Merge pull request #292 from yangzhen233/feature/rerank-model

Feature/rerank model

README.md

@@ -31,7 +31,7 @@
 
 |     模块     | 现有能力 
 |:----------:|---
-|  **模型管理**  | 多模型接入(OpenAI/DeepSeek/通义/智谱)、多模态理解、Coze/DIFY/FastGPT平台集成 
+|  **模型管理**  | 多模型接入(OpenAI/DeepSeek/通义/智谱/MiniMax)、多模态理解、Coze/DIFY/FastGPT平台集成
 |  **知识管理**  | 本地RAG + 向量库(Milvus/Weaviate/Qdrant)  + 文档解析
 |  **工具管理**  | Mcp协议集成、Skills能力 + 可扩展工具生态                             
 |  **流程编排**  | 可视化工作流设计器、节点拖拽编排、SSE流式执行,目前已经支持模型调用,邮件发送,人工审核等节点  

README_EN.md

@@ -34,7 +34,7 @@
 
 | Module | Current Capabilities |
 |:---:|---|
-| **Model Management** | Multi-model integration (OpenAI/DeepSeek/Tongyi/Zhipu), multi-modal understanding, Coze/DIFY/FastGPT platform integration |
+| **Model Management** | Multi-model integration (OpenAI/DeepSeek/Tongyi/Zhipu/MiniMax), multi-modal understanding, Coze/DIFY/FastGPT platform integration |
 | **Knowledge Base** | Local RAG + Vector DB (Milvus/Weaviate/Qdrant) + Document parsing |
 | **Tool Management** | MCP protocol integration, Skills capability + Extensible tool ecosystem |
 | **Workflow Orchestration** | Visual workflow designer, drag-and-drop node orchestration, SSE streaming execution, currently supports model calls, email sending, manual review nodes |

docs/docker/ruoyi-ai/docker-compose-all.yaml

@@ -65,7 +65,7 @@ services:
       - "28080:8080"
     environment:
       QUERY_DEFAULTS_LIMIT: 25
-      AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: true
+      AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: "true"
       PERSISTENCE_DATA_PATH: /var/lib/weaviate
       DEFAULT_VECTORIZER_MODULE: none
       ENABLE_MODULES: text2vec-cohere,text2vec-huggingface,text2vec-palm,text2vec-openai,generative-openai,generative-cohere,generative-palm,ref2vec-centroid,reranker-cohere,qna-openai

docs/script/sql/ruoyi-ai-v3_mysql8.sql

@@ -72,8 +72,9 @@ CREATE TABLE `chat_model`  (
 -- ----------------------------
 -- Records of chat_model
 -- ----------------------------
-INSERT INTO `chat_model` VALUES (2000585866022060033, 'chat', 'deepseek/deepseek-v3.2', 'ppio', 'deepseek', NULL, 'Y', 'https://api.ppinfra.com/openai', 'sk_xx', 103, 1, '2025-12-15 23:16:54', 1, '2026-03-15 19:18:48', 'DeepSeek-V3.2 是一款在高效推理、复杂推理能力与智能体场景中表现突出的领先模型。其基于 DeepSeek Sparse Attention（DSA）稀疏注意力机制，在显著降低计算开销的同时优化长上下文性能；通过可扩展强化学习框架，整体能力达到 GPT-5 同级，高算力版本 V3.2-Speciale 更在推理表现上接近 Gemini-3.0-Pro；同时，模型依托大型智能体任务合成管线，具备更强的工具调用与多步骤决策能力，并在 2025 年 IMO 与 IOI 中取得金牌级表现。作为 MaaS 平台，我们已对 DeepSeek-V3.2 完成深度适配，通过动态调度、批处理加速、低延迟推理与企业级 SLA 保障，进一步增强其在企业生产环境中的稳定性、性价比与可控性，适用于搜索、问答、智能体、代码、数据处理等多类高价值场景。', 0);
+INSERT INTO `chat_model` VALUES (2000585866022060033, 'chat', 'zai-org/glm-5', 'ppio', 'zai-org/glm-5', NULL, 'Y', 'https://api.ppio.com/openai', 'sk_xx', 103, 1, '2025-12-15 23:16:54', 1, '2026-03-15 19:18:48', 'DeepSeek-V3.2 是一款在高效推理、复杂推理能力与智能体场景中表现突出的领先模型。其基于 DeepSeek Sparse Attention（DSA）稀疏注意力机制，在显著降低计算开销的同时优化长上下文性能；通过可扩展强化学习框架，整体能力达到 GPT-5 同级，高算力版本 V3.2-Speciale 更在推理表现上接近 Gemini-3.0-Pro；同时，模型依托大型智能体任务合成管线，具备更强的工具调用与多步骤决策能力，并在 2025 年 IMO 与 IOI 中取得金牌级表现。作为 MaaS 平台，我们已对 DeepSeek-V3.2 完成深度适配，通过动态调度、批处理加速、低延迟推理与企业级 SLA 保障，进一步增强其在企业生产环境中的稳定性、性价比与可控性，适用于搜索、问答、智能体、代码、数据处理等多类高价值场景。', 0);
-INSERT INTO `chat_model` VALUES (2007528268536287233, 'vector', 'baai/bge-m3', 'ppio', 'bge-m3', 1024, 'N', 'https://api.ppinfra.com/openai', 'sk_xx', 103, 1, '2026-01-04 03:03:32', 1, '2026-03-15 19:18:51', 'BGE-M3 是一款具备多维度能力的文本嵌入模型，可同时实现密集检索、多向量检索和稀疏检索三大核心功能。该模型设计上兼容超过100种语言，并支持从短句到长达8192词元的长文本等多种输入形式。在跨语言检索任务中，BGE-M3展现出显著优势，其性能在MIRACL、MKQA等国际基准测试中位居前列。此外，针对长文档检索场景，该模型在MLDR、NarritiveQA等数据集上的表现同样达到行业领先水平。', 0);
+INSERT INTO `chat_model` VALUES (2007528268536287233, 'vector', 'baai/bge-m3', 'ppio', 'bge-m3', 1024, 'N', 'https://api.ppio.com/openai', 'sk_xx', 103, 1, '2026-01-04 03:03:32', 1, '2026-03-15 19:18:51', 'BGE-M3 是一款具备多维度能力的文本嵌入模型，可同时实现密集检索、多向量检索和稀疏检索三大核心功能。该模型设计上兼容超过100种语言，并支持从短句到长达8192词元的长文本等多种输入形式。在跨语言检索任务中，BGE-M3展现出显著优势，其性能在MIRACL、MKQA等国际基准测试中位居前列。此外，针对长文档检索场景，该模型在MLDR、NarritiveQA等数据集上的表现同样达到行业领先水平。', 0);
+INSERT INTO `chat_model` VALUES (2045735140488847361, 'chat', 'deepseek-chat', 'custom_api', 'deepseek-chat', NULL, NULL, 'https://api.deepseek.com', 'sk_xx', 103, 1, '2026-04-19 13:24:00', 1, '2026-04-19 13:24:00', 'deepseek对话模型', 0);
 
 -- ----------------------------
 -- Table structure for chat_provider
@@ -95,22 +96,26 @@ CREATE TABLE `chat_provider`  (
                                   `update_time` datetime NULL DEFAULT NULL COMMENT '更新时间',
                                   `remark` varchar(500) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '备注',
                                   `version` int NULL DEFAULT NULL COMMENT '版本',
-                                  `del_flag` char(1) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT '0' COMMENT '删除标志（0代表存在 1代表删除）',
+                                  `del_flag` char(1) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT '0' COMMENT '删除标志',
                                   `update_ip` varchar(128) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '更新IP',
                                   `tenant_id` bigint NOT NULL DEFAULT 0 COMMENT '租户Id',
                                   PRIMARY KEY (`id`) USING BTREE,
-                                  UNIQUE INDEX `unique_provider_code`(`provider_code` ASC, `tenant_id` ASC) USING BTREE,
+                                  UNIQUE INDEX `unique_provider_code`(`provider_code` ASC, `tenant_id` ASC, `del_flag` ASC) USING BTREE,
                                   INDEX `idx_status`(`status` ASC) USING BTREE
-) ENGINE = InnoDB AUTO_INCREMENT = 2008460994477690882 CHARACTER SET = utf8mb4 COLLATE = utf8mb4_0900_ai_ci COMMENT = '厂商管理表' ROW_FORMAT = DYNAMIC;
+) ENGINE = InnoDB AUTO_INCREMENT = 2045727230803255298 CHARACTER SET = utf8mb4 COLLATE = utf8mb4_0900_ai_ci COMMENT = '厂商管理表' ROW_FORMAT = DYNAMIC;
 
 -- ----------------------------
 -- Records of chat_provider
 -- ----------------------------
-INSERT INTO `chat_provider` VALUES (1, 'OpenAI', 'openai', 'https://ruoyiai-1254149996.cos.ap-guangzhou.myqcloud.com/2026/02/25/01091be272334383a1efd9bc22b73ee6.png', 'OpenAI官方API服务商', 'https://api.openai.com', '0', 1, NULL, '2025-12-14 21:48:11', '1', '1', '2026-02-25 20:46:59', 'OpenAI厂商', NULL, '0', NULL, 0);
+INSERT INTO `chat_provider` VALUES (1, 'OpenAI', 'openai', 'https://ruoyiai-1254149996.cos.ap-guangzhou.myqcloud.com/2026/02/25/01091be272334383a1efd9bc22b73ee6.png', 'OpenAI官方API服务商', 'https://api.openai.com', '0', 1, 103, '2025-12-14 21:48:11', '1', '1', '2026-02-25 20:46:59', 'OpenAI厂商', NULL, '0', NULL, 0);
-INSERT INTO `chat_provider` VALUES (2, '阿里云百炼', 'qianwen', 'https://ruoyiai-1254149996.cos.ap-guangzhou.myqcloud.com/2026/02/25/de2aa7e649de44f3ba5c6380ac6acd04.png', '阿里云百炼大模型服务', 'https://dashscope.aliyuncs.com', '0', 2, NULL, '2025-12-14 21:48:11', '1', '1', '2026-02-25 20:49:13', '阿里云厂商', NULL, '0', NULL, 0);
+INSERT INTO `chat_provider` VALUES (11, '深度求索', 'deepseek', 'https://ruoyiai-1254149996.cos.ap-guangzhou.myqcloud.com/2026/04/19/5ba8c30f153246898a4d7dc7b846de8d.png', 'DeepSeek官方API', 'https://api.deepseek.com', '0', 0, 103, '2026-04-19 12:52:34', '1', '1', '2026-04-19 13:13:25', 'DeepSeek官方API', NULL, '0', NULL, 0);
-INSERT INTO `chat_provider` VALUES (3, '智谱AI', 'zhipu', 'https://ruoyi-ai-1254149996.cos.ap-guangzhou.myqcloud.com/2025/12/15/a43e98fb7b3b4861b8caa6184e6fa40a.png', '智谱AI大模型服务', 'https://open.bigmodel.cn', '0', 3, NULL, '2025-12-14 21:48:11', '1', '1', '2026-02-06 00:49:14', '智谱AI厂商', NULL, '1', NULL, 0);
+INSERT INTO `chat_provider` VALUES (12, '智谱AI', 'zhipu', 'https://ruoyiai-1254149996.cos.ap-guangzhou.myqcloud.com/2026/04/19/da071783c9284fdd9ed1ce1b57b3c75c.png', '智谱AI大模型服务', 'https://open.bigmodel.cn', '0', 4, 103, '2025-12-14 21:48:11', '1', '1', '2026-04-19 13:14:00', '智谱AI厂商', NULL, '0', NULL, 0);
-INSERT INTO `chat_provider` VALUES (5, 'ollama', 'ollama', 'https://ruoyiai-1254149996.cos.ap-guangzhou.myqcloud.com/2026/02/25/afecabebc8014d80b0f06b4796a74c5d.png', 'ollama大模型', 'http://127.0.0.1:11434', '0', 5, NULL, '2025-12-14 21:48:11', '1', '1', '2026-02-25 20:48:48', 'ollama厂商', NULL, '0', NULL, 0);
+INSERT INTO `chat_provider` VALUES (13, '小米MIMO', 'xiaomi', 'https://ruoyiai-1254149996.cos.ap-guangzhou.myqcloud.com/2026/04/19/18dd39365ce244e3ae5e030da036760e.png', '小米官方API', 'https://api.xiaomimimo.com/anthropic/v1/messages', '0', 3, 103, '2026-04-19 12:48:24', '1', '1', '2026-04-19 13:14:22', '小米官方API', NULL, '0', NULL, 0);
-INSERT INTO `chat_provider` VALUES (2000585060904435714, 'PPIO', 'ppio', 'https://ruoyiai-1254149996.cos.ap-guangzhou.myqcloud.com/2026/02/25/049bb6a507174f73bba4b8d8b9e55b8a.png', 'api聚合厂商', 'https://api.ppinfra.com/openai', '0', 5, 103, '2025-12-15 23:13:42', '1', '1', '2026-02-25 20:49:01', 'api聚合厂商', NULL, '0', NULL, 0);
+INSERT INTO `chat_provider` VALUES (14, '阿里云百炼', 'qianwen', 'https://ruoyiai-1254149996.cos.ap-guangzhou.myqcloud.com/2026/02/25/de2aa7e649de44f3ba5c6380ac6acd04.png', '阿里云百炼大模型服务', 'https://dashscope.aliyuncs.com', '0', 2, 103, '2025-12-14 21:48:11', '1', '1', '2026-02-25 20:49:13', '阿里云厂商', NULL, '0', NULL, 0);
+INSERT INTO `chat_provider` VALUES (15, 'PPIO', 'ppio', 'https://ruoyiai-1254149996.cos.ap-guangzhou.myqcloud.com/2026/02/25/049bb6a507174f73bba4b8d8b9e55b8a.png', 'api聚合厂商', 'https://api.ppinfra.com/openai', '0', 5, 103, '2025-12-15 23:13:42', '1', '1', '2026-02-25 20:49:01', 'api聚合厂商', NULL, '0', NULL, 0);
+INSERT INTO `chat_provider` VALUES (16, 'MiniMax', 'minimax', 'https://ruoyiai-1254149996.cos.ap-guangzhou.myqcloud.com/2026/04/19/fdc712e90e0e4d78b05862ad230884e5.png', 'MiniMax大模型服务，支持M2.7、M2.5等模型', 'https://api.minimax.io/v1', '0', 6, 103, '2026-04-19 12:50:12', '1', '1', '2026-04-19 13:14:59', 'MiniMax厂商', NULL, '0', NULL, 0);
+INSERT INTO `chat_provider` VALUES (17, 'ollama', 'ollama', 'https://ruoyiai-1254149996.cos.ap-guangzhou.myqcloud.com/2026/02/25/afecabebc8014d80b0f06b4796a74c5d.png', 'ollama大模型', 'http://127.0.0.1:11434', '0', 7, 103, '2025-12-14 21:48:11', '1', '1', '2026-02-25 20:48:48', 'ollama厂商', NULL, '0', NULL, 0);
+INSERT INTO `chat_provider` VALUES (18, '自定义厂商', 'custom_api', 'https://ruoyiai-1254149996.cos.ap-guangzhou.myqcloud.com/2026/04/19/c1a8e122510f4e2f90deb36958af710b.png', 'OPENAI兼容格式', '自定义', '0', 8, 103, '2026-04-19 12:35:57', '1', '1', '2026-04-19 13:17:20', 'OPENAI兼容格式', NULL, '0', NULL, 0);
 
 -- ----------------------------
 -- Table structure for chat_session

docs/script/sql/update/updat-0419.sql

@@ -0,0 +1,92 @@
+/*
+ Navicat Premium Dump SQL
+
+ Source Server         : localhost-mysql
+ Source Server Type    : MySQL
+ Source Server Version : 80045 (8.0.45)
+ Source Host           : localhost:3306
+ Source Schema         : ruoyi-ai
+
+ Target Server Type    : MySQL
+ Target Server Version : 80045 (8.0.45)
+ File Encoding         : 65001
+
+ Date: 19/04/2026 13:36:41
+*/
+
+SET NAMES utf8mb4;
+SET FOREIGN_KEY_CHECKS = 0;
+
+-- ----------------------------
+-- Table structure for chat_model
+-- ----------------------------
+DROP TABLE IF EXISTS `chat_model`;
+CREATE TABLE `chat_model`  (
+                               `id` bigint NOT NULL COMMENT '主键',
+                               `category` varchar(20) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '模型分类',
+                               `model_name` varchar(50) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '模型名称',
+                               `provider_code` varchar(20) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '模型供应商',
+                               `model_describe` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '模型描述',
+                               `model_dimension` int NULL DEFAULT NULL COMMENT '模型维度',
+                               `model_show` char(1) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '是否显示',
+                               `api_host` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '请求地址',
+                               `api_key` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '密钥',
+                               `create_dept` bigint NULL DEFAULT NULL COMMENT '创建部门',
+                               `create_by` bigint NULL DEFAULT NULL COMMENT '创建者',
+                               `create_time` datetime NULL DEFAULT NULL COMMENT '创建时间',
+                               `update_by` bigint NULL DEFAULT NULL COMMENT '更新者',
+                               `update_time` datetime NULL DEFAULT NULL COMMENT '更新时间',
+                               `remark` varchar(500) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '备注',
+                               `tenant_id` bigint NOT NULL DEFAULT 0 COMMENT '租户Id',
+                               PRIMARY KEY (`id`) USING BTREE
+) ENGINE = InnoDB CHARACTER SET = utf8mb4 COLLATE = utf8mb4_0900_ai_ci COMMENT = '模型管理' ROW_FORMAT = DYNAMIC;
+
+-- ----------------------------
+-- Records of chat_model
+-- ----------------------------
+INSERT INTO `chat_model` VALUES (2000585866022060033, 'chat', 'zai-org/glm-5', 'ppio', 'zai-org/glm-5', NULL, 'Y', 'https://api.ppio.com/openai', 'sk_xx', 103, 1, '2025-12-15 23:16:54', 1, '2026-03-15 19:18:48', 'DeepSeek-V3.2 是一款在高效推理、复杂推理能力与智能体场景中表现突出的领先模型。其基于 DeepSeek Sparse Attention（DSA）稀疏注意力机制，在显著降低计算开销的同时优化长上下文性能；通过可扩展强化学习框架，整体能力达到 GPT-5 同级，高算力版本 V3.2-Speciale 更在推理表现上接近 Gemini-3.0-Pro；同时，模型依托大型智能体任务合成管线，具备更强的工具调用与多步骤决策能力，并在 2025 年 IMO 与 IOI 中取得金牌级表现。作为 MaaS 平台，我们已对 DeepSeek-V3.2 完成深度适配，通过动态调度、批处理加速、低延迟推理与企业级 SLA 保障，进一步增强其在企业生产环境中的稳定性、性价比与可控性，适用于搜索、问答、智能体、代码、数据处理等多类高价值场景。', 0);
+INSERT INTO `chat_model` VALUES (2007528268536287233, 'vector', 'baai/bge-m3', 'ppio', 'bge-m3', 1024, 'N', 'https://api.ppio.com/openai', 'sk_xx', 103, 1, '2026-01-04 03:03:32', 1, '2026-03-15 19:18:51', 'BGE-M3 是一款具备多维度能力的文本嵌入模型，可同时实现密集检索、多向量检索和稀疏检索三大核心功能。该模型设计上兼容超过100种语言，并支持从短句到长达8192词元的长文本等多种输入形式。在跨语言检索任务中，BGE-M3展现出显著优势，其性能在MIRACL、MKQA等国际基准测试中位居前列。此外，针对长文档检索场景，该模型在MLDR、NarritiveQA等数据集上的表现同样达到行业领先水平。', 0);
+INSERT INTO `chat_model` VALUES (2045735140488847361, 'chat', 'deepseek-chat', 'custom_api', 'deepseek-chat', NULL, NULL, 'https://api.deepseek.com', 'sk_xx', 103, 1, '2026-04-19 13:24:00', 1, '2026-04-19 13:24:00', 'deepseek对话模型', 0);
+
+-- ----------------------------
+-- Table structure for chat_provider
+-- ----------------------------
+DROP TABLE IF EXISTS `chat_provider`;
+CREATE TABLE `chat_provider`  (
+                                  `id` bigint NOT NULL AUTO_INCREMENT COMMENT '主键',
+                                  `provider_name` varchar(100) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NOT NULL COMMENT '厂商名称',
+                                  `provider_code` varchar(50) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NOT NULL COMMENT '厂商编码',
+                                  `provider_icon` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '厂商图标',
+                                  `provider_desc` varchar(500) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '厂商描述',
+                                  `api_host` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT 'API地址',
+                                  `status` char(1) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT '0' COMMENT '状态（0正常 1停用）',
+                                  `sort_order` int NULL DEFAULT 0 COMMENT '排序',
+                                  `create_dept` bigint NULL DEFAULT NULL COMMENT '创建部门',
+                                  `create_time` datetime NULL DEFAULT NULL COMMENT '创建时间',
+                                  `create_by` varchar(64) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT '' COMMENT '创建者',
+                                  `update_by` varchar(64) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT '' COMMENT '更新者',
+                                  `update_time` datetime NULL DEFAULT NULL COMMENT '更新时间',
+                                  `remark` varchar(500) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '备注',
+                                  `version` int NULL DEFAULT NULL COMMENT '版本',
+                                  `del_flag` char(1) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT '0' COMMENT '删除标志',
+                                  `update_ip` varchar(128) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '更新IP',
+                                  `tenant_id` bigint NOT NULL DEFAULT 0 COMMENT '租户Id',
+                                  PRIMARY KEY (`id`) USING BTREE,
+                                  UNIQUE INDEX `unique_provider_code`(`provider_code` ASC, `tenant_id` ASC, `del_flag` ASC) USING BTREE,
+                                  INDEX `idx_status`(`status` ASC) USING BTREE
+) ENGINE = InnoDB AUTO_INCREMENT = 2045727230803255298 CHARACTER SET = utf8mb4 COLLATE = utf8mb4_0900_ai_ci COMMENT = '厂商管理表' ROW_FORMAT = DYNAMIC;
+
+-- ----------------------------
+-- Records of chat_provider
+-- ----------------------------
+INSERT INTO `chat_provider` VALUES (1, 'OpenAI', 'openai', 'https://ruoyiai-1254149996.cos.ap-guangzhou.myqcloud.com/2026/02/25/01091be272334383a1efd9bc22b73ee6.png', 'OpenAI官方API服务商', 'https://api.openai.com', '0', 1, 103, '2025-12-14 21:48:11', '1', '1', '2026-02-25 20:46:59', 'OpenAI厂商', NULL, '0', NULL, 0);
+INSERT INTO `chat_provider` VALUES (11, '深度求索', 'deepseek', 'https://ruoyiai-1254149996.cos.ap-guangzhou.myqcloud.com/2026/04/19/5ba8c30f153246898a4d7dc7b846de8d.png', 'DeepSeek官方API', 'https://api.deepseek.com', '0', 0, 103, '2026-04-19 12:52:34', '1', '1', '2026-04-19 13:13:25', 'DeepSeek官方API', NULL, '0', NULL, 0);
+INSERT INTO `chat_provider` VALUES (12, '智谱AI', 'zhipu', 'https://ruoyiai-1254149996.cos.ap-guangzhou.myqcloud.com/2026/04/19/da071783c9284fdd9ed1ce1b57b3c75c.png', '智谱AI大模型服务', 'https://open.bigmodel.cn', '0', 4, 103, '2025-12-14 21:48:11', '1', '1', '2026-04-19 13:14:00', '智谱AI厂商', NULL, '0', NULL, 0);
+INSERT INTO `chat_provider` VALUES (13, '小米MIMO', 'xiaomi', 'https://ruoyiai-1254149996.cos.ap-guangzhou.myqcloud.com/2026/04/19/18dd39365ce244e3ae5e030da036760e.png', '小米官方API', 'https://api.xiaomimimo.com/anthropic/v1/messages', '0', 3, 103, '2026-04-19 12:48:24', '1', '1', '2026-04-19 13:14:22', '小米官方API', NULL, '0', NULL, 0);
+INSERT INTO `chat_provider` VALUES (14, '阿里云百炼', 'qianwen', 'https://ruoyiai-1254149996.cos.ap-guangzhou.myqcloud.com/2026/02/25/de2aa7e649de44f3ba5c6380ac6acd04.png', '阿里云百炼大模型服务', 'https://dashscope.aliyuncs.com', '0', 2, 103, '2025-12-14 21:48:11', '1', '1', '2026-02-25 20:49:13', '阿里云厂商', NULL, '0', NULL, 0);
+INSERT INTO `chat_provider` VALUES (15, 'PPIO', 'ppio', 'https://ruoyiai-1254149996.cos.ap-guangzhou.myqcloud.com/2026/02/25/049bb6a507174f73bba4b8d8b9e55b8a.png', 'api聚合厂商', 'https://api.ppinfra.com/openai', '0', 5, 103, '2025-12-15 23:13:42', '1', '1', '2026-02-25 20:49:01', 'api聚合厂商', NULL, '0', NULL, 0);
+INSERT INTO `chat_provider` VALUES (16, 'MiniMax', 'minimax', 'https://ruoyiai-1254149996.cos.ap-guangzhou.myqcloud.com/2026/04/19/fdc712e90e0e4d78b05862ad230884e5.png', 'MiniMax大模型服务，支持M2.7、M2.5等模型', 'https://api.minimax.io/v1', '0', 6, 103, '2026-04-19 12:50:12', '1', '1', '2026-04-19 13:14:59', 'MiniMax厂商', NULL, '0', NULL, 0);
+INSERT INTO `chat_provider` VALUES (17, 'ollama', 'ollama', 'https://ruoyiai-1254149996.cos.ap-guangzhou.myqcloud.com/2026/02/25/afecabebc8014d80b0f06b4796a74c5d.png', 'ollama大模型', 'http://127.0.0.1:11434', '0', 7, 103, '2025-12-14 21:48:11', '1', '1', '2026-02-25 20:48:48', 'ollama厂商', NULL, '0', NULL, 0);
+INSERT INTO `chat_provider` VALUES (18, '自定义厂商', 'custom_api', 'https://ruoyiai-1254149996.cos.ap-guangzhou.myqcloud.com/2026/04/19/c1a8e122510f4e2f90deb36958af710b.png', 'OPENAI兼容格式', '自定义', '0', 8, 103, '2026-04-19 12:35:57', '1', '1', '2026-04-19 13:17:20', 'OPENAI兼容格式', NULL, '0', NULL, 0);
+
+SET FOREIGN_KEY_CHECKS = 1;

docs/script/sql/update/updat-0420.sql

@@ -0,0 +1,46 @@
+/*
+ Navicat Premium Dump SQL
+
+ Source Server         : localhost-mysql
+ Source Server Type    : MySQL
+ Source Server Version : 80045 (8.0.45)
+ Source Host           : localhost:3306
+ Source Schema         : ruoyi-ai
+
+ Target Server Type    : MySQL
+ Target Server Version : 80045 (8.0.45)
+ File Encoding         : 65001
+
+ Date: 20/04/2026 15:30:00
+*/
+
+SET NAMES utf8mb4;
+SET FOREIGN_KEY_CHECKS = 0;
+
+-- ----------------------------
+-- 新增：重排序模型（chat_model）
+-- ----------------------------
+INSERT INTO `chat_model`
+(id, category, model_name, provider_code, model_describe, model_dimension, model_show, api_host, api_key, create_dept, create_by, create_time, update_by, update_time, remark, tenant_id)
+VALUES(2045071617578237953, 'rerank', 'rerank', 'zhipu', '智谱重排序', NULL, 'Y', 'https://open.bigmodel.cn', 'e9xx', 103, 1, '2026-04-17 17:27:24', 1, '2026-04-20 15:21:48', '智谱重排序', 0);
+
+INSERT INTO `chat_model`
+(id, category, model_name, provider_code, model_describe, model_dimension, model_show, api_host, api_key, create_dept, create_by, create_time, update_by, update_time, remark, tenant_id)
+VALUES(2046119803482902530, 'rerank', 'qwen3-rerank', 'qianwen', '千问3重排序', NULL, NULL, 'https://dashscope.aliyuncs.com', 'sk-xx', 103, 1, '2026-04-20 14:52:31', 1, '2026-04-20 15:03:13', '千问3文本重排序', 0);
+
+-- ----------------------------
+-- 新增：字典类型 - 重排序模型分类
+-- ----------------------------
+INSERT INTO `sys_dict_data`
+(dict_code, tenant_id, dict_sort, dict_label, dict_value, dict_type, css_class, list_class, is_default, create_dept, create_by, create_time, update_by, update_time, remark)
+VALUES(2045070879435259905, '000000', 4, '重排序', 'rerank', 'chat_model_category', NULL, '#000000', 'N', 103, 1, '2026-04-17 17:24:28', 1, '2026-04-19 01:02:20', '重排序模型');
+
+-- ----------------------------
+-- 修改表：knowledge_info 增加重排序相关字段
+-- ----------------------------
+ALTER TABLE `knowledge_info` ADD COLUMN `enable_rerank` tinyint DEFAULT 0 NULL COMMENT '是否启用重排序（0否 1是）';
+ALTER TABLE `knowledge_info` ADD COLUMN `rerank_score_threshold` double NULL COMMENT '重排序相关性分数阈值';
+ALTER TABLE `knowledge_info` ADD COLUMN `rerank_top_n` int NULL COMMENT '重排序后返回的文档数量';
+ALTER TABLE `knowledge_info` ADD COLUMN `rerank_model` varchar(100) NULL COMMENT '重排序模型名称';
+
+SET FOREIGN_KEY_CHECKS = 1;

ruoyi-admin/src/main/resources/application.yml

@@ -265,7 +265,7 @@ demo:
   # 是否开启演示模式（开启后所有写操作将被拦截）
   enabled: false
   # 提示消息
-  message: "演示模式，不允许进行写操作"
+  message: "演示模式，不允许操作"
   # 排除的路径（这些路径不受演示模式限制）
   excludes:
     - /login
@@ -276,7 +276,9 @@ demo:
     - /chat/send
     - /system/session/**
     - /system/message/**
-
+    - /system/attach/**
+    - /system/fragment/**
+    - /system/info/**
 --- # warm-flow工作流配置
 warm-flow:
   # 是否开启工作流，默认true

ruoyi-modules/ruoyi-chat/pom.xml

@@ -174,6 +174,23 @@
             <scope>test</scope>
         </dependency>
 
+        <!-- Test dependencies -->
+        <dependency>
+            <groupId>org.junit.jupiter</groupId>
+            <artifactId>junit-jupiter</artifactId>
+            <scope>test</scope>
+        </dependency>
+        <dependency>
+            <groupId>org.mockito</groupId>
+            <artifactId>mockito-core</artifactId>
+            <scope>test</scope>
+        </dependency>
+        <dependency>
+            <groupId>org.mockito</groupId>
+            <artifactId>mockito-junit-jupiter</artifactId>
+            <scope>test</scope>
+        </dependency>
+
     </dependencies>
 
 </project>

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/controller/chat/ChatModelController.java

@@ -9,6 +9,7 @@ import cn.dev33.satoken.annotation.SaCheckPermission;
 import org.ruoyi.common.chat.service.chat.IChatModelService;
 import org.ruoyi.common.chat.domain.bo.chat.ChatModelBo;
 import org.ruoyi.common.chat.domain.vo.chat.ChatModelVo;
+import org.ruoyi.enums.ChatModeType;
 import org.ruoyi.enums.ModelType;
 import org.springframework.web.bind.annotation.*;
 import org.springframework.validation.annotation.Validated;
@@ -23,6 +24,8 @@ import org.ruoyi.common.log.enums.BusinessType;
 import org.ruoyi.common.excel.utils.ExcelUtil;
 import org.ruoyi.common.mybatis.core.page.TableDataInfo;
 
+import java.util.LinkedHashMap;
+
 /**
  * 模型管理
  *
@@ -55,6 +58,21 @@ public class ChatModelController extends BaseController {
         return R.ok(chatModelService.queryList(bo));
     }
 
+    /**
+     * 获取模型供应商枚举
+     */
+    @GetMapping("/providerOptions")
+    public R<List<LinkedHashMap<String, String>>> providerOptions() {
+        List<LinkedHashMap<String, String>> options = new java.util.ArrayList<>();
+        for (ChatModeType type : ChatModeType.values()) {
+            LinkedHashMap<String, String> item = new LinkedHashMap<>();
+            item.put("label", type.getDescription());
+            item.put("value", type.getCode());
+            options.add(item);
+        }
+        return R.ok(options);
+    }
+
     /**
      * 导出模型管理列表
      */

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/controller/knowledge/KnowledgeGraphInstanceController.java

@@ -1,105 +0,0 @@
-package org.ruoyi.controller.knowledge;
-
-import java.util.List;
-
-import lombok.RequiredArgsConstructor;
-import jakarta.servlet.http.HttpServletResponse;
-import jakarta.validation.constraints.*;
-import cn.dev33.satoken.annotation.SaCheckPermission;
-import org.ruoyi.domain.bo.knowledge.KnowledgeGraphInstanceBo;
-import org.ruoyi.domain.vo.knowledge.KnowledgeGraphInstanceVo;
-import org.ruoyi.service.knowledge.IKnowledgeGraphInstanceService;
-import org.springframework.web.bind.annotation.*;
-import org.springframework.validation.annotation.Validated;
-import org.ruoyi.common.idempotent.annotation.RepeatSubmit;
-import org.ruoyi.common.log.annotation.Log;
-import org.ruoyi.common.web.core.BaseController;
-import org.ruoyi.common.mybatis.core.page.PageQuery;
-import org.ruoyi.common.core.domain.R;
-import org.ruoyi.common.core.validate.AddGroup;
-import org.ruoyi.common.core.validate.EditGroup;
-import org.ruoyi.common.log.enums.BusinessType;
-import org.ruoyi.common.excel.utils.ExcelUtil;
-import org.ruoyi.common.mybatis.core.page.TableDataInfo;
-
-/**
- * 知识图谱实例
- *
- * @author ageerle
- * @date 2025-12-17
- */
-@Validated
-@RequiredArgsConstructor
-@RestController
-@RequestMapping("/system/graphInstance")
-public class KnowledgeGraphInstanceController extends BaseController {
-
-    private final IKnowledgeGraphInstanceService knowledgeGraphInstanceService;
-
-    /**
-     * 查询知识图谱实例列表
-     */
-    @SaCheckPermission("system:graphInstance:list")
-    @GetMapping("/list")
-    public TableDataInfo<KnowledgeGraphInstanceVo> list(KnowledgeGraphInstanceBo bo, PageQuery pageQuery) {
-        return knowledgeGraphInstanceService.queryPageList(bo, pageQuery);
-    }
-
-    /**
-     * 导出知识图谱实例列表
-     */
-    @SaCheckPermission("system:graphInstance:export")
-    @Log(title = "知识图谱实例", businessType = BusinessType.EXPORT)
-    @PostMapping("/export")
-    public void export(KnowledgeGraphInstanceBo bo, HttpServletResponse response) {
-        List<KnowledgeGraphInstanceVo> list = knowledgeGraphInstanceService.queryList(bo);
-        ExcelUtil.exportExcel(list, "知识图谱实例", KnowledgeGraphInstanceVo.class, response);
-    }
-
-    /**
-     * 获取知识图谱实例详细信息
-     *
-     * @param id 主键
-     */
-    @SaCheckPermission("system:graphInstance:query")
-    @GetMapping("/{id}")
-    public R<KnowledgeGraphInstanceVo> getInfo(@NotNull(message = "主键不能为空")
-                                     @PathVariable Long id) {
-        return R.ok(knowledgeGraphInstanceService.queryById(id));
-    }
-
-    /**
-     * 新增知识图谱实例
-     */
-    @SaCheckPermission("system:graphInstance:add")
-    @Log(title = "知识图谱实例", businessType = BusinessType.INSERT)
-    @RepeatSubmit()
-    @PostMapping()
-    public R<Void> add(@Validated(AddGroup.class) @RequestBody KnowledgeGraphInstanceBo bo) {
-        return toAjax(knowledgeGraphInstanceService.insertByBo(bo));
-    }
-
-    /**
-     * 修改知识图谱实例
-     */
-    @SaCheckPermission("system:graphInstance:edit")
-    @Log(title = "知识图谱实例", businessType = BusinessType.UPDATE)
-    @RepeatSubmit()
-    @PutMapping()
-    public R<Void> edit(@Validated(EditGroup.class) @RequestBody KnowledgeGraphInstanceBo bo) {
-        return toAjax(knowledgeGraphInstanceService.updateByBo(bo));
-    }
-
-    /**
-     * 删除知识图谱实例
-     *
-     * @param ids 主键串
-     */
-    @SaCheckPermission("system:graphInstance:remove")
-    @Log(title = "知识图谱实例", businessType = BusinessType.DELETE)
-    @DeleteMapping("/{ids}")
-    public R<Void> remove(@NotEmpty(message = "主键不能为空")
-                          @PathVariable Long[] ids) {
-        return toAjax(knowledgeGraphInstanceService.deleteWithValidByIds(List.of(ids), true));
-    }
-}

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/controller/knowledge/KnowledgeGraphSegmentController.java

@@ -1,105 +0,0 @@
-package org.ruoyi.controller.knowledge;
-
-import java.util.List;
-
-import lombok.RequiredArgsConstructor;
-import jakarta.servlet.http.HttpServletResponse;
-import jakarta.validation.constraints.*;
-import cn.dev33.satoken.annotation.SaCheckPermission;
-import org.ruoyi.domain.bo.knowledge.KnowledgeGraphSegmentBo;
-import org.ruoyi.domain.vo.knowledge.KnowledgeGraphSegmentVo;
-import org.ruoyi.service.knowledge.IKnowledgeGraphSegmentService;
-import org.springframework.web.bind.annotation.*;
-import org.springframework.validation.annotation.Validated;
-import org.ruoyi.common.idempotent.annotation.RepeatSubmit;
-import org.ruoyi.common.log.annotation.Log;
-import org.ruoyi.common.web.core.BaseController;
-import org.ruoyi.common.mybatis.core.page.PageQuery;
-import org.ruoyi.common.core.domain.R;
-import org.ruoyi.common.core.validate.AddGroup;
-import org.ruoyi.common.core.validate.EditGroup;
-import org.ruoyi.common.log.enums.BusinessType;
-import org.ruoyi.common.excel.utils.ExcelUtil;
-import org.ruoyi.common.mybatis.core.page.TableDataInfo;
-
-/**
- * 知识图谱片段
- *
- * @author ageerle
- * @date 2025-12-17
- */
-@Validated
-@RequiredArgsConstructor
-@RestController
-@RequestMapping("/system/graphSegment")
-public class KnowledgeGraphSegmentController extends BaseController {
-
-    private final IKnowledgeGraphSegmentService knowledgeGraphSegmentService;
-
-    /**
-     * 查询知识图谱片段列表
-     */
-    @SaCheckPermission("system:graphSegment:list")
-    @GetMapping("/list")
-    public TableDataInfo<KnowledgeGraphSegmentVo> list(KnowledgeGraphSegmentBo bo, PageQuery pageQuery) {
-        return knowledgeGraphSegmentService.queryPageList(bo, pageQuery);
-    }
-
-    /**
-     * 导出知识图谱片段列表
-     */
-    @SaCheckPermission("system:graphSegment:export")
-    @Log(title = "知识图谱片段", businessType = BusinessType.EXPORT)
-    @PostMapping("/export")
-    public void export(KnowledgeGraphSegmentBo bo, HttpServletResponse response) {
-        List<KnowledgeGraphSegmentVo> list = knowledgeGraphSegmentService.queryList(bo);
-        ExcelUtil.exportExcel(list, "知识图谱片段", KnowledgeGraphSegmentVo.class, response);
-    }
-
-    /**
-     * 获取知识图谱片段详细信息
-     *
-     * @param id 主键
-     */
-    @SaCheckPermission("system:graphSegment:query")
-    @GetMapping("/{id}")
-    public R<KnowledgeGraphSegmentVo> getInfo(@NotNull(message = "主键不能为空")
-                                     @PathVariable Long id) {
-        return R.ok(knowledgeGraphSegmentService.queryById(id));
-    }
-
-    /**
-     * 新增知识图谱片段
-     */
-    @SaCheckPermission("system:graphSegment:add")
-    @Log(title = "知识图谱片段", businessType = BusinessType.INSERT)
-    @RepeatSubmit()
-    @PostMapping()
-    public R<Void> add(@Validated(AddGroup.class) @RequestBody KnowledgeGraphSegmentBo bo) {
-        return toAjax(knowledgeGraphSegmentService.insertByBo(bo));
-    }
-
-    /**
-     * 修改知识图谱片段
-     */
-    @SaCheckPermission("system:graphSegment:edit")
-    @Log(title = "知识图谱片段", businessType = BusinessType.UPDATE)
-    @RepeatSubmit()
-    @PutMapping()
-    public R<Void> edit(@Validated(EditGroup.class) @RequestBody KnowledgeGraphSegmentBo bo) {
-        return toAjax(knowledgeGraphSegmentService.updateByBo(bo));
-    }
-
-    /**
-     * 删除知识图谱片段
-     *
-     * @param ids 主键串
-     */
-    @SaCheckPermission("system:graphSegment:remove")
-    @Log(title = "知识图谱片段", businessType = BusinessType.DELETE)
-    @DeleteMapping("/{ids}")
-    public R<Void> remove(@NotEmpty(message = "主键不能为空")
-                          @PathVariable Long[] ids) {
-        return toAjax(knowledgeGraphSegmentService.deleteWithValidByIds(List.of(ids), true));
-    }
-}

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/domain/bo/knowledge/KnowledgeInfoBo.java

@@ -77,10 +77,33 @@ public class KnowledgeInfoBo extends BaseEntity {
      */
     private String embeddingModel;
 
+    /**
+     * 是否启用重排序（0 否 1是）
+     */
+    private Integer enableRerank;
+
+    /**
+     * 重排序模型名称
+     */
+    private String rerankModel;
+
+    /**
+     * 重排序后返回的文档数量
+     */
+    private Integer rerankTopN;
+
+    /**
+     * 重排序相关性分数阈值
+     */
+    private Double rerankScoreThreshold;
+
+
     /**
      * 备注
      */
     private String remark;
 
 
+
+
 }

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/domain/bo/rerank/RerankRequest.java

@@ -0,0 +1,44 @@
+package org.ruoyi.domain.bo.rerank;
+
+import lombok.AllArgsConstructor;
+import lombok.Builder;
+import lombok.Data;
+import lombok.NoArgsConstructor;
+
+import java.util.List;
+
+/**
+ * 重排序请求参数
+ *
+ * @author yang
+ * @date 2026-04-19
+ */
+@Data
+@Builder
+@NoArgsConstructor
+@AllArgsConstructor
+public class RerankRequest {
+
+    /**
+     * 查询文本
+     */
+    private String query;
+
+    /**
+     * 候选文档列表
+     */
+    private List<String> documents;
+
+    /**
+     * 返回的文档数量（topN）
+     * 如果不指定，默认返回所有文档
+     */
+    private Integer topN;
+
+    /**
+     * 是否返回原始文档内容
+     * 默认为 true
+     */
+    @Builder.Default
+    private Boolean returnDocuments = true;
+}

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/domain/bo/rerank/RerankResult.java

@@ -0,0 +1,72 @@
+package org.ruoyi.domain.bo.rerank;
+
+import lombok.AllArgsConstructor;
+import lombok.Builder;
+import lombok.Data;
+import lombok.NoArgsConstructor;
+
+import java.util.List;
+
+/**
+ * 重排序结果
+ *
+ * @author yang
+ * @date 2026-04-19
+ */
+@Data
+@Builder
+@NoArgsConstructor
+@AllArgsConstructor
+public class RerankResult {
+
+    /**
+     * 重排序后的文档结果列表
+     */
+    private List<RerankDocument> documents;
+
+    /**
+     * 原始请求中的文档总数
+     */
+    private Integer totalDocuments;
+
+    /**
+     * 重排序耗时（毫秒）
+     */
+    private Long durationMs;
+
+    /**
+     * 单个重排序文档结果
+     */
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    public static class RerankDocument {
+
+        /**
+         * 文档在原始列表中的索引位置
+         */
+        private Integer index;
+
+        /**
+         * 相关性分数（通常 0-1 之间，越高越相关）
+         */
+        private Double relevanceScore;
+
+        /**
+         * 文档内容
+         */
+        private String document;
+    }
+
+    /**
+     * 创建空结果
+     */
+    public static RerankResult empty() {
+        return RerankResult.builder()
+                .documents(List.of())
+                .totalDocuments(0)
+                .durationMs(0L)
+                .build();
+    }
+}

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/domain/bo/vector/QueryVectorBo.java

@@ -51,4 +51,30 @@ public class QueryVectorBo {
      */
     private String baseUrl;
 
+
+    // ========== 重排序相关参数 ==========
+
+    /**
+     * 是否启用重排序
+     * 默认为 false
+     */
+    private Boolean enableRerank = false;
+
+    /**
+     * 重排序模型名称
+     */
+    private String rerankModelName;
+
+    /**
+     * 重排序后返回的文档数量（topN）
+     * 如果不指定，默认与 maxResults 相同
+     */
+    private Integer rerankTopN;
+
+    /**
+     * 重排序相关性分数阈值
+     * 低于此阈值的文档将被过滤
+     */
+    private Double rerankScoreThreshold;
+
 }

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/domain/dto/request/AliBaiLianRerankRequest.java

@@ -0,0 +1,55 @@
+package org.ruoyi.domain.dto.request;
+
+import com.fasterxml.jackson.annotation.JsonInclude;
+import com.fasterxml.jackson.annotation.JsonProperty;
+import com.fasterxml.jackson.core.JsonProcessingException;
+import com.fasterxml.jackson.databind.ObjectMapper;
+
+import java.util.List;
+
+/**
+ * 阿里百炼重排序请求DTO（OpenAI兼容格式）
+ *
+ * @author yang
+ * @date 2026-04-20
+ */
+@JsonInclude(JsonInclude.Include.NON_NULL)
+public record AliBaiLianRerankRequest(
+        String model,
+        List<String> documents,
+        String query,
+        @JsonProperty("top_n")
+        Integer topN,
+        String instruct,
+        @JsonProperty("return_documents")
+        Boolean returnDocuments
+) {
+    private static final ObjectMapper OBJECT_MAPPER = new ObjectMapper();
+
+    /**
+     * 创建文本重排序请求
+     */
+    public static AliBaiLianRerankRequest create(String modelName, String query,
+                                                  List<String> documents, Integer topN,
+                                                  Boolean returnDocuments) {
+        return new AliBaiLianRerankRequest(
+                modelName,
+                documents,
+                query,
+                topN != null ? topN : documents.size(),
+                null,
+                returnDocuments != null ? returnDocuments : true
+        );
+    }
+
+    /**
+     * 转换为JSON字符串
+     */
+    public String toJson() {
+        try {
+            return OBJECT_MAPPER.writeValueAsString(this);
+        } catch (JsonProcessingException e) {
+            throw new IllegalArgumentException("序列化阿里百炼重排序请求失败", e);
+        }
+    }
+}

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/domain/dto/request/ZhipuRerankRequest.java

@@ -0,0 +1,48 @@
+package org.ruoyi.domain.dto.request;
+
+import com.fasterxml.jackson.core.JsonProcessingException;
+import com.fasterxml.jackson.databind.ObjectMapper;
+
+import java.util.List;
+
+/**
+ * 智谱AI重排序请求DTO
+ *
+ * @author yang
+ * @date 2026-04-19
+ */
+public record ZhipuRerankRequest(
+        String model,
+        String query,
+        List<String> documents,
+        Integer top_n,
+        Boolean return_documents
+) {
+    private static final ObjectMapper OBJECT_MAPPER = new ObjectMapper();
+
+    /**
+     * 创建智谱重排序请求
+     */
+    public static ZhipuRerankRequest create(String modelName, String query,
+                                            List<String> documents, Integer topN,
+                                            Boolean returnDocuments) {
+        return new ZhipuRerankRequest(
+                modelName,
+                query,
+                documents,
+                topN != null ? topN : documents.size(),
+                returnDocuments != null ? returnDocuments : true
+        );
+    }
+
+    /**
+     * 转换为JSON字符串
+     */
+    public String toJson() {
+        try {
+            return OBJECT_MAPPER.writeValueAsString(this);
+        } catch (JsonProcessingException e) {
+            throw new IllegalArgumentException("序列化智谱重排序请求失败", e);
+        }
+    }
+}

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/domain/dto/response/AliBaiLianRerankResponse.java

@@ -0,0 +1,81 @@
+package org.ruoyi.domain.dto.response;
+
+import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
+import com.fasterxml.jackson.annotation.JsonProperty;
+import org.ruoyi.domain.bo.rerank.RerankResult;
+
+import java.util.List;
+import java.util.Map;
+import java.util.stream.Collectors;
+
+/**
+ * 阿里百炼重排序响应DTO（OpenAI兼容格式）
+ *
+ * @author yang
+ * @date 2026-04-20
+ */
+@JsonIgnoreProperties(ignoreUnknown = true)
+public record AliBaiLianRerankResponse(
+        String id,
+        String object,
+        List<ResultItem> results,
+        UsageInfo usage
+) {
+    /**
+     * 单个重排序结果项
+     */
+    @JsonIgnoreProperties(ignoreUnknown = true)
+    public record ResultItem(
+            Integer index,
+            @JsonProperty("relevance_score")
+            Double relevanceScore,
+            Object document
+    ) {
+        /**
+         * 获取文档文本内容
+         */
+        public String getDocumentText() {
+            if (document == null) return null;
+            if (document instanceof String) return (String) document;
+            if (document instanceof Map) {
+                Object text = ((Map<?, ?>) document).get("text");
+                return text != null ? text.toString() : null;
+            }
+            return document.toString();
+        }
+    }
+
+    /**
+     * Token使用信息
+     */
+    @JsonIgnoreProperties(ignoreUnknown = true)
+    public record UsageInfo(
+            @JsonProperty("total_tokens")
+            Integer totalTokens,
+            @JsonProperty("prompt_tokens")
+            Integer promptTokens
+    ) {}
+
+    /**
+     * 转换为通用RerankResult
+     */
+    public RerankResult toRerankResult(int totalDocs, long durationMs) {
+        if (results == null || results.isEmpty()) {
+            return RerankResult.empty();
+        }
+
+        List<RerankResult.RerankDocument> documents = results.stream()
+                .map(item -> RerankResult.RerankDocument.builder()
+                        .index(item.index())
+                        .relevanceScore(item.relevanceScore())
+                        .document(item.getDocumentText())
+                        .build())
+                .collect(Collectors.toList());
+
+        return RerankResult.builder()
+                .documents(documents)
+                .totalDocuments(totalDocs)
+                .durationMs(durationMs)
+                .build();
+    }
+}

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/domain/dto/response/ZhipuRerankResponse.java

@@ -0,0 +1,68 @@
+package org.ruoyi.domain.dto.response;
+
+import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
+import com.fasterxml.jackson.annotation.JsonProperty;
+import org.ruoyi.domain.bo.rerank.RerankResult;
+
+import java.util.List;
+import java.util.stream.Collectors;
+
+/**
+ * 智谱AI重排序响应DTO
+ *
+ * @author yang
+ * @date 2026-04-19
+ */
+@JsonIgnoreProperties(ignoreUnknown = true)
+public record ZhipuRerankResponse(
+        String model,
+        String object,
+        List<ResultItem> results,
+        UsageInfo usage
+) {
+    /**
+     * 单个重排序结果项
+     */
+    public record ResultItem(
+            Integer index,
+            @JsonProperty("relevance_score")
+            Double relevanceScore,
+            String document
+    ) {}
+
+    /**
+     * Token使用信息
+     */
+    @JsonIgnoreProperties(ignoreUnknown = true)
+    public record UsageInfo(
+            @JsonProperty("total_tokens")
+            Integer totalTokens,
+            @JsonProperty("input_tokens")
+            Integer inputTokens,
+            @JsonProperty("output_tokens")
+            Integer outputTokens
+    ) {}
+
+    /**
+     * 转换为通用RerankResult
+     */
+    public RerankResult toRerankResult(int totalDocs, long durationMs) {
+        if (results == null || results.isEmpty()) {
+            return RerankResult.empty();
+        }
+
+        List<RerankResult.RerankDocument> documents = results.stream()
+                .map(item -> RerankResult.RerankDocument.builder()
+                        .index(item.index())
+                        .relevanceScore(item.relevanceScore())
+                        .document(item.document())
+                        .build())
+                .collect(Collectors.toList());
+
+        return RerankResult.builder()
+                .documents(documents)
+                .totalDocuments(totalDocs)
+                .durationMs(durationMs)
+                .build();
+    }
+}

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/domain/entity/knowledge/KnowledgeInfo.java

@@ -78,6 +78,26 @@ public class KnowledgeInfo extends BaseEntity {
      */
     private String embeddingModel;
 
+    /**
+     * 是否启用重排序（0 否 1是）
+     */
+    private Integer enableRerank;
+
+    /**
+     * 重排序模型名称
+     */
+    private String rerankModel;
+
+    /**
+     * 重排序后返回的文档数量
+     */
+    private Integer rerankTopN;
+
+    /**
+     * 重排序相关性分数阈值
+     */
+    private Double rerankScoreThreshold;
+
     /**
      * 备注
      */

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/domain/vo/knowledge/KnowledgeInfoVo.java

@@ -94,6 +94,30 @@ public class KnowledgeInfoVo implements Serializable {
     @ExcelProperty(value = "向量模型")
     private String embeddingModel;
 
+    /**
+     * 是否启用重排序（0 否 1是）
+     */
+    @ExcelProperty(value = "是否启用重排序")
+    private Integer enableRerank;
+
+    /**
+     * 重排序模型名称
+     */
+    @ExcelProperty(value = "重排序模型")
+    private String rerankModel;
+
+    /**
+     * 重排序后返回的文档数量
+     */
+    @ExcelProperty(value = "重排序返回数量")
+    private Integer rerankTopN;
+
+    /**
+     * 重排序相关性分数阈值
+     */
+    @ExcelProperty(value = "重排序分数阈值")
+    private Double rerankScoreThreshold;
+
     /**
      * 备注
      */

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/enums/ChatModeType.java

@@ -15,7 +15,10 @@ public enum ChatModeType {
     DEEP_SEEK("deepseek", "深度求索"),
     QIAN_WEN("qianwen", "通义千问"),
     OPEN_AI("openai", "openai"),
-    PPIO("ppio", "ppio");
+    PPIO("ppio", "ppio"),
+    CUSTOM_API("custom_api", "自定义API"),
+    MINIMAX("minimax", "MiniMax"),
+    XIAOMI("xiaomi", "小米MiMo");
     private final String code;
     private final String description;
 

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/factory/RerankModelFactory.java

@@ -0,0 +1,106 @@
+package org.ruoyi.factory;
+
+import lombok.RequiredArgsConstructor;
+import lombok.extern.slf4j.Slf4j;
+import org.ruoyi.common.chat.domain.vo.chat.ChatModelVo;
+import org.ruoyi.common.chat.service.chat.IChatModelService;
+import org.ruoyi.service.rerank.RerankModelService;
+import org.springframework.beans.factory.NoSuchBeanDefinitionException;
+import org.springframework.context.ApplicationContext;
+import org.springframework.stereotype.Service;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Map;
+import java.util.concurrent.ConcurrentHashMap;
+
+/**
+ * 重排序模型工厂服务类
+ * 参考设计模式：EmbeddingModelFactory
+ * 负责创建和管理重排序模型实例
+ *
+ * @author yang
+ * @date 2026-04-19
+ */
+@Service
+@RequiredArgsConstructor
+@Slf4j
+public class RerankModelFactory {
+
+    private final ApplicationContext applicationContext;
+
+    private final IChatModelService chatModelService;
+
+    /**
+     * 模型缓存，使用ConcurrentHashMap保证线程安全
+     */
+    private final Map<String, RerankModelService> modelCache = new ConcurrentHashMap<>();
+
+    /**
+     * 创建重排序模型实例
+     * 如果模型已存在于缓存中，则直接返回；否则创建新的实例
+     *
+     * @param rerankModelName 重排序模型名称
+     */
+    public RerankModelService createModel(String rerankModelName) {
+        return modelCache.computeIfAbsent(rerankModelName, name -> {
+            ChatModelVo modelConfig = chatModelService.selectModelByName(rerankModelName);
+
+            if (modelConfig == null) {
+                throw new IllegalArgumentException("未找到重排序模型配置，name=" + name);
+            }
+            return createModelInstance(modelConfig.getProviderCode(), modelConfig);
+        });
+    }
+
+    /**
+     * 刷新模型缓存
+     * 根据给定的模型ID从缓存中移除对应的模型
+     *
+     * @param modelId 模型的唯一标识ID
+     */
+    public void refreshModel(Long modelId) {
+        modelCache.remove(modelId);
+    }
+
+    /**
+     * 获取所有支持模型工厂的列表
+     *
+     * @return 支持的模型工厂名称列表
+     */
+    public List<String> getSupportedFactories() {
+        return new ArrayList<>(applicationContext.getBeansOfType(RerankModelService.class)
+                .keySet());
+    }
+
+    /**
+     * 创建具体的模型实例
+     * 根据提供的工厂名称和配置信息创建并配置模型实例
+     *
+     * @param factory 工厂名称，用于标识模型类型（providerCode）
+     * @param config  模型配置信息
+     * @return RerankModelService 配置好的模型实例
+     * @throws IllegalArgumentException 当无法获取指定的模型实例时抛出
+     */
+    private RerankModelService createModelInstance(String factory, ChatModelVo config) {
+        try {
+            // 优先尝试使用 providerCode + "Rerank" 作为 Bean 名称
+            // 例如：zhipu -> zhipuRerank，jina -> jinaRerank
+            String rerankBeanName = factory + "Rerank";
+            RerankModelService model = applicationContext.getBean(rerankBeanName, RerankModelService.class);
+            model.configure(config);
+            log.info("成功创建重排序模型: factory={}, modelName={}", rerankBeanName, config.getModelName());
+            return model;
+        } catch (NoSuchBeanDefinitionException e) {
+            // 如果找不到，尝试使用原始的 providerCode
+            try {
+                RerankModelService model = applicationContext.getBean(factory, RerankModelService.class);
+                model.configure(config);
+                log.info("成功创建重排序模型: factory={}, modelName={}", factory, config.getModelName());
+                return model;
+            } catch (NoSuchBeanDefinitionException ex) {
+                throw new IllegalArgumentException("获取不到重排序模型: " + factory + " 或 " + factory + "Rerank", ex);
+            }
+        }
+    }
+}

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/chat/AbstractChatService.java

@@ -1,9 +1,13 @@
 package org.ruoyi.service.chat;
 
+import dev.langchain4j.model.chat.ChatModel;
 import dev.langchain4j.model.chat.StreamingChatModel;
+import dev.langchain4j.model.openai.OpenAiChatModel;
 import org.ruoyi.common.chat.domain.dto.request.ChatRequest;
 import org.ruoyi.common.chat.domain.vo.chat.ChatModelVo;
 
+import java.time.Duration;
+
 /**
  * 聊天消息Service接口
  *
@@ -21,6 +25,23 @@ public interface AbstractChatService {
      */
     StreamingChatModel buildStreamingChatModel(ChatModelVo chatModelVo, ChatRequest chatRequest);
 
+    /**
+     * 创建同步聊天模型（供 Agent/SupervisorAgent 使用）
+     * 默认实现使用 OpenAI 兼容协议，适用于 OpenAI、DeepSeek、PPIO 等兼容接口的 provider。
+     * ZhiPu、QianWen、Ollama 等需覆盖此方法使用各自 SDK。
+     *
+     * @param chatModelVo 模型配置
+     * @return 同步聊天模型实例
+     */
+    default ChatModel buildChatModel(ChatModelVo chatModelVo) {
+        return OpenAiChatModel.builder()
+            .baseUrl(chatModelVo.getApiHost())
+            .apiKey(chatModelVo.getApiKey())
+            .modelName(chatModelVo.getModelName())
+            .timeout(Duration.ofSeconds(120))
+            .build();
+    }
+
     /**
      * 获取服务提供商名称
      */

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/chat/impl/ChatServiceFacade.java

@@ -54,6 +54,7 @@ import org.ruoyi.service.chat.AbstractChatService;
 import org.ruoyi.service.chat.IChatMessageService;
 import org.ruoyi.service.chat.impl.memory.PersistentChatMemoryStore;
 import org.ruoyi.service.knowledge.IKnowledgeInfoService;
+import org.ruoyi.service.retrieval.KnowledgeRetrievalService;
 import org.ruoyi.service.vector.VectorStoreService;
 import org.springframework.stereotype.Service;
 import org.springframework.web.servlet.mvc.method.annotation.SseEmitter;
@@ -89,6 +90,8 @@ public class ChatServiceFacade implements IChatService {
 
     private final VectorStoreService vectorStoreService;
 
+    private final KnowledgeRetrievalService knowledgeRetrievalService;
+
     private final SseEmitterManager sseEmitterManager;
 
     private final IChatMessageService chatMessageService;
@@ -452,8 +455,8 @@ public class ChatServiceFacade implements IChatService {
             // 构建向量查询参数
             QueryVectorBo queryVectorBo = buildQueryVectorBo(chatRequest, knowledgeInfoVo, chatModel);
 
-            // 获取向量查询结果（知识库内容作为系统上下文，放在历史消息之后）
+            // 使用知识库检索服务（支持重排序）
-            List<String> nearestList = vectorStoreService.getQueryVector(queryVectorBo);
+            List<String> nearestList = knowledgeRetrievalService.retrieveTexts(queryVectorBo);
             for (String prompt : nearestList) {
                 // 知识库内容作为系统上下文添加
                 messages.add(new AiMessage(prompt));
@@ -480,6 +483,13 @@ public class ChatServiceFacade implements IChatService {
         queryVectorBo.setVectorModelName(knowledgeInfoVo.getVectorModel());
         queryVectorBo.setEmbeddingModelName(knowledgeInfoVo.getEmbeddingModel());
         queryVectorBo.setMaxResults(knowledgeInfoVo.getRetrieveLimit());
+
+        // 设置重排序参数
+        queryVectorBo.setEnableRerank(knowledgeInfoVo.getEnableRerank() != null && knowledgeInfoVo.getEnableRerank() == 1);
+        queryVectorBo.setRerankModelName(knowledgeInfoVo.getRerankModel());
+        queryVectorBo.setRerankTopN(knowledgeInfoVo.getRerankTopN());
+        queryVectorBo.setRerankScoreThreshold(knowledgeInfoVo.getRerankScoreThreshold());
+
         return queryVectorBo;
     }
 

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/chat/impl/provider/CustomApiServiceImpl.java

@@ -0,0 +1,72 @@
+package org.ruoyi.service.chat.impl.provider;
+
+import cn.hutool.core.util.StrUtil;
+import dev.langchain4j.model.chat.ChatModel;
+import dev.langchain4j.model.chat.StreamingChatModel;
+import dev.langchain4j.model.openai.OpenAiChatModel;
+import dev.langchain4j.model.openai.OpenAiStreamingChatModel;
+import lombok.RequiredArgsConstructor;
+import lombok.extern.slf4j.Slf4j;
+import org.ruoyi.common.chat.domain.dto.request.ChatRequest;
+import org.ruoyi.common.chat.domain.vo.chat.ChatModelVo;
+import org.ruoyi.enums.ChatModeType;
+import org.ruoyi.observability.MyChatModelListener;
+import org.ruoyi.service.chat.AbstractChatService;
+import org.springframework.stereotype.Service;
+
+import java.time.Duration;
+import java.util.List;
+
+/**
+ * 自定义 API 服务调用
+ *
+ * 适用于 OpenAI 兼容接口或仅通过通用 HTTP 协议接入的第三方大模型服务。
+ * 通过模型配置中的 apiHost / apiKey / modelName 即可复用，不需要再写死具体供应商。
+ *
+ * @author better
+ */
+@Service
+@Slf4j
+@RequiredArgsConstructor
+public class CustomApiServiceImpl implements AbstractChatService {
+
+    private static final Duration DEFAULT_TIMEOUT = Duration.ofSeconds(180);
+
+    @Override
+    public StreamingChatModel buildStreamingChatModel(ChatModelVo chatModelVo, ChatRequest chatRequest) {
+        return OpenAiStreamingChatModel.builder()
+            .baseUrl(normalizeBaseUrl(chatModelVo.getApiHost()))
+            .apiKey(defaultIfBlank(chatModelVo.getApiKey(), "EMPTY"))
+            .modelName(chatModelVo.getModelName())
+            .timeout(DEFAULT_TIMEOUT)
+            .listeners(List.of(new MyChatModelListener()))
+            .returnThinking(chatRequest.getEnableThinking())
+            .build();
+    }
+
+    @Override
+    public ChatModel buildChatModel(ChatModelVo chatModelVo) {
+        return OpenAiChatModel.builder()
+            .baseUrl(normalizeBaseUrl(chatModelVo.getApiHost()))
+            .apiKey(defaultIfBlank(chatModelVo.getApiKey(), "EMPTY"))
+            .modelName(chatModelVo.getModelName())
+            .timeout(DEFAULT_TIMEOUT)
+            .build();
+    }
+
+    @Override
+    public String getProviderName() {
+        return ChatModeType.CUSTOM_API.getCode();
+    }
+
+    private String normalizeBaseUrl(String baseUrl) {
+        if (StrUtil.isBlank(baseUrl)) {
+            throw new IllegalArgumentException("自定义API的请求地址(apiHost)不能为空");
+        }
+        return baseUrl.endsWith("/") ? baseUrl.substring(0, baseUrl.length() - 1) : baseUrl;
+    }
+
+    private String defaultIfBlank(String value, String defaultValue) {
+        return StrUtil.isBlank(value) ? defaultValue : value;
+    }
+}

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/chat/impl/provider/MiMoServiceImpl.java

@@ -0,0 +1,47 @@
+package org.ruoyi.service.chat.impl.provider;
+
+
+import dev.langchain4j.model.chat.StreamingChatModel;
+import dev.langchain4j.model.openai.OpenAiStreamingChatModel;
+import lombok.RequiredArgsConstructor;
+import lombok.extern.slf4j.Slf4j;
+import org.ruoyi.common.chat.domain.dto.request.ChatRequest;
+import org.ruoyi.common.chat.domain.vo.chat.ChatModelVo;
+import org.ruoyi.enums.ChatModeType;
+import org.ruoyi.observability.MyChatModelListener;
+import org.ruoyi.service.chat.AbstractChatService;
+import org.springframework.stereotype.Service;
+
+import java.util.List;
+
+
+/**
+ * 小米MiMo服务调用
+ * <p>
+ * 小米提供OpenAI兼容的API接口，支持MiMo等模型。
+ *
+ * @author ageerle
+ * @date 2026/4/19
+ */
+@Service
+@Slf4j
+@RequiredArgsConstructor
+public class MiMoServiceImpl implements AbstractChatService {
+
+    @Override
+    public StreamingChatModel buildStreamingChatModel(ChatModelVo chatModelVo, ChatRequest chatRequest) {
+        return OpenAiStreamingChatModel.builder()
+            .baseUrl(chatModelVo.getApiHost())
+            .apiKey(chatModelVo.getApiKey())
+            .modelName(chatModelVo.getModelName())
+            .listeners(List.of(new MyChatModelListener()))
+            .returnThinking(chatRequest.getEnableThinking())
+            .build();
+    }
+
+    @Override
+    public String getProviderName() {
+        return ChatModeType.XIAOMI.getCode();
+    }
+
+}

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/chat/impl/provider/MinimaxServiceImpl.java

@@ -0,0 +1,44 @@
+package org.ruoyi.service.chat.impl.provider;
+
+import dev.langchain4j.model.chat.StreamingChatModel;
+import dev.langchain4j.model.openai.OpenAiStreamingChatModel;
+import lombok.extern.slf4j.Slf4j;
+import org.ruoyi.common.chat.domain.dto.request.ChatRequest;
+import org.ruoyi.common.chat.domain.vo.chat.ChatModelVo;
+import org.ruoyi.enums.ChatModeType;
+import org.ruoyi.observability.MyChatModelListener;
+import org.ruoyi.service.chat.AbstractChatService;
+import org.springframework.stereotype.Service;
+
+import java.util.List;
+
+/**
+ * MiniMax服务调用
+ * <p>
+ * MiniMax提供OpenAI兼容的API接口，支持MiniMax-M2.7、MiniMax-M2.5等模型。
+ * API地址：https://api.minimax.io/v1
+ *
+ * @author octopus
+ * @date 2026/3/21
+ */
+@Service
+@Slf4j
+public class MinimaxServiceImpl implements AbstractChatService {
+
+    @Override
+    public StreamingChatModel buildStreamingChatModel(ChatModelVo chatModelVo, ChatRequest chatRequest) {
+        return OpenAiStreamingChatModel.builder()
+            .baseUrl(chatModelVo.getApiHost())
+            .apiKey(chatModelVo.getApiKey())
+            .modelName(chatModelVo.getModelName())
+            .listeners(List.of(new MyChatModelListener()))
+            .returnThinking(chatRequest.getEnableThinking())
+            .build();
+    }
+
+    @Override
+    public String getProviderName() {
+        return ChatModeType.MINIMAX.getCode();
+    }
+
+}

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/chat/impl/provider/OllamaServiceImpl.java

@@ -1,7 +1,9 @@
 package org.ruoyi.service.chat.impl.provider;
 
 
+import dev.langchain4j.model.chat.ChatModel;
 import dev.langchain4j.model.chat.StreamingChatModel;
+import dev.langchain4j.model.ollama.OllamaChatModel;
 import dev.langchain4j.model.ollama.OllamaStreamingChatModel;
 import lombok.RequiredArgsConstructor;
 import lombok.extern.slf4j.Slf4j;
@@ -37,6 +39,14 @@ public class OllamaServiceImpl implements AbstractChatService {
                 .build();
     }
 
+    @Override
+    public ChatModel buildChatModel(ChatModelVo chatModelVo) {
+        return OllamaChatModel.builder()
+            .baseUrl(chatModelVo.getApiHost())
+            .modelName(chatModelVo.getModelName())
+            .build();
+    }
+
     @Override
     public String getProviderName() {
         return ChatModeType.OLLAMA.getCode();

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/chat/impl/provider/QianWenChatServiceImpl.java

@@ -1,7 +1,9 @@
 package org.ruoyi.service.chat.impl.provider;
 
 
+import dev.langchain4j.community.model.dashscope.QwenChatModel;
 import dev.langchain4j.community.model.dashscope.QwenStreamingChatModel;
+import dev.langchain4j.model.chat.ChatModel;
 import dev.langchain4j.model.chat.StreamingChatModel;
 import lombok.RequiredArgsConstructor;
 import lombok.extern.slf4j.Slf4j;
@@ -38,6 +40,14 @@ public class QianWenChatServiceImpl implements AbstractChatService {
                 .build();
     }
 
+    @Override
+    public ChatModel buildChatModel(ChatModelVo chatModelVo) {
+        return QwenChatModel.builder()
+            .apiKey(chatModelVo.getApiKey())
+            .modelName(chatModelVo.getModelName())
+            .build();
+    }
+
     @Override
     public String getProviderName() {
         return ChatModeType.QIAN_WEN.getCode();

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/chat/impl/provider/ZhiPuChatServiceImpl.java

@@ -1,7 +1,9 @@
 package org.ruoyi.service.chat.impl.provider;
 
 
+import dev.langchain4j.community.model.zhipu.ZhipuAiChatModel;
 import dev.langchain4j.community.model.zhipu.ZhipuAiStreamingChatModel;
+import dev.langchain4j.model.chat.ChatModel;
 import dev.langchain4j.model.chat.StreamingChatModel;
 import lombok.RequiredArgsConstructor;
 import lombok.extern.slf4j.Slf4j;
@@ -35,6 +37,14 @@ public class ZhiPuChatServiceImpl implements AbstractChatService {
             .build();
     }
 
+    @Override
+    public ChatModel buildChatModel(ChatModelVo chatModelVo) {
+        return ZhipuAiChatModel.builder()
+            .apiKey(chatModelVo.getApiKey())
+            .model(chatModelVo.getModelName())
+            .build();
+    }
+
     @Override
     public String getProviderName() {
         return ChatModeType.ZHI_PU.getCode();

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/embed/impl/MinimaxEmbeddingProvider.java

@@ -0,0 +1,17 @@
+package org.ruoyi.service.embed.impl;
+
+import org.springframework.stereotype.Component;
+
+/**
+ * MiniMax嵌入模型（兼容OpenAI接口）
+ * <p>
+ * 支持embo-01模型，1536维度向量。
+ * API地址：https://api.minimax.io/v1
+ *
+ * @author octopus
+ * @date 2026/3/21
+ */
+@Component("minimax")
+public class MinimaxEmbeddingProvider extends OpenAiEmbeddingProvider {
+
+}

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/embed/impl/ZhipuAiEmbeddingProvider.java

@@ -0,0 +1,48 @@
+package org.ruoyi.service.embed.impl;
+
+
+
+import dev.langchain4j.community.model.zhipu.ZhipuAiEmbeddingModel;
+import dev.langchain4j.data.embedding.Embedding;
+import dev.langchain4j.data.segment.TextSegment;
+import dev.langchain4j.model.embedding.EmbeddingModel;
+import dev.langchain4j.model.output.Response;
+import org.ruoyi.common.chat.domain.vo.chat.ChatModelVo;
+import org.ruoyi.enums.ModalityType;
+import org.ruoyi.service.embed.BaseEmbedModelService;
+import org.springframework.stereotype.Component;
+
+import java.util.List;
+import java.util.Set;
+
+/**
+ * @Author:yang
+ * @Date:
+ * @Description: 智谱AI嵌入模型
+ */
+@Component("zhipu")
+public class ZhipuAiEmbeddingProvider implements BaseEmbedModelService {
+    protected ChatModelVo chatModelVo;
+
+    @Override
+    public void configure(ChatModelVo config) {
+        this.chatModelVo = config;
+    }
+
+    @Override
+    public Set<ModalityType> getSupportedModalities() {
+        return Set.of(ModalityType.TEXT);
+    }
+
+    @Override
+    public Response<List<Embedding>> embedAll(List<TextSegment> textSegments) {
+        EmbeddingModel model = ZhipuAiEmbeddingModel.builder()
+            .baseUrl(chatModelVo.getApiHost())
+            .apiKey(chatModelVo.getApiKey())
+            .model(chatModelVo.getModelName())
+            .dimensions(chatModelVo.getModelDimension())
+            .build();
+
+        return model.embedAll(textSegments);
+    }
+}

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/rerank/RerankModelService.java

@@ -0,0 +1,70 @@
+package org.ruoyi.service.rerank;
+
+import dev.langchain4j.data.segment.TextSegment;
+import dev.langchain4j.model.output.Response;
+import dev.langchain4j.model.scoring.ScoringModel;
+import org.ruoyi.common.chat.domain.vo.chat.ChatModelVo;
+import org.ruoyi.domain.bo.rerank.RerankRequest;
+import org.ruoyi.domain.bo.rerank.RerankResult;
+
+import java.util.List;
+
+/**
+ * 重排序模型服务接口
+ * 继承 langchain4j 的 ScoringModel 接口
+ * 参考设计模式：BaseEmbedModelService
+ *
+ * @author Yzm
+ * @date 2026-04-19
+ */
+public interface RerankModelService extends ScoringModel {
+
+    /**
+     * 根据配置信息配置重排序模型
+     *
+     * @param config 包含模型配置信息的 ChatModelVo 对象
+     */
+    void configure(ChatModelVo config);
+
+    /**
+     * 执行重排序（批量文档）
+     * 这是业务层使用的便捷方法
+     *
+     * @param rerankRequest 重排序请求，包含查询文本和候选文档列表
+     * @return 重排序结果，包含排序后的文档和相关性分数
+     */
+    RerankResult rerank(RerankRequest rerankRequest);
+
+    /**
+     * 实现 ScoringModel 接口的 scoreAll 方法
+     * 将 ScoringModel 的调用转换为重排序调用
+     */
+    @Override
+    default Response<List<Double>> scoreAll(List<TextSegment> segments, String query) {
+        // 将 TextSegment 转换为文档字符串列表
+        List<String> documents = segments.stream()
+                .map(TextSegment::text)
+                .toList();
+
+        RerankRequest request = RerankRequest.builder()
+                .query(query)
+                .documents(documents)
+                .topN(documents.size())
+                .returnDocuments(false)
+                .build();
+
+        RerankResult result = rerank(request);
+
+        // 提取分数列表，按原始顺序排列
+        List<Double> scores = new java.util.ArrayList<>(
+                java.util.Collections.nCopies(documents.size(), 0.0));
+
+        for (RerankResult.RerankDocument doc : result.getDocuments()) {
+            if (doc.getIndex() != null && doc.getIndex() < documents.size()) {
+                scores.set(doc.getIndex(), doc.getRelevanceScore());
+            }
+        }
+
+        return Response.from(scores);
+    }
+}

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/rerank/impl/AliBaiLianRerankModelService.java

@@ -0,0 +1,115 @@
+package org.ruoyi.service.rerank.impl;
+
+import com.fasterxml.jackson.databind.ObjectMapper;
+import lombok.extern.slf4j.Slf4j;
+import okhttp3.*;
+import org.ruoyi.common.chat.domain.vo.chat.ChatModelVo;
+import org.ruoyi.domain.bo.rerank.RerankRequest;
+import org.ruoyi.domain.bo.rerank.RerankResult;
+import org.ruoyi.domain.dto.request.AliBaiLianRerankRequest;
+import org.ruoyi.domain.dto.response.AliBaiLianRerankResponse;
+import org.ruoyi.service.rerank.RerankModelService;
+import org.springframework.stereotype.Component;
+
+import java.io.IOException;
+import java.util.concurrent.TimeUnit;
+
+/**
+ * 阿里百炼重排序模型实现
+ * 参考设计模式：AliBaiLianMultiEmbeddingProvider
+ *
+ * @author yang
+ * @date 2026-04-20
+ */
+@Slf4j
+@Component("qianwenRerank")
+public class AliBaiLianRerankModelService implements RerankModelService {
+
+    private final OkHttpClient okHttpClient;
+    private final ObjectMapper objectMapper = new ObjectMapper();
+    private ChatModelVo chatModelVo;
+
+    public AliBaiLianRerankModelService() {
+        this.okHttpClient = new OkHttpClient.Builder()
+                .connectTimeout(30, TimeUnit.SECONDS)
+                .readTimeout(60, TimeUnit.SECONDS)
+                .writeTimeout(30, TimeUnit.SECONDS)
+                .build();
+    }
+
+    @Override
+    public void configure(ChatModelVo config) {
+        this.chatModelVo = config;
+    }
+
+    @Override
+    public RerankResult rerank(RerankRequest rerankRequest) {
+        long startTime = System.currentTimeMillis();
+
+        try {
+            // 构建请求
+            AliBaiLianRerankRequest request = buildRequest(rerankRequest);
+            AliBaiLianRerankResponse response = executeRequest(request);
+
+            return response.toRerankResult(
+                    rerankRequest.getDocuments().size(),
+                    System.currentTimeMillis() - startTime
+            );
+
+        } catch (Exception e) {
+            log.error("阿里百炼重排序失败: {}", e.getMessage(), e);
+            throw new RuntimeException("重排序服务调用失败: " + e.getMessage(), e);
+        }
+    }
+
+    /**
+     * 构建请求对象
+     */
+    private AliBaiLianRerankRequest buildRequest(RerankRequest rerankRequest) {
+        return AliBaiLianRerankRequest.create(
+                chatModelVo.getModelName(),
+                rerankRequest.getQuery(),
+                rerankRequest.getDocuments(),
+                rerankRequest.getTopN(),
+                rerankRequest.getReturnDocuments()
+        );
+    }
+
+    /**
+     * 执行HTTP请求并解析响应
+     */
+    private AliBaiLianRerankResponse executeRequest(AliBaiLianRerankRequest request) throws IOException {
+        String jsonBody = request.toJson();
+        RequestBody body = RequestBody.create(jsonBody, MediaType.get("application/json"));
+
+        // 阿里百炼重排序 OpenAI兼容端点
+        String url = chatModelVo.getApiHost() + "/compatible-api/v1/reranks";
+        Request httpRequest = new Request.Builder()
+                .url(url)
+                .addHeader("Authorization", "Bearer " + chatModelVo.getApiKey())
+                .addHeader("Content-Type", "application/json")
+                .post(body)
+                .build();
+
+        try (Response response = okHttpClient.newCall(httpRequest).execute()) {
+            if (!response.isSuccessful()) {
+                String err = response.body() != null ? response.body().string() : "无错误信息";
+                throw new IllegalArgumentException("阿里百炼API调用失败: " + response.code() + " - " + err);
+            }
+
+            ResponseBody responseBody = response.body();
+            if (responseBody == null) {
+                throw new IllegalArgumentException("响应体为空");
+            }
+
+            return parseResponse(responseBody.string());
+        }
+    }
+
+    /**
+     * 解析响应
+     */
+    private AliBaiLianRerankResponse parseResponse(String responseBody) throws IOException {
+        return objectMapper.readValue(responseBody, AliBaiLianRerankResponse.class);
+    }
+}

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/rerank/impl/ZhiPuRerankModelService.java

@@ -0,0 +1,163 @@
+package org.ruoyi.service.rerank.impl;
+
+import com.fasterxml.jackson.databind.ObjectMapper;
+import io.jsonwebtoken.Jwts;
+import io.jsonwebtoken.security.MacAlgorithm;
+import lombok.extern.slf4j.Slf4j;
+import okhttp3.*;
+import org.ruoyi.common.chat.domain.vo.chat.ChatModelVo;
+import org.ruoyi.domain.bo.rerank.RerankRequest;
+import org.ruoyi.domain.bo.rerank.RerankResult;
+import org.ruoyi.domain.dto.request.ZhipuRerankRequest;
+import org.ruoyi.domain.dto.response.ZhipuRerankResponse;
+import org.ruoyi.service.rerank.RerankModelService;
+import org.springframework.stereotype.Component;
+
+import javax.crypto.spec.SecretKeySpec;
+import java.io.IOException;
+import java.lang.reflect.Constructor;
+import java.nio.charset.StandardCharsets;
+import java.util.concurrent.TimeUnit;
+
+/**
+ * 智谱AI 重排序模型实现
+ * 参考设计模式：AliBaiLianMultiEmbeddingProvider
+ *
+ * @author yang
+ * @date 2026-04-19
+ */
+@Slf4j
+@Component("zhipuRerank")
+public class ZhiPuRerankModelService implements RerankModelService {
+
+    private final OkHttpClient okHttpClient;
+    private final ObjectMapper objectMapper = new ObjectMapper();
+    private ChatModelVo chatModelVo;
+
+    public ZhiPuRerankModelService() {
+        this.okHttpClient = new OkHttpClient.Builder()
+                .connectTimeout(30, TimeUnit.SECONDS)
+                .readTimeout(60, TimeUnit.SECONDS)
+                .writeTimeout(30, TimeUnit.SECONDS)
+                .build();
+    }
+
+    @Override
+    public void configure(ChatModelVo config) {
+        this.chatModelVo = config;
+    }
+
+    @Override
+    public RerankResult rerank(RerankRequest rerankRequest) {
+        long startTime = System.currentTimeMillis();
+
+        try {
+            // 构建请求
+            ZhipuRerankRequest request = buildRequest(rerankRequest);
+            ZhipuRerankResponse response = executeRequest(request);
+
+            return response.toRerankResult(
+                    rerankRequest.getDocuments().size(),
+                    System.currentTimeMillis() - startTime
+            );
+
+        } catch (Exception e) {
+            log.error("智谱重排序失败: {}", e.getMessage(), e);
+            throw new RuntimeException("重排序服务调用失败: " + e.getMessage(), e);
+        }
+    }
+
+    /**
+     * 构建请求对象
+     */
+    private ZhipuRerankRequest buildRequest(RerankRequest rerankRequest) {
+        return ZhipuRerankRequest.create(
+                chatModelVo.getModelName(),
+                rerankRequest.getQuery(),
+                rerankRequest.getDocuments(),
+                rerankRequest.getTopN(),
+                rerankRequest.getReturnDocuments()
+        );
+    }
+
+    /**
+     * 执行HTTP请求并解析响应
+     */
+    private ZhipuRerankResponse executeRequest(ZhipuRerankRequest request) throws IOException {
+        String jsonBody = request.toJson();
+        RequestBody body = RequestBody.create(jsonBody, MediaType.get("application/json"));
+
+        // 生成智谱认证Token
+        String token = generateToken(chatModelVo.getApiKey());
+
+        // 智谱重排序固定端点路径
+        String url = chatModelVo.getApiHost() + "/api/paas/v4/rerank";
+        Request httpRequest = new Request.Builder()
+                .url(url)
+                .addHeader("Authorization", token)
+                .post(body)
+                .build();
+
+        try (Response response = okHttpClient.newCall(httpRequest).execute()) {
+            if (!response.isSuccessful()) {
+                String err = response.body() != null ? response.body().string() : "无错误信息";
+                throw new IllegalArgumentException("智谱API调用失败: " + response.code() + " - " + err);
+            }
+
+            ResponseBody responseBody = response.body();
+            if (responseBody == null) {
+                throw new IllegalArgumentException("响应体为空");
+            }
+
+            return parseResponse(responseBody.string());
+        }
+    }
+
+    /**
+     * 解析响应
+     */
+    private ZhipuRerankResponse parseResponse(String responseBody) throws IOException {
+        return objectMapper.readValue(responseBody, ZhipuRerankResponse.class);
+    }
+
+    /**
+     * 生成智谱JWT Token
+     */
+    private String generateToken(String apiKey) {
+        try {
+            String[] apiKeyParts = apiKey.split("\\.");
+            String keyId = apiKeyParts[0];
+            String secret = apiKeyParts[1];
+
+            long expireMillis = 1000L * 60 * 30; // 30分钟
+            java.util.Map<String, Object> payload = new java.util.HashMap<>();
+            payload.put("api_key", keyId);
+            payload.put("exp", System.currentTimeMillis() + expireMillis);
+            payload.put("timestamp", System.currentTimeMillis());
+
+            // 使用反射创建 MacAlgorithm（兼容不同版本的 jjwt）
+            MacAlgorithm macAlgorithm;
+            try {
+                Class<?> c = Class.forName("io.jsonwebtoken.impl.security.DefaultMacAlgorithm");
+                Constructor<?> ctor = c.getDeclaredConstructor(String.class, String.class, int.class);
+                ctor.setAccessible(true);
+                macAlgorithm = (MacAlgorithm) ctor.newInstance("HS256", "HmacSHA256", 128);
+            } catch (Exception e) {
+                macAlgorithm = Jwts.SIG.HS256;
+            }
+
+            String token = Jwts.builder()
+                    .header()
+                    .add("alg", "HS256")
+                    .add("sign_type", "SIGN")
+                    .and()
+                    .content(objectMapper.writeValueAsString(payload))
+                    .signWith(new SecretKeySpec(secret.getBytes(StandardCharsets.UTF_8), "HmacSHA256"), macAlgorithm)
+                    .compact();
+
+            return "Bearer " + token;
+        } catch (Exception e) {
+            throw new RuntimeException("生成智谱Token失败: " + e.getMessage(), e);
+        }
+    }
+}

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/retrieval/KnowledgeRetrievalService.java

@@ -0,0 +1,24 @@
+package org.ruoyi.service.retrieval;
+
+import org.ruoyi.domain.bo.vector.QueryVectorBo;
+
+import java.util.List;
+
+/**
+ * 知识库检索服务接口
+ * 整合粗召回（向量检索）和重排序流程
+ *
+ * @author yang
+ * @date 2026-04-19
+ */
+public interface KnowledgeRetrievalService {
+
+    /**
+     * 执行知识库检索，返回文本内容
+     * 流程：向量粗召回 -> 重排序（可选） -> 返回结果
+     *
+     * @param queryVectorBo 查询参数
+     * @return 文本内容列表
+     */
+    List<String> retrieveTexts(QueryVectorBo queryVectorBo);
+}

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/retrieval/impl/KnowledgeRetrievalServiceImpl.java

@@ -0,0 +1,135 @@
+package org.ruoyi.service.retrieval.impl;
+
+import lombok.RequiredArgsConstructor;
+import lombok.extern.slf4j.Slf4j;
+import org.ruoyi.domain.bo.rerank.RerankRequest;
+import org.ruoyi.domain.bo.rerank.RerankResult;
+import org.ruoyi.domain.bo.vector.QueryVectorBo;
+import org.ruoyi.factory.RerankModelFactory;
+import org.ruoyi.service.rerank.RerankModelService;
+import org.ruoyi.service.retrieval.KnowledgeRetrievalService;
+import org.ruoyi.service.vector.VectorStoreService;
+import org.springframework.stereotype.Service;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/**
+ * 知识库检索服务实现
+ * 整合粗召回（向量检索）和重排序流程
+ *
+ * @author yang
+ * @date 2026-04-19
+ */
+@Slf4j
+@Service
+@RequiredArgsConstructor
+public class KnowledgeRetrievalServiceImpl implements KnowledgeRetrievalService {
+
+    private final VectorStoreService vectorStoreService;
+    private final RerankModelFactory rerankModelFactory;
+
+    /**
+     * 粗召回默认扩大倍数
+     * 如果启用重排序，粗召回会获取更多结果供重排序筛选
+     */
+    private static final int RERANK_EXPANSION_FACTOR = 3;
+
+    @Override
+    public List<String> retrieveTexts(QueryVectorBo queryVectorBo) {
+        log.info("开始知识库检索, kid={}, query={}", queryVectorBo.getKid(), queryVectorBo.getQuery());
+
+        // 1. 粗召回阶段 - 向量检索
+        List<String> coarseResults = coarseRetrieval(queryVectorBo);
+        log.debug("粗召回返回 {} 条结果", coarseResults.size());
+
+        if (coarseResults.isEmpty()) {
+            return coarseResults;
+        }
+
+        // 2. 重排序阶段（可选）
+        if (Boolean.TRUE.equals(queryVectorBo.getEnableRerank()) &&
+                queryVectorBo.getRerankModelName() != null) {
+            return rerank(queryVectorBo, coarseResults);
+        }
+
+        return coarseResults;
+    }
+
+    /**
+     * 粗召回阶段 - 向量检索
+     */
+    private List<String> coarseRetrieval(QueryVectorBo queryVectorBo) {
+        // 如果启用重排序，扩大粗召回数量
+        int originalMaxResults = queryVectorBo.getMaxResults();
+        int expandedResults = originalMaxResults;
+        if (Boolean.TRUE.equals(queryVectorBo.getEnableRerank()) &&
+                queryVectorBo.getRerankModelName() != null) {
+            expandedResults = originalMaxResults * RERANK_EXPANSION_FACTOR;
+            log.debug("启用重排序，粗召回数量从 {} 扩大到 {}", originalMaxResults, expandedResults);
+        }
+
+        // 临时修改查询数量
+        queryVectorBo.setMaxResults(expandedResults);
+        try {
+            return vectorStoreService.getQueryVector(queryVectorBo);
+        } finally {
+            // 恢复原始值
+            queryVectorBo.setMaxResults(originalMaxResults);
+        }
+    }
+
+    /**
+     * 重排序阶段
+     */
+    private List<String> rerank(QueryVectorBo queryVectorBo, List<String> coarseResults) {
+        long startTime = System.currentTimeMillis();
+
+        try {
+            // 1. 通过工厂获取重排序模型
+            RerankModelService rerankModel = rerankModelFactory.createModel(queryVectorBo.getRerankModelName());
+
+            // 2. 构建重排序请求
+            int topN = queryVectorBo.getRerankTopN() != null ?
+                    queryVectorBo.getRerankTopN() : queryVectorBo.getMaxResults();
+
+            RerankRequest rerankRequest = RerankRequest.builder()
+                    .query(queryVectorBo.getQuery())
+                    .documents(coarseResults)
+                    .topN(topN)
+                    .returnDocuments(true)
+                    .build();
+
+            log.info("执行重排序, model={}, documents={}, topN={}",
+                    queryVectorBo.getRerankModelName(), coarseResults.size(), topN);
+
+            // 3. 执行重排序
+            RerankResult rerankResult = rerankModel.rerank(rerankRequest);
+
+            // 4. 转换重排序结果
+            List<String> finalResults = new ArrayList<>();
+            for (RerankResult.RerankDocument doc : rerankResult.getDocuments()) {
+                // 应用分数阈值过滤
+                if (queryVectorBo.getRerankScoreThreshold() != null &&
+                        doc.getRelevanceScore() < queryVectorBo.getRerankScoreThreshold()) {
+                    continue;
+                }
+
+                if (doc.getDocument() != null) {
+                    finalResults.add(doc.getDocument());
+                }
+            }
+
+            long duration = System.currentTimeMillis() - startTime;
+            log.info("重排序完成, 返回 {} 条结果, 耗时 {}ms", finalResults.size(), duration);
+
+            return finalResults;
+
+        } catch (Exception e) {
+            log.error("重排序失败: {}", e.getMessage(), e);
+            // 重排序失败时返回原始粗召回结果（截取到期望数量）
+            int limit = Math.min(queryVectorBo.getMaxResults(), coarseResults.size());
+            return new ArrayList<>(coarseResults.subList(0, limit));
+        }
+    }
+}

ruoyi-modules/ruoyi-chat/src/test/java/org/ruoyi/enums/ChatModeTypeTest.java

@@ -0,0 +1,42 @@
+package org.ruoyi.enums;
+
+import org.junit.jupiter.api.Test;
+
+import static org.junit.jupiter.api.Assertions.*;
+
+/**
+ * Unit tests for ChatModeType enum
+ */
+class ChatModeTypeTest {
+
+    @Test
+    void minimaxEnumExists() {
+        ChatModeType minimax = ChatModeType.MINIMAX;
+        assertNotNull(minimax);
+    }
+
+    @Test
+    void minimaxCode_isMinimax() {
+        assertEquals("minimax", ChatModeType.MINIMAX.getCode());
+    }
+
+    @Test
+    void minimaxDescription_isMiniMax() {
+        assertEquals("MiniMax", ChatModeType.MINIMAX.getDescription());
+    }
+
+    @Test
+    void allProviders_haveUniqueCode() {
+        ChatModeType[] values = ChatModeType.values();
+        long uniqueCodes = java.util.Arrays.stream(values)
+            .map(ChatModeType::getCode)
+            .distinct()
+            .count();
+        assertEquals(values.length, uniqueCodes, "All providers must have unique codes");
+    }
+
+    @Test
+    void valueOf_minimax() {
+        assertEquals(ChatModeType.MINIMAX, ChatModeType.valueOf("MINIMAX"));
+    }
+}

ruoyi-modules/ruoyi-chat/src/test/java/org/ruoyi/integration/MinimaxIntegrationTest.java

@@ -0,0 +1,70 @@
+package org.ruoyi.integration;
+
+import dev.langchain4j.model.chat.StreamingChatModel;
+import org.junit.jupiter.api.BeforeEach;
+import org.junit.jupiter.api.Test;
+import org.junit.jupiter.api.condition.EnabledIfEnvironmentVariable;
+import org.ruoyi.common.chat.domain.dto.request.ChatRequest;
+import org.ruoyi.common.chat.domain.vo.chat.ChatModelVo;
+import org.ruoyi.service.chat.impl.provider.MinimaxServiceImpl;
+
+import static org.junit.jupiter.api.Assertions.*;
+
+/**
+ * Integration tests for MiniMax provider.
+ * These tests require a valid MINIMAX_API_KEY environment variable.
+ */
+@EnabledIfEnvironmentVariable(named = "MINIMAX_API_KEY", matches = ".+")
+class MinimaxIntegrationTest {
+
+    private MinimaxServiceImpl minimaxService;
+    private String apiKey;
+
+    @BeforeEach
+    void setUp() {
+        minimaxService = new MinimaxServiceImpl();
+        apiKey = System.getenv("MINIMAX_API_KEY");
+    }
+
+    @Test
+    void buildStreamingChatModel_withRealApiKey_M27() {
+        ChatModelVo modelVo = new ChatModelVo();
+        modelVo.setApiHost("https://api.minimax.io/v1");
+        modelVo.setApiKey(apiKey);
+        modelVo.setModelName("MiniMax-M2.7");
+
+        ChatRequest request = new ChatRequest();
+        request.setEnableThinking(false);
+
+        StreamingChatModel model = minimaxService.buildStreamingChatModel(modelVo, request);
+        assertNotNull(model, "Should create streaming model with real API key");
+    }
+
+    @Test
+    void buildStreamingChatModel_withRealApiKey_M25() {
+        ChatModelVo modelVo = new ChatModelVo();
+        modelVo.setApiHost("https://api.minimax.io/v1");
+        modelVo.setApiKey(apiKey);
+        modelVo.setModelName("MiniMax-M2.5");
+
+        ChatRequest request = new ChatRequest();
+        request.setEnableThinking(false);
+
+        StreamingChatModel model = minimaxService.buildStreamingChatModel(modelVo, request);
+        assertNotNull(model, "Should create streaming model with M2.5");
+    }
+
+    @Test
+    void buildStreamingChatModel_withRealApiKey_M25Highspeed() {
+        ChatModelVo modelVo = new ChatModelVo();
+        modelVo.setApiHost("https://api.minimax.io/v1");
+        modelVo.setApiKey(apiKey);
+        modelVo.setModelName("MiniMax-M2.5-highspeed");
+
+        ChatRequest request = new ChatRequest();
+        request.setEnableThinking(false);
+
+        StreamingChatModel model = minimaxService.buildStreamingChatModel(modelVo, request);
+        assertNotNull(model, "Should create streaming model with M2.5-highspeed");
+    }
+}

ruoyi-modules/ruoyi-chat/src/test/java/org/ruoyi/service/chat/impl/provider/MinimaxServiceImplTest.java

@@ -0,0 +1,76 @@
+package org.ruoyi.service.chat.impl.provider;
+
+import dev.langchain4j.model.chat.StreamingChatModel;
+import org.junit.jupiter.api.BeforeEach;
+import org.junit.jupiter.api.Test;
+import org.ruoyi.common.chat.domain.dto.request.ChatRequest;
+import org.ruoyi.common.chat.domain.vo.chat.ChatModelVo;
+import org.ruoyi.enums.ChatModeType;
+
+import static org.junit.jupiter.api.Assertions.*;
+
+/**
+ * Unit tests for MinimaxServiceImpl
+ */
+class MinimaxServiceImplTest {
+
+    private MinimaxServiceImpl minimaxService;
+
+    @BeforeEach
+    void setUp() {
+        minimaxService = new MinimaxServiceImpl();
+    }
+
+    @Test
+    void getProviderName_returnsMinimaxCode() {
+        assertEquals("minimax", minimaxService.getProviderName());
+        assertEquals(ChatModeType.MINIMAX.getCode(), minimaxService.getProviderName());
+    }
+
+    @Test
+    void buildStreamingChatModel_returnsNonNull() {
+        ChatModelVo modelVo = new ChatModelVo();
+        modelVo.setApiHost("https://api.minimax.io/v1");
+        modelVo.setApiKey("test-api-key");
+        modelVo.setModelName("MiniMax-M2.7");
+
+        ChatRequest request = new ChatRequest();
+        request.setEnableThinking(false);
+
+        StreamingChatModel model = minimaxService.buildStreamingChatModel(modelVo, request);
+        assertNotNull(model);
+    }
+
+    @Test
+    void buildStreamingChatModel_withThinkingEnabled() {
+        ChatModelVo modelVo = new ChatModelVo();
+        modelVo.setApiHost("https://api.minimax.io/v1");
+        modelVo.setApiKey("test-api-key");
+        modelVo.setModelName("MiniMax-M2.5");
+
+        ChatRequest request = new ChatRequest();
+        request.setEnableThinking(true);
+
+        StreamingChatModel model = minimaxService.buildStreamingChatModel(modelVo, request);
+        assertNotNull(model);
+    }
+
+    @Test
+    void buildStreamingChatModel_withHighspeedModel() {
+        ChatModelVo modelVo = new ChatModelVo();
+        modelVo.setApiHost("https://api.minimax.io/v1");
+        modelVo.setApiKey("test-api-key");
+        modelVo.setModelName("MiniMax-M2.5-highspeed");
+
+        ChatRequest request = new ChatRequest();
+        request.setEnableThinking(false);
+
+        StreamingChatModel model = minimaxService.buildStreamingChatModel(modelVo, request);
+        assertNotNull(model);
+    }
+
+    @Test
+    void implementsAbstractChatService() {
+        assertInstanceOf(org.ruoyi.service.chat.AbstractChatService.class, minimaxService);
+    }
+}

ruoyi-modules/ruoyi-chat/src/test/java/org/ruoyi/service/embed/impl/MinimaxEmbeddingProviderTest.java

@@ -0,0 +1,55 @@
+package org.ruoyi.service.embed.impl;
+
+import org.junit.jupiter.api.BeforeEach;
+import org.junit.jupiter.api.Test;
+import org.ruoyi.common.chat.domain.vo.chat.ChatModelVo;
+import org.ruoyi.enums.ModalityType;
+import org.ruoyi.service.embed.BaseEmbedModelService;
+
+import java.util.Set;
+
+import static org.junit.jupiter.api.Assertions.*;
+
+/**
+ * Unit tests for MinimaxEmbeddingProvider
+ */
+class MinimaxEmbeddingProviderTest {
+
+    private MinimaxEmbeddingProvider provider;
+
+    @BeforeEach
+    void setUp() {
+        provider = new MinimaxEmbeddingProvider();
+    }
+
+    @Test
+    void implementsBaseEmbedModelService() {
+        assertInstanceOf(BaseEmbedModelService.class, provider);
+    }
+
+    @Test
+    void extendsOpenAiEmbeddingProvider() {
+        assertInstanceOf(OpenAiEmbeddingProvider.class, provider);
+    }
+
+    @Test
+    void getSupportedModalities_returnsText() {
+        Set<ModalityType> modalities = provider.getSupportedModalities();
+        assertNotNull(modalities);
+        assertTrue(modalities.contains(ModalityType.TEXT));
+        assertEquals(1, modalities.size());
+    }
+
+    @Test
+    void configure_setsModelConfig() {
+        ChatModelVo config = new ChatModelVo();
+        config.setApiHost("https://api.minimax.io/v1");
+        config.setApiKey("test-api-key");
+        config.setModelName("embo-01");
+        config.setModelDimension(1536);
+
+        provider.configure(config);
+        // configure sets internal state; verify no exception thrown
+        assertNotNull(provider);
+    }
+}

ruoyi-modules/ruoyi-chat/src/test/java/org/ruoyi/service/rerank/impl/AliBaiLianRerankModelServiceTest.java

@@ -0,0 +1,126 @@
+package org.ruoyi.service.rerank.impl;
+
+import org.junit.jupiter.api.BeforeEach;
+import org.junit.jupiter.api.Test;
+import org.ruoyi.common.chat.domain.vo.chat.ChatModelVo;
+import org.ruoyi.domain.bo.rerank.RerankRequest;
+import org.ruoyi.domain.bo.rerank.RerankResult;
+
+import java.util.Arrays;
+import java.util.List;
+
+import static org.junit.jupiter.api.Assertions.*;
+
+/**
+ * 阿里百炼重排序模型测试类
+ * 运行前请设置环境变量 DASHSCOPE_API_KEY 或直接修改 apiKey
+ */
+class AliBaiLianRerankModelServiceTest {
+
+    private AliBaiLianRerankModelService service;
+
+    // 请替换为你的 API Key
+    private static final String API_KEY = System.getenv("DASHSCOPE_API_KEY");
+    private static final String API_HOST = "https://dashscope.aliyuncs.com";
+    private static final String MODEL_NAME = "qwen3-rerank";
+
+    @BeforeEach
+    void setUp() {
+        service = new AliBaiLianRerankModelService();
+    }
+
+    @Test
+    void testConfigure() {
+        ChatModelVo config = createConfig();
+        service.configure(config);
+        assertNotNull(service);
+    }
+
+    @Test
+    void testRerank() {
+        // 跳过测试如果没有配置 API Key
+        if (API_KEY == null || API_KEY.isEmpty()) {
+            System.out.println("跳过测试: 未设置环境变量 DASHSCOPE_API_KEY");
+            return;
+        }
+
+        ChatModelVo config = createConfig();
+        service.configure(config);
+
+        List<String> documents = Arrays.asList(
+                "文本排序模型广泛用于搜索引擎和推荐系统中，它们根据文本相关性对候选文本进行排序",
+                "量子计算是计算科学的一个前沿领域",
+                "预训练语言模型的发展给文本排序模型带来了新的进展"
+        );
+
+        RerankRequest request = RerankRequest.builder()
+                .query("什么是文本排序模型")
+                .documents(documents)
+                .topN(2)
+                .returnDocuments(true)
+                .build();
+
+        RerankResult result = service.rerank(request);
+
+        System.out.println("=== 重排序结果 ===");
+        System.out.println("总文档数: " + result.getTotalDocuments());
+        System.out.println("耗时: " + result.getDurationMs() + "ms");
+
+        result.getDocuments().forEach(doc -> {
+            System.out.println("索引: " + doc.getIndex() +
+                    ", 相关性分数: " + doc.getRelevanceScore() +
+                    ", 文档: " + doc.getDocument());
+        });
+
+        assertNotNull(result);
+        assertNotNull(result.getDocuments());
+        assertFalse(result.getDocuments().isEmpty());
+        assertEquals(2, result.getDocuments().size());
+    }
+
+    @Test
+    void testRerankWithFullDocuments() {
+        if (API_KEY == null || API_KEY.isEmpty()) {
+            System.out.println("跳过测试: 未设置环境变量 DASHSCOPE_API_KEY");
+            return;
+        }
+
+        ChatModelVo config = createConfig();
+        service.configure(config);
+
+        List<String> documents = Arrays.asList(
+                "Java是一种广泛使用的编程语言",
+                "Python是人工智能领域最流行的语言",
+                "Go语言由Google开发，适合并发编程"
+        );
+
+        RerankRequest request = RerankRequest.builder()
+                .query("哪种语言适合AI开发")
+                .documents(documents)
+                .build();
+
+        RerankResult result = service.rerank(request);
+
+        System.out.println("=== 重排序结果2 ===");
+        result.getDocuments().forEach(doc -> {
+            System.out.println("索引: " + doc.getIndex() +
+                    ", 分数: " + doc.getRelevanceScore() +
+                    ", 文档: " + doc.getDocument());
+        });
+
+        assertNotNull(result);
+        assertEquals(3, result.getDocuments().size());
+
+        // Python相关文档应该排在前面
+        assertEquals(1, result.getDocuments().get(0).getIndex());
+        assertTrue(result.getDocuments().get(0).getRelevanceScore() > 0.5);
+    }
+
+    private ChatModelVo createConfig() {
+        ChatModelVo config = new ChatModelVo();
+        config.setApiHost(API_HOST);
+        config.setApiKey(API_KEY != null ? API_KEY : "test-api-key");
+        config.setModelName(MODEL_NAME);
+        return config;
+    }
+}

ruoyi-modules/ruoyi-chat/src/test/java/org/ruoyi/service/rerank/impl/AliBaiLianRerankTestMain.java

@@ -0,0 +1,73 @@
+package org.ruoyi.service.rerank.impl;
+
+import org.ruoyi.common.chat.domain.vo.chat.ChatModelVo;
+import org.ruoyi.domain.bo.rerank.RerankRequest;
+import org.ruoyi.domain.bo.rerank.RerankResult;
+
+import java.util.Arrays;
+import java.util.List;
+
+/**
+ * 阿里百炼重排序模型测试 - Main方法直接运行
+ * 运行前请设置 API_KEY
+ */
+public class AliBaiLianRerankTestMain {
+
+    // 请替换为你的 API Key
+    private static final String API_KEY = "sk-your-api-key-here";
+    private static final String API_HOST = "https://dashscope.aliyuncs.com";
+    private static final String MODEL_NAME = "qwen3-rerank";
+
+    public static void main(String[] args) {
+        AliBaiLianRerankModelService service = new AliBaiLianRerankModelService();
+
+        // 配置
+        ChatModelVo config = new ChatModelVo();
+        config.setApiHost(API_HOST);
+        config.setApiKey(API_KEY);
+        config.setModelName(MODEL_NAME);
+        service.configure(config);
+
+        // 测试数据
+        List<String> documents = Arrays.asList(
+                "文本排序模型广泛用于搜索引擎和推荐系统中，它们根据文本相关性对候选文本进行排序",
+                "量子计算是计算科学的一个前沿领域",
+                "预训练语言模型的发展给文本排序模型带来了新的进展"
+        );
+
+        RerankRequest request = RerankRequest.builder()
+                .query("什么是文本排序模型")
+                .documents(documents)
+                .topN(2)
+                .returnDocuments(true)
+                .build();
+
+        System.out.println("=== 开始测试阿里百炼重排序 ===");
+        System.out.println("API Host: " + API_HOST);
+        System.out.println("Model: " + MODEL_NAME);
+        System.out.println("Query: 什么是文本排序模型");
+        System.out.println();
+
+        try {
+            RerankResult result = service.rerank(request);
+
+            System.out.println("=== 重排序结果 ===");
+            System.out.println("总文档数: " + result.getTotalDocuments());
+            System.out.println("耗时: " + result.getDurationMs() + "ms");
+            System.out.println();
+
+            result.getDocuments().forEach(doc -> {
+                System.out.println("索引: " + doc.getIndex());
+                System.out.println("相关性分数: " + doc.getRelevanceScore());
+                System.out.println("文档: " + doc.getDocument());
+                System.out.println("---");
+            });
+
+            System.out.println("=== 测试成功 ===");
+
+        } catch (Exception e) {
+            System.err.println("=== 测试失败 ===");
+            e.printStackTrace();
+        }
+    }
+}