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["url"])
+        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}",

src/models/rerank_model.py

@@ -25,7 +25,7 @@ def sigmoid(x):
 class OnlineRerank:
     def __init__(self, **kwargs):
         model_info = config.reranker_names[config.reranker]
-        self.url = get_docker_safe_url(model_info["url"])
+        self.url = get_docker_safe_url(model_info["base_url"])
         self.model = model_info["name"]
 
         api_key = os.getenv(model_info["api_key"], model_info["api_key"])

src/static/models.yaml

@@ -111,44 +111,36 @@ MODEL_NAMES:
 
 
 EMBED_MODEL_INFO:
-  zhipu/zhipu-embedding-2:
-    name: embedding-2
-    dimension: 1024
-
-  zhipu/zhipu-embedding-3:
-    name: embedding-3
-    dimension: 2048
-
   siliconflow/BAAI/bge-m3:
     name: BAAI/bge-m3
     dimension: 1024
-    url: https://api.siliconflow.cn/v1/embeddings
+    base_url: https://api.siliconflow.cn/v1/embeddings
     api_key: SILICONFLOW_API_KEY
 
   vllm/Qwen/Qwen3-Embedding-0.6B:
     name: Qwen3-Embedding-0.6B
     dimension: 1024
-    url: http://localhost:8081/v1/embeddings
+    base_url: http://localhost:8081/v1/embeddings
     api_key: no_api_key
 
   ollama/nomic-embed-text:
     name: nomic-embed-text
-    url: http://localhost:11434/api/embed
+    base_url: http://localhost:11434/api/embed
     dimension: 768
 
   ollama/bge-m3:
     name: bge-m3
-    url: http://localhost:11434/api/embed
+    base_url: http://localhost:11434/api/embed
     dimension: 1024
 
 RERANKER_LIST:
 
   siliconflow/BAAI/bge-reranker-v2-m3:
     name: BAAI/bge-reranker-v2-m3
-    url: https://api.siliconflow.cn/v1/rerank
+    base_url: https://api.siliconflow.cn/v1/rerank
     api_key: SILICONFLOW_API_KEY
 
   vllm/Qwen/Qwen3-Reranker-0.6B:
     name: Qwen/Qwen3-Reranker-0.6B
-    url: http://localhost:8081/v1/rerank
+    base_url: http://localhost:8081/v1/rerank
     api_key: no_api_key

web/src/views/DataBaseView.vue

@@ -8,11 +8,11 @@
       </template>
     </HeaderComponent>
 
-    <a-modal :open="state.openNewDatabaseModel" title="新建知识库" @ok="createDatabase" @cancel="cancelCreateDatabase">
+    <a-modal :open="state.openNewDatabaseModel" title="新建知识库" @ok="createDatabase" @cancel="cancelCreateDatabase" class="new-database-modal">
       <h3>知识库名称<span style="color: var(--error-color)">*</span></h3>
       <a-input v-model:value="newDatabase.name" placeholder="新建知识库名称" />
       <h3>嵌入模型</h3>
-      <a-select v-model:value="newDatabase.embed_model_name" :options="embedModelOptions" />
+      <a-select v-model:value="newDatabase.embed_model_name" :options="embedModelOptions" style="width: 100%;" />
       <h3 style="margin-top: 20px;">知识库描述</h3>
       <p style="color: var(--gray-700); font-size: 14px;">在智能体流程中，这里的描述会作为工具的描述。智能体会根据知识库的标题和描述来选择合适的工具。所以这里描述的越详细，智能体越容易选择到合适的工具。</p>
       <a-textarea
@@ -85,8 +85,8 @@ const state = reactive({
 })
 
 const embedModelOptions = computed(() => {
-  return Object.keys(configStore.config?.embed_model_info || {}).map(key => ({
+  return Object.keys(configStore.config?.embed_model_names || {}).map(key => ({
-    label: configStore.config?.embed_model_info[key]?.name || key,
+    label: `${key} (${configStore.config?.embed_model_names[key]?.dimension})`,
     value: key,
   }))
 })
@@ -94,7 +94,7 @@ const embedModelOptions = computed(() => {
 const emptyEmbedInfo = {
   name: '',
   description: '',
-  embed_model_name: '',
+  embed_model_name: configStore.config?.embed_model,
 }
 
 const newDatabase = reactive({
@@ -270,4 +270,10 @@ onMounted(() => {
 .database-container {
   padding: 0;
 }
+
+.new-database-modal {
+  h3 {
+    margin-top: 10px;
+  }
+}
 </style>