diff --git a/docs/DEVELOPER_GUIDE-en.md b/docs/DEVELOPER_GUIDE-en.md
index 6f204cba8..7a17ee556 100644
--- a/docs/DEVELOPER_GUIDE-en.md
+++ b/docs/DEVELOPER_GUIDE-en.md
@@ -178,13 +178,16 @@ Configuration prefix: `spring.ai.alibaba.data-agent.vector-store`
 
 | Configuration Item | Description | Default Value |
 |-------------------|-------------|---------------|
-| `default-similarity-threshold` | Global default similarity threshold | 0.4 |
-| `table-similarity-threshold` | Table recall similarity threshold | 0.2 |
+| `default-similarity-threshold` | Global default similarity threshold (used by business knowledge, agent knowledge, etc.) | 0.4 |
+| `table-similarity-threshold` | Table recall similarity threshold (kept low to avoid missing tables during recall) | 0.2 |
 | `batch-del-topk-limit` | Maximum documents for batch deletion | 5000 |
 | `default-topk-limit` | Global default max documents returned (currently only used by business knowledge and agent knowledge) | 8 |
 | `table-topk-limit` | Maximum documents for table recall | 10 |
-| `enable-hybrid-search` | Enable hybrid search | false |
-| `elasticsearch-min-score` | ES keyword search minimum score threshold | 0.5 |
+| `embedding-dimension` | Expected embedding dimension for the persistent vector store; must match the embedding model's output dimension. A value of `0` disables the check (the in-memory store defaults to 0) | 0 |
+| `enable-hybrid-search` | Enable hybrid search (vector retrieval + ES keyword retrieval); only effective with Elasticsearch | false |
+| `hybrid-search-timeout-ms` | Maximum wait time (ms) for each retrieval branch in hybrid search | 3000 |
+| `elasticsearch-min-score` | ES keyword search minimum score threshold, used to filter out low-relevance documents | 0.5 |
+| `file-path` | Local serialization file path for `SimpleVectorStore` (in-memory store only) | `./vectorstore/vectorstore.json` |
 
 #### Vector Store Dependency Extension
 
@@ -202,8 +205,74 @@ The project uses in-memory vector store (`SimpleVectorStore`) by default. To use
 
 2. **Configure Properties**: Add the corresponding vector store connection configuration in `application.yml`. For specific parameters, refer to [Spring AI Official Documentation](https://springdoc.cn/spring-ai/api/vectordbs.html).
 
-2. **Configure `spring.ai.vectorstore.type`**. You can find the specific value after importing the vector store starter above by searching for `VectorStoreAutoConfiguration` auto-configuration class. For example, for `es` it's `ElasticsearchVectorStoreAutoConfiguration`, and you can see that `spring.ai.vectorstore.type` expects `elasticsearch`.
+3. **Configure `spring.ai.vectorstore.type`**. You can find the specific value after importing the vector store starter above by searching for the `VectorStoreAutoConfiguration` auto-configuration class. For example, for `es` it's `ElasticsearchVectorStoreAutoConfiguration`, and you can see that `spring.ai.vectorstore.type` expects `elasticsearch`.
+
+4. **Configure `embedding-dimension`**: When using a persistent vector store, set `spring.ai.alibaba.data-agent.vector-store.embedding-dimension` to match your embedding model's output dimension (e.g. `1024`). This validates the dimension at startup and avoids retrieval failures after documents are written.
+
+#### Ready-to-Use Configuration Examples
+
+The project ships two ready-to-activate vector store example profiles under `data-agent-management/src/main/resources/`. Activate the corresponding profile via `spring.profiles.active` or the `SPRING_PROFILES_ACTIVE` environment variable — no manual connection configuration required.
+
+**Milvus (`application-milvus.yml`)**
+
+```yaml
+spring:
+  ai:
+    vectorstore:
+      type: milvus
+      milvus:
+        client:
+          host: ${MILVUS_HOST:127.0.0.1}
+          port: ${MILVUS_PORT:19530}
+        database-name: ${MILVUS_DATABASE:default}
+        collection-name: ${MILVUS_COLLECTION:vector_store}
+        embedding-dimension: ${MILVUS_DIMENSION:1024}
+        initialize-schema: true
+    alibaba:
+      data-agent:
+        vector-store:
+          embedding-dimension: ${MILVUS_DIMENSION:1024}
+```
+
+The `spring-ai-starter-vector-store-milvus` dependency is already bundled in `data-agent-management/pom.xml`, so no extra dependency is needed — just activate the profile:
+
+```bash
+export SPRING_PROFILES_ACTIVE=milvus
+# Optional: override the default connection info
+export MILVUS_HOST=127.0.0.1
+export MILVUS_PORT=19530
+```
+
+**Elasticsearch (`application-elasticsearch.yml`)**
+
+```yaml
+spring:
+  elasticsearch:
+    uris: ${ELASTICSEARCH_URIS:http://127.0.0.1:9200}
+    username: ${ELASTICSEARCH_USERNAME:}
+    password: ${ELASTICSEARCH_PASSWORD:}
+  ai:
+    vectorstore:
+      type: elasticsearch
+      elasticsearch:
+        index-name: ${ELASTICSEARCH_INDEX_NAME:spring-ai-document-index}
+        dimensions: ${ELASTICSEARCH_DIMENSIONS:1024}
+        initialize-schema: ${ELASTICSEARCH_INITIALIZE_SCHEMA:true}
+    alibaba:
+      data-agent:
+        vector-store:
+          embedding-dimension: ${ELASTICSEARCH_DIMENSIONS:1024}
+```
+
+The `spring-ai-starter-vector-store-elasticsearch` dependency is already bundled in `data-agent-management/pom.xml`, so no extra dependency is needed — just activate the profile:
+
+```bash
+export SPRING_PROFILES_ACTIVE=elasticsearch
+# Optional: override the default connection info
+export ELASTICSEARCH_URIS=http://127.0.0.1:9200
+```
 
+> Tip: Elasticsearch supports hybrid search. After activating the ES profile, set `spring.ai.alibaba.data-agent.vector-store.enable-hybrid-search` to `true` to enable the weighted fusion of vector retrieval and keyword retrieval.
 
 #### ES Schema Configuration Example
 Below is the Elasticsearch Schema structure. Other vector stores (like Milvus, PGVector) can reference this structure to create their Schema, paying special attention to the data types of fields in `metadata`.
diff --git a/docs/DEVELOPER_GUIDE.md b/docs/DEVELOPER_GUIDE.md
index 8bc557c72..30ccc4046 100644
--- a/docs/DEVELOPER_GUIDE.md
+++ b/docs/DEVELOPER_GUIDE.md
@@ -178,13 +178,16 @@ public class AgentVectorStoreService {
 
 | 配置项 | 说明 | 默认值 |
 |--------|------|--------|
-| `default-similarity-threshold` | 全局默认相似度阈值 | 0.4 |
-| `table-similarity-threshold` | 召回表的相似度阈值 | 0.2 |
+| `default-similarity-threshold` | 全局默认相似度阈值（用于业务知识、智能体知识等） | 0.4 |
+| `table-similarity-threshold` | 召回表的相似度阈值（设置较低以尽量避免表召回遗漏） | 0.2 |
 | `batch-del-topk-limit` | 批量删除时的最大文档数量 | 5000 |
 | `default-topk-limit` | 全局默认查询返回的最大文档数量（目前只有业务知识和智能体知识在使用） | 8 |
 | `table-topk-limit` | 召回表的最大文档数量 | 10 |
-| `enable-hybrid-search` | 是否启用混合搜索 | false |
-| `elasticsearch-min-score` | ES关键词搜索的最小分数阈值 | 0.5 |
+| `embedding-dimension` | 持久化向量库期望的向量维度校验值，需与嵌入模型输出维度一致；设为 `0` 时关闭校验（内存向量库默认即为 0） | 0 |
+| `enable-hybrid-search` | 是否启用混合搜索（向量检索 + ES 关键词检索），仅在使用 Elasticsearch 时生效 | false |
+| `hybrid-search-timeout-ms` | 混合检索中每个检索分支的最大等待时间（毫秒） | 3000 |
+| `elasticsearch-min-score` | ES 关键词搜索的最小分数阈值，用于过滤相关性较低的文档 | 0.5 |
+| `file-path` | `SimpleVectorStore` 本地序列化文件地址（仅内存向量库使用） | `./vectorstore/vectorstore.json` |
 
 #### 向量库依赖扩展
 
@@ -202,8 +205,74 @@ public class AgentVectorStoreService {
    
 2. **配置属性**: 在 `application.yml` 中添加对应向量库的连接配置。具体参数请参考 [Spring AI 官方文档](https://springdoc.cn/spring-ai/api/vectordbs.html)。
 
-2. **配置 `spring.ai.vectorstore.type`**。具体填写的值可以在引入上面的向量库starter后自行搜索 `VectorStoreAutoConfiguration`自动配置类，比如`es`的是`ElasticsearchVectorStoreAutoConfiguration`，该类里面可以看见`spring.ai.vectorstore.type`期望的是`elasticsearch`。
+3. **配置 `spring.ai.vectorstore.type`**。具体填写的值可以在引入上面的向量库 starter 后自行搜索 `VectorStoreAutoConfiguration` 自动配置类，比如 `es` 的是 `ElasticsearchVectorStoreAutoConfiguration`，该类里面可以看见 `spring.ai.vectorstore.type` 期望的是 `elasticsearch`。
+
+4. **配置 `embedding-dimension`**: 使用持久化向量库时，建议将 `spring.ai.alibaba.data-agent.vector-store.embedding-dimension` 设置为与嵌入模型输出维度一致的值（如 `1024`），以便启动时校验维度是否匹配，避免写入后检索异常。
+
+#### 开箱即用的配置示例
+
+项目已内置两个可直接激活的向量库示例 Profile，位于 `data-agent-management/src/main/resources/`。通过 `spring.profiles.active` 或环境变量 `SPRING_PROFILES_ACTIVE` 激活对应 Profile 即可，无需手动编写连接配置。
+
+**Milvus (`application-milvus.yml`)**
+
+```yaml
+spring:
+  ai:
+    vectorstore:
+      type: milvus
+      milvus:
+        client:
+          host: ${MILVUS_HOST:127.0.0.1}
+          port: ${MILVUS_PORT:19530}
+        database-name: ${MILVUS_DATABASE:default}
+        collection-name: ${MILVUS_COLLECTION:vector_store}
+        embedding-dimension: ${MILVUS_DIMENSION:1024}
+        initialize-schema: true
+    alibaba:
+      data-agent:
+        vector-store:
+          embedding-dimension: ${MILVUS_DIMENSION:1024}
+```
+
+`spring-ai-starter-vector-store-milvus` 依赖已包含在 `data-agent-management/pom.xml` 中，无需额外引入，激活 Profile 即可：
+
+```bash
+export SPRING_PROFILES_ACTIVE=milvus
+# 可选：覆盖默认连接信息
+export MILVUS_HOST=127.0.0.1
+export MILVUS_PORT=19530
+```
+
+**Elasticsearch (`application-elasticsearch.yml`)**
+
+```yaml
+spring:
+  elasticsearch:
+    uris: ${ELASTICSEARCH_URIS:http://127.0.0.1:9200}
+    username: ${ELASTICSEARCH_USERNAME:}
+    password: ${ELASTICSEARCH_PASSWORD:}
+  ai:
+    vectorstore:
+      type: elasticsearch
+      elasticsearch:
+        index-name: ${ELASTICSEARCH_INDEX_NAME:spring-ai-document-index}
+        dimensions: ${ELASTICSEARCH_DIMENSIONS:1024}
+        initialize-schema: ${ELASTICSEARCH_INITIALIZE_SCHEMA:true}
+    alibaba:
+      data-agent:
+        vector-store:
+          embedding-dimension: ${ELASTICSEARCH_DIMENSIONS:1024}
+```
+
+`spring-ai-starter-vector-store-elasticsearch` 依赖已包含在 `data-agent-management/pom.xml` 中，无需额外引入，激活 Profile 即可：
+
+```bash
+export SPRING_PROFILES_ACTIVE=elasticsearch
+# 可选：覆盖默认连接信息
+export ELASTICSEARCH_URIS=http://127.0.0.1:9200
+```
 
+> 提示：Elasticsearch 支持混合检索。激活 ES Profile 后，再将 `spring.ai.alibaba.data-agent.vector-store.enable-hybrid-search` 设为 `true` 即可启用向量检索与关键词检索的混合融合策略。
 
 #### ES Schema 配置示例
 以下为 Elasticsearch 的 Schema 结构。其他向量库（如 Milvus, PGVector）可参考此结构建立 Schema，尤其要注意 `metadata` 中的字段数据类型。
diff --git a/docs/QUICK_START-en.md b/docs/QUICK_START-en.md
index b3708bb14..a82b47ea7 100644
--- a/docs/QUICK_START-en.md
+++ b/docs/QUICK_START-en.md
@@ -189,6 +189,8 @@ Below is the ES schema structure. For other vector stores like Milvus, PG, etc.,
 ### 2.7 Replace Vector Store Implementation
 
 > To replace the default in-memory vector store (for example, with PGVector or Milvus), see [Developer Guide - Vector Store Dependency Extension](DEVELOPER_GUIDE-en.md#vector-store-dependency-extension).
+>
+> The project ships two ready-to-use example profiles, `application-milvus.yml` and `application-elasticsearch.yml`. Switch quickly with `SPRING_PROFILES_ACTIVE=milvus` (or `elasticsearch`); see [Developer Guide - Ready-to-Use Configuration Examples](DEVELOPER_GUIDE-en.md#ready-to-use-configuration-examples).
 
 ### 2.8 Configure the Python Sandbox
 
diff --git a/docs/QUICK_START.md b/docs/QUICK_START.md
index 9754c1193..7d3ed9458 100644
--- a/docs/QUICK_START.md
+++ b/docs/QUICK_START.md
@@ -188,6 +188,8 @@ spring:
 ### 2.7 替换vector-store的实现类
 
 > 关于如何替换默认的内存向量库（如使用 PGVector、Milvus 等），请参考 [开发者指南 - 向量库依赖扩展](DEVELOPER_GUIDE.md#向量库依赖扩展)。
+>
+> 项目已内置 `application-milvus.yml` 与 `application-elasticsearch.yml` 两个开箱即用的示例 Profile，通过 `SPRING_PROFILES_ACTIVE=milvus`（或 `elasticsearch`）即可快速切换，详见 [开发者指南 - 开箱即用的配置示例](DEVELOPER_GUIDE.md#开箱即用的配置示例)。
 
 ### 2.8 配置 Python 沙盒