diff --git a/docs/dev-ops/pgsql/sql/weform_run.sql b/docs/dev-ops/pgsql/sql/weform_run.sql
index d7da1b9..137cc98 100644
--- a/docs/dev-ops/pgsql/sql/weform_run.sql
+++ b/docs/dev-ops/pgsql/sql/weform_run.sql
@@ -19,7 +19,7 @@ CREATE TABLE vector_store (
     id VARCHAR(255) PRIMARY KEY,
     content TEXT,
     metadata JSONB,
-    embedding vector(1536),
+    embedding vector(1024),
 
     -- 关联关系
     project_id BIGINT,
@@ -97,7 +97,7 @@ COMMENT ON TABLE vector_store IS '向量存储表 - 用于RAG文档检索和知
 COMMENT ON COLUMN vector_store.id IS '文档ID（字符串类型，兼容PgVectorStore）';
 COMMENT ON COLUMN vector_store.content IS '文档内容文本';
 COMMENT ON COLUMN vector_store.metadata IS '文档元数据（JSONB格式，兼容PgVectorStore）';
-COMMENT ON COLUMN vector_store.embedding IS '向量嵌入（1536维）';
+COMMENT ON COLUMN vector_store.embedding IS '向量嵌入（1024维，阿里云text-embedding-v4）';
 COMMENT ON COLUMN vector_store.project_id IS '关联项目ID';
 COMMENT ON COLUMN vector_store.timeline_node_id IS '关联时间节点ID';
 COMMENT ON COLUMN vector_store.kb_id IS '关联知识库ID';
@@ -1102,7 +1102,7 @@ CREATE TABLE ai_chat_history (
     -- 消息内容
                                  role VARCHAR(20) NOT NULL,
                                  content TEXT NOT NULL,
-                                 content_embedding vector(1536),
+                                 content_embedding vector(1024),
 
     -- 引用的知识库文档
                                  referenced_doc_ids BIGINT[],
@@ -1170,7 +1170,7 @@ CREATE TABLE ai_analysis_result (
     -- 输入输出
                                     input_data JSONB,
                                     result_data JSONB,
-                                    result_embedding vector(1536),
+                                    result_embedding vector(1024),
 
     -- 模型信息
                                     confidence DECIMAL(5,2),
@@ -1333,7 +1333,7 @@ INSERT INTO sys_role (role_code, role_name, role_type, description, data_scope,
 -- 初始化系统配置
 INSERT INTO sys_config (config_key, config_value, config_type, description) VALUES
                                                                                 ('ai.model.default', 'gpt-4o', 'ai', '默认AI模型'),
-                                                                                ('ai.embedding.model', 'text-embedding-3-small', 'ai', '向量嵌入模型'),
-                                                                                ('ai.embedding.dimension', '1536', 'ai', '向量维度'),
+                                                                                ('ai.embedding.model', 'text-embedding-v4', 'ai', '向量嵌入模型'),
+                                                                                ('ai.embedding.dimension', '1024', 'ai', '向量维度'),
                                                                                 ('ai.rag.top_k', '5', 'ai', 'RAG检索返回数量'),
                                                                                 ('ai.rag.similarity_threshold', '0.7', 'ai', 'RAG相似度阈值');
diff --git a/src/main/java/cn/yinlihupo/common/config/SpringAiConfig.java b/src/main/java/cn/yinlihupo/common/config/SpringAiConfig.java
index 68ff1b3..646fbe2 100644
--- a/src/main/java/cn/yinlihupo/common/config/SpringAiConfig.java
+++ b/src/main/java/cn/yinlihupo/common/config/SpringAiConfig.java
@@ -39,7 +39,7 @@ public class SpringAiConfig {
     @Primary
     public VectorStore vectorStore(JdbcTemplate jdbcTemplate, EmbeddingModel embeddingModel) {
         return PgVectorStore.builder(jdbcTemplate, embeddingModel)
-                .dimensions(1536)  // 向量维度，与配置一致
+                .dimensions(1024)  // 向量维度，阿里云text-embedding-v4生成1024维向量
                 .distanceType(PgVectorStore.PgDistanceType.COSINE_DISTANCE)
                 .initializeSchema(false)  // 禁用自动初始化，使用SQL文件中已创建的表
                 .build();