Merge remote-tracking branch 'origin/main' into agent-update

.github/ISSUE_TEMPLATE/提交一个bug.md

@@ -0,0 +1,35 @@
+---
+name: 提交一个BUG
+about: 提交一个 BUG
+title: "[BUG]"
+labels: bug
+assignees: xerrors
+
+---
+
+### 描述这个 bug
+
+简单描述一下这个 BUG：
+
+### 如何复现
+
+什么情况下出现：
+
+### 相关信息
+
+**1）检查 `docker logs`**
+
+请运行以下命令，并提供部分相关日志：
+
+```sh
+docker logs --tail=100 api-dev
+```
+
+如果问题与模型调用相关，请尝试切换到其他在线模型
+
+**2）相关截图**
+
+
+
+
+**3）其他相关信息**

docker/docker-compose.dev.yml

@@ -61,6 +61,7 @@ services:
       - NEO4J_AUTH=neo4j/0123456789
       - NEO4J_server_bolt_listen__address=0.0.0.0:7687
       - NEO4J_server_http_listen__address=0.0.0.0:7474
+      - ENTITY_EMBEDDING=true
     networks:
       - app-network
 

src/core/graphbase.py

@@ -218,7 +218,19 @@ class GraphDatabase:
 
     def query_by_vector(self, entity_name, threshold=0.9, kgdb_name='neo4j', hops=2, num_of_res=5):
         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 query(tx, text):
+            # 首先检查索引是否存在
+            if not _index_exists(tx, "entityEmbeddings"):
+                raise Exception("向量索引不存在，请先创建索引")
+
             embedding = self.get_embedding(text)
             result = tx.run("""
             CALL db.index.vector.queryNodes('entityEmbeddings', 10, $embedding)
@@ -227,9 +239,14 @@ class GraphDatabase:
             """, embedding=embedding)
             return result.values()
 
-        with self.driver.session() as session:
-            results = session.execute_read(query, entity_name)
-
+        try:
+            with self.driver.session() as session:
+                results = session.execute_read(query, entity_name)
+        except Exception as e:
+            if "向量索引不存在" in str(e):
+                logger.error(f"向量索引不存在，请先创建索引: {e}, {traceback.format_exc()}")
+                return []
+            raise e
 
         # 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]

src/core/retriever.py

@@ -76,6 +76,8 @@ class Retriever:
         results = []
         if refs["meta"].get("use_graph") and config.enable_knowledge_base:
             for entity in refs["entities"]:
+                if entity == "":
+                    continue
                 result = graph_base.query_by_vector(entity)
                 if result != []:
                     results.extend(result)

web/src/components/ChatComponent.vue

@@ -434,7 +434,7 @@ const fetchChatResponse = (user_input, cur_res_id) => {
 
   const params = {
     query: user_input,
-    history: getHistory(),
+    history: getHistory().slice(0, -1), // 去掉最后一条刚添加的用户消息,
     meta: meta,
     cur_res_id: cur_res_id,
   }