Merge pull request #33 from xerrors/dev

Dev 修复图谱bug

README.md

@@ -10,7 +10,6 @@
 
 </div>
 
-
 ## 概述
 
 基于大模型 RAG 知识库与知识图谱的问答平台。Llamaindex + VueJS + Flask + Neo4j。大模型适配 OpenAI、国内主流大模型平台的模型调用、本地 vllm 部署。只需要配置对应服务平台的 `API_KEY` 即可使用。
@@ -18,11 +17,12 @@
 ![image](https://github.com/user-attachments/assets/75010511-4ac5-4924-8268-fea9a589839c)
 
 
-代办清单
+待办清单
 
 - [ ] Ollma Embedding 支持（Open-like Embedding 支持）
-- [x] 知识图谱索引支持自定义 Embedding 模型
+- [ ] embedding 过程添加进度显示
-- [x] DeepSeek-R1 支持
+- [X] 知识图谱索引支持自定义 Embedding 模型
+- [X] DeepSeek-R1 支持
 
 ## 更新日志
 
@@ -39,7 +39,6 @@
 
 
 
-
 ## 快速上手
 
 在启动之前，提供 API 服务商的 API_KEY，并放置在 `src/.env` 文件中。默认使用的是硅基流动。因此务必需要配置 `SILICONFLOW_API_KEY=<SILICONFLOW_API_KEY>`。其余模型的配置可以参考 [src/static/models.yaml](src/static/models.yaml) 中的 env。
@@ -103,15 +102,15 @@ docker compose -f docker/docker-compose.yml --env-file src/.env up --build
 
 模型仅支持通过API调用的模型，如果是需要运行本地模型，则建议使用 vllm 转成 API 服务之后使用。使用前请在 `.env` 配置 APIKEY 后使用，配置项目参考：[src/static/models.yaml](src/static/models.yaml)
 
-| 模型供应商            | 默认模型                                  | 配置项目                                       |
+| 模型供应商             | 默认模型                            | 配置项目                                       |
-| :-------------------- | :---------------------------------------- | :--------------------------------------------- |
+| :--------------------- | :---------------------------------- | :--------------------------------------------- |
-| `siliconflow` (默认) | `Qwen/Qwen2.5-7B-Instruct` (免费)  | `SILICONFLOW_API_KEY`                        |
+| `siliconflow` (默认) | `Qwen/Qwen2.5-7B-Instruct` (免费) | `SILICONFLOW_API_KEY`                        |
-| `openai`            | `gpt-4o`                                | `OPENAI_API_KEY`                             |
+| `openai`             | `gpt-4o`                          | `OPENAI_API_KEY`                             |
-| `deepseek`          | `deepseek-chat`                         | `DEEPSEEK_API_KEY`                           |
+| `deepseek`           | `deepseek-chat`                   | `DEEPSEEK_API_KEY`                           |
-| `arc`（豆包方舟）       | `doubao-1-5-pro-32k-250115`        | `ARK_API_KEY`                            |
+| `arc`（豆包方舟）    | `doubao-1-5-pro-32k-250115`       | `ARK_API_KEY`                                |
-| `zhipu`（智谱清言）       | `glm-4-flash`                   | `ZHIPUAI_API_KEY`                            |
+| `zhipu`（智谱清言）  | `glm-4-flash`                     | `ZHIPUAI_API_KEY`                            |
-| `dashscope`（阿里） | `qwen-max-latest`                       | `DASHSCOPE_API_KEY`                          |
+| `dashscope`（阿里）  | `qwen-max-latest`                 | `DASHSCOPE_API_KEY`                          |
-| `qianfan`（百度）   | `ernie_speed`                           | `QIANFAN_ACCESS_KEY`, `QIANFAN_SECRET_KEY` |
+| `qianfan`（百度）    | `ernie_speed`                     | `QIANFAN_ACCESS_KEY`, `QIANFAN_SECRET_KEY` |
 
 此外，如果想要添加供应商的模型，确认知识 OpenAI 调用的方法之后，只需要在 [src/static/models.yaml](src/static/models.yaml) 中添加对应的模型配置即可。配置示例如下：
 
@@ -140,14 +139,12 @@ docker compose -f docker/docker-compose.yml --env-file src/.env up --build
 > [!Warning]
 > 需要注意，由于知识库和图数据库的构建都依赖于向量模型，如果中途更改向量模型，会导致知识库不可用。此外，知识图谱的向量索引的建立默认使用 embedding-3 构建，因此检索的时候必须使用 embedding-3（现阶段还不支持修改）
 
-
 对于**语言模型**，并不支持直接运行本地语言模型，请使用 vllm 或者 ollama 转成 API 服务之后使用。
 
 对于**向量模型**和**重排序模型**，选择以 `local` 前缀开头的模型，可以不做修改会自动下载模型，如果下载过程中出现问题，请参考 [HF-Mirror](https://hf-mirror.com/) 配置相关内容。（但请注意，如果是 Docker 运行，模型仅会缓存到 Docker 里面）
 
 如果想要使用本地已经下载好的模型，可以在网页的 settings 里面做映射。或者修改 `saves/config/base.yaml` 来配置映射关系。但请记得，本地模型的路径要在 docker-compose 的文件中映射 volumes。
 
-
 ## 知识库支持
 
 本项目支持多种格式的知识库，目前支持的格式有：pdf、txt、md。文件上传后，会首先转换为纯文本，然后使用向量模型将文本转换为向量，然后存储到向量数据库中。这个过程可能会花费不少时间。

docker/web.Dockerfile

@@ -6,8 +6,8 @@ WORKDIR /app
 COPY ./web/package*.json ./
 
 # 安装依赖
-RUN npm install --verbose --force
+# RUN npm install --verbose --force
-# RUN npm install --registry http://mirrors.cloud.tencent.com/npm/ --verbose --force
+RUN npm install --registry http://mirrors.cloud.tencent.com/npm/ --verbose --force
 
 # 复制源代码
 COPY ./web .
@@ -22,8 +22,8 @@ FROM node:latest AS build-stage
 WORKDIR /app
 
 COPY ./web/package*.json ./
-RUN npm install --force
+# RUN npm install --force
-# RUN npm install --registry https://registry.npmmirror.com --force
+RUN npm install --registry https://registry.npmmirror.com --force
 
 COPY ./web .
 RUN npm run build

src/config/__init__.py

@@ -2,11 +2,9 @@ import os
 import json
 import yaml
 from pathlib import Path
-from src.utils.logging_config import setup_logger
+from src.utils.logging_config import logger
 
-logger = setup_logger("Config")
+with open(Path("src/static/models.yaml"), 'r', encoding='utf-8') as f:
-
-with open(Path("src/static/models.yaml"), "r") as f:
     _models = yaml.safe_load(f)
 
 MODEL_NAMES = _models["MODEL_NAMES"]
@@ -53,7 +51,6 @@ class Config(SimpleConfig):
         self.add_item("enable_reranker", default=False, des="是否开启重排序")
         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（需配置 TAVILY_API_KEY）")
         # 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
@@ -90,6 +87,9 @@ class Config(SimpleConfig):
 
     def handle_self(self):
         self.model_names = MODEL_NAMES
+        self.embed_model_names = EMBED_MODEL_INFO
+        self.reranker_names = RERANKER_LIST
+
         model_provider_info = self.model_names.get(self.model_provider, {})
         self.model_dir = os.environ.get("MODEL_DIR", "")
 

src/core/database.py

@@ -2,11 +2,9 @@ import os
 import json
 import time
 from src.plugins import pdf2txt
-from src.utils import hashstr, setup_logger, is_text_pdf
+from src.utils import hashstr, logger, is_text_pdf
 from src.models.embedding import get_embedding_model
 
-logger = setup_logger("DataBaseManager")
-
 
 class DataBaseManager:
 
@@ -21,7 +19,6 @@ class DataBaseManager:
             if self.config.enable_knowledge_graph:
                 from src.core.graphbase import GraphDatabase
                 self.graph_base = GraphDatabase(self.config, self.embed_model)
-                self.graph_base.start()
             else:
                 self.graph_base = None
 

src/core/graphbase.py

@@ -9,9 +9,7 @@ import warnings
 
 from src.plugins import pdf2txt
 from src.plugins.oneke import OneKE
-from src.utils import setup_logger
+from src.utils import logger
-
-logger = setup_logger("server-graphbase")
 
 warnings.filterwarnings("ignore", category=UserWarning)
 
@@ -28,6 +26,16 @@ class GraphDatabase:
         assert embed_model, "embed_model=None"
         self.embed_model = embed_model
         self.embed_model_name = None
+        self.work_dir = os.path.join(config.save_dir, "knowledge_graph", kgdb_name)
+        os.makedirs(self.work_dir, exist_ok=True)
+
+        # 尝试加载已保存的图数据库信息
+        if self.load_graph_info():
+            logger.info(f"已加载图数据库信息：{self.kgdb_name}")
+        else:
+            logger.info(f"未找到已保存的图数据库信息，将创建新的配置")
+
+        self.start()
 
     def start(self):
         uri = os.environ.get("NEO4J_URI", "bolt://localhost:7687")
@@ -38,11 +46,12 @@ class GraphDatabase:
             self.driver = GD.driver(f"{uri}/{self.kgdb_name}", auth=(username, password))
             self.status = "open"
             logger.info(f"Connected to Neo4j at {uri}/{self.kgdb_name}, {self.get_database_info()}")
+            # 连接成功后保存图数据库信息
+            self.save_graph_info()
         except Exception as e:
             logger.error(f"Failed to connect to Neo4j: {e}, {uri}, {self.kgdb_name}, {username}, {password}")
             self.config.enable_knowledge_graph = False
 
-
     def close(self):
         """关闭数据库连接"""
         self.driver.close()
@@ -119,33 +128,39 @@ class GraphDatabase:
         with self.driver.session() as session:
             session.execute_write(create, triples)
 
-    def pdf_file_add_entity(self, file_path, output_path, kgdb_name='neo4j'):
+    # def pdf_file_add_entity(self, file_path, output_path, kgdb_name='neo4j'):
-        self.use_database(kgdb_name)  # 切换到指定数据库
+    #     self.use_database(kgdb_name)  # 切换到指定数据库
-        text_path = pdf2txt(file_path)
+    #     text_path = pdf2txt(file_path)
-        global UIE_MODEL
+    #     global UIE_MODEL
-        if UIE_MODEL is None:
+    #     if UIE_MODEL is None:
-            UIE_MODEL = OneKE()
+    #         UIE_MODEL = OneKE()
-        triples_path = UIE_MODEL.processing_text_to_kg(text_path, output_path)
+    #     triples_path = UIE_MODEL.processing_text_to_kg(text_path, output_path)
-        self.jsonl_file_add_entity(triples_path)
+    #     self.jsonl_file_add_entity(triples_path)
-        return kgdb_name
+    #     return kgdb_name
 
     def txt_add_vector_entity(self, triples, kgdb_name='neo4j'):
         """添加实体三元组"""
         self.use_database(kgdb_name)
         def _index_exists(tx, index_name):
+            """检查索引是否存在"""
             result = tx.run("SHOW INDEXES")
             for record in result:
                 if record["name"] == index_name:
                     return True
             return False
+
         def _create_graph(tx, data):
+            """添加一个三元组"""
             for entry in data:
                 tx.run("""
                 MERGE (h:Entity {name: $h})
                 MERGE (t:Entity {name: $t})
                 MERGE (h)-[r:RELATION {type: $r}]->(t)
                 """, h=entry['h'], t=entry['t'], r=entry['r'])
+
         def _create_vector_index(tx, dim):
+            """创建向量索引"""
+            # NOTE 这里是否是会重复构建索引？
             index_name = "entityEmbeddings"
             if not _index_exists(tx, index_name):
                 tx.run(f"""
@@ -157,24 +172,34 @@ class GraphDatabase:
                 }} }};
                 """)
 
+        # 判断模型名称是否匹配
         from src.config import EMBED_MODEL_INFO
-        embed_info = EMBED_MODEL_INFO[self.config.embed_model]
+        cur_embed_info = EMBED_MODEL_INFO[self.config.embed_model]
-        with self.driver.session() as session:
+        self.embed_model_name = self.embed_model_name or cur_embed_info.get('name')
-            session.execute_write(_create_graph, triples)
+        assert self.embed_model_name == cur_embed_info.get('name') or self.embed_model_name is None, \
-            session.execute_write(_create_vector_index, embed_info.get('dimension'))
+            f"embed_model_name={self.embed_model_name}, {cur_embed_info.get('name')=}"
-            for i, entry in enumerate(triples):
-                logger.info(f"Adding entity {i+1}/{len(triples)}")
-                embedding_h = self.get_embedding(entry['h'])
-                session.execute_write(self.set_embedding, entry['h'], embedding_h)
 
+        with self.driver.session() as session:
+            logger.info(f"Adding entity to {kgdb_name}")
+            session.execute_write(_create_graph, triples)
+            logger.info(f"Creating vector index for {kgdb_name} with {self.config.embed_model}")
+            session.execute_write(_create_vector_index, cur_embed_info['dimension'])
+            # NOTE 这里需要异步处理
+            for i, entry in enumerate(triples):
+                logger.debug(f"Adding entity {i+1}/{len(triples)}")
+                embedding_h = self.get_embedding(entry['h'])
                 embedding_t = self.get_embedding(entry['t'])
+                session.execute_write(self.set_embedding, entry['h'], embedding_h)
                 session.execute_write(self.set_embedding, entry['t'], embedding_t)
 
+            # 数据添加完成后保存图信息
+            self.save_graph_info()
+
     def jsonl_file_add_entity(self, file_path, kgdb_name='neo4j'):
         self.status = "processing"
         kgdb_name = kgdb_name or 'neo4j'
-        self.embed_model_name = self.embed_model_name or self.config.embed_model
         self.use_database(kgdb_name)  # 切换到指定数据库
+        logger.info(f"Start adding entity to {kgdb_name} with {file_path}")
 
         def read_triples(file_path):
             with open(file_path, 'r', encoding='utf-8') as file:
@@ -186,6 +211,8 @@ class GraphDatabase:
         self.txt_add_vector_entity(triples, kgdb_name)
 
         self.status = "open"
+        # 更新并保存图数据库信息
+        self.save_graph_info()
         return kgdb_name
 
     def delete_entity(self, entity_name=None, kgdb_name="neo4j"):
@@ -213,27 +240,14 @@ class GraphDatabase:
 
     def query_node(self, entity_name, hops=2, **kwargs):
         # TODO 添加判断节点数量为 0 停止检索
+
+        logger.debug(f"Query graph node {entity_name} with {hops=}")
         if kwargs.get("exact_match"):
             raise NotImplemented("not implement for `exact_match`")
         else:
             return self.query_by_vector(entity_name=entity_name, **kwargs)
 
     def query_by_vector(self, entity_name, threshold=0.9, kgdb_name='neo4j', hops=2, num_of_res=5):
-        results = self.query_by_vector_tep(entity_name=entity_name)
-
-        # 筛选出分数高于阈值的实体
-        qualified_entities = [result[0] for result in results[:num_of_res] if result[1] > threshold]
-
-        # 对每个合格的实体进行查询
-        all_query_results = []
-        for entity in qualified_entities:
-            query_result = self.query_specific_entity(entity_name=entity, hops=hops, kgdb_name=kgdb_name)
-            all_query_results.extend(query_result)
-
-        return all_query_results
-
-    def query_by_vector_tep(self, entity_name, kgdb_name='neo4j'):
-        """向量查询"""
         self.use_database(kgdb_name)
         def query(tx, text):
             embedding = self.get_embedding(text)
@@ -245,15 +259,28 @@ class GraphDatabase:
             return result.values()
 
         with self.driver.session() as session:
-            return session.execute_read(query, entity_name)
+            results = session.execute_read(query, entity_name)
+
+
+        # 筛选出分数高于阈值的实体
+        qualified_entities = [result[0] for result in results[:num_of_res] if result[1] > threshold]
+        logger.debug(f"Graph Query Entities: {entity_name}, {qualified_entities=}")
+
+        # 对每个合格的实体进行查询
+        all_query_results = []
+        for entity in qualified_entities:
+            query_result = self.query_specific_entity(entity_name=entity, hops=hops, kgdb_name=kgdb_name)
+            all_query_results.extend(query_result)
+
+        return all_query_results
 
     def query_specific_entity(self, entity_name, kgdb_name='neo4j', hops=2):
-        """查询指定实体三元组信息"""
+        """查询指定实体三元组信息（无向关系）"""
         self.use_database(kgdb_name)
         def query(tx, entity_name, hops):
             result = tx.run(f"""
-            MATCH (n {{name: $entity_name}})-[r*1..{hops}]->(m)
+            MATCH (n {{name: $entity_name}})-[r*1..{hops}]-(m)
-            RETURN n.name AS node_name, r, m.name AS neighbor_name
+            RETURN n, r, m
             """, entity_name=entity_name)
             return result.values()
 
@@ -316,11 +343,9 @@ class GraphDatabase:
             return session.execute_read(query, node_name, hops)
 
     def get_embedding(self, text):
-        inputs = [text]
         with torch.no_grad():
-            outputs = self.embed_model.encode(inputs)
+            outputs = self.embed_model.encode([text])[0]
-        embeddings = outputs[0] # 假设取平均作为文本的嵌入向量
+            return outputs
-        return embeddings
 
     def set_embedding(self, tx, entity_name, embedding):
         tx.run("""
@@ -328,160 +353,71 @@ class GraphDatabase:
         CALL db.create.setNodeVectorProperty(e, 'embedding', $embedding)
         """, name=entity_name, embedding=embedding)
 
-    # def format_query_results(self, results):
+    def save_graph_info(self):
-    #     formatted_results = []
+        """
-    #     for row in results:
+        将图数据库的基本信息保存到工作目录中的JSON文件
-    #         n, rs, m = row
+        保存的信息包括：数据库名称、状态、嵌入模型名称等
-    #         entity_a = n['name']
+        """
-    #         entity_b = m['name']
+        try:
-    #         for rel in rs:
+            # 获取数据库信息
-    #             relationship = rel.type
+            db_info = None
-    #             formatted_results.append(f"实体 {entity_a} 和 实体 {entity_b} 的关系是 {relationship}")
+            if self.status == "open" and self.driver:
-    #     return formatted_results
+                try:
+                    db_info = self.get_database_info(self.kgdb_name)
+                except Exception as e:
+                    logger.warning(f"无法获取数据库信息：{e}")
 
+            # 构建要保存的信息字典
+            graph_info = {
+                "kgdb_name": self.kgdb_name,
+                "status": self.status,
+                "embed_model_name": self.embed_model_name,
+                "last_updated": None,  # 这里可以添加时间戳
+                "database_info": db_info
+            }
+
+            # 添加时间戳
+            from datetime import datetime
+            graph_info["last_updated"] = datetime.now().isoformat()
+
+            # 保存到JSON文件
+            info_file_path = os.path.join(self.work_dir, "graph_info.json")
+            with open(info_file_path, 'w', encoding='utf-8') as f:
+                json.dump(graph_info, f, ensure_ascii=False, indent=2)
+
+            logger.info(f"图数据库信息已保存到：{info_file_path}")
+            return True
+        except Exception as e:
+            logger.error(f"保存图数据库信息失败：{e}")
+            return False
+
+    def load_graph_info(self):
+        """
+        从工作目录中的JSON文件加载图数据库的基本信息
+        返回True表示加载成功，False表示加载失败
+        """
+        try:
+            info_file_path = os.path.join(self.work_dir, "graph_info.json")
+            if not os.path.exists(info_file_path):
+                logger.warning(f"图数据库信息文件不存在：{info_file_path}")
+                return False
+
+            with open(info_file_path, 'r', encoding='utf-8') as f:
+                graph_info = json.load(f)
+
+            # 更新对象属性
+            if graph_info.get("embed_model_name"):
+                self.embed_model_name = graph_info["embed_model_name"]
+
+            # 如果需要，可以加载更多信息
+            # 注意：这里不更新self.kgdb_name，因为它是在初始化时设置的
+
+            logger.info(f"已加载图数据库信息，最后更新时间：{graph_info.get('last_updated')}")
+            return True
+        except Exception as e:
+            logger.error(f"加载图数据库信息失败：{e}")
+            return False
 
 
 if __name__ == "__main__":
-    config = None
+    pass
-
-    kgdb_name = "neo4j"
-
-    class EmbeddingModel(FlagModel):
-        def __init__(self, config, **kwargs):
-
-            model_name_or_path = "/data2024/yyyl/model/BAAI/bge-large-zh-v1.5/"
-
-            super().__init__(model_name_or_path, use_fp16=False, **kwargs)
-
-
-    model = EmbeddingModel(config)
-    # 初始化知识图谱数据库
-    kgdb = GraphDatabase(config, model)
-    # 创建新的数据库
-    # kgdb.create_graph_database("db2")
-
-    # 返回指定数据库信息
-    # info = kgdb.get_database_info(kgdb_name)
-    # print(info)
-
-    # triples = [
-    #         {
-    #             "h": "CCC",
-    #             "t": "EE",
-    #             "r": "同学"
-    #         },
-    #         {
-    #             "h": "EE",
-    #             "t": "RR",
-    #             "r": "同事"
-    #         }
-    #         ]
-
-    # kgdb.query_by_vector("z")
-    # def format_query_results(results):
-    #     formatted_results = {"nodes": [], "edges": []}
-
-    #     # 用于存储所有唯一的节点信息
-    #     node_dict = {}
-
-    #     for item in results:
-    #         # 确保item[1]是一个非空的列表
-    #         if isinstance(item[1], list) and len(item[1]) > 0:
-    #             relationship = item[1][0]
-    #             rel_id = relationship.element_id
-    #             nodes = relationship.nodes
-    #             if len(nodes) == 2:
-    #                 node1, node2 = nodes
-
-    #                 # 提取源节点和目标节点信息
-    #                 node1_id = node1.element_id
-    #                 node2_id = node2.element_id
-    #                 node1_name = item[0]  # 假设节点名称和列表中的第一个元素相同
-    #                 node2_name = item[2] if len(item) > 2 else 'unknown'
-
-    #                 # 记录节点信息
-    #                 if node1_id not in node_dict:
-    #                     node_dict[node1_id] = {"id": node1_id, "name": node1_name}
-    #                 if node2_id not in node_dict:
-    #                     node_dict[node2_id] = {"id": node2_id, "name": node2_name}
-
-    #                 # 确定关系类型
-
-    #                 relationship_type = relationship._properties.get('type', 'unknown')
-    #                 if relationship_type == 'unknown':
-    #                     relationship_type = relationship.type
-
-    #                 # 记录边的信息
-    #                 formatted_results["edges"].append({
-    #                     "id": rel_id,
-    #                     "type": relationship_type,
-    #                     "source_id": node1_id,
-    #                     "target_id": node2_id,
-    #                     "source_name": node1_name,
-    #                     "target_name": node2_name
-    #                 })
-
-    #     # 将唯一的节点信息添加到结果中
-    #     formatted_results["nodes"] = list(node_dict.values())
-
-    #     return formatted_results
-
-    # entities = ['jqy', '维c']
-    # results = []
-    # for entitie in entities:
-    #     result = kgdb.query_by_vector(entitie)
-    #     if result != []:
-    #         results.extend(result)
-    # print(format_query_results(results))
-
-
-    # kgdb.txt_add_vector_entity(triples, model)
-    # print("Extend the Graph data base")
-
-    # kgdb.jsonl_file_add_entity("/data2024/yyyl/ProjectAthena/tep.jsonl", kgdb_name)
-    # print("Extend the Graph data base")
-
-    # triples_path = "output.jsonl"
-    # def read_triples(file_path):
-    #     with open(file_path, 'r', encoding='utf-8') as file:
-    #         for line in file:
-    #             item = json.loads(line.strip())
-    #             yield [item]
-    # for trio in read_triples(triples_path):
-    #     kgdb.txt_add_entity(trio)
-
-    # 通过文件添加三元组数据
-    # kgdb.file_add_entity("/data2024/yyyl/ProjectAthena/test.pdf", "/data2024/yyyl/ProjectAthena/output.jsonl", kgdb_name)
-    # print("Extend the Graph data base")
-
-    # 删除数据库信息
-    # kgdb.delete_entity()
-    # print("Clear the Graph data base")
-
-    # 查询所有节点和关系
-    # results = kgdb.query_all_nodes_and_relationships(kgdb_name)
-    # print(results)
-
-    # 查询特定实体及其关系
-    # results = kgdb.query_specific_entity("kllll", kgdb_name)
-    # print(results)
-
-    # 查询特定实体及其关系
-    # results = kgdb.query_by_vector_tep("z", model, tokenizer)
-    # print(results)
-
-    # 查询特定关系类型的所有节点
-    # results = kgdb.query_by_relationship_type("作用或食用效果", kgdb_name)
-    # print(results)
-
-    # 模糊查询
-    # results = kgdb.query_entity_like("三七", kgdb_name)
-    # print(results)
-
-    # 查询节点信息
-    # results = kgdb.query_entity_like("三七提取物", kgdb_name)
-    # print(results)
-
-    # 关闭数据库连接
-    kgdb.close()
-

src/core/knowledgebase.py

@@ -1,8 +1,7 @@
 import os
 
 from pymilvus import MilvusClient, MilvusException
-from src.utils import setup_logger, hashstr
+from src.utils import logger, hashstr
-logger = setup_logger("KnowledgeBase")
 
 
 class KnowledgeBase:
@@ -68,7 +67,7 @@ class KnowledgeBase:
             logger.error(f"Collection {collection_name} not found, create it")
             # self.add_collection(collection_name)
 
-        vectors = self.embed_model.encode(docs)
+        vectors = self.embed_model.batch_encode(docs)
 
         data = [{
             "id": int(random.random() * 1e12),

src/core/retriever.py

@@ -1,7 +1,5 @@
 from src.models.rerank_model import get_reranker
-from src.utils.logging_config import setup_logger
+from src.utils.logging_config import logger
-
-logger = setup_logger("server-common")
 
 
 class Retriever:
@@ -19,7 +17,6 @@ class Retriever:
             self.web_searcher = WebSearcher()
 
     def retrieval(self, query, history, meta):
-
         refs = {"query": query, "history": history, "meta": meta}
         refs["model_name"] = self.config.model_name
         refs["entities"] = self.reco_entities(query, history, refs)
@@ -66,8 +63,8 @@ class Retriever:
 
     def query_classification(self, query):
         """判断是否需要查询
-        - 对于完全基于用户给定信息的任务，称之为“足够”“sufficient”，不需要检索；
+        - 对于完全基于用户给定信息的任务，称之为"足够""sufficient"，不需要检索；
-        - 否则，称之为“不足”“insufficient”，可能需要检索，
+        - 否则，称之为"不足""insufficient"，可能需要检索，
         """
         raise NotImplementedError
 
@@ -107,12 +104,14 @@ class Retriever:
         distance_threshold = meta.get("distanceThreshold", 0)
         top_k = meta.get("topK", 5)
 
+        # 检索
         all_kb_res = self.dbm.knowledge_base.search(rw_query, db_name, limit=max_query_count)
         for r in all_kb_res:
             r["file"] = kb.id2file(r["entity"]["file_id"])
 
         kb_res = [r for r in all_kb_res if r["distance"] > distance_threshold]
 
+        # 重排序
         if self.config.enable_reranker and len(kb_res) > 0:
             texts = [r["entity"]["text"] for r in kb_res]
             rerank_scores = self.reranker.compute_score([rw_query, texts], normalize=True)
@@ -176,7 +175,7 @@ class Retriever:
 
         return entities
 
-    def _extract_relationship_info(self, relationship, source_name, target_name):
+    def _extract_relationship_info(self, relationship, source_name=None, target_name=None, node_dict=None):
         """
         提取关系信息并返回格式化的节点和边信息
         """
@@ -189,6 +188,9 @@ class Retriever:
         source_id = source.element_id
         target_id = target.element_id
 
+        source_name = node_dict[source_id]["name"] if source_name is None else source_name
+        target_name = node_dict[target_id]["name"] if target_name is None else target_name
+
         relationship_type = relationship._properties.get("type", "unknown")
         if relationship_type == "unknown":
             relationship_type = relationship.type
@@ -230,24 +232,33 @@ class Retriever:
         return formatted_results
 
     def format_query_results(self, results):
+        logger.debug(f"Graph Query Results: {results}")
         formatted_results = {"nodes": [], "edges": []}
         node_dict = {}
 
         for item in results:
-            if not isinstance(item[1], list) or not item[1]:
+            # 检查数据格式
+            if len(item) < 2 or not isinstance(item[1], list):
                 continue
 
-            relationship = item[1][0]
+            node_dict[item[0].element_id] = dict(id=item[0].element_id, name=item[0]._properties.get("name", "Unknown"))
-            source_name = item[0]
+            node_dict[item[2].element_id] = dict(id=item[2].element_id, name=item[2]._properties.get("name", "Unknown"))
-            target_name = item[2] if len(item) > 2 else "unknown"
 
-            node_info, edge_info = self._extract_relationship_info(relationship, source_name, target_name)
+            # 处理关系列表中的每个关系
-            if node_info is None or edge_info is None:
+            for i, relationship in enumerate(item[1]):
-                continue
+                try:
+                    # 提取关系信息
+                    node_info, edge_info = self._extract_relationship_info(relationship, node_dict=node_dict)
+                    if node_info is None or edge_info is None:
+                        continue
 
-            node_dict.update({node["id"]: node for node in node_info})
+                    # 添加边
-            formatted_results["edges"].append(edge_info)
+                    formatted_results["edges"].append(edge_info)
+                except Exception as e:
+                    logger.error(f"处理关系时出错: {e}, 关系: {relationship}")
+                    continue
 
+        # 将节点字典转换为列表
         formatted_results["nodes"] = list(node_dict.values())
 
         return formatted_results

src/core/startup.py

@@ -3,9 +3,7 @@ from src.core import DataBaseManager
 from src.core.retriever import Retriever
 from src.models import select_model
 from src.config import Config
-from src.utils import setup_logger
+from src.utils import logger
-
-logger = setup_logger("Startup")
 
 
 class Startup:

src/main.py

@@ -1,11 +1,13 @@
 import uvicorn
 from dotenv import load_dotenv
+
+load_dotenv("src/.env")
+
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 from src.routers import router
-from src.utils.logging_config import setup_logger
+from src.utils.logging_config import logger
 
-load_dotenv()
 
 app = FastAPI()
 app.include_router(router)
@@ -20,9 +22,6 @@ app.add_middleware(
 )
 
 
-logger = setup_logger("server:main")
-
-
 if __name__ == "__main__":
     uvicorn.run(app, host="0.0.0.0", port=5000, threads=10, workers=10)
 

src/models/__init__.py

@@ -18,6 +18,7 @@ def select_model(config, model_provider=None, model_name=None):
         "siliconflow",
         "zhipu",
         "lingyiwanwu",
+        "together.ai",
     ]:
         return OpenAIBase(
             api_key=os.getenv(model_info["env"][0]),

src/models/chat_model.py

@@ -1,9 +1,6 @@
 import os
 from openai import OpenAI
-from src.utils.logging_config import setup_logger
+from src.utils import logger
-
-
-logger = setup_logger(__name__)
 
 class OpenAIBase():
     def __init__(self, api_key, base_url, model_name):

src/models/embedding.py

@@ -4,12 +4,9 @@ import requests
 from FlagEmbedding import FlagModel
 
 from src.config import EMBED_MODEL_INFO
-from src.utils.logging_config import setup_logger
+from src.utils import hashstr, logger
-from src.utils import hashstr
 
 
-logger = setup_logger("EmbeddingModel")
-
 class LocalEmbeddingModel(FlagModel):
     def __init__(self, config, **kwargs):
         info = EMBED_MODEL_INFO[config.embed_model]
@@ -31,6 +28,7 @@ class RemoteEmbeddingModel:
     embed_state = {}
 
     def batch_encode(self, messages, batch_size=20):
+        logger.info(f"Batch encoding {len(messages)} messages")
         data = []
 
         if len(messages) > batch_size:

src/models/ollama_embedding.py

@@ -4,9 +4,7 @@ import numpy as np
 from typing import List, Union, Dict
 
 from src.models.embedding import RemoteEmbeddingModel
-from src.utils.logging_config import setup_logger
+from src.utils.logging_config import logger
-
-logger = setup_logger("OllamaEmbedding")
 
 class OllamaEmbedding(RemoteEmbeddingModel):
     """

src/models/rerank_model.py

@@ -5,10 +5,7 @@ import numpy as np
 from FlagEmbedding import FlagReranker
 
 from src.config import RERANKER_LIST
-from src.utils.logging_config import setup_logger
+from src.utils.logging_config import logger
-
-
-logger = setup_logger("RerankModel")
 
 
 class LocalReranker(FlagReranker):

src/plugins/oneke.py

@@ -11,8 +11,7 @@ from transformers import (
     BitsAndBytesConfig
 )
 
-from src.utils import setup_logger
+from src.utils import logger
-logger = setup_logger("OneKE")
 
 dotenv.load_dotenv()
 

src/routers/base_router.py

@@ -5,11 +5,11 @@ base = APIRouter()
 from fastapi import FastAPI, HTTPException
 from fastapi.responses import JSONResponse
 from fastapi import Request, Body
-from src.core import HistoryManager
-from src.utils.logging_config import setup_logger
-from src.core.startup import startup
 
-logger = setup_logger("server-base")
+from src.core import HistoryManager
+from src.core.startup import startup
+from src.utils import logger
+
 
 @base.get("/")
 async def route_index():
@@ -39,6 +39,6 @@ def get_log():
         last_lines = deque(f, maxlen=1000)
 
     log = ''.join(last_lines)
-    return {"log": log}
+    return {"log": log, "message": "success", "log_file": LOG_FILE}
 
 

src/routers/chat_router.py

@@ -5,14 +5,12 @@ from fastapi.responses import StreamingResponse, Response
 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.logging_config import logger
 
 chat = APIRouter(prefix="/chat")
-logger = setup_logger("server-chat")
 # 创建线程池
 executor = ThreadPoolExecutor()
 
-refs_pool = {}
 
 @chat.get("/")
 async def chat_get():
@@ -26,6 +24,7 @@ def chat_post(
         cur_res_id: str = Body(...)):
 
     history_manager = HistoryManager(history)
+    logger.debug(f"Received query: {query} with meta: {meta}")
 
     def make_chunk(content=None, **kwargs):
         return json.dumps({
@@ -112,10 +111,4 @@ async def call(query: str = Body(...), meta: dict = Body(None)):
     response = await predict_async(query)
     logger.debug({"query": query, "response": response.content})
 
-    return {"response": response.content}
+    return {"response": response.content}
-
-@chat.get("/refs")
-def get_refs(cur_res_id: str):
-    global refs_pool
-    refs = refs_pool.pop(cur_res_id, None)
-    return {"refs": refs}

src/routers/data_router.py

@@ -2,12 +2,11 @@ import os
 from typing import List, Optional
 from fastapi import APIRouter, File, UploadFile, HTTPException, Depends, Body
 
-from src.utils import setup_logger, hashstr
+from src.utils import logger, hashstr
 from src.core.startup import startup
 
 data = APIRouter(prefix="/data")
 
-logger = setup_logger("server-database")
 
 @data.get("/")
 def get_databases():
@@ -100,7 +99,6 @@ async def get_graph_info():
 
 @data.get("/graph/node")
 async def get_graph_node(entity_name: str):
-    logger.debug(f"Get graph node {entity_name}")
     result = startup.dbm.graph_base.query_node(entity_name=entity_name)
     return {"result": startup.retriever.format_query_results(result), "message": "success"}
 
@@ -113,7 +111,7 @@ async def get_graph_nodes(kgdb_name: str, num: int):
     result = startup.dbm.graph_base.get_sample_nodes(kgdb_name, num)
     return {"result": startup.retriever.format_general_results(result), "message": "success"}
 
-@data.post("/graph/add")
+@data.post("/graph/add-by-jsonl")
 async def add_graph_entity(file_path: str = Body(...), kgdb_name: Optional[str] = Body(None)):
     if not startup.config.enable_knowledge_graph:
         raise HTTPException(status_code=400, detail="Knowledge graph is not enabled")

src/routers/tool_router.py

@@ -3,11 +3,10 @@ from fastapi import APIRouter, Body
 from pydantic import BaseModel
 from typing import List, Dict, Any, Optional
 
-from src.utils import setup_logger
+from src.utils import logger
 
 tool = APIRouter(prefix="/tool")
 
-logger = setup_logger("server-tools")
 
 class Tool(BaseModel):
     name: str

src/static/models.yaml

@@ -50,6 +50,18 @@ MODEL_NAMES:
       - deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
       - Qwen/Qwen2.5-72B-Instruct
       - Qwen/Qwen2.5-7B-Instruct
+  together.ai:
+    name: Together.ai
+    url: https://api.together.ai/models
+    base_url: https://api.together.xyz/v1/
+    default: meta-llama/Llama-3.3-70B-Instruct-Turbo-Free
+    env:
+      - TOGETHER_API_KEY
+    models:
+      - meta-llama/Llama-3.3-70B-Instruct-Turbo
+      - meta-llama/Llama-3.3-70B-Instruct-Turbo-Free
+      - deepseek-ai/DeepSeek-R1-Distill-Llama-70B-free
+
   qianfan:
     name: 百度千帆 (QianFan)
     url: https://open.bigmodel.cn/dev/api

src/utils/__init__.py

@@ -1,6 +1,6 @@
 import time
 import random
-from src.utils.logging_config import setup_logger, logger
+from src.utils.logging_config import logger
 
 def is_text_pdf(pdf_path):
     import fitz

src/utils/web_search.py

@@ -1,9 +1,7 @@
 import os
 from typing import List, Dict
 from tavily import TavilyClient
-from src.utils.logging_config import setup_logger
+from src.utils.logging_config import logger
-
-logger = setup_logger("web-search")
 
 class WebSearcher:
     def __init__(self):

test/data/A_Dream_of_Red_Mansions_tiny.jsonl

@@ -0,0 +1,5 @@
+{"h": "贾代善", "t": "贾源", "r": "子"}
+{"h": "娄氏", "t": "贾源", "r": "重孙媳妇"}
+{"h": "贾母", "t": "贾代善", "r": "妻"}
+{"h": "老姨奶奶", "t": "贾代善", "r": "妾"}
+{"h": "贾敏", "t": "贾代善", "r": "女"}

web/src/components/ChatComponent.vue

@@ -1,6 +1,6 @@
 <template>
   <div class="chat"  ref="chatContainer">
-    <div class="header">
+    <div class="chat-header">
       <div class="header__left">
         <div
           v-if="!state.isSidebarOpen"
@@ -154,7 +154,7 @@
 </template>
 
 <script setup>
-import { reactive, ref, onMounted, toRefs, nextTick, computed, watch } from 'vue'
+import { reactive, ref, onMounted, toRefs, nextTick, onUnmounted, watch } from 'vue'
 import {
   SendOutlined,
   MenuOutlined,
@@ -197,7 +197,11 @@ const configStore = useConfigStore()
 
 const { conv, state } = toRefs(props)
 const chatContainer = ref(null)
+
 const isStreaming = ref(false)
+const userIsScrolling = ref(false);
+const shouldAutoScroll = ref(true);
+
 const panel = ref(null)
 const modelCard = ref(null)
 const examples = ref([
@@ -295,10 +299,23 @@ const renameTitle = () => {
   }
 }
 
+const handleUserScroll = () => {
+  // 计算我们是否接近底部（100像素以内）
+  const isNearBottom = chatContainer.value.scrollHeight - chatContainer.value.scrollTop - chatContainer.value.clientHeight < 100;
+
+  // 如果用户不在底部，则仅将其标记为用户滚动
+  userIsScrolling.value = !isNearBottom;
+
+  // 如果用户再次滚动到底部，请恢复自动滚动
+  shouldAutoScroll.value = isNearBottom;
+};
+
 const scrollToBottom = () => {
-  setTimeout(() => {
+  if (shouldAutoScroll.value) {
-    chatContainer.value.scrollTop = chatContainer.value.scrollHeight - chatContainer.value.clientHeight
+    setTimeout(() => {
-  }, 10)
+      chatContainer.value.scrollTop = chatContainer.value.scrollHeight - chatContainer.value.clientHeight;
+    }, 10);
+  }
 }
 
 const generateRandomHash = (length) => {
@@ -502,22 +519,6 @@ const fetchChatResponse = (user_input, cur_res_id) => {
   });
 }
 
-const fetchRefs = (cur_res_id) => {
-  return new Promise((resolve, reject) => {
-    fetch(`/api/chat/refs?cur_res_id=${cur_res_id}`, {
-      method: "GET",
-      headers: {
-        "Content-Type": "application/json"
-      },
-    }).then(response => response.json())
-      .then(data => {
-        resolve(data.refs)
-      })
-      .catch((error) => {
-        reject(error)
-      })
-  })
-}
 
 // 更新后的 sendMessage 函数
 const sendMessage = () => {
@@ -531,6 +532,8 @@ const sendMessage = () => {
     isStreaming.value = true;
     appendUserMessage(user_input);
     appendAiMessage("", null);
+    forceScrollToBottom();
+
     const cur_res_id = conv.value.messages[conv.value.messages.length - 1].id;
     conv.value.inputText = '';
     meta.db_name = dbName;
@@ -553,15 +556,15 @@ const retryMessage = (id) => {
   sendMessage();
 }
 
-const autoSend = (msg) => {
-  conv.value.inputText = msg
-  sendMessage()
-}
 
 // 从本地存储加载数据
 onMounted(() => {
   scrollToBottom()
   loadDatabases()
+
+  chatContainer.value.addEventListener('scroll', handleUserScroll);
+
+  // 从本地存储加载数据
   const storedMeta = localStorage.getItem('meta');
   if (storedMeta) {
     const parsedMeta = JSON.parse(storedMeta);
@@ -569,6 +572,20 @@ onMounted(() => {
   }
 });
 
+onUnmounted(() => {
+  if (chatContainer.value) {
+    chatContainer.value.removeEventListener('scroll', handleUserScroll);
+  }
+});
+
+// 添加新函数来处理特定的滚动行为
+const forceScrollToBottom = () => {
+  shouldAutoScroll.value = true;
+  setTimeout(() => {
+    chatContainer.value.scrollTop = chatContainer.value.scrollHeight - chatContainer.value.clientHeight;
+  }, 10);
+};
+
 // 监听 meta 对象的变化，并保存到本地存储
 watch(
   () => meta,
@@ -593,7 +610,7 @@ watch(
   flex: 5 5 200px;
   overflow-y: scroll;
 
-  .header {
+  .chat-header {
     user-select: none;
     position: sticky;
     top: 0;
@@ -728,7 +745,7 @@ watch(
 
 .chat-box {
   width: 100%;
-  max-width: 900px;
+  max-width: 800px;
   margin: 0 auto;
   flex-grow: 1;
   padding: 1rem 2rem;
@@ -819,7 +836,7 @@ watch(
     flex-direction: column;
     width: 100%;
     height: auto;
-    max-width: 900px;
+    max-width: 800px;
     margin: 0 auto;
     padding: 0.25rem 0.5rem;
     border: 2px solid var(--gray-200);
@@ -1008,14 +1025,14 @@ watch(
     height: calc(100vh - 60px);
   }
 
-  .chat-container .chat .header {
+  .chat-container .chat .chat-header {
     background: var(--main-light-4);
     .header__left, .header__right {
-      gap: 20px;
+      gap: 24px;
     }
 
     .nav-btn {
-      font-size: 1.5rem;
+      font-size: 1.3rem;
       padding: 0;
 
       &:hover {
@@ -1057,19 +1074,29 @@ watch(
 </style>
 
 <style lang="less">
-.message-box pre {
-  border-radius: 8px;
-  font-size: 14px;
-  border: 1px solid var(--main-light-3);
-  padding: 1rem;
-
-  &:has(code.hljs) {
-    padding: 0;
-  }
-}
-
 .message-md {
+  color: var(--gray-900);
   max-width: 100%;
+
+  pre {
+    border-radius: 8px;
+    font-size: 14px;
+    border: 1px solid var(--main-light-3);
+    padding: 1rem;
+
+    &:has(code.hljs) {
+      padding: 0;
+    }
+
+    code.hljs {
+      background-color: var(--gray-100);
+    }
+  }
+
+  strong {
+    color: var(--gray-800);
+  }
+
   h1, h2, h3, h4, h5, h6 {
     font-size: 1rem;
   }
@@ -1083,6 +1110,15 @@ watch(
   ol, ul {
     padding-left: 1rem;
   }
+
+  hr {
+    margin-bottom: 1rem;
+  }
+
+  a {
+    color: var(--main-800);
+    margin: auto 2px;
+  }
 }
 </style>
 

web/src/layouts/AppLayout.vue

@@ -337,8 +337,12 @@ div.header, #app-router-view {
     div.header {
       display: none;
     }
-  }
 
+    .debug-panel {
+      bottom: 10rem;
+    }
+
+  }
   .app-layout div.header-mobile {
     display: flex;
     flex-direction: row;
@@ -348,6 +352,7 @@ div.header, #app-router-view {
     align-items: center;
     flex: 0 0 60px;
     border-right: none;
+    height: 40px;
 
     .nav-item {
       text-decoration: none;

web/src/views/ChatView.vue

@@ -256,10 +256,11 @@ onMounted(() => {
 }
 
 @media (max-width: 520px) {
-  .conversation {
+  .conversations {
     position: absolute;
     z-index: 101;
     width: 300px;
+    height: 100%;
     border-radius: 0 16px 16px 0;
     box-shadow: 0 0 10px 1px rgba(0, 0, 0, 0.05);
   }

web/src/views/DataBaseInfoView.vue

@@ -59,7 +59,9 @@
               <template v-if="column.key === 'filename'">
                 <a-button class="main-btn" type="link" @click="openFileDetail(record)">{{ text }}</a-button>
               </template>
-              <template v-else-if="column.key === 'type'"><span :class="text">{{ text.toUpperCase() }}</span></template>
+              <template v-else-if="column.key === 'type'">
+                <span :class="['span-type', text]">{{ text.toUpperCase() }}</span>
+              </template>
               <template v-else-if="column.key === 'status' && text === 'done'">
                 <CheckCircleFilled style="color: #41A317;"/>
               </template>
@@ -813,7 +815,7 @@ onMounted(() => {
     padding: 0;
   }
 
-  .pdf, .txt, .md {
+  .span-type {
     color: white;
     padding: 2px 4px;
     border-radius: 4px;
@@ -821,6 +823,7 @@ onMounted(() => {
     font-weight: bold;
     opacity: 0.8;
     user-select: none;
+    background: #005F77;
   }
 
   .pdf {
@@ -831,6 +834,16 @@ onMounted(() => {
     background: #068033;
   }
 
+  .docx, .doc {
+    background: #2C59B7;
+  }
+
+  .md {
+    background: #020817;
+  }
+
+
+
   button.main-btn {
     font-weight: bold;
     font-size: 14px;

web/src/views/GraphView.vue

@@ -50,12 +50,13 @@
       @ok="addDocumentByFile"
       @cancel="() => state.showModal = false"
       ok-text="添加到图数据库" cancel-text="取消"
+      :ok-button-props="{ disabled: disabled }"
       :confirm-loading="state.precessing">
       <div v-if="graphInfo?.embed_model_name">
         <p>当前图数据库向量模型：{{ graphInfo?.embed_model_name }}</p>
-        <p>当前所选择的向量模型是 {{ configStore.config.embed_model }}</p>
+        <p>当前所选择的向量模型是 {{ cur_embed_model }}</p>
       </div>
-      <p v-else>第一次创建之后将无法修改向量模型，当前向量模型 {{ configStore.config.embed_model }}</p>
+      <p v-else>第一次创建之后将无法修改向量模型，当前向量模型 {{ cur_embed_model }}</p>
       <div class="upload">
         <a-upload-dragger
           class="upload-dragger"
@@ -63,7 +64,7 @@
           name="file"
           :fileList="fileList"
           :max-count="1"
-          :disabled="state.precessing || (graphInfo?.embed_model_name && graphInfo?.embed_model_name !== configStore.config.embed_model)"
+          :disabled="disabled"
           action="/api/data/upload"
           @change="handleFileUpload"
           @drop="handleDrop"
@@ -87,6 +88,12 @@ import { UploadOutlined } from '@ant-design/icons-vue';
 import HeaderComponent from '@/components/HeaderComponent.vue';
 
 const configStore = useConfigStore()
+const cur_embed_model = computed(() => configStore.config.embed_model_names[configStore.config.embed_model].name)
+const disabled = computed(() => {
+  if (state.precessing) return true
+  if (graphInfo?.value?.embed_model_name !== cur_embed_model.value) return true
+  return false
+})
 
 let graphInstance
 const graphInfo = ref(null)
@@ -152,7 +159,7 @@ const getGraphData = () => {
 const addDocumentByFile = () => {
   state.precessing = true
   const files = fileList.value.filter(file => file.status === 'done').map(file => file.response.file_path)
-  fetch('/api/data/graph/add', {
+  fetch('/api/data/graph/add-by-jsonl', {
     method: 'POST',
     headers: {
       "Content-Type": "application/json"  // 添加 Content-Type 头
@@ -178,7 +185,7 @@ const loadSampleNodes = () => {
     .then((res) => {
       if (res.ok) {
         return res.json();
-      } else if (!configStore.config.enable_knowledge_graph) {
+      } else if (configStore?.config && !configStore?.config.enable_knowledge_graph) {
         throw new Error('请前往设置页面配置启用知识图谱')
       } else {
         throw new Error("加载失败");

web/src/views/SettingView.vue

@@ -14,15 +14,15 @@
       </template>
     </HeaderComponent>
     <div class="setting-container layout-container">
-      <div class="sider">
+      <div class="sider" v-if="state.windowWidth > 520">
         <a-button type="text" :class="{ activesec: state.section === 'base'}" @click="state.section='base'" :icon="h(SettingOutlined)"> 基本设置 </a-button>
         <a-button type="text" :class="{ activesec: state.section === 'model'}" @click="state.section='model'" :icon="h(CodeOutlined)"> 模型配置 </a-button>
         <a-button type="text" :class="{ activesec: state.section === 'path'}" @click="state.section='path'" :icon="h(FolderOutlined)"> 路径配置 </a-button>
       </div>
-      <div class="setting" v-if="state.section === 'base'">
+      <div class="setting" v-if="state.windowWidth <= 520 || state.section === 'base'">
         <h3>基础模型配置</h3>
         <div class="section">
-          <div class="card">
+          <div class="card card-select">
             <span class="label">{{ items?.embed_model.des }}</span>
             <a-select style="width: 300px"
               :value="configStore.config?.embed_model"
@@ -34,7 +34,7 @@
               </a-select-option>
             </a-select>
           </div>
-          <div class="card">
+          <div class="card card-select">
             <span class="label">{{ items?.reranker.des }}</span>
             <a-select style="width: 300px"
               :value="configStore.config?.reranker"
@@ -81,7 +81,7 @@
         </div>
         <h3>检索配置</h3>
         <div class="section">
-          <div class="card">
+          <div class="card card-select">
             <span class="label">{{ items?.use_rewrite_query.des }}</span>
             <a-select style="width: 200px"
               :value="configStore.config?.use_rewrite_query"
@@ -95,7 +95,7 @@
           </div>
         </div>
       </div>
-      <div class="setting" v-if="state.section === 'model'">
+      <div class="setting" v-if="state.windowWidth <= 520 || state.section === 'model'">
         <h3>模型配置</h3>
         <p>请在 <code>src/.env</code> 文件中配置对应的 APIKEY</p>
         <div class="model-provider-card">
@@ -184,7 +184,7 @@
           </div>
         </div>
       </div>
-      <div class="setting" v-if="state.section ==='path'">
+      <div class="setting" v-if="state.windowWidth <= 520 || state.section ==='path'">
         <h3>本地模型配置</h3>
         <p>如果是 Docker 启动，务必确保在环境变量中设置了 MODEL_DIR，或者设置了 volumes 映射</p>
         <TableConfigComponent
@@ -198,7 +198,7 @@
 
 <script setup>
 import { message } from 'ant-design-vue';
-import { computed, reactive, ref, h, watch } from 'vue'
+import { computed, reactive, ref, h, watch, onMounted, onUnmounted } from 'vue'
 import { useConfigStore } from '@/stores/config';
 import {
   ReloadOutlined,
@@ -231,16 +231,17 @@ const customModel = reactive({
 })
 const state = reactive({
   loading: false,
-  section: 'base'
+  section: 'base',
+  windowWidth: window?.innerWidth || 0
 })
 
 // 筛选 modelStatus 中为真的key
 const modelKeys = computed(() => {
-  return Object.keys(modelStatus.value).filter(key => modelStatus.value[key])
+  return Object.keys(modelStatus.value || {}).filter(key => modelStatus.value?.[key])
 })
 
 const notModelKeys = computed(() => {
-  return Object.keys(modelStatus.value).filter(key => !modelStatus.value[key])
+  return Object.keys(modelStatus.value || {}).filter(key => !modelStatus.value?.[key])
 })
 
 const generateRandomHash = (length) => {
@@ -360,6 +361,19 @@ const handleCancelCustomModel = () => {
   customModel.visible = false
 }
 
+const updateWindowWidth = () => {
+  state.windowWidth = window?.innerWidth || 0
+}
+
+onMounted(() => {
+  updateWindowWidth()
+  window.addEventListener('resize', updateWindowWidth)
+})
+
+onUnmounted(() => {
+  window.removeEventListener('resize', updateWindowWidth)
+})
+
 const sendRestart = () => {
   console.log('Restarting...')
   message.loading({ content: '重新加载模型中', key: "restart", duration: 0 });
@@ -618,7 +632,18 @@ const sendRestart = () => {
 
     }
   }
+}
 
+@media (max-width: 520px) {
+  .setting-container {
+    flex-direction: column;
+  }
+
+  .card.card-select {
+    gap: 0.75rem;
+    align-items: flex-start;
+    flex-direction: column;
+  }
 }
 </style>
 

web/vite.config.js

@@ -14,7 +14,7 @@ export default defineConfig(({ mode }) => {
     server: {
       proxy: {
         '^/api': {
-          target: env.VITE_API_URL || 'http://localhost:5000',
+          target: env.VITE_API_URL || 'http://localhost:5050',
           changeOrigin: true,
           rewrite: (path) => path.replace(/^\/api/, '')
         }