Merge pull request #29 from xerrors/dev

feat: 添加了基于 tavil 的 web 搜索

.gitignore

@@ -35,4 +35,7 @@ web/package-lock.json
 saves
 notebooks
 graphrag
-docker/volumes
+docker/volumes
+
+
+.cursorrules

requirements.txt

@@ -24,4 +24,5 @@ opencv-python-headless
 docx2txt
 uvicorn[standard]
 fastapi
-python-multipart
+python-multipart
+tavily-python

src/config/__init__.py

@@ -17,7 +17,7 @@ RERANKER_LIST = _models["RERANKER_LIST"]
 class SimpleConfig(dict):
 
     def __key(self, key):
-        return "" if key is None else key.lower()
+        return "" if key is None else key.lower()  # 目前忘记了这里为什么要 lower 了，只能说配置项最好不要有大写的
 
     def __str__(self):
         return json.dumps(self)
@@ -53,7 +53,7 @@ class Config(SimpleConfig):
         self.add_item("enable_knowledge_base", default=False, des="是否开启知识库")
         self.add_item("enable_knowledge_graph", default=False, des="是否开启知识图谱")
         self.add_item("enable_search_engine", default=False, des="是否开启搜索引擎")
-
+        self.add_item("enable_web_search", default=False, des="是否开启网页搜索")
         # 模型配置
         ## 注意这里是模型名，而不是具体的模型路径，默认使用 HuggingFace 的路径
         ## 如果需要自定义路径，则在 config/base.yaml 中配置 model_local_paths
@@ -61,6 +61,7 @@ class Config(SimpleConfig):
         self.add_item("model_name", default=None, des="模型名称")
         self.add_item("embed_model", default="zhipu-embedding-3", des="Embedding 模型", choices=list(EMBED_MODEL_INFO.keys()))
         self.add_item("reranker", default="bge-reranker-v2-m3", des="Re-Ranker 模型", choices=list(RERANKER_LIST.keys()))
+        self.add_item("use_rewrite_query", default="off", des="重写查询", choices=["off", "on", "hyde"])
         self.add_item("model_local_paths", default={}, des="本地模型路径")
         ### <<< 默认配置结束
 
@@ -113,7 +114,7 @@ class Config(SimpleConfig):
         self.valuable_model_provider = [k for k, v in self.model_provider_status.items() if v]
         assert len(self.valuable_model_provider) > 0, f"No model provider available, please check your `.env` file. API_KEY_LIST: {conds}"
 
-        
+
 
     def load(self):
         """根据传入的文件覆盖掉默认配置"""

src/config/base.yaml

@@ -0,0 +1,17 @@
+# 基础配置
+stream: true
+save_dir: saves
+
+# 功能开关
+enable_reranker: false
+enable_knowledge_base: false
+enable_knowledge_graph: false
+enable_search_engine: false
+enable_web_search: false
+
+# 模型配置
+model_provider: "deepseek"  # 设置为 deepseek
+model_name: "deepseek-chat"  # 设置默认模型名称
+embed_model: "zhipu-embedding-3"
+reranker: "bge-reranker-v2-m3"
+model_local_paths: {} 

src/config/models.yaml

@@ -18,6 +18,7 @@ MODEL_NAMES:
       - DEEPSEEK_API_KEY
     models:
       - deepseek-chat
+      - deepseek-reasoner
   zhipu:
     name: 智谱AI (Zhipu)
     url: https://open.bigmodel.cn/dev/api
@@ -81,6 +82,10 @@ EMBED_MODEL_INFO:
     default_path: BAAI/bge-large-zh-v1.5
     dimension: 1024
     query_instruction: "为这个句子生成表示以用于检索相关文章："
+  bge-m3:
+    name: bge-m3
+    default_path: BAAI/bge-m3
+    dimension: 1024
   zhipu-embedding-2:
     name: zhipu-embedding-2
     default_path: embedding-2

src/core/retriever.py

@@ -118,7 +118,11 @@ class Retriever:
 
     def rewrite_query(self, query, history, refs):
         """重写查询"""
-        rewrite_query_span = refs["meta"].get("rewriteQuery", "off")
+        if refs["meta"].get("mode") == "search":  # 如果是搜索模式，就使用 meta 的配置，否则就使用全局的配置
+            rewrite_query_span = refs["meta"].get("use_rewrite_query", "off")
+        else:
+            rewrite_query_span = refs["meta"]["config"].get("use_rewrite_query", "off")
+
         if rewrite_query_span == "off":
             rewritten_query = query
         else:

src/core/startup.py

@@ -1,3 +1,4 @@
+import os
 from src.core import DataBaseManager
 from src.core.retriever import Retriever
 from src.models import select_model
@@ -9,10 +10,29 @@ logger = setup_logger("Startup")
 
 class Startup:
     def __init__(self):
+        self.config = Config("config/base.yaml")
+        self._check_environment()
         self.start()
 
+    def _check_environment(self):
+        """检查必要的环境变量"""
+        required_vars = {
+            "zhipu": ["ZHIPUAI_API_KEY"],
+            "openai": ["OPENAI_API_KEY"],
+            "deepseek": ["DEEPSEEK_API_KEY"],
+        }
+        
+        provider = self.config.model_provider
+        if provider in required_vars:
+            missing = [var for var in required_vars[provider] if not os.getenv(var)]
+            if missing:
+                logger.error(f"Missing required environment variables for {provider}: {missing}")
+                raise ValueError(f"Missing required environment variables: {missing}")
+                
+        if self.config.enable_web_search and not os.getenv("TAVILY_API_KEY"):
+            logger.warning("TAVILY_API_KEY not set, web search will be disabled")
+
     def start(self):
-        self.config = Config()
         self.model = select_model(self.config)
         self.dbm = DataBaseManager(self.config)
         self.retriever = Retriever(self.config, self.dbm, self.model)

src/models/__init__.py

@@ -1,46 +1,23 @@
 from src.utils.logging_config import logger
+from src.models.chat_model import OpenModel, DeepSeek, Zhipu, Qianfan, DashScope, SiliconFlow
+from src.models.embedding import get_embedding_model
 
 
 def select_model(config):
-
-    model_provider = config.model_provider
-    model_name = config.model_name
-
-    logger.info(f"Selecting model from {model_provider} with {model_name}")
-
-    if model_provider == "deepseek":
-        from src.models.chat_model import DeepSeek
-        return DeepSeek(model_name)
-
-    elif model_provider == "zhipu":
-        from src.models.chat_model import Zhipu
-        return Zhipu(model_name)
-
-    elif model_provider == "qianfan":
-        from src.models.chat_model import Qianfan
-        return Qianfan(model_name)
-
-    elif model_provider == "dashscope":
-        from src.models.chat_model import DashScope
-        return DashScope(model_name)
-
-    elif model_provider == "openai":
-        from src.models.chat_model import OpenModel
-        return OpenModel(model_name)
-
-    elif model_provider == "siliconflow":
-        from src.models.chat_model import SiliconFlow
-        return SiliconFlow(model_name)
-
-    elif model_provider == "custom":
-        model_info = next((x for x in config.custom_models if x["custom_id"] == model_name), None)
-        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)
-
-    elif model_provider is None:
-        raise ValueError("Model provider not specified, please modify `model_provider` in `src/config/base.yaml`")
+    """
+    根据配置选择模型
+    """
+    if config.model_provider == "deepseek":
+        return DeepSeek(config.model_name)
+    elif config.model_provider == "zhipu":
+        return Zhipu(config.model_name)
+    elif config.model_provider == "openai":
+        return OpenModel(config.model_name)
+    elif config.model_provider == "qianfan":
+        return Qianfan(config.model_name)
+    elif config.model_provider == "dashscope":
+        return DashScope(config.model_name)
+    elif config.model_provider == "siliconflow":
+        return SiliconFlow(config.model_name)
     else:
-        raise ValueError(f"Model provider {model_provider} not supported")
+        raise ValueError(f"Unsupported model provider: {config.model_provider}")

src/models/chat_model.py

@@ -1,6 +1,7 @@
 import os
 from openai import OpenAI
 from src.utils.logging_config import setup_logger
+from zhipuai import ZhipuAI
 
 
 logger = setup_logger(__name__)
@@ -50,8 +51,8 @@ class OpenModel(OpenAIBase):
 class DeepSeek(OpenAIBase):
     def __init__(self, model_name=None):
         model_name = model_name or "deepseek-chat"
-        api_key = os.getenv("DEEPSEEK_API_KEY")
-        base_url = "https://api.deepseek.com"
+        api_key = os.getenv("DEEPSEEK_API_KEY", "your-default-api-key")
+        base_url = os.getenv("DEEPSEEK_API_BASE", "https://api.deepseek.com/v1")
         super().__init__(api_key=api_key, base_url=base_url, model_name=model_name)
 
 
@@ -165,6 +166,14 @@ class DashScope:
         return response.output.choices[0].message
 
 
+class ChatModel:
+    def __init__(self, config):
+        if config.model_provider == "zhipu":
+            self.client = ZhipuAI(api_key=os.getenv("ZHIPUAI_API_KEY"))
+        elif config.model_provider == "openai":
+            self.client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+
+
 if __name__ == "__main__":
     model = SiliconFlow()
     for a in model.predict("你好", stream=True):

src/models/embedding.py

@@ -15,11 +15,7 @@ GLOBAL_EMBED_STATE = {}
 class EmbeddingModel(FlagModel):
     def __init__(self, model_info, config, **kwargs):
         self.info = model_info
-        model_name_or_path = handle_local_model(
-            paths=config.model_local_paths,
-            model_name=model_info["name"],
-            default_path=model_info.get("default_path", None))
-
+        model_name_or_path = config.model_local_paths.get(model_info["name"], model_info.get("default_path", None))
         logger.info(f"Loading embedding model {model_info['name']} from {model_name_or_path}")
 
         super().__init__(model_name_or_path,
@@ -34,11 +30,7 @@ class Reranker(FlagReranker):
 
         assert config.reranker in RERANKER_LIST.keys(), f"Unsupported Reranker: {config.reranker}, only support {RERANKER_LIST.keys()}"
 
-        model_name_or_path = handle_local_model(
-            paths=config.model_local_paths,
-            model_name=config.reranker,
-            default_path=RERANKER_LIST[config.reranker])
-
+        model_name_or_path = config.model_local_paths.get(config.reranker, default_path=RERANKER_LIST[config.reranker])
         logger.info(f"Loading Reranker model {config.reranker} from {model_name_or_path}")
 
         super().__init__(model_name_or_path, use_fp16=True, **kwargs)
@@ -113,6 +105,4 @@ def get_embedding_model(config):
 
 def handle_local_model(paths, model_name, default_path):
     model_path = paths.get(model_name, default_path)
-    if os.getenv("MODEL_ROOT_DIR") and not os.path.isabs(model_path):
-        model_path = os.path.join(os.getenv("MODEL_ROOT_DIR"), model_path)
     return model_path

src/models/ollama_embedding.py

@@ -0,0 +1,179 @@
+import os
+import requests
+import numpy as np
+from typing import List, Union, Dict
+from src.utils.logging_config import setup_logger
+
+logger = setup_logger("OllamaEmbedding")
+
+class OllamaEmbedding:
+    """
+    使用 Ollama API 进行文本嵌入的类
+    """
+    def __init__(self, model_info: Dict, config) -> None:
+        """
+        初始化 Ollama Embedding 模型
+        
+        Args:
+            model_info: 模型信息字典
+            config: 配置对象
+        """
+        self.config = config
+        self.model_info = model_info
+        self.base_url = os.getenv("OLLAMA_BASE_URL", "http://localhost:11434")
+        self.model_name = model_info.get("name", "nomic-embed-text")
+        self.query_instruction_for_retrieval = "为这个句子生成表示以用于检索相关文章："
+        logger.info(f"Ollama Embedding model {self.model_name} initialized")
+
+    def _get_embedding(self, text: str) -> List[float]:
+        """
+        获取单个文本的嵌入向量
+        
+        Args:
+            text: 输入文本
+            
+        Returns:
+            嵌入向量
+        """
+        url = f"{self.base_url}/api/embeddings"
+        try:
+            response = requests.post(url, json={
+                "model": self.model_name,
+                "prompt": text
+            })
+            response.raise_for_status()
+            return response.json()["embedding"]
+        except Exception as e:
+            logger.error(f"Error getting embedding: {str(e)}")
+            raise
+
+    def predict(self, messages: List[str]) -> List[List[float]]:
+        """
+        批量获取文本嵌入向量
+        
+        Args:
+            messages: 文本列表
+            
+        Returns:
+            嵌入向量列表
+        """
+        embeddings = []
+        batch_size = 20
+
+        for i in range(0, len(messages), batch_size):
+            batch = messages[i:i + batch_size]
+            logger.info(f"Processing batch {i//batch_size + 1}, size: {len(batch)}")
+            
+            batch_embeddings = []
+            for text in batch:
+                embedding = self._get_embedding(text)
+                batch_embeddings.append(embedding)
+            
+            embeddings.extend(batch_embeddings)
+            
+        return embeddings
+
+    def encode(self, messages: Union[str, List[str]]) -> List[List[float]]:
+        """
+        编码文本
+        
+        Args:
+            messages: 单个文本或文本列表
+            
+        Returns:
+            嵌入向量列表
+        """
+        if isinstance(messages, str):
+            messages = [messages]
+        return self.predict(messages)
+
+    def encode_queries(self, queries: List[str]) -> List[List[float]]:
+        """
+        编码查询文本
+        
+        Args:
+            queries: 查询文本列表
+            
+        Returns:
+            查询文本的嵌入向量列表
+        """
+        return self.predict(queries)
+
+
+class OllamaReranker:
+    """
+    使用 Ollama API 进行文本重排序的类
+    """
+    def __init__(self, config) -> None:
+        """
+        初始化 Ollama Reranker
+        
+        Args:
+            config: 配置对象
+        """
+        self.config = config
+        self.base_url = os.getenv("OLLAMA_BASE_URL", "http://localhost:11434")
+        self.model_name = config.reranker
+        logger.info(f"Ollama Reranker model {self.model_name} initialized")
+
+    def compute_score(self, query: str, passage: str) -> float:
+        """
+        计算查询和文本段落之间的相关性分数
+        
+        Args:
+            query: 查询文本
+            passage: 段落文本
+            
+        Returns:
+            相关性分数
+        """
+        prompt = f"Query: {query}\nPassage: {passage}\nRate the relevance of the passage to the query on a scale of 0 to 1:"
+        
+        try:
+            response = requests.post(
+                f"{self.base_url}/api/generate",
+                json={
+                    "model": self.model_name,
+                    "prompt": prompt,
+                    "stream": False
+                }
+            )
+            response.raise_for_status()
+            
+            # 提取生成的数字作为分数
+            result = response.json()["response"].strip()
+            try:
+                score = float(result)
+                return min(max(score, 0), 1)  # 确保分数在 0-1 之间
+            except ValueError:
+                logger.warning(f"Could not parse score from response: {result}")
+                return 0.0
+                
+        except Exception as e:
+            logger.error(f"Error computing rerank score: {str(e)}")
+            return 0.0
+
+    def rerank(self, query: str, passages: List[str], top_n: int = None) -> List[Dict]:
+        """
+        重新排序文本段落
+        
+        Args:
+            query: 查询文本
+            passages: 段落文本列表
+            top_n: 返回前 n 个结果
+            
+        Returns:
+            排序后的结果列表，每个元素包含索引和分数
+        """
+        scores = []
+        for i, passage in enumerate(passages):
+            score = self.compute_score(query, passage)
+            scores.append({"index": i, "score": score})
+        
+        # 按分数降序排序
+        sorted_results = sorted(scores, key=lambda x: x["score"], reverse=True)
+        
+        if top_n:
+            sorted_results = sorted_results[:top_n]
+            
+        return sorted_results 

src/plugins/oneke.py

@@ -16,8 +16,6 @@ logger = setup_logger("OneKE")
 
 dotenv.load_dotenv()
 
-MODEL_NAME_OR_PATH = os.path.join(os.getenv('MODEL_ROOT_DIR', './'), 'OneKE')
-
 instruction_mapper = {
     'NERzh': "你是专门进行实体抽取的专家。请从input中抽取出符合schema定义的实体，不存在的实体类型返回空列表。请按照JSON字符串的格式回答。",
     'REzh': "你是专门进行关系抽取的专家。请从input中抽取出符合schema定义的关系三元组。请按照JSON字符串的格式回答。",
@@ -42,7 +40,7 @@ class OneKE:
     def __init__(self, config=None):
 
         self.config = config
-        model_name_or_path = config.model_local_paths.get('oneke', "zjunlp/OneKE")
+        model_name_or_path = config.model_local_paths.get('zjunlp/OneKE', "zjunlp/OneKE")
         logger.info(f"Loading KGC model OneKE from {model_name_or_path}")
 
         model_config = AutoConfig.from_pretrained(model_name_or_path, trust_remote_code=True)

src/routers/chat_router.py

@@ -6,6 +6,7 @@ from concurrent.futures import ThreadPoolExecutor
 from src.core import HistoryManager
 from src.core.startup import startup
 from src.utils.logging_config import setup_logger
+from src.utils.web_search import WebSearcher
 
 chat = APIRouter(prefix="/chat")
 logger = setup_logger("server-chat")
@@ -13,6 +14,7 @@ logger = setup_logger("server-chat")
 executor = ThreadPoolExecutor()
 
 refs_pool = {}
+web_searcher = WebSearcher()
 
 @chat.get("/")
 async def chat_get():
@@ -37,19 +39,50 @@ def chat_post(
         }, ensure_ascii=False).encode('utf-8') + b"\n"
 
     def generate_response():
+        modified_query = query
+        
+        # 处理网页搜索
+        if meta and meta.get("enable_web_search"):
+            chunk = make_chunk("正在进行网络搜索...", "searching", history=None)
+            yield chunk
 
-        if meta.get("enable_retrieval"):
+            try:
+                search_results = web_searcher.search(query)
+                if search_results:
+                    search_context = web_searcher.format_search_results(search_results)
+                    # 将搜索结果添加到查询中
+                    modified_query = f"""基于以下网络搜索结果回答问题：
+
+                    {search_context}
+
+                    用户问题：{query}
+
+                    请综合以上搜索结果，给出准确、客观的回答。如果搜索结果与问题相关性不大，请直接基于你的知识回答。
+                    """
+                    logger.info(f"Web search results added to query")
+                else:
+                    logger.warning("No web search results found")
+            except Exception as e:
+                logger.error(f"Web search error: {str(e)}")
+                chunk = make_chunk("网络搜索失败，将直接回答问题。", "loading", history=None)
+                yield chunk
+
+        # 处理知识库检索
+        if meta and meta.get("enable_retrieval"):
             chunk = make_chunk("", "searching", history=None)
             yield chunk
 
+<<<<<<< HEAD
+            meta["config"] = startup.config
             new_query, refs = startup.retriever(query, history_manager.messages, meta)
+=======
+            modified_query, refs = startup.retriever(modified_query, history_manager.messages, meta)
+>>>>>>> feature/web_search
             refs_pool[cur_res_id] = refs
-        else:
-            new_query = query
 
-        messages = history_manager.get_history_with_msg(new_query, max_rounds=meta.get('history_round'))
-        history_manager.add_user(query)
-        logger.debug(f"Web history: {history_manager.messages}")
+        messages = history_manager.get_history_with_msg(modified_query, max_rounds=meta.get('history_round'))
+        history_manager.add_user(query)  # 注意这里使用原始查询
+        logger.debug(f"Final query: {modified_query}")
 
         content = ""
         for delta in startup.model.predict(messages, stream=True):

src/utils/web_search.py

@@ -0,0 +1,69 @@
+import os
+from typing import List, Dict
+from tavily import TavilyClient
+from src.utils.logging_config import setup_logger
+
+logger = setup_logger("web-search")
+
+class WebSearcher:
+    def __init__(self):
+        api_key = os.getenv("TAVILY_API_KEY", "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")
+        if not api_key:
+            raise ValueError("TAVILY_API_KEY environment variable is not set")
+        self.client = TavilyClient(api_key)
+        logger.info("WebSearcher initialized with Tavily client")
+
+    def search(self, query: str, max_results: int = 1) -> List[Dict]:
+        """
+        使用 Tavily 搜索相关内容
+        
+        Args:
+            query: 搜索查询
+            max_results: 最大返回结果数
+            
+        Returns:
+            搜索结果列表
+        """
+        try:
+            search_results = self.client.search(
+                query=query,
+                search_depth="basic",
+                max_results=max_results
+            )
+            
+            # 提取需要的信息
+            formatted_results = []
+            for result in search_results['results'][:max_results]:
+                formatted_results.append({
+                    'title': result.get('title', ''),
+                    'content': result.get('content', ''),
+                    'url': result.get('url', ''),
+                    'score': result.get('score', 0)
+                })
+                
+            return formatted_results
+            
+        except Exception as e:
+            logger.error(f"Error during web search: {str(e)}")
+            return []
+
+    def format_search_results(self, results: List[Dict]) -> str:
+        """
+        将搜索结果格式化为文本
+        
+        Args:
+            results: 搜索结果列表
+            
+        Returns:
+            格式化后的文本
+        """
+        if not results:
+            return "没有找到相关的网络搜索结果。"
+            
+        formatted_text = "以下是相关的网络搜索结果：\n\n"
+        for i, result in enumerate(results, 1):
+            formatted_text += f"{i}. {result['title']}\n"
+            formatted_text += f"   {result['content']}\n"
+            formatted_text += f"   来源: {result['url']}\n\n"
+            
+        return formatted_text 

web/src/components/ChatComponent.vue

@@ -81,9 +81,12 @@
           <div class="flex-center" @click="meta.use_web = !meta.use_web" v-if="configStore.config.enable_search_engine && meta.enable_retrieval">
             搜索引擎（Bing） <div @click.stop><a-switch v-model:checked="meta.use_web" /></div>
           </div>
-          <div class="flex-center" v-if="configStore.config.enable_knowledge_base && meta.enable_retrieval">
-            重写查询 <a-segmented v-model:value="meta.rewriteQuery" :options="['off', 'on', 'hyde']"/>
+          <div class="flex-center" @click="meta.enable_web_search = !meta.enable_web_search">
+            网页搜索 <div @click.stop><a-switch v-model:checked="meta.enable_web_search" /></div>
           </div>
+          <!-- <div class="flex-center" v-if="configStore.config.enable_knowledge_base && meta.enable_retrieval">
+            重写查询 <a-segmented v-model:value="meta.use_rewrite_query" :options="['off', 'on', 'hyde']"/>
+          </div> -->
         </div>
       </div>
     </div>
@@ -166,6 +169,8 @@ import {
   GlobalOutlined,
   FileTextOutlined,
   RobotOutlined,
+  EditOutlined,
+  PlusOutlined,
 } from '@ant-design/icons-vue'
 import { onClickOutside } from '@vueuse/core'
 import { Marked } from 'marked';
@@ -191,11 +196,7 @@ const panel = ref(null)
 const modelCard = ref(null)
 const examples = ref([
   '写一个冒泡排序',
-  '肉碱的分子量是多少？直接回答',
-  '总结大蒜的功效是什么？',
   '今天天气怎么样？',
-  '吃饭吃出苍蝇可以索赔吗？',
-  '帮我写一个请假条',
   '贾宝玉今年多少岁？',
 ])
 
@@ -210,12 +211,14 @@ const meta = reactive(JSON.parse(localStorage.getItem('meta')) || {
   enable_retrieval: false,
   use_graph: false,
   use_web: false,
+  enable_web_search: false,
   graph_name: "neo4j",
-  rewriteQuery: "off",
+  // use_rewrite_query: "off",
   selectedKB: null,
   stream: true,
   summary_title: true,
   history_round: 5,
+  db_name: null,
 })
 
 const marked = new Marked(
@@ -327,35 +330,26 @@ const appendAiMessage = (message, refs=null) => {
 
 const updateMessage = (info) => {
   const message = conv.value.messages.find((message) => message.id === info.id);
-
   if (message) {
-    // 只有在 text 不为空时更新
-    if (info.text !== null && info.text !== undefined && info.text !== '') {
-      message.text = info.text;
-    }
-
-    // 只有在 refs 不为空时更新
-    if (info.refs !== null && info.refs !== undefined) {
-      message.refs = info.refs;
-    }
-
-    if (info.model_name !== null && info.model_name !== undefined && info.model_name !== '') {
-      message.model_name = info.model_name;
-    }
-
-    // 只有在 status 不为空时更新
-    if (info.status !== null && info.status !== undefined && info.status !== '') {
-      message.status = info.status;
-    }
-
-    if (info.meta !== null && info.meta !== undefined) {
-      message.meta = info.meta;
+    try {
+      if (info.text !== null && info.text !== undefined && info.text !== '') {
+        message.text = info.text;
+      }
+      if (info.status !== null && info.status !== undefined && info.status !== '') {
+        message.status = info.status;
+      }
+      if (info.meta !== null && info.meta !== undefined) {
+        message.meta = info.meta;
+      }
+      scrollToBottom();
+    } catch (error) {
+      console.error('Error updating message:', error);
+      message.status = 'error';
+      message.text = '消息更新失败';
     }
   } else {
-    console.error('Message not found');
+    console.error('Message not found:', info.id);
   }
-
-  scrollToBottom();
 };
 
 
@@ -399,21 +393,21 @@ const loadDatabases = () => {
 }
 
 // 新函数用于处理 fetch 请求
-const fetchChatResponse = (user_input, cur_res_id) => {
+const fetchChatResponse = (requestData) => {
   fetch('/api/chat/', {
     method: 'POST',
-    body: JSON.stringify({
-      query: user_input,
-      history: conv.value.history,
-      meta: meta,
-      cur_res_id: cur_res_id,
-    }),
     headers: {
       'Content-Type': 'application/json'
-    }
+    },
+    body: JSON.stringify(requestData)
   })
-  .then((response) => {
-    if (!response.body) throw new Error("ReadableStream not supported.");
+  .then(response => {
+    if (!response.ok) {
+      throw new Error(`HTTP error! status: ${response.status}`);
+    }
+    if (!response.body) {
+      throw new Error("ReadableStream not supported.");
+    }
     const reader = response.body.getReader();
     const decoder = new TextDecoder("utf-8");
     let buffer = '';
@@ -421,22 +415,22 @@ const fetchChatResponse = (user_input, cur_res_id) => {
     const readChunk = () => {
       return reader.read().then(({ done, value }) => {
         if (done) {
-          const message = conv.value.messages.find((message) => message.id === cur_res_id)
+          const message = conv.value.messages.find((message) => message.id === requestData.cur_res_id)
           console.log(message)
           if (message.meta.enable_retrieval) {
             console.log("fetching refs")
-            fetchRefs(cur_res_id).then((data) => {
+            fetchRefs(requestData.cur_res_id).then((data) => {
               console.log(data)
               updateMessage({
-                id: cur_res_id,
+                id: requestData.cur_res_id,
                 refs: data,
                 status: "finished",
               });
-              groupRefs(cur_res_id);
+              groupRefs(requestData.cur_res_id);
             })
           } else {
             updateMessage({
-              id: cur_res_id,
+              id: requestData.cur_res_id,
               status: "finished",
             });
           }
@@ -455,7 +449,7 @@ const fetchChatResponse = (user_input, cur_res_id) => {
             try {
               const data = JSON.parse(line);
               updateMessage({
-                id: cur_res_id,
+                id: requestData.cur_res_id,
                 text: data.response,
                 model_name: data.model_name,
                 status: data.status,
@@ -482,12 +476,14 @@ const fetchChatResponse = (user_input, cur_res_id) => {
     readChunk();
   })
   .catch((error) => {
-    console.error(error);
+    console.error('Error in fetchChatResponse:', error);
     updateMessage({
-      id: cur_res_id,
+      id: requestData.cur_res_id,
       status: "error",
+      text: `请求错误：${error.message}`,
     });
     isStreaming.value = false;
+    message.error(`请求失败：${error.message}`);
   });
 }
 
@@ -518,9 +514,30 @@ const sendMessage = () => {
     appendAiMessage("", null);
     const cur_res_id = conv.value.messages[conv.value.messages.length - 1].id;
     conv.value.inputText = '';
-    meta.db_name = dbName;
+    
+    // 准备发送的数据
+    const requestData = {
+      query: user_input,
+      history: conv.value.history,
+      cur_res_id: cur_res_id,
+      meta: {
+        enable_retrieval: meta.enable_retrieval,
+        use_graph: meta.use_graph,
+        use_web: meta.use_web,
+        enable_web_search: meta.enable_web_search,
+        graph_name: meta.graph_name,
+        rewriteQuery: meta.rewriteQuery,
+        selectedKB: meta.selectedKB,
+        stream: meta.stream,
+        summary_title: meta.summary_title,
+        history_round: meta.history_round,
+        db_name: dbName,
+      }
+    };
 
-    fetchChatResponse(user_input, cur_res_id)
+    console.log('Sending request with data:', requestData); // 添加日志
+
+    fetchChatResponse(requestData);
   } else {
     console.log('请输入消息');
   }
@@ -648,6 +665,17 @@ watch(
     &:hover {
       background-color: var(--main-light-3);
     }
+
+    .anticon {
+      margin-right: 8px;
+      font-size: 16px;
+    }
+    
+    .ant-switch {
+      &.ant-switch-checked {
+        background-color: var(--main-500);
+      }
+    }
   }
 }
 
@@ -977,7 +1005,15 @@ watch(
   }
 }
 
-
+.controls {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+  
+  .search-switch {
+    margin-right: 8px;
+  }
+}
 </style>
 
 <style lang="less">

web/src/components/RefsComponent.vue

@@ -2,8 +2,8 @@
   <div class="refs" v-if="showRefs">
     <div class="tags">
       <span class="item btn" @click="copyText(msg.text)"><CopyOutlined /></span>
-      <span class="item btn" @click="likeThisResponse(msg)"><LikeOutlined /></span>
-      <span class="item btn" @click="dislikeThisResponse(msg)"><DislikeOutlined /></span>
+      <!-- <span class="item btn" @click="likeThisResponse(msg)"><LikeOutlined /></span> -->
+      <!-- <span class="item btn" @click="dislikeThisResponse(msg)"><DislikeOutlined /></span> -->
       <span class="item"><GlobalOutlined /> {{ msg.model_name }}</span>
       <span
         class="item btn"

web/src/components/TableConfigComponent.vue

@@ -0,0 +1,202 @@
+<template>
+  <a-card class="config-card" style="max-width: 960px">
+    <a-form layout="vertical">
+      <div
+        v-for="(item, index) in configList"
+        :key="index"
+        class="config-item"
+      >
+        <a-row :gutter="[16, 8]" align="middle">
+          <a-col :span="8">
+            <a-input
+              v-model:value="item.key"
+              placeholder="模型名称"
+              readonly
+              class="key-input"
+            />
+          </a-col>
+          <a-col :span="14">
+            <a-input
+              v-model:value="item.value"
+              @change="updateValue(index)"
+              placeholder="模型本地路径"
+              class="value-input"
+            />
+          </a-col>
+          <a-col :span="2" class="delete-btn-col">
+            <a-button
+              type="link"
+              danger
+              class="delete-btn"
+              @click="deleteConfig(index)"
+            >
+              <DeleteOutlined />
+            </a-button>
+          </a-col>
+        </a-row>
+      </div>
+
+      <a-button block @click="addConfig" class="add-btn" :disabled="isAdding">
+        <PlusOutlined /> 添加路径映射
+      </a-button>
+    </a-form>
+
+    <a-modal
+      title="添加路径映射"
+      v-model:visible="addConfigModalVisible"
+      @ok="confirmAddConfig"
+      class="config-modal"
+    >
+      <a-form layout="vertical">
+        <a-form-item label="模型名称（与Huggingface名称一致，比如 BAAI/bge-large-zh-v1.5" required>
+          <a-input
+            v-model:value="newConfig.key"
+            placeholder="请输入模型名称"
+            class="modal-input"
+          />
+        </a-form-item>
+        <a-form-item label="模型本地路径（绝对路径，比如 /hdd/models/BAAI/bge-large-zh-v1.5）" required>
+          <a-input
+            v-model:value="newConfig.value"
+            placeholder="请输入模型本地路径"
+            class="modal-input"
+          />
+        </a-form-item>
+      </a-form>
+    </a-modal>
+  </a-card>
+</template>
+
+<script setup>
+import { ref, reactive, computed, watch } from 'vue';
+import { message } from 'ant-design-vue';
+import { DeleteOutlined, PlusOutlined } from '@ant-design/icons-vue';
+
+const props = defineProps({
+  config: {
+    type: Object,
+    default: () => ({})
+  }
+});
+
+const emit = defineEmits(['update:config']);
+
+// 配置列表
+const configList = reactive([]);
+
+// 初始化配置列表
+props.config && Object.entries(props.config).forEach(([key, value]) => {
+  configList.push({ key, value });
+});
+
+// 控制模态框显示
+const addConfigModalVisible = ref(false);
+
+// 新增配置项数据
+const newConfig = ref({ key: '', value: '' });
+
+// 添加配置
+const addConfig = () => {
+  addConfigModalVisible.value = true;
+};
+
+// 确认添加配置
+const confirmAddConfig = () => {
+  if (newConfig.value.key === '' || newConfig.value.value === '') {
+    message.warning('键或值不能为空');
+    return;
+  }
+  if (configList.some(item => item.key === newConfig.value.key)) {
+    message.warning('键已存在');
+    return;
+  }
+  configList.push({ key: newConfig.value.key, value: newConfig.value.value });
+  addConfigModalVisible.value = false;
+  newConfig.value = { key: '', value: '' };
+};
+
+// 删除配置
+const deleteConfig = (index) => {
+  configList.splice(index, 1);
+};
+
+// 更新值
+const updateValue = (index) => {
+  // 值的更新实时反映在 configList 中，无需额外处理
+};
+
+// 将配置列表转换为对象
+const configObject = computed(() => {
+  return configList.reduce((acc, item) => {
+    acc[item.key] = item.value;
+    return acc;
+  }, {});
+});
+
+// 监听配置变化并传递回父组件
+watch(configObject, (newValue) => {
+  emit('update:config', newValue);
+}, { deep: true });
+</script>
+
+<style scoped>
+.config-card {
+  background-color: var(--gray-10);
+  border-radius: 8px;
+  border: 1px solid var(--gray-300);
+}
+
+.config-item {
+  border-bottom: 1px solid #f0f0f0;
+  padding: 12px 0;
+  transition: background-color 0.3s ease;
+}
+
+.config-item:hover {
+  background-color: #fafafa;
+}
+
+.config-item:last-child {
+  border-bottom: none;
+}
+
+.key-input {
+  background-color: #f8f8f8;
+  border-color: #e8e8e8;
+}
+
+.delete-btn-col {
+  display: flex;
+  justify-content: center;
+  align-items: center;
+}
+
+.delete-btn {
+  opacity: 0.6;
+  transition: opacity 0.3s ease;
+}
+
+.delete-btn:hover {
+  opacity: 1;
+}
+
+.add-btn {
+  margin-top: 16px;
+  height: 40px;
+  transition: all 0.3s ease;
+  width: auto;
+}
+
+.modal-input {
+  margin-bottom: 8px;
+}
+
+:deep(.ant-modal-content) {
+  border-radius: 8px;
+}
+
+:deep(.ant-card-body) {
+  padding: 16px;
+}
+</style>
+

web/src/views/DataBaseInfoView.vue

@@ -132,7 +132,7 @@
                 <div class="params-group">
                   <div class="params-item col">
                     <p>重写查询<small>（修改后需重新检索）</small>：</p>
-                    <a-segmented v-model:value="meta.rewriteQuery" :options="rewriteQueryOptions">
+                    <a-segmented v-model:value="meta.use_rewrite_query" :options="use_rewrite_queryOptions">
                       <template #label="{ payload }">
                         <div>
                           <p style="margin: 4px 0">{{ payload.subTitle }}</p>
@@ -251,13 +251,13 @@ const meta = reactive({
   mode: 'search',
   maxQueryCount: 30,
   filter: true,
-  rewriteQuery: 'off',
+  use_rewrite_query: 'off',
   rerankThreshold: 0.1,
   distanceThreshold: 0.3,
   topK: 10,
 });
 
-const rewriteQueryOptions = ref([
+const use_rewrite_queryOptions = ref([
   { value: 'off', payload: { title: 'off', subTitle: '不启用' } },
   { value: 'on', payload: { title: 'on', subTitle: '启用重写' } },
   { value: 'hyde', payload: { title: 'hyde', subTitle: '伪文档生成' } },
@@ -546,10 +546,7 @@ watch(() => meta, () => {
 
 // 添加示例查询
 const queryExamples = ref([
-  '食品添加剂的安全性如何?',
-  '如何识别和预防食物中毒?',
-  '转基因食品对人体健康有什么影响?',
-  '如何正确储存和处理生鲜食品?'
+  '贾宝玉的丫鬟有哪些？',
 ]);
 
 // 使用示例查询的方法

web/src/views/SettingView.vue

@@ -79,6 +79,21 @@
             />
           </div>
         </div>
+        <h3>检索配置</h3>
+        <div class="section">
+          <div class="card">
+            <span class="label">{{ items?.use_rewrite_query.des }}</span>
+            <a-select style="width: 200px"
+              :value="configStore.config?.use_rewrite_query"
+              @change="handleChange('use_rewrite_query', $event)"
+            >
+              <a-select-option
+                v-for="(name, idx) in items?.use_rewrite_query.choices" :key="idx"
+                :value="name">{{ name }}
+              </a-select-option>
+            </a-select>
+          </div>
+        </div>
       </div>
       <div class="setting" v-if="state.section === 'model'">
         <h3>模型配置</h3>
@@ -161,16 +176,20 @@
         </div>
         <div class="model-provider-card" v-for="(item, key) in notModelKeys" :key="key">
           <div class="card-header">
-            <h3>{{ modelNames[item].name }}</h3>
-            <a :href="modelNames[item].url" target="_blank">详情</a>
+            <h3 style="font-weight: 400">{{ modelNames[item].name }}</h3>
+            <a :href="modelNames[item].url" target="_blank"><InfoCircleOutlined /></a>
             <div class="missing-keys">
-              需配置 <span v-for="(key, idx) in modelNames[item].env" :key="idx">{{ key }}</span>
+              <small>需配置</small> <span v-for="(key, idx) in modelNames[item].env" :key="idx">{{ key }}</span>
             </div>
           </div>
         </div>
       </div>
       <div class="setting" v-if="state.section ==='path'">
-        <h3>暂无配置</h3>
+        <h3>本地模型配置</h3>
+        <TableConfigComponent
+          :config="configStore.config?.model_local_paths"
+          @update:config="handleModelLocalPathsUpdate"
+        />
       </div>
     </div>
   </div>
@@ -191,6 +210,7 @@ import {
   InfoCircleOutlined,
 } from '@ant-design/icons-vue';
 import HeaderComponent from '@/components/HeaderComponent.vue';
+import TableConfigComponent from '@/components/TableConfigComponent.vue';
 import { notification, Button } from 'ant-design-vue';
 
 const configStore = useConfigStore()
@@ -231,6 +251,10 @@ const generateRandomHash = (length) => {
   return hash;
 }
 
+const handleModelLocalPathsUpdate = (config) => {
+  handleChange('model_local_paths', config)
+}
+
 const handleChange = (key, e) => {
   if (key == 'enable_knowledge_graph' && e && !configStore.config.enable_knowledge_base) {
     message.error('启动知识图谱必须请先启用知识库功能')
@@ -249,7 +273,8 @@ const handleChange = (key, e) => {
         || key == 'model_provider'
         || key == 'model_name'
         || key == 'embed_model'
-        || key == 'reranker') {
+        || key == 'reranker'
+        || key == 'model_local_paths') {
     if (!isNeedRestart.value) {
       isNeedRestart.value = true
       notification.info({