wrapper = new LambdaQueryWrapper<>();\r\n wrapper.and(w -> w\r\n .apply(\"material_code LIKE {0}\", \"%\" + escaped + \"%\")\r\n .or()\r\n .apply(\"material_name LIKE {0}\", \"%\" + escaped + \"%\")\r\n .or()\r\n .apply(\"spec LIKE {0}\", \"%\" + escaped + \"%\")\r\n );\r\n wrapper.select(Material::getId);\r\n return selectList(wrapper).stream()\r\n .map(Material::getId)\r\n .collect(Collectors.toList());\r\n }\r\n\r\n}\r\n"}],"isError":false,"timestamp":1780017933583}}
{"type":"message","id":"62f396c9","parentId":"376e2bbd","timestamp":"2026-05-29T01:25:33.583Z","message":{"role":"toolResult","toolCallId":"call_4d47ea19647d4e97ac05b221","toolName":"read","content":[{"type":"text","text":"package com.witsoft.erp.common.util;\n\n/**\n * LIKE 查询特殊字符转义工具类\n * \n * SQL LIKE 查询中，% 和 _ 是通配符，需要转义。\n * 本工具转义 \\ % _ 三个字符，配合 ESCAPE '\\' 使用。\n */\npublic class LikeEscapeUtil {\n\n /**\n * 转义 LIKE 查询中的特殊字符\n *
\n * SQL LIKE 通配符：% _ 需要转义\n * 数据中出现的特殊字符：空格、%、x、_、-、/、\\ 均不影响 LIKE 查询（% 和 _ 已被转义）\n * \n * @param keyword 原始关键字\n * @return 转义后的关键字\n */\n public static String escapeLike(String keyword) {\n if (keyword == null) {\n return null;\n }\n return keyword\n .replace(\"\\\\\", \"\\\\\\\\\")\n .replace(\"%\", \"\\\\%\")\n .replace(\"_\", \"\\\\_\");\n }\n}"}],"isError":false,"timestamp":1780017933583}}
{"type":"message","id":"7f6a9bf1","parentId":"62f396c9","timestamp":"2026-05-29T01:25:53.287Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Now I have a full picture of the existing codebase. Let me analyze the situation and provide a comprehensive answer about the \"近似查询\" (approximate/fuzzy query) approach.\n\nThe user is asking about a query approximation strategy for materials, with these requirements:\n1. AI should return name and spec\n2. Exact match by name\n3. Fuzzy match by name\n4. Fuzzy match by spec\n5. When multiple results are returned, analyze priority\n\nLet me now look at what the current code already does and propose a comprehensive solution.\n\nCurrent state:\n- `MaterialServiceImpl.searchByNameLike(String keyword)` - does a combined LIKE on material_code, material_name, spec with `OR` - so all three fields are searched with the same keyword\n- `MaterialMapper.searchMaterialIdsByKeyword` - same combined LIKE\n- `MaterialSelectorServiceImpl` - keyword matching in `matchesKeyword()` method\n- `AiRecognitionServiceImpl.matchMaterialItem()` - complex multi-level fuzzy matching strategy\n\nThe user wants a more structured approach. Let me think about what would be a good design.\n\nThe key architectural question is: when the user/ai provides a query string that might match multiple materials by different criteria (exact name match, fuzzy name match, fuzzy spec match), how should we prioritize results?\n\nA good approach would be something like:\n\n1. **Separate the matching strategies** - don't use a single OR LIKE\n2. **Define priority tiers**:\n - Tier 1: Exact name match\n - Tier 2: Name starts with / prefix match\n - Tier 3: Name contains keyword (fuzzy)\n - Tier 4: Spec contains keyword\n - Tier 5: Code contains keyword\n3. **Return with priority label** so the caller knows which tier matched\n\nLet me propose a clean solution.","thinkingSignature":"reasoning_content"},{"type":"text","text":"现在我对整个查询链路有完整了解了。给你一个综合分析。\n\n---\n\n## 现有查询链路分析\n\n目前项目中有**三处**物料近似查询逻辑，分散在不同地方：\n\n| 位置 | 方式 | 问题 |\n|---|---|---|\n| `MaterialService.searchByNameLike()` | 一个 `keyword` 同时 LIKE 编码/名称/规格（OR） | 三条路混在一起，不知道哪个优先级 |\n| `MaterialSelectorServiceImpl.matchesKeyword()` | 内存中 `contains` 匹配代码/名称/规格 | 无优先级，纯集合过滤 |\n| `AiRecognitionServiceImpl.matchMaterialItem()` | 多层降级搜索（名称→名称+规格→分词→汉字片段） | 每次只取第一个，放弃多候选 |\n\n核心痛点：**没有按匹配类型分优先级排序，多条结果混在一起无法区分。**\n\n---\n\n## 推荐方案：结构化近似查询\n\n核心思路：**一个查询不只返回「有没有匹配到」，还要返回「匹配的场次（match_type）」。**\n\n### 1. 定义匹配优先级枚举\n\n```java\npublic enum MatchPriority {\n EXACT_NAME(1, \"名称精确匹配\"), // material_name = keyword\n PREFIX_NAME(2, \"名称前缀匹配\"), // material_name LIKE 'keyword%'\n FUZZY_NAME(3, \"名称模糊匹配\"), // material_name LIKE '%keyword%'\n FUZZY_SPEC(4, \"规格模糊匹配\"), // spec LIKE '%keyword%'\n FUZZY_CODE(5, \"编码模糊匹配\"); // material_code LIKE '%keyword%'\n\n public final int rank;\n public final String desc;\n\n MatchPriority(int rank, String desc) {\n this.rank = rank;\n this.desc = desc;\n }\n}\n```\n\n### 2. SQL 分步查询（按优先级降级）\n\n```sql\n-- Step 1: 精确匹配名称\nSELECT id, material_code, material_name, spec, unit, 'EXACT_NAME' AS match_type\n FROM md_material\n WHERE material_name = :keyword\n AND status = 0\n\n-- Step 2: 名称前缀匹配（如果有增删查改场景）\nSELECT id, ..., 'PREFIX_NAME' AS match_type\n FROM md_material\n WHERE material_name LIKE :keyword || '%'\n AND status = 0\n\n-- Step 3: 名称模糊匹配\nSELECT id, ..., 'FUZZY_NAME' AS match_type\n FROM md_material\n WHERE material_name LIKE '%' || :keyword || '%'\n AND status = 0\n\n-- Step 4: 规格模糊匹配\nSELECT id, ..., 'FUZZY_SPEC' AS match_type\n FROM md_material\n WHERE spec LIKE '%' || :keyword || '%'\n AND status = 0\n\n-- Step 5: 编码模糊匹配（兜底）\nSELECT id, ..., 'FUZZY_CODE' AS match_type\n FROM md_material\n WHERE material_code LIKE '%' || :keyword || '%'\n AND status = 0\n```\n\n> **为什么不全放一个 OR？** 因为 OR 出来的结果你无法知道是哪条条件命中的，排序时无法区分精确匹配和模糊匹配。\n\n### 3. 返回 DTO\n\n```java\n@Data\npublic class MaterialMatchResult {\n private Long id;\n private String materialCode;\n private String materialName;\n private String spec;\n private String unit;\n \n /** 命中类型（EXACT_NAME / FUZZY_SPEC 等） */\n private MatchPriority matchType;\n \n /** 排序权重（越小越优先，展示时按此排序） */\n private Integer matchRank;\n \n /** 总结果数（方便前端决策是否展示\"搜索到 N 条\"） */\n private Integer totalMatches;\n}\n```\n\n### 4. Service 实现架构\n\n```java\n@Override\npublic List searchSimilar(String keyword) {\n String escaped = escapeLike(keyword);\n List results = new ArrayList<>();\n Set addedIds = new HashSet<>(); // 防重复\n\n // 按优先级依次查询，已匹配的 ID 不再出现在更低优先级中\n // 1. 名称精确匹配\n searchAndAdd(results, addedIds, \n wrapper -> wrapper.eq(Material::getMaterialName, keyword),\n MatchPriority.EXACT_NAME);\n\n // 2. 名称前缀匹配\n searchAndAdd(results, addedIds,\n wrapper -> wrapper.apply(\"material_name LIKE {0}\", escaped + \"%\"),\n MatchPriority.PREFIX_NAME);\n\n // 3. 名称模糊匹配\n searchAndAdd(results, addedIds,\n wrapper -> wrapper.apply(\"material_name LIKE {0}\", \"%\" + escaped + \"%\"),\n MatchPriority.FUZZY_NAME);\n\n // 4. 规格模糊匹配\n searchAndAdd(results, addedIds,\n wrapper -> wrapper.apply(\"spec LIKE {0}\", \"%\" + escaped + \"%\"),\n MatchPriority.FUZZY_SPEC);\n\n // 5. 编码模糊匹配（兜底）\n searchAndAdd(results, addedIds,\n wrapper -> wrapper.apply(\"material_code LIKE {0}\", \"%\" + escaped + \"%\"),\n MatchPriority.FUZZY_CODE);\n\n return results;\n}\n\nprivate void searchAndAdd(List results, Set addedIds,\n Consumer> condition, MatchPriority priority) {\n LambdaQueryWrapper wrapper = new LambdaQueryWrapper<>();\n condition.accept(wrapper);\n wrapper.eq(Material::getStatus, 0);\n wrapper.orderByDesc(Material::getCreateTime);\n wrapper.last(\"LIMIT 20\");\n\n List list = baseMapper.selectList(wrapper);\n for (Material m : list) {\n if (addedIds.add(m.getId())) {\n MaterialMatchResult r = new MaterialMatchResult();\n // 填充字段\n r.setMatchType(priority);\n r.setMatchRank(priority.rank);\n results.add(r);\n }\n }\n}\n```\n\n### 5. AI 场景的适配（`AiRecognitionServiceImpl`）\n\n现有逻辑每次只取 `candidates.get(0)`，改为：\n\n```java\n// 不再只取第一个，改用结构化搜索\nList candidates = materialService.searchSimilar(normalized);\n\nif (candidates.isEmpty()) {\n // 降级：分词搜索（和现有逻辑一致）\n ...\n}\n\n// 取优先级最高的候选\nMaterialMatchResult best = candidates.get(0);\n```\n\n并且 `RecognitionItem` 中可以新增一个 `matchType` 字段，前端可以看到匹配质量（精确/模糊）。\n\n---\n\n## 优先级总览\n\n| 优先级 | 匹配方式 | SQL 条件 | 场景示例（输入\"8.5mm玻璃棒\"） |\n|---|---|---|---|\n| 🥇 1 | 名称精确匹配 | `name = keyword` | 数据库刚好有物料叫\"8.5mm玻璃棒\" |\n| 🥇 2 | 名称前缀匹配 | `name LIKE 'keyword%'` | 前缀匹配（较少用，视业务需求可跳过） |\n| 🥇 3 | 名称模糊匹配 | `name LIKE '%keyword%'` | 名称中包含\"8.5mm玻璃棒\" |\n| 🥇 4 | 规格模糊匹配 | `spec LIKE '%keyword%'` | 规格字段中有\"8.5mm\"或\"玻璃棒\" |\n| 🥇 5 | 编码模糊匹配 | `code LIKE '%keyword%'` | 编码中包含该关键字（兜底） |\n\n**输出自然形成排序：** 精确匹配排最前，越模糊越靠后。\n\n---\n\n## 与现有代码的兼容\n\n- `searchByNameLike()` 保留不动（现有调用者需要原始 OR 行为），新增 `searchSimilar()`\n- `MaterialSelectorServiceImpl.matchesKeyword()` 同理保留，不改\n- 只改 `AiRecognitionServiceImpl` 和物料搜索前端用的地方\n\n---\n\n要我把这个方案落地成代码？还是你先看方案，有地方需要调整再聊？"}],"api":"openai-completions","provider":"new-api","model":"deepseek-v4-flash","usage":{"input":958,"output":2124,"cacheRead":35584,"cacheWrite":0,"reasoningTokens":377,"totalTokens":38666,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1780017933600,"responseId":"chatcmpl-aa6864f7-43cc-4f36-b462-ae56b7f496cd"}}
{"type":"message","id":"6f53d1c0","parentId":"7f6a9bf1","timestamp":"2026-05-29T01:30:33.710Z","message":{"role":"user","content":[{"type":"text","text":"[Fri 2026-05-29 09:30 GMT+8] 保存方案文档到 tiny yangxuan/设计"}],"timestamp":1780018233603}}
{"type":"message","id":"6fef19eb","parentId":"6f53d1c0","timestamp":"2026-05-29T01:30:43.129Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"The user wants me to save the analysis/design document to a specific path. Let me interpret \"tiny yangxuan/设计\" - this likely means the tiny-erp project's design docs, and \"yangxuan/设计\" suggests a path structure. Let me think about where to put this.\n\nLooking at the context, the workspace is `/home/yangxuan/.openclaw/workspace/java`. The user said \"tiny yangxuan/设计\" - I think \"tiny\" refers to the tiny-erp project. Given the path convention, I'll save it under the workspace or a reasonable location. But actually, re-reading: \"保存方案文档到 tiny yangxuan/设计\" - this could mean:\n1. Save to the tiny-erp project's design folder with \"yangxuan/设计\" subpath\n2. Or save relative to workspace\n\nGiven the workspace is for \"java\" and the tiny-erp project is at `/home/yangxuan/Projects/IdeaProjects/tiny-erp`, I think the user wants me to save it to the tiny-erp project. But \"yangxuan/设计\" - maybe a design docs folder?\n\nLet me check if there's a design/docs folder in the tiny-erp project, or I'll create one.\n\nActually, re-reading more carefully: \"保存方案文档到 tiny yangxuan/设计\" - \"tiny\" is the project alias (from TOOLS.md: `tiny` / `tiny-erp` → `/home/yangxuan/Projects/IdeaProjects/tiny-erp`), so \"tiny yangxuan/设计\" means save to the tiny-erp project at path `yangxuan/设计/`. This is likely relative to the tiny-erp project root.\n\nLet me check if that path exists.","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_6e2d01514735431a8a8f8d8d","name":"exec","arguments":{"command":"ls -la /home/yangxuan/Projects/IdeaProjects/tiny-erp/yangxuan/ 2>/dev/null || echo \"NOT_EXISTS\"","workdir":"/home/yangxuan/Projects/IdeaProjects/tiny-erp"},"partialArgs":"{\"command\": \"ls -la /home/yangxuan/Projects/IdeaProjects/tiny-erp/yangxuan/ 2>/dev/null || echo \\\"NOT_EXISTS\\\"\", \"workdir\": \"/home/yangxuan/Projects/IdeaProjects/tiny-erp\"}"}],"api":"openai-completions","provider":"new-api","model":"deepseek-v4-flash","usage":{"input":37647,"output":483,"cacheRead":0,"cacheWrite":0,"reasoningTokens":374,"totalTokens":38130,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1780018233910,"responseId":"chatcmpl-0462ac89-d4ec-4ac0-a7de-f21284e1addd"}}
{"type":"message","id":"dfd63bad","parentId":"6fef19eb","timestamp":"2026-05-29T01:30:43.846Z","message":{"role":"toolResult","toolCallId":"call_6e2d01514735431a8a8f8d8d","toolName":"exec","content":[{"type":"text","text":"total 48\ndrwxrwxr-x 8 yangxuan yangxuan 4096 May 26 14:50 .\ndrwxrwxr-x 10 yangxuan yangxuan 4096 May 28 20:53 ..\ndrwxrwxr-x 2 yangxuan yangxuan 4096 May 26 10:42 API\n-rw-rw-r-- 1 yangxuan yangxuan 2763 May 25 18:56 README.md\ndrwxrwxr-x 2 yangxuan yangxuan 4096 May 29 09:23 其他\ndrwxrwxr-x 2 yangxuan yangxuan 4096 May 25 19:01 数据库\ndrwxr-xr-x 2 yangxuan yangxuan 4096 May 26 19:42 测试\n-rw-rw-r-- 1 yangxuan yangxuan 2771 May 26 14:56 物料信息填充公共服务总结.md\ndrwxrwxr-x 8 yangxuan yangxuan 4096 May 26 10:32 设计\ndrwxr-xr-x 8 yangxuan yangxuan 4096 May 25 18:58 需求\n-rw-rw-r-- 1 yangxuan yangxuan 5162 May 25 18:49 项目规范.md"}],"details":{"status":"completed","exitCode":0,"durationMs":207,"aggregated":"total 48\ndrwxrwxr-x 8 yangxuan yangxuan 4096 May 26 14:50 .\ndrwxrwxr-x 10 yangxuan yangxuan 4096 May 28 20:53 ..\ndrwxrwxr-x 2 yangxuan yangxuan 4096 May 26 10:42 API\n-rw-rw-r-- 1 yangxuan yangxuan 2763 May 25 18:56 README.md\ndrwxrwxr-x 2 yangxuan yangxuan 4096 May 29 09:23 其他\ndrwxrwxr-x 2 yangxuan yangxuan 4096 May 25 19:01 数据库\ndrwxr-xr-x 2 yangxuan yangxuan 4096 May 26 19:42 测试\n-rw-rw-r-- 1 yangxuan yangxuan 2771 May 26 14:56 物料信息填充公共服务总结.md\ndrwxrwxr-x 8 yangxuan yangxuan 4096 May 26 10:32 设计\ndrwxr-xr-x 8 yangxuan yangxuan 4096 May 25 18:58 需求\n-rw-rw-r-- 1 yangxuan yangxuan 5162 May 25 18:49 项目规范.md","cwd":"/home/yangxuan/Projects/IdeaProjects/tiny-erp"},"isError":false,"timestamp":1780018243846}}
{"type":"message","id":"0fcae377","parentId":"dfd63bad","timestamp":"2026-05-29T01:30:46.377Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Good, the directory `设计` already exists under `yangxuan/`. Let me check what's already in it.","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_6b68771c08894e71b545affa","name":"exec","arguments":{"command":"ls -la /home/yangxuan/Projects/IdeaProjects/tiny-erp/yangxuan/设计/","workdir":"/home/yangxuan/Projects/IdeaProjects/tiny-erp"},"partialArgs":"{\"command\": \"ls -la /home/yangxuan/Projects/IdeaProjects/tiny-erp/yangxuan/设计/\", \"workdir\": \"/home/yangxuan/Projects/IdeaProjects/tiny-erp\"}"}],"api":"openai-completions","provider":"new-api","model":"deepseek-v4-flash","usage":{"input":438,"output":124,"cacheRead":37632,"cacheWrite":0,"reasoningTokens":25,"totalTokens":38194,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1780018243848,"responseId":"chatcmpl-75593c6d-7d5f-49d2-bb91-5fdb5dccd207"}}
{"type":"message","id":"6626aabc","parentId":"0fcae377","timestamp":"2026-05-29T01:30:47.122Z","message":{"role":"toolResult","toolCallId":"call_6b68771c08894e71b545affa","toolName":"exec","content":[{"type":"text","text":"total 56\ndrwxrwxr-x 8 yangxuan yangxuan 4096 May 26 10:32 .\ndrwxrwxr-x 8 yangxuan yangxuan 4096 May 26 14:50 ..\n-rw-r--r-- 1 yangxuan yangxuan 21349 May 23 17:17 01-开发方案.md\ndrwxrwxr-x 2 yangxuan yangxuan 4096 May 25 18:57 主数据模块\ndrwxrwxr-x 2 yangxuan yangxuan 4096 May 26 11:25 库存模块\ndrwxrwxr-x 2 yangxuan yangxuan 4096 May 25 18:57 系统模块\ndrwxrwxr-x 2 yangxuan yangxuan 4096 May 28 10:40 财务模块\ndrwxrwxr-x 2 yangxuan yangxuan 4096 May 25 18:57 采购模块\ndrwxrwxr-x 2 yangxuan yangxuan 4096 May 25 18:57 销售模块"}],"details":{"status":"completed","exitCode":0,"durationMs":211,"aggregated":"total 56\ndrwxrwxr-x 8 yangxuan yangxuan 4096 May 26 10:32 .\ndrwxrwxr-x 8 yangxuan yangxuan 4096 May 26 14:50 ..\n-rw-r--r-- 1 yangxuan yangxuan 21349 May 23 17:17 01-开发方案.md\ndrwxrwxr-x 2 yangxuan yangxuan 4096 May 25 18:57 主数据模块\ndrwxrwxr-x 2 yangxuan yangxuan 4096 May 26 11:25 库存模块\ndrwxrwxr-x 2 yangxuan yangxuan 4096 May 25 18:57 系统模块\ndrwxrwxr-x 2 yangxuan yangxuan 4096 May 28 10:40 财务模块\ndrwxrwxr-x 2 yangxuan yangxuan 4096 May 25 18:57 采购模块\ndrwxrwxr-x 2 yangxuan yangxuan 4096 May 25 18:57 销售模块","cwd":"/home/yangxuan/Projects/IdeaProjects/tiny-erp"},"isError":false,"timestamp":1780018247121}}
{"type":"message","id":"995ed6a7","parentId":"6626aabc","timestamp":"2026-05-29T01:31:05.083Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"The user wants me to save the analysis document about the \"查询近似规则\" (fuzzy/approximate search) to the `tiny-erp/yangxuan/设计/` directory. I'll write a comprehensive design document there.","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_49b53b043c024d128d4ec852","name":"write","arguments":{"path":"/home/yangxuan/Projects/IdeaProjects/tiny-erp/yangxuan/设计/02-物料近似查询方案.md","content":"# 物料近似查询方案\n\n> 2026-05-29 更新\n\n## 一、背景\n\n现有物料查询存在多个入口，但缺乏统一的近似匹配策略：\n\n| 位置 | 方式 | 问题 |\n|------|------|------|\n| `MaterialService.searchByNameLike()` | 一个 `keyword` 同时 LIKE 编码/名称/规格（OR） | 三条路混在一起，无法区分命中类型，排序无优先级 |\n| `MaterialSelectorServiceImpl.matchesKeyword()` | 内存中 `contains` 匹配代码/名称/规格 | 无优先级，纯集合过滤 |\n| `AiRecognitionServiceImpl.matchMaterialItem()` | 多层降级搜索 | 每次只取第一个匹配，放弃多候选分析 |\n\n核心痛点：**无法知道命中哪个字段，无法按匹配质量排序，多条结果混在一起。**\n\n## 二、匹配优先级定义\n\n按精确度从高到低，定义 5 级匹配优先级：\n\n| 优先级 | 匹配方式 | SQL 条件 | 示意场景 |\n|--------|----------|----------|----------|\n| 🥇 1 | 名称精确匹配 | `material_name = keyword` | 输入\"8.5mm玻璃棒\" → 数据库刚好有这个名称 |\n| 🥇 2 | 名称前缀匹配 | `material_name LIKE 'keyword%'` | \"玻璃棒\" → \"玻璃棒 8.5mm\" |\n| 🥇 3 | 名称模糊匹配 | `material_name LIKE '%keyword%'` | \"玻璃棒\" → \"高硼硅玻璃棒\" |\n| 🥇 4 | 规格模糊匹配 | `spec LIKE '%keyword%'` | \"玻璃棒\" → 物料名称为\"石英管\"但规格字段含\"玻璃棒\" |\n| 🥇 5 | 编码模糊匹配 | `material_code LIKE '%keyword%'` | 兜底，编码中包含该关键字 |\n\n名称精确匹配最高，编码模糊匹配最低。\n\n## 三、数据结构\n\n### 3.1 匹配优先级枚举\n\n```java\npublic enum MatchPriority {\n EXACT_NAME(1, \"名称精确匹配\"),\n PREFIX_NAME(2, \"名称前缀匹配\"),\n FUZZY_NAME(3, \"名称模糊匹配\"),\n FUZZY_SPEC(4, \"规格模糊匹配\"),\n FUZZY_CODE(5, \"编码模糊匹配\");\n\n public final int rank; // 排序权重，越小越优先\n public final String desc;\n\n MatchPriority(int rank, String desc) {\n this.rank = rank;\n this.desc = desc;\n }\n}\n```\n\n### 3.2 统一返回 DTO\n\n```java\n@Data\npublic class MaterialMatchResult {\n private Long id;\n private String materialCode;\n private String materialName;\n private String spec;\n private String unit;\n\n /** 命中类型（EXACT_NAME / FUZZY_SPEC 等） */\n private MatchPriority matchType;\n\n /** 排序权重（rank 值，越小越优先） */\n private Integer matchRank;\n\n /** 关联数据（物料类型名称、分类名称等，后续由 MaterialFillService 填充） */\n private MaterialInfoDTO materialInfo;\n}\n```\n\n## 四、核心查询流程\n\n### 4.1 分步查询 + 去重\n\n核心思路：按优先级从高到低依次查询，**每个更低优先级只搜索未被命中过的物料 ID**，最终结果自然按优先级排序。\n\n```\n输入 keyword\n\nStep 1: SELECT ... WHERE material_name = :keyword\n → 加入结果集，记录：匹配类型 = EXACT_NAME\n → 记录已命中的 ID 集合\n\nStep 2: SELECT ... WHERE material_name LIKE :keyword || '%'\n → 过滤掉已命中的 ID，剩余加入结果集\n → 匹配类型 = PREFIX_NAME\n\nStep 3: SELECT ... WHERE material_name LIKE '%' || :keyword || '%'\n → 过滤已命中 ID\n → 匹配类型 = FUZZY_NAME\n\nStep 4: SELECT ... WHERE spec LIKE '%' || :keyword || '%'\n → 过滤已命中 ID\n → 匹配类型 = FUZZY_SPEC\n\nStep 5: SELECT ... WHERE material_code LIKE '%' || :keyword || '%'\n → 过滤已命中 ID\n → 匹配类型 = FUZZY_CODE\n\n返回 List（有序，按 matchRank 升序）\n```\n\n### 4.2 为什么不用单个 OR？\n\n单个 OR 查询无法区分每条结果是命中 name 还是 spec 还是 code，排序时无法给精确匹配更高权重。分步查询天然得到优先级排序的结果。\n\n## 五、接口定义\n\n### 5.1 Service 层新增\n\n```java\npublic interface MaterialService extends IService {\n\n // 保留现有方法（不破坏调用者）\n Page pageWithStock(MaterialPageQueryReqDTO reqDTO);\n List searchMaterialIdsByKeyword(String keyword);\n List searchByNameLike(String keyword);\n\n // 新增：结构化近似查询，结果按匹配优先级排序\n List searchSimilar(String keyword);\n}\n```\n\n### 5.2 限流保护\n\n- 每个步骤 LIMIT 20\n- 最终结果上限：建议 30-50 条\n- 关键字空或过短（< 2 字符）时返回空\n\n## 六、现有调用处的迁移方案\n\n### 6.1 AI 识别场景（`AiRecognitionServiceImpl`）\n\n现有逻辑每次只取 `candidates.get(0)`，改为走结构化搜索：\n\n```java\n// 改前\nList candidates = materialService.searchByNameLike(normalized);\n// ... multi-level fallback ...\nif (!candidates.isEmpty()) {\n Material best = candidates.get(0);\n // ...\n}\n\n// 改后\nList candidates = materialService.searchSimilar(normalized);\nif (candidates.isEmpty()) {\n // 降级：分词搜索（保留现有降级逻辑）\n}\nMaterialMatchResult best = candidates.get(0);\n// 同时可将 matchType 带回前端\n```\n\n在 `RecognitionItem` 中增加 `matchType` 字段，前端可视化展示匹配质量。\n\n### 6.2 物料选择器（`MaterialSelectorServiceImpl`）\n\n入库/出库选择器的 `matchesKeyword()` 内存过滤改为按优先级匹配：\n\n```java\n// 改前：内存中 contains 匹配\n.filter(dto -> matchesKeyword(dto, keyword))\n\n// 改后：按优先级搜索并排序（或者在 SQL 层就按优先级查）\n```\n\n### 6.3 库存查询场景（`StockServiceImpl`）\n\n当前库存分页查询中通过 `searchMaterialIdsByKeyword` 获取物料 ID 再 JOIN 库存表：\n\n```java\n// 改前\nList matchedMaterialIds = materialMapper.searchMaterialIdsByKeyword(dto.getKeyword());\n\n// 改后：加入排序，保持匹配优先级排序\nList matched = materialService.searchSimilar(dto.getKeyword());\nList matchedMaterialIds = matched.stream().map(MaterialMatchResult::getId).toList();\n```\n\n## 七、特殊字符处理\n\n统一使用 `LikeEscapeUtil` 转义 `\\` `%` `_` 三个字符后拼接 `%` 通配符。\n\n```sql\n-- 示例：keyword = \"8.5%玻璃_棒\"\n-- 转义后：material_name LIKE '%8.5\\%玻璃\\_棒%' ESCAPE '\\'\n```\n\n## 八、与现有代码的兼容性\n\n- **`searchByNameLike()` 保留不动** — 现有调用方不需要改\n- **`searchMaterialIdsByKeyword()` 保留不动** — 兼容旧查询路径\n- **`matchesKeyword()` 保留不动** — 选择器内存过滤仍可用\n- 新增 `searchSimilar()` 方法，调用方按需迁移\n\n## 九、PostgreSQL 性能考虑\n\n- 物料表 `md_material` 预计规模不超过 10 万条\n- `material_name`、`spec`、`material_code` 建议建索引（**已建则忽略**）\n- 分步查询每次命中特定索引，不会出现全表扫描\n\n```sql\n-- 建议索引\nCREATE INDEX idx_md_material_name ON md_material(material_name);\nCREATE INDEX idx_md_material_spec ON md_material(spec);\nCREATE INDEX idx_md_material_code ON md_material(material_code);\n```\n\n## 十、前端展示建议\n\n返回结果携带 `matchType`，前端根据匹配类型给出视觉提示：\n\n| 匹配类型 | 展示样式 |\n|----------|----------|\n| EXACT_NAME | 正常高亮（绿底/粗体） |\n| PREFIX_NAME / FUZZY_NAME | 仅高亮匹配部分 |\n| FUZZY_SPEC | 灰色标注\"规格匹配\" |\n| FUZZY_CODE | 灰色标注\"编码匹配\" |\n\n同时展示每条结果的 `score/matchType`，帮助用户判断匹配质量。"},"partialArgs":"{\"path\": \"/home/yangxuan/Projects/IdeaProjects/tiny-erp/yangxuan/设计/02-物料近似查询方案.md\", \"content\": \"# 物料近似查询方案\\n\\n> 2026-05-29 更新\\n\\n## 一、背景\\n\\n现有物料查询存在多个入口，但缺乏统一的近似匹配策略：\\n\\n| 位置 | 方式 | 问题 |\\n|------|------|------|\\n| `MaterialService.searchByNameLike()` | 一个 `keyword` 同时 LIKE 编码/名称/规格（OR） | 三条路混在一起，无法区分命中类型，排序无优先级 |\\n| `MaterialSelectorServiceImpl.matchesKeyword()` | 内存中 `contains` 匹配代码/名称/规格 | 无优先级，纯集合过滤 |\\n| `AiRecognitionServiceImpl.matchMaterialItem()` | 多层降级搜索 | 每次只取第一个匹配，放弃多候选分析 |\\n\\n核心痛点：**无法知道命中哪个字段，无法按匹配质量排序，多条结果混在一起。**\\n\\n## 二、匹配优先级定义\\n\\n按精确度从高到低，定义 5 级匹配优先级：\\n\\n| 优先级 | 匹配方式 | SQL 条件 | 示意场景 |\\n|--------|----------|----------|----------|\\n| 🥇 1 | 名称精确匹配 | `material_name = keyword` | 输入\\\"8.5mm玻璃棒\\\" → 数据库刚好有这个名称 |\\n| 🥇 2 | 名称前缀匹配 | `material_name LIKE 'keyword%'` | \\\"玻璃棒\\\" → \\\"玻璃棒 8.5mm\\\" |\\n| 🥇 3 | 名称模糊匹配 | `material_name LIKE '%keyword%'` | \\\"玻璃棒\\\" → \\\"高硼硅玻璃棒\\\" |\\n| 🥇 4 | 规格模糊匹配 | `spec LIKE '%keyword%'` | \\\"玻璃棒\\\" → 物料名称为\\\"石英管\\\"但规格字段含\\\"玻璃棒\\\" |\\n| 🥇 5 | 编码模糊匹配 | `material_code LIKE '%keyword%'` | 兜底，编码中包含该关键字 |\\n\\n名称精确匹配最高，编码模糊匹配最低。\\n\\n## 三、数据结构\\n\\n### 3.1 匹配优先级枚举\\n\\n```java\\npublic enum MatchPriority {\\n EXACT_NAME(1, \\\"名称精确匹配\\\"),\\n PREFIX_NAME(2, \\\"名称前缀匹配\\\"),\\n FUZZY_NAME(3, \\\"名称模糊匹配\\\"),\\n FUZZY_SPEC(4, \\\"规格模糊匹配\\\"),\\n FUZZY_CODE(5, \\\"编码模糊匹配\\\");\\n\\n public final int rank; // 排序权重，越小越优先\\n public final String desc;\\n\\n MatchPriority(int rank, String desc) {\\n this.rank = rank;\\n this.desc = desc;\\n }\\n}\\n```\\n\\n### 3.2 统一返回 DTO\\n\\n```java\\n@Data\\npublic class MaterialMatchResult {\\n private Long id;\\n private String materialCode;\\n private String materialName;\\n private String spec;\\n private String unit;\\n\\n /** 命中类型（EXACT_NAME / FUZZY_SPEC 等） */\\n private MatchPriority matchType;\\n\\n /** 排序权重（rank 值，越小越优先） */\\n private Integer matchRank;\\n\\n /** 关联数据（物料类型名称、分类名称等，后续由 MaterialFillService 填充） */\\n private MaterialInfoDTO materialInfo;\\n}\\n```\\n\\n## 四、核心查询流程\\n\\n### 4.1 分步查询 + 去重\\n\\n核心思路：按优先级从高到低依次查询，**每个更低优先级只搜索未被命中过的物料 ID**，最终结果自然按优先级排序。\\n\\n```\\n输入 keyword\\n\\nStep 1: SELECT ... WHERE material_name = :keyword\\n → 加入结果集，记录：匹配类型 = EXACT_NAME\\n → 记录已命中的 ID 集合\\n\\nStep 2: SELECT ... WHERE material_name LIKE :keyword || '%'\\n → 过滤掉已命中的 ID，剩余加入结果集\\n → 匹配类型 = PREFIX_NAME\\n\\nStep 3: SELECT ... WHERE material_name LIKE '%' || :keyword || '%'\\n → 过滤已命中 ID\\n → 匹配类型 = FUZZY_NAME\\n\\nStep 4: SELECT ... WHERE spec LIKE '%' || :keyword || '%'\\n → 过滤已命中 ID\\n → 匹配类型 = FUZZY_SPEC\\n\\nStep 5: SELECT ... WHERE material_code LIKE '%' || :keyword || '%'\\n → 过滤已命中 ID\\n → 匹配类型 = FUZZY_CODE\\n\\n返回 List（有序，按 matchRank 升序）\\n```\\n\\n### 4.2 为什么不用单个 OR？\\n\\n单个 OR 查询无法区分每条结果是命中 name 还是 spec 还是 code，排序时无法给精确匹配更高权重。分步查询天然得到优先级排序的结果。\\n\\n## 五、接口定义\\n\\n### 5.1 Service 层新增\\n\\n```java\\npublic interface MaterialService extends IService {\\n\\n // 保留现有方法（不破坏调用者）\\n Page pageWithStock(MaterialPageQueryReqDTO reqDTO);\\n List searchMaterialIdsByKeyword(String keyword);\\n List searchByNameLike(String keyword);\\n\\n // 新增：结构化近似查询，结果按匹配优先级排序\\n List searchSimilar(String keyword);\\n}\\n```\\n\\n### 5.2 限流保护\\n\\n- 每个步骤 LIMIT 20\\n- 最终结果上限：建议 30-50 条\\n- 关键字空或过短（< 2 字符）时返回空\\n\\n## 六、现有调用处的迁移方案\\n\\n### 6.1 AI 识别场景（`AiRecognitionServiceImpl`）\\n\\n现有逻辑每次只取 `candidates.get(0)`，改为走结构化搜索：\\n\\n```java\\n// 改前\\nList candidates = materialService.searchByNameLike(normalized);\\n// ... multi-level fallback ...\\nif (!candidates.isEmpty()) {\\n Material best = candidates.get(0);\\n // ...\\n}\\n\\n// 改后\\nList candidates = materialService.searchSimilar(normalized);\\nif (candidates.isEmpty()) {\\n // 降级：分词搜索（保留现有降级逻辑）\\n}\\nMaterialMatchResult best = candidates.get(0);\\n// 同时可将 matchType 带回前端\\n```\\n\\n在 `RecognitionItem` 中增加 `matchType` 字段，前端可视化展示匹配质量。\\n\\n### 6.2 物料选择器（`MaterialSelectorServiceImpl`）\\n\\n入库/出库选择器的 `matchesKeyword()` 内存过滤改为按优先级匹配：\\n\\n```java\\n// 改前：内存中 contains 匹配\\n.filter(dto -> matchesKeyword(dto, keyword))\\n\\n// 改后：按优先级搜索并排序（或者在 SQL 层就按优先级查）\\n```\\n\\n### 6.3 库存查询场景（`StockServiceImpl`）\\n\\n当前库存分页查询中通过 `searchMaterialIdsByKeyword` 获取物料 ID 再 JOIN 库存表：\\n\\n```java\\n// 改前\\nList matchedMaterialIds = materialMapper.searchMaterialIdsByKeyword(dto.getKeyword());\\n\\n// 改后：加入排序，保持匹配优先级排序\\nList matched = materialService.searchSimilar(dto.getKeyword());\\nList matchedMaterialIds = matched.stream().map(MaterialMatchResult::getId).toList();\\n```\\n\\n## 七、特殊字符处理\\n\\n统一使用 `LikeEscapeUtil` 转义 `\\\\` `%` `_` 三个字符后拼接 `%` 通配符。\\n\\n```sql\\n-- 示例：keyword = \\\"8.5%玻璃_棒\\\"\\n-- 转义后：material_name LIKE '%8.5\\\\%玻璃\\\\_棒%' ESCAPE '\\\\'\\n```\\n\\n## 八、与现有代码的兼容性\\n\\n- **`searchByNameLike()` 保留不动** — 现有调用方不需要改\\n- **`searchMaterialIdsByKeyword()` 保留不动** — 兼容旧查询路径\\n- **`matchesKeyword()` 保留不动** — 选择器内存过滤仍可用\\n- 新增 `searchSimilar()` 方法，调用方按需迁移\\n\\n## 九、PostgreSQL 性能考虑\\n\\n- 物料表 `md_material` 预计规模不超过 10 万条\\n- `material_name`、`spec`、`material_code` 建议建索引（**已建则忽略**）\\n- 分步查询每次命中特定索引，不会出现全表扫描\\n\\n```sql\\n-- 建议索引\\nCREATE INDEX idx_md_material_name ON md_material(material_name);\\nCREATE INDEX idx_md_material_spec ON md_material(spec);\\nCREATE INDEX idx_md_material_code ON md_material(material_code);\\n```\\n\\n## 十、前端展示建议\\n\\n返回结果携带 `matchType`，前端根据匹配类型给出视觉提示：\\n\\n| 匹配类型 | 展示样式 |\\n|----------|----------|\\n| EXACT_NAME | 正常高亮（绿底/粗体） |\\n| PREFIX_NAME / FUZZY_NAME | 仅高亮匹配部分 |\\n| FUZZY_SPEC | 灰色标注\\\"规格匹配\\\" |\\n| FUZZY_CODE | 灰色标注\\\"编码匹配\\\" |\\n\\n同时展示每条结果的 `score/matchType`，帮助用户判断匹配质量。\"}"}],"api":"openai-completions","provider":"new-api","model":"deepseek-v4-flash","usage":{"input":545,"output":2098,"cacheRead":37888,"cacheWrite":0,"reasoningTokens":49,"totalTokens":40531,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1780018247125,"responseId":"chatcmpl-736acbf4-8027-41c9-80de-08cf329f8ce3"}}
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{"type":"message","id":"904c85b3","parentId":"6f846074","timestamp":"2026-05-29T01:31:09.534Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Document saved successfully. Let me confirm the path and summarize.","thinkingSignature":"reasoning_content"},{"type":"text","text":"已保存到 `tiny-erp/yangxuan/设计/02-物料近似查询方案.md` ✅\n\n文档覆盖了：\n1. **现有代码痛点分析** — 三个入口各有各的问题\n2. **5 级匹配优先级定义** — 精确名称 > 前缀 > 模糊名称 > 规格 > 编码\n3. **分步查询 + 去重实现方案** — 避免 OR 混在一起无法排序\n4. **枚举 + DTO 结构**\n5. **三处现有调用点的迁移路径** — AI 识别、物料选择器、库存查询\n6. **兼容性说明** — 旧方法保留不动，新增 `searchSimilar()`\n7. **索引建议** 和 **前端展示建议**"}],"api":"openai-completions","provider":"new-api","model":"deepseek-v4-flash","usage":{"input":2129,"output":175,"cacheRead":38400,"cacheWrite":0,"reasoningTokens":12,"totalTokens":40704,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1780018265570,"responseId":"chatcmpl-b43acb25-e3af-43eb-98df-fb031cc294f6"}}
{"type":"message","id":"1f4c53ef","parentId":"904c85b3","timestamp":"2026-05-29T01:36:39.085Z","message":{"role":"user","content":[{"type":"text","text":"[Fri 2026-05-29 09:36 GMT+8] ## 交互优化\n- 返回前端id 无法填充页面\n - 客户供应商需要查询名称\n - 物料使用com.witsoft.erp.module.md.material.service.MaterialFillService.getMaterialInfoMap填充\n - 单据类型使用 数据字典中类型\n - 使用local 库测试\n - [02-物料近似查询方案.md](yangxuan/设计/02-物料近似查询方案.md)"}],"timestamp":1780018598971}}
{"type":"message","id":"0769cc9f","parentId":"1f4c53ef","timestamp":"2026-05-29T01:36:49.750Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"The user is asking me to read the current document and then discuss/handle an interaction optimization. Let me first read the document they referenced, and also understand what they're talking about.\n\nLet me re-read the conversation. The user said:\n\n- Return frontend id can't fill page\n- Customer/supplier need to query name\n- Material uses `MaterialFillService.getMaterialInfoMap` to fill\n- Document type uses data dictionary type\n- Test on local database\n\nSo it seems like they're talking about the AI recognition feature, where the current implementation returns IDs but the frontend can't directly use those IDs to fill in names on the page. They want:\n\n1. For counterparties (customer/supplier): instead of just returning ID, also query and return the name\n2. For materials: use the existing `MaterialFillService.getMaterialInfoMap` to fill in material info\n3. For document type: use data dictionary values\n4. Test on the local database\n\nWait, let me re-read the AiRecognitionServiceImpl.java - it already does some of this. Let me look more carefully at what might be missing.\n\nActually, looking again at the code I already read:\n\nThe `AiRecognitionResultDTO` already has:\n- `Counterparty` with `id`, `name`, `recognizedName`, `exactMatch`\n- `RecognitionItem` with `materialId`, `materialName`, `materialCode`, `spec`, `unit`\n- `typeValue`, `typeName`\n\nSo it seems like they already return names. But maybe the issue is that:\n\n1. For counterparty matching - if it's not exact match, it returns the recognized name without an ID, and the frontend can't map it\n2. For material matching - it returns materialId but the frontend can't display materialCode/spec/unit properly\n3. document type needs to be from data dictionary properly\n\nLet me look at the existing code more carefully to understand the actual problem. The user says \"返回前端id 无法填充页面\" - the frontend can't fill the page with just IDs.\n\nLet me re-examine the AiRecognitionServiceImpl code more carefully, especially the material matching part.\n\nFrom what I saw earlier, the `matchMaterialItem` method in `AiRecognitionServiceImpl`:\n1. Searches materials by name\n2. If found, builds a `RecognitionItem` with materialId, materialName etc.\n3. But it uses `MaterialFillService.getMaterialInfoMap` to fill in the material info\n\nWait, I didn't see the full method because it was truncated. Let me read the full file.\n\nActually, let me look at this more carefully. The user is talking about an interaction optimization. Let me re-read:\n\n1. \"返回前端id 无法填充页面\" - Returning just IDs to frontend can't fill the page\n2. \"客户供应商需要查询名称\" - Customer/supplier need to query names\n3. \"物料使用MaterialFillService.getMaterialInfoMap填充\" - Materials use MaterialFillService.getMaterialInfoMap to fill\n4. \"单据类型使用数据字典中类型\" - Document type should use data dictionary types\n5. \"使用local库测试\" - Test on local database\n\nSo the user wants me to analyze the AiRecognitionResultDTO and the flow to see what's wrong, and then optimize it.\n\nLet me read the full matchMaterialItem method to understand the current implementation better.","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_2d536132adca4def9e1ebb45","name":"read","arguments":{"path":"/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/ai/service/impl/AiRecognitionServiceImpl.java","offset":310},"partialArgs":"{\"path\": \"/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/ai/service/impl/AiRecognitionServiceImpl.java\", \"offset\": 310}"}],"api":"openai-completions","provider":"new-api","model":"deepseek-v4-flash","usage":{"input":40786,"output":815,"cacheRead":0,"cacheWrite":0,"reasoningTokens":718,"totalTokens":41601,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1780018599297,"responseId":"chatcmpl-03d5605e-4f3a-4c58-a5d4-395fe80ad1de"}}
{"type":"message","id":"1cc62cc2","parentId":"0769cc9f","timestamp":"2026-05-29T01:36:50.301Z","message":{"role":"toolResult","toolCallId":"call_2d536132adca4def9e1ebb45","toolName":"read","content":[{"type":"text","text":" }\n chineseParts.sort((a, b) -> Integer.compare(b.length(), a.length()));\n for (String cp : chineseParts) {\n if (cp.length() >= 2) {\n candidates = materialService.searchByNameLike(cp);\n if (!candidates.isEmpty()) break;\n }\n }\n }\n\n if (candidates.isEmpty()) {\n // 完全无法匹配：返回 AI 识别的原始信息\n return AiRecognitionResultDTO.RecognitionItem.builder()\n .materialName(materialName)\n .spec(spec)\n .recognizedName(materialName)\n .quantity(quantity)\n .price(price)\n .unit(unit)\n .exactMatch(false)\n .build();\n }\n\n // 取最佳匹配，并用 MaterialFillService 补充信息\n Material best = candidates.get(0);\n MaterialInfoDTO info = materialFillService.getMaterialInfo(best.getId());\n\n AiRecognitionResultDTO.RecognitionItem.RecognitionItemBuilder builder = AiRecognitionResultDTO.RecognitionItem.builder()\n .materialId(best.getId())\n .materialName(info != null ? info.getMaterialName() : best.getMaterialName())\n .materialCode(info != null ? info.getMaterialCode() : null)\n .spec(info != null ? info.getSpec() : best.getSpec())\n .unit(info != null ? info.getUnit() : best.getUnit())\n .recognizedName(materialName)\n .quantity(quantity)\n .price(price)\n .exactMatch(best.getMaterialName().replaceAll(\"\\\\s+\", \"\").equals(normalized));\n\n return builder.build();\n }\n\n // ========== 名称处理工具 ==========\n\n /** 归一化名称：去空格、全角转半角 */\n private String normalizeName(String name) {\n if (name == null) return \"\";\n // 去掉所有空白字符\n String result = name.replaceAll(\"\\\\s+\", \"\");\n // 全角转半角\n StringBuilder sb = new StringBuilder();\n for (char c : result.toCharArray()) {\n if (c >= 'Ａ' && c <= 'Ｚ') sb.append((char) (c - 'Ａ' + 'A'));\n else if (c >= 'ａ' && c <= 'ｚ') sb.append((char) (c - 'ａ' + 'a'));\n else if (c >= '０' && c <= '９') sb.append((char) (c - '０' + '0'));\n else sb.append(c);\n }\n return sb.toString();\n }\n\n /** 分词：按非中文/非数字/非字母字符分割 */\n private List tokenize(String s) {\n List tokens = new ArrayList<>();\n StringBuilder cur = new StringBuilder();\n for (char c : s.toCharArray()) {\n if (Character.isLetterOrDigit(c)) {\n cur.append(c);\n } else {\n if (cur.length() > 0) {\n tokens.add(cur.toString());\n cur.setLength(0);\n }\n }\n }\n if (cur.length() > 0) {\n tokens.add(cur.toString());\n }\n return tokens;\n }\n\n // ========== JSON 工具方法 ==========\n\n private String extractJson(String text) {\n if (text == null) return null;\n text = text.replaceAll(\"```json\\\\s*\", \"\").replaceAll(\"```\\\\s*\", \"\");\n text = text.trim();\n if (text.startsWith(\"{\") && text.endsWith(\"}\")) {\n return text;\n }\n int start = text.indexOf('{');\n int end = text.lastIndexOf('}');\n if (start >= 0 && end > start) {\n return text.substring(start, end + 1);\n }\n return null;\n }\n\n @SuppressWarnings(\"unchecked\")\n private Map parseSimpleJson(String json) {\n Map result = new LinkedHashMap<>();\n json = json.trim();\n if (json.startsWith(\"{\") && json.endsWith(\"}\")) {\n json = json.substring(1, json.length() - 1).trim();\n }\n List segments = splitTopLevel(json);\n for (String seg : segments) {\n int colonIdx = seg.indexOf(':');\n if (colonIdx < 0) continue;\n String key = seg.substring(0, colonIdx).trim();\n String value = seg.substring(colonIdx + 1).trim();\n key = unquote(key);\n if (value.startsWith(\"\\\"\")) {\n result.put(key, unquote(value));\n } else if (value.startsWith(\"[\")) {\n result.put(key, parseArray(value));\n } else if (value.startsWith(\"{\")) {\n result.put(key, parseSimpleJson(value));\n } else if (value.equals(\"null\")) {\n result.put(key, null);\n } else {\n try {\n if (value.contains(\".\")) {\n result.put(key, new BigDecimal(value));\n } else {\n result.put(key, Long.parseLong(value));\n }\n } catch (NumberFormatException e) {\n result.put(key, value);\n }\n }\n }\n return result;\n }\n\n private List> parseArray(String arrayStr) {\n List> list = new ArrayList<>();\n arrayStr = arrayStr.trim();\n if (arrayStr.startsWith(\"[\") && arrayStr.endsWith(\"]\")) {\n arrayStr = arrayStr.substring(1, arrayStr.length() - 1).trim();\n }\n int braceDepth = 0;\n int start = 0;\n for (int i = 0; i < arrayStr.length(); i++) {\n char c = arrayStr.charAt(i);\n if (c == '{') braceDepth++;\n else if (c == '}') braceDepth--;\n else if (c == ',' && braceDepth == 0) {\n String item = arrayStr.substring(start, i).trim();\n if (!item.isEmpty()) list.add(parseSimpleJson(item));\n start = i + 1;\n }\n }\n String last = arrayStr.substring(start).trim();\n if (!last.isEmpty()) list.add(parseSimpleJson(last));\n return list;\n }\n\n private List splitTopLevel(String s) {\n List parts = new ArrayList<>();\n int depth = 0, bracketDepth = 0;\n boolean inString = false;\n int start = 0;\n for (int i = 0; i < s.length(); i++) {\n char c = s.charAt(i);\n if (c == '\"' && (i == 0 || s.charAt(i - 1) != '\\\\')) inString = !inString;\n else if (!inString) {\n if (c == '{') depth++;\n else if (c == '}') depth--;\n else if (c == '[') bracketDepth++;\n else if (c == ']') bracketDepth--;\n else if (c == ',' && depth == 0 && bracketDepth == 0) {\n parts.add(s.substring(start, i));\n start = i + 1;\n }\n }\n }\n String last = s.substring(start).trim();\n if (!last.isEmpty()) parts.add(last);\n return parts;\n }\n\n private String unquote(String s) {\n s = s.trim();\n if (s.startsWith(\"\\\"\") && s.endsWith(\"\\\"\")) {\n return s.substring(1, s.length() - 1);\n }\n return s;\n }\n\n private BigDecimal toBigDecimal(Object obj) {\n if (obj == null) return null;\n if (obj instanceof BigDecimal) return (BigDecimal) obj;\n if (obj instanceof Number) return BigDecimal.valueOf(((Number) obj).doubleValue());\n if (obj instanceof String) {\n try { return new BigDecimal((String) obj); }\n catch (NumberFormatException e) { return null; }\n }\n return null;\n }\n}"}],"isError":false,"timestamp":1780018610301}}
{"type":"message","id":"d00fbfa0","parentId":"1cc62cc2","timestamp":"2026-05-29T01:37:01.900Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"The user wants me to understand the full AiRecognitionServiceImpl code. Let me also look at the Controller and other related files to understand how the recognition result flows to the frontend.","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_1dd3f9c4cc7a483795b877f4","name":"exec","arguments":{"command":"find /home/yangxuan/Projects/IdeaProjects/tiny-erp/src -type f -name \"AiRecognition*\" | grep -v target","workdir":"/home/yangxuan/Projects/IdeaProjects/tiny-erp"},"partialArgs":"{\"command\": \"find /home/yangxuan/Projects/IdeaProjects/tiny-erp/src -type f -name \\\"AiRecognition*\\\" | grep -v target\", \"workdir\": \"/home/yangxuan/Projects/IdeaProjects/tiny-erp\"}"}],"api":"openai-completions","provider":"new-api","model":"deepseek-v4-flash","usage":{"input":1915,"output":144,"cacheRead":40704,"cacheWrite":0,"reasoningTokens":37,"totalTokens":42763,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1780018616555,"responseId":"chatcmpl-11ee4d2d-2c5d-4690-85f6-655c332a2fba"}}
{"type":"message","id":"936b187f","parentId":"d00fbfa0","timestamp":"2026-05-29T01:37:02.633Z","message":{"role":"toolResult","toolCallId":"call_1dd3f9c4cc7a483795b877f4","toolName":"exec","content":[{"type":"text","text":"/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/ai/dto/AiRecognitionResultDTO.java\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/ai/service/impl/AiRecognitionServiceImpl.java\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/ai/service/AiRecognitionService.java"}],"details":{"status":"completed","exitCode":0,"durationMs":215,"aggregated":"/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/ai/dto/AiRecognitionResultDTO.java\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/ai/service/impl/AiRecognitionServiceImpl.java\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/ai/service/AiRecognitionService.java","cwd":"/home/yangxuan/Projects/IdeaProjects/tiny-erp"},"isError":false,"timestamp":1780018622633}}
{"type":"message","id":"36fa566e","parentId":"936b187f","timestamp":"2026-05-29T01:37:15.074Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"The user wants me to:\n\n1. Fix the issue where returning IDs to the frontend doesn't populate the page - need to return names\n2. Customer/supplier - need to query their names\n3. Materials - use `MaterialFillService.getMaterialInfoMap()` for filling\n4. Document type - use data dictionary type\n5. Test with local DB\n6. Reference the [02-物料近似查询方案.md](yangxuan/设计/02-物料近似查询方案.md) design doc\n\nLet me understand the full picture. The user is saying the current `AiRecognitionServiceImpl` returns IDs but the frontend can't populate the page because it only has IDs, not the names.\n\nLet me check the `AiRecognitionResultDTO` more carefully and understand what's being returned.\n\nLooking at the code:\n- `Counterparty` has `id` and `name` - seems okay\n- `RecognitionItem` has `materialId`, `materialName`, `materialCode`, `spec`, `unit` - seems okay\n- But `typeValue`/`typeName` - typeValue is a dict value, but is there a proper dict type mapping?\n\nLet me look at:\n1. The current AiRecognitionController to see what's returned\n2. The MaterialFillService to understand how it works\n3. The dict service\n4. The frontend code to understand what it needs\n\nActually, wait - re-reading the user's message more carefully:\n\n> - 返回前端id 无法填充页面 - 客户供应商需要查询名称\n\nThis means: the frontend gets back IDs but can't populate the page. The customer/supplier names need to be queried.\n\nLet me look at the current flow:\n\n1. `AiRecognitionServiceImpl.recognize()` returns `AiRecognitionResultDTO`\n2. This is returned to frontend\n3. Frontend tries to auto-fill a form\n\nThe issue is: the frontend needs the **names** of customers/suppliers to fill the form, not just IDs. Let me check what `Counterparty` looks like.\n\nLooking at `AiRecognitionResultDTO.Counterparty`:\n```java\npublic static class Counterparty {\n private Long id;\n private String name;\n private String recognizedName;\n private boolean exactMatch;\n}\n```\n\nIt already has `name`. Hmm, so what's the issue?\n\nLet me re-read: \"返回前端id 无法填充页面 - 客户供应商需要查询名称\"\n\nMaybe the issue is that the frontend fields expect names, but the backend is just returning IDs? Or maybe the `getMaterialInfo` call in the current code isn't properly populating everything?\n\nLet me look at the `MaterialFillService.getMaterialInfo` vs `getMaterialInfoMap`.\n\nActually, looking more carefully at the AI recognition code:\n```java\nMaterialInfoDTO info = materialFillService.getMaterialInfo(best.getId());\n```\n\nIt uses `getMaterialInfo` (single) rather than `getMaterialInfoMap` (batch). The user says to use `getMaterialInfoMap`. Let me check MaterialFillService.\n\nAlso, let me check what data dictionary usage exists for the document type.\n\nLet me also check the frontend code.\n\nLet me look at things more carefully. I think there are several things to improve:\n\n1. **MaterialFillService**: Change from `getMaterialInfo` to `getMaterialInfoMap` for batch loading\n2. **Document type**: Use data dictionary properly (currently hardcoded)\n3. **Counterparty matching**: Ensure names are properly returned\n4. **Test with local DB**\n\nLet me check what exists and then make the changes.","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_413cd3f86d63498c909941c6","name":"read","arguments":{"path":"/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/md/material/service/MaterialFillService.java"},"partialArgs":"{\"path\": \"/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/md/material/service/MaterialFillService.java\"}"},{"type":"toolCall","id":"call_1c74f442ceca4fdc94cb7bc3","name":"read","arguments":{"path":"/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/ai/service/AiRecognitionService.java"},"partialArgs":"{\"path\": \"/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/ai/service/AiRecognitionService.java\"}"},{"type":"toolCall","id":"call_94b05d5bb62547758eec6bf8","name":"exec","arguments":{"command":"find /home/yangxuan/Projects/IdeaProjects/tiny-erp/src -type f -name \"AiRecognitionController.java\" -o -name \"AiRecognition*Controller*\" | grep -v target","workdir":"/home/yangxuan/Projects/IdeaProjects/tiny-erp"},"partialArgs":"{\"command\": \"find /home/yangxuan/Projects/IdeaProjects/tiny-erp/src -type f -name \\\"AiRecognitionController.java\\\" -o -name \\\"AiRecognition*Controller*\\\" | grep -v target\", \"workdir\": \"/home/yangxuan/Projects/IdeaProjects/tiny-erp\"}"}],"api":"openai-completions","provider":"new-api","model":"deepseek-v4-flash","usage":{"input":352,"output":1011,"cacheRead":42496,"cacheWrite":0,"reasoningTokens":763,"totalTokens":43859,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1780018622635,"responseId":"chatcmpl-322b884f-d2aa-4025-9e66-7c0b927e02df"}}
{"type":"message","id":"b5bbc117","parentId":"36fa566e","timestamp":"2026-05-29T01:37:16.407Z","message":{"role":"toolResult","toolCallId":"call_413cd3f86d63498c909941c6","toolName":"read","content":[{"type":"text","text":"package com.witsoft.erp.module.md.material.service;\n\nimport com.witsoft.erp.module.md.material.dto.MaterialInfoDTO;\n\nimport java.util.List;\nimport java.util.Map;\n\n/**\n * 物料信息填充服务\n * 用于在列表接口中批量填充物料相关信息\n */\npublic interface MaterialFillService {\n\n /**\n * 根据物料 ID 列表批量查询物料信息\n *\n * @param materialIds 物料 ID 列表\n * @return Map<物料 ID, 物料信息>\n */\n Map getMaterialInfoMap(List materialIds);\n\n /**\n * 根据物料 ID 获取物料信息\n *\n * @param materialId 物料 ID\n * @return 物料信息，不存在返回 null\n */\n MaterialInfoDTO getMaterialInfo(Long materialId);\n\n /**\n * 获取物料类型字典映射\n *\n * @return Map<字典值，字典标签>\n */\n Map getMaterialTypeDictMap();\n}\n"}],"isError":false,"timestamp":1780018636407}}
{"type":"message","id":"a33af350","parentId":"b5bbc117","timestamp":"2026-05-29T01:37:16.408Z","message":{"role":"toolResult","toolCallId":"call_1c74f442ceca4fdc94cb7bc3","toolName":"read","content":[{"type":"text","text":"package com.witsoft.erp.module.ai.service;\n\nimport com.witsoft.erp.module.ai.dto.AiRecognitionResultDTO;\nimport org.springframework.web.multipart.MultipartFile;\n\n/**\n * AI 单据识别服务接口\n */\npublic interface AiRecognitionService {\n\n /**\n * 识别单据图片，返回结构化的识别结果（供应商/客户/物料已模糊匹配 ID）\n */\n AiRecognitionResultDTO recognize(MultipartFile file);\n}"}],"isError":false,"timestamp":1780018636408}}
{"type":"message","id":"faadc6aa","parentId":"a33af350","timestamp":"2026-05-29T01:37:16.408Z","message":{"role":"toolResult","toolCallId":"call_94b05d5bb62547758eec6bf8","toolName":"exec","content":[{"type":"text","text":"\n\n(Command exited with code 1)"}],"details":{"status":"completed","exitCode":1,"durationMs":412,"aggregated":"\n\n(Command exited with code 1)","cwd":"/home/yangxuan/Projects/IdeaProjects/tiny-erp"},"isError":false,"timestamp":1780018636408}}
{"type":"message","id":"d47ccae9","parentId":"faadc6aa","timestamp":"2026-05-29T01:37:24.723Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me understand the task. The user wants me to:\n\n1. Read the design document `02-物料近似查询方案.md`\n2. Fix the AI recognition result so that:\n - Client/Supplier: returns name (not just ID)\n - Material: uses `MaterialFillService.getMaterialInfoMap()` to fill name, code, spec, unit\n - Document type (单据类型) should use data dictionary values\n - Test with local database\n\nThe issue is that when the front end receives the result, it can't populate the form because:\n1. Customer/supplier info only returns `id` without `name`\n2. Material info only returns `materialId` without filling in `materialName`, `materialCode`, `spec`, `unit`\n3. Document type needs to use data dictionary\n\nLet me look at the AI recognition result DTO more carefully, and the controller to understand the full flow.\n\nActually, wait - looking at the `AiRecognitionResultDTO`, it already has:\n- `Counterparty` with `id`, `name`, `recognizedName`, `exactMatch`\n- `RecognitionItem` with `materialId`, `materialName`, `materialCode`, `spec`, `unit`\n\nAnd in the implementation `matchMaterialItem()`, it's already using `materialFillService.getMaterialInfo(best.getId())` to fill material info.\n\nAnd `matchSupplier()` / `matchCustomer()` already return `Counterparty` with `name` from the database.\n\nSo what's the actual issue? Let me look at the AiRecognitionController to see what happens after recognition.\n\nLet me also check what's happening in the frontend to understand the \"cannot fill page\" issue.\n\nActually, re-reading the user's message more carefully:\n\n- \"返回前端id 无法填充页面\" - returning only ID can't fill the form\n- \"客户供应商需要查询名称\" - customer/supplier needs to query name (maybe the returned name isn't being used?)\n- \"物料使用MaterialFillService填充\" - material needs to use MaterialFillService\n- \"单据类型使用数据字典中类型\" - document type should use data dictionary values\n- \"使用local 库测试\" - test with local database\n\nLet me look at the current code more carefully. The `matchMaterialItem` does use `materialFillService.getMaterialInfo(best.getId())` for single item. But I see it's using `getMaterialInfo` singleton, not `getMaterialInfoMap` batch.\n\nAlso, I should check if the material fill is using the dict service for material type.\n\nLet me look at the `materialFillService.getMaterialInfoMap` and the `MaterialFillServiceImpl` to understand the filling.\n\nActually let me re-read the AiRecognitionServiceImpl more carefully. The issue might be:\n\n1. When multiple items are returned from AI, the matching is done one by one with individual `searchByNameLike()` + `getMaterialInfo()` - not using batch filling\n2. The counterparty returns just id without filling in the full name from database (or maybe the name is wrong)\n3. The typeValue/typeName might not be using proper data dict values\n\nLet me look at the controller first.","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_9c8f0fcf6e404a44a2f4241e","name":"exec","arguments":{"command":"find /home/yangxuan/Projects/IdeaProjects/tiny-erp/src -type f -name \"*AiRecognition*\" -o -name \"*AiController*\" -o -name \"*ai*Controller*\" | grep -v target","workdir":"/home/yangxuan/Projects/IdeaProjects/tiny-erp"},"partialArgs":"{\"command\": \"find /home/yangxuan/Projects/IdeaProjects/tiny-erp/src -type f -name \\\"*AiRecognition*\\\" -o -name \\\"*AiController*\\\" -o -name \\\"*ai*Controller*\\\" | grep -v target\", \"workdir\": \"/home/yangxuan/Projects/IdeaProjects/tiny-erp\"}"},{"type":"toolCall","id":"call_6518ff9758b94858956647ff","name":"read","arguments":{"path":"/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/md/material/service/impl/MaterialFillServiceImpl.java"},"partialArgs":"{\"path\": \"/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/md/material/service/impl/MaterialFillServiceImpl.java\"}"}],"api":"openai-completions","provider":"new-api","model":"deepseek-v4-flash","usage":{"input":699,"output":857,"cacheRead":42752,"cacheWrite":0,"reasoningTokens":665,"totalTokens":44308,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1780018636410,"responseId":"chatcmpl-c7f6656f-c687-4c02-978c-ef90bf936d20"}}
{"type":"message","id":"0879d556","parentId":"d47ccae9","timestamp":"2026-05-29T01:37:25.871Z","message":{"role":"toolResult","toolCallId":"call_9c8f0fcf6e404a44a2f4241e","toolName":"exec","content":[{"type":"text","text":"/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/ai/dto/AiRecognitionResultDTO.java\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/ai/service/impl/AiRecognitionServiceImpl.java\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/ai/service/AiRecognitionService.java\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/ai/controller/AiController.java\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/test/java/com/witsoft/erp/module/ai/AiControllerTest.java"}],"details":{"status":"completed","exitCode":0,"durationMs":419,"aggregated":"/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/ai/dto/AiRecognitionResultDTO.java\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/ai/service/impl/AiRecognitionServiceImpl.java\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/ai/service/AiRecognitionService.java\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/ai/controller/AiController.java\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/test/java/com/witsoft/erp/module/ai/AiControllerTest.java","cwd":"/home/yangxuan/Projects/IdeaProjects/tiny-erp"},"isError":false,"timestamp":1780018645870}}
{"type":"message","id":"3572eed3","parentId":"0879d556","timestamp":"2026-05-29T01:37:25.871Z","message":{"role":"toolResult","toolCallId":"call_6518ff9758b94858956647ff","toolName":"read","content":[{"type":"text","text":"package com.witsoft.erp.module.md.material.service.impl;\n\nimport com.baomidou.mybatisplus.core.conditions.query.LambdaQueryWrapper;\nimport com.witsoft.erp.module.md.category.entity.MaterialCategory;\nimport com.witsoft.erp.module.md.category.mapper.MaterialCategoryMapper;\nimport com.witsoft.erp.module.md.material.dto.MaterialInfoDTO;\nimport com.witsoft.erp.module.md.material.entity.Material;\nimport com.witsoft.erp.module.md.material.mapper.MaterialMapper;\nimport com.witsoft.erp.module.md.material.service.MaterialFillService;\nimport com.witsoft.erp.module.system.dict.entity.DictData;\nimport com.witsoft.erp.module.system.dict.mapper.DictDataMapper;\nimport lombok.RequiredArgsConstructor;\nimport org.springframework.stereotype.Service;\n\nimport java.util.Collections;\nimport java.util.List;\nimport java.util.Map;\nimport java.util.stream.Collectors;\n\n/**\n * 物料信息填充服务实现类\n */\n@Service\n@RequiredArgsConstructor\npublic class MaterialFillServiceImpl implements MaterialFillService {\n\n private final MaterialMapper materialMapper;\n private final MaterialCategoryMapper categoryMapper;\n private final DictDataMapper dictDataMapper;\n\n @Override\n public Map getMaterialInfoMap(List materialIds) {\n if (materialIds == null || materialIds.isEmpty()) {\n return Collections.emptyMap();\n }\n\n // 批量查询物料\n List materials = materialMapper.selectBatchIds(materialIds);\n if (materials.isEmpty()) {\n return Collections.emptyMap();\n }\n\n // 提取分类 ID 列表\n List categoryIds = materials.stream()\n .map(Material::getCategoryId)\n .filter(id -> id != null)\n .distinct()\n .collect(Collectors.toList());\n\n // 批量查询分类\n final Map categoryMap;\n if (!categoryIds.isEmpty()) {\n List categories = categoryMapper.selectBatchIds(categoryIds);\n categoryMap = categories.stream()\n .collect(Collectors.toMap(MaterialCategory::getId, MaterialCategory::getCategoryName, (v1, v2) -> v1));\n } else {\n categoryMap = Collections.emptyMap();\n }\n\n // 查询物料类型字典\n final Map materialTypeDictMap = getMaterialTypeDictMap();\n\n // 构建物料信息 Map\n return materials.stream()\n .map(material -> {\n MaterialInfoDTO dto = new MaterialInfoDTO();\n dto.setId(material.getId());\n dto.setMaterialCode(material.getMaterialCode());\n dto.setMaterialName(material.getMaterialName());\n dto.setSpec(material.getSpec());\n dto.setUnit(material.getUnit());\n dto.setMaterialType(material.getMaterialType());\n dto.setMaterialTypeName(materialTypeDictMap.get(material.getMaterialType()));\n dto.setCategoryId(material.getCategoryId());\n dto.setCategoryName(categoryMap.get(material.getCategoryId()));\n dto.setMinSafeQty(material.getMinSafeQty());\n return dto;\n })\n .collect(Collectors.toMap(MaterialInfoDTO::getId, m -> m));\n }\n\n @Override\n public MaterialInfoDTO getMaterialInfo(Long materialId) {\n if (materialId == null) {\n return null;\n }\n Material material = materialMapper.selectById(materialId);\n if (material == null) {\n return null;\n }\n\n MaterialInfoDTO dto = new MaterialInfoDTO();\n dto.setId(material.getId());\n dto.setMaterialCode(material.getMaterialCode());\n dto.setMaterialName(material.getMaterialName());\n dto.setSpec(material.getSpec());\n dto.setUnit(material.getUnit());\n dto.setMaterialType(material.getMaterialType());\n\n // 查询分类名称\n if (material.getCategoryId() != null) {\n MaterialCategory category = categoryMapper.selectById(material.getCategoryId());\n if (category != null) {\n dto.setCategoryId(category.getId());\n dto.setCategoryName(category.getCategoryName());\n }\n }\n\n // 查询物料类型名称\n Map materialTypeDictMap = getMaterialTypeDictMap();\n dto.setMaterialTypeName(materialTypeDictMap.get(material.getMaterialType()));\n dto.setMinSafeQty(material.getMinSafeQty());\n\n return dto;\n }\n\n @Override\n public Map getMaterialTypeDictMap() {\n List typeDictList = dictDataMapper.selectList(\n new LambdaQueryWrapper()\n .eq(DictData::getDictType, \"material_type\")\n .eq(DictData::getStatus, 0)\n .orderByAsc(DictData::getSort)\n );\n return typeDictList.stream()\n .collect(Collectors.toMap(DictData::getDictValue, DictData::getDictLabel, (v1, v2) -> v1));\n }\n}\n"}],"isError":false,"timestamp":1780018645871}}
{"type":"message","id":"dee62cd0","parentId":"3572eed3","timestamp":"2026-05-29T01:37:33.472Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Now let me understand the current state better. The user wants:\n\n1. **Return 名称 and 规格 from AI recognition** - the current code already does this for materials via MaterialFillService. But for the DTO level (AiRecognitionResultDTO), the fields are already there.\n\n2. **Customer/Supplier need to query name** - currently `matchCounterparty` returns `Counterparty` with id + name, but the comment says \"返回id但前端无法填充页面\". The issue is likely that the frontend expects 名称 but only gets id. Let me look at the actual issue more carefully.\n\n3. **Material uses MaterialFillService.getMaterialInfoMap to fill** - The current code already uses `materialFillService.getMaterialInfo()` for single material. The DTO has materialCode, materialName, spec, unit, etc. So this seems OK.\n\n4. **单据类型使用数据字典** - Currently `AiRecognitionServiceImpl` hardcodes type name: `\"采购入库\"` etc. This should use dictionary service.\n\n5. **Use local DB for testing** - need to check current datasource config.\n\nLet me look at the frontend to understand what's expected.","thinkingSignature":"reasoning_content"},{"type":"text","text":"让我先看看前端期望的返回结构，以及 Controller/测试文件"},{"type":"toolCall","id":"call_d335dd39c3444b8bab10a702","name":"read","arguments":{"path":"/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/ai/controller/AiController.java"},"partialArgs":"{\"path\": \"/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/ai/controller/AiController.java\"}"},{"type":"toolCall","id":"call_46addebd33f246528f1da701","name":"read","arguments":{"path":"/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/test/java/com/witsoft/erp/module/ai/AiControllerTest.java"},"partialArgs":"{\"path\": \"/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/test/java/com/witsoft/erp/module/ai/AiControllerTest.java\"}"}],"api":"openai-completions","provider":"new-api","model":"deepseek-v4-flash","usage":{"input":1687,"output":401,"cacheRead":43264,"cacheWrite":0,"reasoningTokens":247,"totalTokens":45352,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1780018645873,"responseId":"chatcmpl-687afcda-64ae-4f38-86d2-8af5699e8aa7"}}
{"type":"message","id":"1613d2cd","parentId":"dee62cd0","timestamp":"2026-05-29T01:37:34.405Z","message":{"role":"toolResult","toolCallId":"call_d335dd39c3444b8bab10a702","toolName":"read","content":[{"type":"text","text":"package com.witsoft.erp.module.ai.controller;\n\nimport com.witsoft.erp.common.core.Result;\nimport com.witsoft.erp.module.ai.dto.AiChatResponseDTO;\nimport com.witsoft.erp.module.ai.dto.AiRecognitionResultDTO;\nimport com.witsoft.erp.module.ai.dto.ImageUploadResponseDTO;\nimport com.witsoft.erp.module.ai.service.AiRecognitionService;\nimport com.witsoft.erp.module.ai.service.AiService;\nimport lombok.RequiredArgsConstructor;\nimport lombok.extern.slf4j.Slf4j;\nimport org.springframework.http.MediaType;\nimport org.springframework.web.bind.annotation.*;\nimport org.springframework.web.multipart.MultipartFile;\n\nimport java.util.List;\n\n/**\n * AI 对话控制器\n */\n@Slf4j\n@RestController\n@RequestMapping(\"/ai\")\n@RequiredArgsConstructor\npublic class AiController {\n\n private final AiService aiService;\n private final AiRecognitionService aiRecognitionService;\n\n /**\n * 图片上传 → 转 base64 返回\n */\n @PostMapping(\"/upload\")\n public Result uploadImage(@RequestParam(\"file\") MultipartFile file) {\n String originalName = file.getOriginalFilename();\n String mimeType = file.getContentType() != null ? file.getContentType() : \"image/png\";\n String base64 = aiService.imageToBase64(file);\n String dataUrl = \"data:\" + mimeType + \";base64,\" + base64;\n\n ImageUploadResponseDTO resp = ImageUploadResponseDTO.builder()\n .base64(base64)\n .dataUrl(dataUrl)\n .originalName(originalName)\n .size(file.getSize())\n .build();\n\n log.info(\"图片上传成功：name={}, size={}, dataUrlLen={}\", originalName, file.getSize(), dataUrl.length());\n return Result.ok(resp);\n }\n\n /**\n * 纯文本对话\n */\n @PostMapping(\"/chat\")\n public Result chat(@RequestParam(\"text\") String text) {\n AiChatResponseDTO resp = aiService.chatText(text);\n log.info(\"AI text 对话：promptTokens={}, completionTokens={}\",\n resp.getPromptTokens(), resp.getCompletionTokens());\n return Result.ok(resp);\n }\n\n /**\n * 多模态对话（文本 + 图片）\n * 使用 multipart/form-data，text 和 images 分开传\n */\n @PostMapping(value = \"/chat-multi\", consumes = MediaType.MULTIPART_FORM_DATA_VALUE)\n public Result chatMulti(\n @RequestParam(value = \"text\", required = false) String text,\n @RequestParam(value = \"images\", required = false) List images) {\n\n AiChatResponseDTO resp = aiService.chatWithImages(text, images);\n log.info(\"AI multi 对话：promptTokens={}, completionTokens={}\",\n resp.getPromptTokens(), resp.getCompletionTokens());\n return Result.ok(resp);\n }\n\n /**\n * 单据识别：上传单据照片 → 返回结构化识别结果（含模糊匹配的 ID）\n */\n @PostMapping(value = \"/recognize\", consumes = MediaType.MULTIPART_FORM_DATA_VALUE)\n public Result recognize(@RequestParam(\"file\") MultipartFile file) {\n AiRecognitionResultDTO resp = aiRecognitionService.recognize(file);\n log.info(\"AI 单据识别完成：type={}({}), counterparty={}\",\n resp.getTypeName(), resp.getTypeValue(), resp.getCounterparty() != null ? resp.getCounterparty().getName() : \"null\");\n return Result.ok(resp);\n }\n}"}],"isError":false,"timestamp":1780018654405}}
{"type":"message","id":"a774ec3b","parentId":"1613d2cd","timestamp":"2026-05-29T01:37:34.406Z","message":{"role":"toolResult","toolCallId":"call_46addebd33f246528f1da701","toolName":"read","content":[{"type":"text","text":"package com.witsoft.erp.module.ai;\n\nimport com.witsoft.erp.ErpApplication;\nimport com.witsoft.erp.common.core.Result;\nimport com.witsoft.erp.module.ai.dto.AiChatResponseDTO;\nimport com.witsoft.erp.module.ai.dto.AiRecognitionResultDTO;\nimport com.witsoft.erp.module.ai.dto.ImageUploadResponseDTO;\nimport com.witsoft.erp.test.base.BaseControllerTest;\nimport lombok.extern.slf4j.Slf4j;\nimport org.junit.jupiter.api.*;\nimport org.springframework.boot.test.context.SpringBootTest;\nimport org.springframework.core.ParameterizedTypeReference;\nimport org.springframework.core.io.ByteArrayResource;\nimport org.springframework.core.io.ClassPathResource;\nimport org.springframework.http.*;\nimport org.springframework.util.LinkedMultiValueMap;\nimport org.springframework.util.MultiValueMap;\n\nimport javax.imageio.ImageIO;\nimport java.awt.*;\nimport java.awt.image.BufferedImage;\nimport java.io.ByteArrayOutputStream;\nimport java.io.InputStream;\nimport java.util.List;\n\nimport static org.junit.jupiter.api.Assertions.*;\n\n/**\n * {@link com.witsoft.erp.module.ai.controller.AiController} 集成测试\n */\n@Slf4j\n@SpringBootTest(classes = ErpApplication.class, webEnvironment = SpringBootTest.WebEnvironment.RANDOM_PORT)\n@TestMethodOrder(MethodOrderer.OrderAnnotation.class)\nclass AiControllerTest extends BaseControllerTest {\n\n /** 生成 32x32 红色测试 PNG */\n private static byte[] createTestPng() {\n try {\n BufferedImage img = new BufferedImage(32, 32, BufferedImage.TYPE_INT_RGB);\n Graphics2D g = img.createGraphics();\n g.setColor(Color.RED);\n g.fillRect(0, 0, 32, 32);\n g.dispose();\n ByteArrayOutputStream baos = new ByteArrayOutputStream();\n ImageIO.write(img, \"png\", baos);\n return baos.toByteArray();\n } catch (Exception e) {\n throw new RuntimeException(e);\n }\n }\n\n private static final byte[] TEST_PNG_BYTES = createTestPng();\n\n @Test\n @Order(1)\n @DisplayName(\"1. 用户登录\")\n void testLogin() {\n login();\n }\n\n @Test\n @Order(2)\n @DisplayName(\"2. 图片上传 → base64\")\n void testUploadImage() {\n assumeToken();\n\n HttpHeaders headers = new HttpHeaders();\n headers.setContentType(MediaType.MULTIPART_FORM_DATA);\n if (authToken != null) {\n headers.setBearerAuth(authToken);\n }\n\n MultiValueMap body = new LinkedMultiValueMap<>();\n body.add(\"file\", new ByteArrayResource(TEST_PNG_BYTES) {\n @Override\n public String getFilename() {\n return \"test-red.png\";\n }\n });\n\n HttpEntity> entity = new HttpEntity<>(body, headers);\n\n ResponseEntity> resp = restTemplate.exchange(\n url(\"/ai/upload\"), HttpMethod.POST, entity,\n new ParameterizedTypeReference>() {});\n\n assertOk(resp);\n ImageUploadResponseDTO data = resp.getBody().getData();\n assertNotNull(data);\n assertNotNull(data.getBase64(), \"base64 不应为空\");\n assertNotNull(data.getDataUrl(), \"dataUrl 不应为空\");\n assertTrue(data.getDataUrl().startsWith(\"data:image/png;base64,\"), \"dataUrl 应 png 格式\");\n assertEquals(\"test-red.png\", data.getOriginalName());\n assertTrue(data.getSize() > 0);\n log.info(\"图片上传成功：base64Len={}, size={}\", data.getBase64().length(), data.getSize());\n }\n\n @Test\n @Order(3)\n @DisplayName(\"3. AI 纯文本对话\")\n void testChatText() {\n assumeToken();\n\n ResponseEntity> resp = restTemplate.exchange(\n url(\"/ai/chat?text=你好，请用一句话介绍你自己\"), HttpMethod.POST,\n new HttpEntity<>(authHeaders()),\n new ParameterizedTypeReference>() {});\n\n assertOk(resp);\n AiChatResponseDTO data = resp.getBody().getData();\n assertNotNull(data);\n assertNotNull(data.getReply(), \"AI 回复不应为空\");\n assertTrue(data.getReply().length() > 0, \"AI 回复应有内容\");\n log.info(\"AI 文本对话回复：{}\", data.getReply().substring(0, Math.min(100, data.getReply().length())));\n log.info(\"token 消耗：prompt={}, completion={}, total={}\",\n data.getPromptTokens(), data.getCompletionTokens(), data.getTotalTokens());\n }\n\n @Test\n @Order(5)\n @DisplayName(\"5. AI 单据识别 - 到货单图片 → 匹配供应商 + 物料\")\n void testRecognize() {\n assumeToken();\n\n // 从 classpath 读取测试图片\n byte[] imageBytes;\n try (InputStream is = new ClassPathResource(\"test_png.png\").getInputStream()) {\n imageBytes = is.readAllBytes();\n } catch (Exception e) {\n throw new RuntimeException(\"无法读取 test_png.png\", e);\n }\n assertTrue(imageBytes.length > 0);\n\n HttpHeaders headers = new HttpHeaders();\n headers.setContentType(MediaType.MULTIPART_FORM_DATA);\n if (authToken != null) {\n headers.setBearerAuth(authToken);\n }\n\n MultiValueMap body = new LinkedMultiValueMap<>();\n body.add(\"file\", new ByteArrayResource(imageBytes) {\n @Override\n public String getFilename() {\n return \"test_png.png\";\n }\n });\n\n HttpEntity> entity = new HttpEntity<>(body, headers);\n\n ResponseEntity> resp = restTemplate.exchange(\n url(\"/ai/recognize\"), HttpMethod.POST, entity,\n new ParameterizedTypeReference>() {});\n\n assertOk(resp);\n AiRecognitionResultDTO data = resp.getBody().getData();\n assertNotNull(data, \"识别结果不应为空\");\n assertNotNull(data.getTypeValue(), \"应识别单据类型\");\n\n log.info(\"AI 单据识别结果：\");\n log.info(\" 类型：{} (value={})\", data.getTypeName(), data.getTypeValue());\n\n // 验证供应商匹配\n AiRecognitionResultDTO.Counterparty cp = data.getCounterparty();\n if (cp != null) {\n log.info(\" 供应商/客户：id={}, name={}, 原始={}, 精确匹配={}\",\n cp.getId(), cp.getName(), cp.getRecognizedName(), cp.isExactMatch());\n assertNotNull(cp.getId(), \"应匹配到供应商 ID\");\n assertEquals(\"老兵水晶店\", cp.getName(), \"供应商名称应一致\");\n }\n\n // 验证物料匹配\n List items = data.getItems();\n assertNotNull(items, \"物料明细不应为空\");\n assertFalse(items.isEmpty(), \"应识别出物料明细\");\n log.info(\" 物料明细（{} 条）：\", items.size());\n for (AiRecognitionResultDTO.RecognitionItem item : items) {\n log.info(\" - materialId={}, name={}, code={}, spec={}, unit={}, quantity={}, price={}, 原始={}, 精确={}\",\n item.getMaterialId(), item.getMaterialName(), item.getMaterialCode(),\n item.getSpec(), item.getUnit(),\n item.getQuantity(), item.getPrice(), item.getRecognizedName(), item.isExactMatch());\n }\n\n // 本地库有数据，验证物料应匹配成功\n long matchedCount = items.stream().filter(i -> i.getMaterialId() != null).count();\n long unmatchedCount = items.stream().filter(i -> i.getMaterialId() == null).count();\n log.info(\" 匹配结果：{} 条已匹配, {} 条未匹配\", matchedCount, unmatchedCount);\n\n log.info(\"✓ 单据识别验证通过\");\n }\n}"}],"isError":false,"timestamp":1780018654405}}
{"type":"message","id":"6e6b5dea","parentId":"a774ec3b","timestamp":"2026-05-29T01:37:58.959Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me understand what the user wants:\n\n1. **返回前端 id 无法填充页面** - The current DTO returns IDs for counterparty (supplier/customer) and materials, but the frontend can't fill the form with just IDs. It needs the names too.\n\n2. **客户供应商需要查询名称** - For customers and suppliers, the DTO needs to return the actual name (not just ID), so the frontend can display it.\n\n3. **物料使用 MaterialFillService.getMaterialInfoMap 填充** - For materials, use `MaterialFillService.getMaterialInfoMap()` to fill in material information.\n\n4. **单据类型使用数据字典中类型** - The document type should use values from the data dictionary.\n\n5. **使用 local 库测试** - Test using the local database.\n\nLet me look at the current `AiRecognitionResultDTO` to understand what it returns and what needs to change:\n\nLooking at the current code:\n- `Counterparty` already has `id`, `name`, `recognizedName`, `exactMatch` - seems like it already returns names\n- `RecognitionItem` already has `materialId`, `materialName`, `materialCode`, `spec`, `unit` - also seems fine\n\nWait, let me re-read the user's request more carefully:\n\n1. \"返回前端id无法填充页面\" - maybe the issue is that the Counterparty only returns an ID without name when match fails? Let me check.\n\nLooking at `AiRecognitionResultDTO.Counterparty`:\n```java\npublic static class Counterparty {\n private Long id;\n private String name; // 匹配到的名称\n private String recognizedName;\n private boolean exactMatch;\n}\n```\n\nAnd in `AiRecognitionResultDTO.RecognitionItem`:\n```java\npublic static class RecognitionItem {\n private Long materialId;\n private String materialName;\n private String materialCode;\n private String spec;\n private String unit;\n private String recognizedName;\n private BigDecimal quantity;\n private BigDecimal price;\n private boolean exactMatch;\n}\n```\n\nLooking at `matchCounterparty` in the impl, when it fails:\n```java\nreturn AiRecognitionResultDTO.Counterparty.builder()\n .name(recognizedName)\n .recognizedName(recognizedName)\n .exactMatch(false)\n .build();\n```\nThis returns name but no ID. That's probably OK.\n\nFor materials, when it fails:\n```java\nreturn AiRecognitionResultDTO.RecognitionItem.builder()\n .materialName(materialName)\n .spec(spec)\n .recognizedName(materialName)\n .quantity(quantity)\n .price(price)\n .unit(unit)\n .exactMatch(false)\n .build();\n```\nAlso returns name but no ID.\n\nHmm, but the user says \"返回前端id无法填充页面\" - maybe the issue is about a different part of the system? Let me think about this differently.\n\nThe user mentions these specific points:\n1. IDs alone can't fill the page → need name + code + spec etc.\n2. Customer/supplier need name query → use service to look up the actual name\n3. Materials use `MaterialFillService.getMaterialInfoMap` to fill info\n4. Document type uses data dictionary\n5. Use local database for testing\n\nLooking at the current code, it already does some of this. But let me look at the full context again. The user is asking me to implement changes that address these interaction/UX issues.\n\nLet me check: when the AI matches a counterparty, the current code returns `Counterparty` with id + name. Is the issue that the frontend can't use just an ID and needs the full name? That seems to already be there...\n\nWait, I think the user might be talking about a NEW feature or interaction point - not just the AI recognition. They might be talking about a general \"approximate query\" interaction where:\n\n1. User types something\n2. System does approximate match\n3. Returns structured results with display info (name, spec, code, unit, etc.) so the frontend can properly display and fill forms\n\nLet me re-read: \"返回前端id无法填充页面\" - this means \"returning an ID to the frontend can't fill the page\" - so the current behavior of returning IDs is insufficient. The frontend needs display names/descriptions.\n\nAnd \"客户供应商需要查询名称\" - for customer/supplier, we need to query and return the name.\n\n\"物料使用com.witsoft.erp.module.md.material.service.MaterialFillService.getMaterialInfoMap填充\" - for materials, use MaterialFillService to fill the info.\n\nI think the user wants me to improve the `AiRecognitionResultDTO` and its usage so that:\n1. Counterparty returns both id + name (already done)\n2. Materials use MaterialFillService to fill material info (already done)\n3. Document type uses dict values (need to check this)\n\nLet me check the `typeValue` handling. In `AiRecognitionServiceImpl`:\n```java\nprivate static final Map TYPE_MAPPING = Map.of(\n \"purchase_inbound\", 10,\n \"sales_outbound\", 10\n);\n\nprivate Integer toTypeValue(String type) {\n return TYPE_MAPPING.getOrDefault(type, 10);\n}\n```\nThis hardcodes the mapping. The user wants to use the data dictionary instead of hardcoded values.\n\nActually wait, looking at the `toTypeName` method, it also has hardcoded values:\n```java\nprivate String toTypeName(Integer typeValue) {\n if (typeValue == null) return null;\n if (typeValue == 10) return \"采购入库\";\n if (typeValue == 20) return \"退货入库\";\n if (typeValue == 30) return \"其他入库\";\n return null;\n}\n```\n\nAnd `sales_outbound` maps to `10` too which is same as `purchase_inbound`, which seems wrong.\n\nThe user wants to:\n1. Query customer/supplier names properly → already done with `matchSupplier`/`matchCustomer`\n2. Use `MaterialFillService` for material info → already done with `materialFillService.getMaterialInfo(best.getId())`\n3. Use data dictionary for document types → need to replace hardcoded type mapping\n4. Ensure the frontend gets enough display info → need to ensure DTO has name, code, spec, unit etc.\n\nActually, let me reconsider. Maybe the user wants me to:\n1. Look at what the frontend expects\n2. Fix the current code to provide better structured data\n3. Write/update tests to verify with local DB\n\nLet me look at the test to understand the expected flow better.\n\nThe test (`AiControllerTest.testRecognize`) expects:\n- Counterparty id not null, name = \"老兵水晶店\"\n- Material items have materialId, materialName, materialCode, spec, unit, quantity, price\n\nSo the DTO already seems to provide name, code, spec, unit, etc.\n\nBut wait - the user says the issues are:\n1. IDs alone can't fill forms → need names\n2. Customer/supplier need name queries\n3. Material uses MaterialFillService\n4. Document type uses dict\n\nLooking more carefully at the existing code, I think the problem might be in how the AI recognition works end-to-end. Let me look at what the frontend might expect:\n\nFor a \"new inbound order\" or \"new outbound order\" page, the frontend probably:\n1. User uploads a receipt image\n2. AI recognizes it and returns structured data\n3. Frontend auto-fills the form with the recognized data\n4. The form needs: supplier name (or ID for autocomplete), warehouse, items with material name/code/spec/unit, quantities, prices, etc.\n\nIf the AI returns just IDs, the frontend can't render them without additional API calls. But our code already returns both id and name.\n\nLet me focus on what I think the user actually wants fixed:\n\n1. **单据类型使用数据字典** → Instead of hardcoded type mapping, use the `DictService` or `DictDataMapper` to look up document type from the data dictionary\n2. **Ensure the DTO properly returns display info** → Double-check all returned fields\n3. **Use MaterialFillService properly** → Already done, but let me verify it's comprehensive\n4. **Test with local DB** → The test uses `BaseControllerTest` which should use local DB\n\nActually, let me just start implementing. The user seems to want me to improve the code based on these points, and test it.\n\nKey changes needed:\n1. **Document type from dict**: Replace hardcoded `TYPE_MAPPING` and `toTypeName` with dict service lookup\n2. **Ensure counterparty returns name** for supplier/customer - already mostly there\n3. **Ensure material info is filled** via MaterialFillService - already there\n4. **Write/update tests** for local DB testing\n\nLet me also look at what document types are available in the dict system:\n\nLet me look at the dict_data table for document types.\n\nAlso, I need to understand what \"单据类型\" means in this context. The current code has `typeValue=10` for both `purchase_inbound` and `sales_outbound`, which maps to \"采购入库\" - this is clearly wrong for sales.\n\nLooking more carefully at the code, `TYPE_MAPPING` has:\n- `purchase_inbound` → 10 \n- `sales_outbound` → 10 (same as purchase_inbound, seems wrong)\n\nAnd `toTypeName`:\n- 10 → \"采购入库\"\n- 20 → \"退货入库\" \n- 30 → \"其他入库\"\n\nThis doesn't even account for sales outbound types. The user wants this to use data dictionary values instead.\n\nLet me look at what dict types exist in the system for inbound/outbound types:\n\nLet me check the SQL for inbound type dict and outbound type dict data.\n\nOK, I think I need to understand the dict structure first. Let me check the existing dict data in the system.\n\nActually, let me just check the SQL data to understand the dict structure for document types.\n\nLet me also look at the `DictService` to understand how to use it.\n\nLet me look at the system's DictService.","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_64fedfdbe667489f8660afe2","name":"exec","arguments":{"command":"grep -rn \"inbound_type\\|outbound_type\\|business_type\\|bill_type\\|单据类型\" /home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/ --include=\"*.sql\" | head -20","workdir":"/home/yangxuan/Projects/IdeaProjects/tiny-erp"},"partialArgs":"{\"command\": \"grep -rn \\\"inbound_type\\\\|outbound_type\\\\|business_type\\\\|bill_type\\\\|单据类型\\\" /home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/ --include=\\\"*.sql\\\" | head -20\", \"workdir\": \"/home/yangxuan/Projects/IdeaProjects/tiny-erp\"}"},{"type":"toolCall","id":"call_0aaf98c75b714616859ea79f","name":"exec","arguments":{"command":"find /home/yangxuan/Projects/IdeaProjects/tiny-erp/src -type f -name \"DictService.java\" -o -name \"DictDataMapper.java\" | grep -v target","workdir":"/home/yangxuan/Projects/IdeaProjects/tiny-erp"},"partialArgs":"{\"command\": \"find /home/yangxuan/Projects/IdeaProjects/tiny-erp/src -type f -name \\\"DictService.java\\\" -o -name \\\"DictDataMapper.java\\\" | grep -v target\", \"workdir\": \"/home/yangxuan/Projects/IdeaProjects/tiny-erp\"}"}],"api":"openai-completions","provider":"new-api","model":"deepseek-v4-flash","usage":{"input":2936,"output":2423,"cacheRead":44800,"cacheWrite":0,"reasoningTokens":2195,"totalTokens":50159,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1780018654408,"responseId":"chatcmpl-abc89529-1259-4279-9fd8-965ec05fdf26"}}
{"type":"message","id":"cefb912f","parentId":"6e6b5dea","timestamp":"2026-05-29T01:38:00.304Z","message":{"role":"toolResult","toolCallId":"call_64fedfdbe667489f8660afe2","toolName":"exec","content":[{"type":"text","text":"/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/mock-inout-data.sql:7:-- inbound_type: 10-采购入库，20-退货入库，30-其他入库\r\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/mock-inout-data.sql:8:-- outbound_type: 10-销售出库，20-领料出库，30-其他出库\r\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/mock-inout-data.sql:62: INSERT INTO im_inbound (id,inbound_no,inbound_type,inbound_date,supplier_id,supplier_name,warehouse_id,warehouse_name,status,total_amount,remark,creator,create_time,updater,update_time,deleted,ecid) VALUES (inbound_id, inbound_no, 10, inbound_date, sup_id, '江苏料新', wh_id, '原料仓', 2, qty*price, '模拟入库', 'admin', now(), 'admin', now(), 0, 'default');\r\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/mock-inout-data.sql:83: INSERT INTO im_inbound (id,inbound_no,inbound_type,inbound_date,supplier_id,supplier_name,warehouse_id,warehouse_name,status,total_amount,remark,creator,create_time,updater,update_time,deleted,ecid) VALUES (inbound_id, inbound_no, 10, inbound_date, sup_id, '梓伊水晶', wh_id, '原料仓', 2, qty*price, '模拟入库', 'admin', now(), 'admin', now(), 0, 'default');\r\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/mock-inout-data.sql:104: INSERT INTO im_inbound (id,inbound_no,inbound_type,inbound_date,supplier_id,supplier_name,warehouse_id,warehouse_name,status,total_amount,remark,creator,create_time,updater,update_time,deleted,ecid) VALUES (inbound_id, inbound_no, 10, inbound_date, sup_id, '老兵水晶店', wh_id, '原料仓', 2, qty*price, '模拟入库', 'admin', now(), 'admin', now(), 0, 'default');\r\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/mock-inout-data.sql:125: INSERT INTO im_outbound (id,outbound_no,outbound_type,outbound_date,customer_id,customer_name,warehouse_id,warehouse_name,status,total_amount,remark,creator,create_time,updater,update_time,deleted,ecid) VALUES (outbound_id, outbound_no, 10, outbound_date, cus_id, '老朱 001', wh_id, '成品仓', 2, qty*price, '模拟出库', 'admin', now(), 'admin', now(), 0, 'default');\r\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/mock-inout-data.sql:146: INSERT INTO im_outbound (id,outbound_no,outbound_type,outbound_date,customer_id,customer_name,warehouse_id,warehouse_name,status,total_amount,remark,creator,create_time,updater,update_time,deleted,ecid) VALUES (outbound_id, outbound_no, 10, outbound_date, cus_id, '布莱恩宋锡元', wh_id, '成品仓', 2, qty*price, '模拟出库', 'admin', now(), 'admin', now(), 0, 'default');\r\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/mock-inout-data.sql:167: INSERT INTO im_outbound (id,outbound_no,outbound_type,outbound_date,customer_id,customer_name,warehouse_id,warehouse_name,status,total_amount,remark,creator,create_time,updater,update_time,deleted,ecid) VALUES (outbound_id, outbound_no, 10, outbound_date, cus_id, 'LYMSAC2023', wh_id, '成品仓', 2, qty*price, '模拟出库', 'admin', now(), 'admin', now(), 0, 'default');\r\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/mock-slow-moving-data.sql:149: INSERT INTO im_inbound (id, inbound_no, inbound_type, inbound_date, supplier_id, warehouse_id, total_amount, remark, creator, create_time, updater, update_time, deleted, ecid)\r\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/mock-slow-moving-data.sql:192: INSERT INTO im_outbound (id, outbound_no, outbound_type, outbound_date, customer_id, warehouse_id, total_amount, remark, creator, create_time, updater, update_time, deleted, ecid)\r\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/10-system.sql:184: business_type SMALLINT,\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/10-system.sql:210:COMMENT ON COLUMN sys_oper_log.business_type IS '业务类型（0-其它，1-新增，2-修改，3-删除，4-导出，5-导入，6-审核）';\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/99-data.sql:64:SELECT nextval('sys_dict_type_seq'), '入库类型', 'inbound_type', 10, 0, 'default', 'system', CURRENT_TIMESTAMP\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/99-data.sql:65:WHERE NOT EXISTS (SELECT 1 FROM sys_dict_type WHERE dict_type = 'inbound_type' AND ecid = 'default');\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/99-data.sql:69:SELECT nextval('sys_dict_type_seq'), '出库类型', 'outbound_type', 11, 0, 'default', 'system', CURRENT_TIMESTAMP\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/99-data.sql:70:WHERE NOT EXISTS (SELECT 1 FROM sys_dict_type WHERE dict_type = 'outbound_type' AND ecid = 'default');\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/99-data.sql:88:SELECT nextval('sys_dict_data_seq'), 'inbound_type', '采购入库', '10', 1, 0, 'default', 'system', CURRENT_TIMESTAMP\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/99-data.sql:89:WHERE NOT EXISTS (SELECT 1 FROM sys_dict_data WHERE dict_type = 'inbound_type' AND dict_value = '10' AND ecid = 'default');\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/99-data.sql:92:SELECT nextval('sys_dict_data_seq'), 'inbound_type', '退货入库', '20', 2, 0, 'default', 'system', CURRENT_TIMESTAMP\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/99-data.sql:93:WHERE NOT EXISTS (SELECT 1 FROM sys_dict_data WHERE dict_type = 'inbound_type' AND dict_value = '20' AND ecid = 'default');"}],"details":{"status":"completed","exitCode":0,"durationMs":408,"aggregated":"/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/mock-inout-data.sql:7:-- inbound_type: 10-采购入库，20-退货入库，30-其他入库\r\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/mock-inout-data.sql:8:-- outbound_type: 10-销售出库，20-领料出库，30-其他出库\r\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/mock-inout-data.sql:62: INSERT INTO im_inbound (id,inbound_no,inbound_type,inbound_date,supplier_id,supplier_name,warehouse_id,warehouse_name,status,total_amount,remark,creator,create_time,updater,update_time,deleted,ecid) VALUES (inbound_id, inbound_no, 10, inbound_date, sup_id, '江苏料新', wh_id, '原料仓', 2, qty*price, '模拟入库', 'admin', now(), 'admin', now(), 0, 'default');\r\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/mock-inout-data.sql:83: INSERT INTO im_inbound (id,inbound_no,inbound_type,inbound_date,supplier_id,supplier_name,warehouse_id,warehouse_name,status,total_amount,remark,creator,create_time,updater,update_time,deleted,ecid) VALUES (inbound_id, inbound_no, 10, inbound_date, sup_id, '梓伊水晶', wh_id, '原料仓', 2, qty*price, '模拟入库', 'admin', now(), 'admin', now(), 0, 'default');\r\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/mock-inout-data.sql:104: INSERT INTO im_inbound (id,inbound_no,inbound_type,inbound_date,supplier_id,supplier_name,warehouse_id,warehouse_name,status,total_amount,remark,creator,create_time,updater,update_time,deleted,ecid) VALUES (inbound_id, inbound_no, 10, inbound_date, sup_id, '老兵水晶店', wh_id, '原料仓', 2, qty*price, '模拟入库', 'admin', now(), 'admin', now(), 0, 'default');\r\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/mock-inout-data.sql:125: INSERT INTO im_outbound (id,outbound_no,outbound_type,outbound_date,customer_id,customer_name,warehouse_id,warehouse_name,status,total_amount,remark,creator,create_time,updater,update_time,deleted,ecid) VALUES (outbound_id, outbound_no, 10, outbound_date, cus_id, '老朱 001', wh_id, '成品仓', 2, qty*price, '模拟出库', 'admin', now(), 'admin', now(), 0, 'default');\r\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/mock-inout-data.sql:146: INSERT INTO im_outbound (id,outbound_no,outbound_type,outbound_date,customer_id,customer_name,warehouse_id,warehouse_name,status,total_amount,remark,creator,create_time,updater,update_time,deleted,ecid) VALUES (outbound_id, outbound_no, 10, outbound_date, cus_id, '布莱恩宋锡元', wh_id, '成品仓', 2, qty*price, '模拟出库', 'admin', now(), 'admin', now(), 0, 'default');\r\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/mock-inout-data.sql:167: INSERT INTO im_outbound (id,outbound_no,outbound_type,outbound_date,customer_id,customer_name,warehouse_id,warehouse_name,status,total_amount,remark,creator,create_time,updater,update_time,deleted,ecid) VALUES (outbound_id, outbound_no, 10, outbound_date, cus_id, 'LYMSAC2023', wh_id, '成品仓', 2, qty*price, '模拟出库', 'admin', now(), 'admin', now(), 0, 'default');\r\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/mock-slow-moving-data.sql:149: INSERT INTO im_inbound (id, inbound_no, inbound_type, inbound_date, supplier_id, warehouse_id, total_amount, remark, creator, create_time, updater, update_time, deleted, ecid)\r\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/mock-slow-moving-data.sql:192: INSERT INTO im_outbound (id, outbound_no, outbound_type, outbound_date, customer_id, warehouse_id, total_amount, remark, creator, create_time, updater, update_time, deleted, ecid)\r\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/10-system.sql:184: business_type SMALLINT,\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/10-system.sql:210:COMMENT ON COLUMN sys_oper_log.business_type IS '业务类型（0-其它，1-新增，2-修改，3-删除，4-导出，5-导入，6-审核）';\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/99-data.sql:64:SELECT nextval('sys_dict_type_seq'), '入库类型', 'inbound_type', 10, 0, 'default', 'system', CURRENT_TIMESTAMP\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/99-data.sql:65:WHERE NOT EXISTS (SELECT 1 FROM sys_dict_type WHERE dict_type = 'inbound_type' AND ecid = 'default');\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/99-data.sql:69:SELECT nextval('sys_dict_type_seq'), '出库类型', 'outbound_type', 11, 0, 'default', 'system', CURRENT_TIMESTAMP\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/99-data.sql:70:WHERE NOT EXISTS (SELECT 1 FROM sys_dict_type WHERE dict_type = 'outbound_type' AND ecid = 'default');\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/99-data.sql:88:SELECT nextval('sys_dict_data_seq'), 'inbound_type', '采购入库', '10', 1, 0, 'default', 'system', CURRENT_TIMESTAMP\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/99-data.sql:89:WHERE NOT EXISTS (SELECT 1 FROM sys_dict_data WHERE dict_type = 'inbound_type' AND dict_value = '10' AND ecid = 'default');\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/99-data.sql:92:SELECT nextval('sys_dict_data_seq'), 'inbound_type', '退货入库', '20', 2, 0, 'default', 'system', CURRENT_TIMESTAMP\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/scripts/postgresql/99-data.sql:93:WHERE NOT EXISTS (SELECT 1 FROM sys_dict_data WHERE dict_type = 'inbound_type' AND dict_value = '20' AND ecid = 'default');","cwd":"/home/yangxuan/Projects/IdeaProjects/tiny-erp"},"isError":false,"timestamp":1780018680303}}
{"type":"message","id":"d4af6a8d","parentId":"cefb912f","timestamp":"2026-05-29T01:38:00.305Z","message":{"role":"toolResult","toolCallId":"call_0aaf98c75b714616859ea79f","toolName":"exec","content":[{"type":"text","text":"/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/system/dict/service/DictService.java\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/system/dict/mapper/DictDataMapper.java"}],"details":{"status":"completed","exitCode":0,"durationMs":407,"aggregated":"/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/system/dict/service/DictService.java\n/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/system/dict/mapper/DictDataMapper.java","cwd":"/home/yangxuan/Projects/IdeaProjects/tiny-erp"},"isError":false,"timestamp":1780018680304}}
{"type":"message","id":"f696779c","parentId":"d4af6a8d","timestamp":"2026-05-29T01:38:07.242Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me understand the full picture. The user wants to fix the AI recognition service so that:\n\n1. **客户/供应商** — currently returns `id` but the frontend needs the name too for filling. The `Counterparty` already has `name`, so need to check if it's filling properly from database.\n\n2. **物料** — should use `MaterialFillService.getMaterialInfoMap()` to enrich material info. Looking at current code, it already uses `materialFillService.getMaterialInfo(best.getId())` for each item individually. But the user wants batch query via `getMaterialInfoMap()`.\n\n3. **单据类型** — should use data dictionary values (`inbound_type` / `outbound_type`). Currently hardcoded mapping `toTypeValue`/`toTypeName` which is wrong - AI returns \"purchase_inbound\"/\"sales_outbound\" and maps to hardcoded values.\n\n4. **测试** — use local database.\n\nLet me re-read the requirements more carefully:\n\n> - 返回前端id 无法填充页面\n> - 客户供应商需要查询名称\n> - 物料使用MaterialFillService.getMaterialInfoMap填充\n> - 单据类型使用 数据字典中类型\n> - 使用local 库测试\n\nSo the issues are:\n1. Counterparty: currently uses `Counterparty` with `id` + `name`, but need to verify the name is actually being returned correctly. Let me check the `searchByNameLike` implementations for customer and supplier.\n\n2. Material: already uses `getMaterialInfo(best.getId())` per-item but not `getMaterialInfoMap()` batch. Should batch all matched material IDs.\n\n3. Document type: currently hardcoded, should use `inbound_type`/`outbound_type` dictionary from DB.\n\nLet me check the existing supplier/customer `searchByNameLike`:","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_3dac51f6a4d3497daad0f736","name":"read","arguments":{"path":"/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/md/supplier/service/impl/SupplierServiceImpl.java"},"partialArgs":"{\"path\": \"/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/md/supplier/service/impl/SupplierServiceImpl.java\"}"},{"type":"toolCall","id":"call_3fcd46f4543f4da8b7f2d651","name":"read","arguments":{"path":"/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/md/customer/service/impl/CustomerServiceImpl.java"},"partialArgs":"{\"path\": \"/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/md/customer/service/impl/CustomerServiceImpl.java\"}"}],"api":"openai-completions","provider":"new-api","model":"deepseek-v4-flash","usage":{"input":2173,"output":542,"cacheRead":47616,"cacheWrite":0,"reasoningTokens":394,"totalTokens":50331,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1780018680310,"responseId":"chatcmpl-367d85e7-81f8-4580-a775-5136d323c333"}}
{"type":"message","id":"64cf063f","parentId":"f696779c","timestamp":"2026-05-29T01:38:08.178Z","message":{"role":"toolResult","toolCallId":"call_3dac51f6a4d3497daad0f736","toolName":"read","content":[{"type":"text","text":"package com.witsoft.erp.module.md.supplier.service.impl;\n\nimport com.baomidou.mybatisplus.core.conditions.query.LambdaQueryWrapper;\nimport com.baomidou.mybatisplus.extension.plugins.pagination.Page;\nimport com.baomidou.mybatisplus.extension.service.impl.ServiceImpl;\nimport com.witsoft.erp.common.util.CodeGeneratorUtil;\nimport com.witsoft.erp.module.md.supplier.dto.SupplierSaveReqDTO;\nimport com.witsoft.erp.module.md.supplier.entity.Supplier;\nimport com.witsoft.erp.module.md.supplier.mapper.SupplierMapper;\nimport com.witsoft.erp.module.md.supplier.service.SupplierService;\nimport lombok.RequiredArgsConstructor;\nimport org.springframework.jdbc.core.JdbcTemplate;\nimport org.springframework.stereotype.Service;\nimport org.springframework.transaction.annotation.Transactional;\n\nimport java.util.List;\n\n/**\n * 供应商 Service 实现类\n */\n@Service\n@RequiredArgsConstructor\npublic class SupplierServiceImpl extends ServiceImpl implements SupplierService {\n\n private final JdbcTemplate jdbcTemplate;\n\n @Override\n public Page pageList(Supplier supplier, int pageNum, int pageSize) {\n LambdaQueryWrapper wrapper = new LambdaQueryWrapper();\n\n if (supplier.getName() != null) {\n wrapper.like(Supplier::getName, supplier.getName());\n }\n if (supplier.getSupplierCode() != null) {\n wrapper.like(Supplier::getSupplierCode, supplier.getSupplierCode());\n }\n\n wrapper.orderByDesc(Supplier::getCreateTime);\n\n return this.page(new Page<>(pageNum, pageSize), wrapper);\n }\n\n @Override\n @Transactional(rollbackFor = Exception.class)\n public Long saveSupplier(SupplierSaveReqDTO dto) {\n Supplier supplier = new Supplier();\n\n // 如果编码为空，自动生成\n if (dto.getSupplierCode() == null || dto.getSupplierCode().trim().isEmpty()) {\n // 从序列获取下一个值\n Long sequenceValue = jdbcTemplate.queryForObject(\"SELECT nextval('seq_md_supplier_code')\", Long.class);\n supplier.setSupplierCode(CodeGeneratorUtil.generateSupplierCode(sequenceValue));\n } else {\n supplier.setSupplierCode(dto.getSupplierCode());\n }\n\n supplier.setName(dto.getName());\n supplier.setContact(dto.getContact());\n supplier.setMobile(dto.getMobile());\n supplier.setTelephone(dto.getTelephone());\n supplier.setPhone(dto.getPhone());\n supplier.setEmail(dto.getEmail());\n supplier.setFax(dto.getFax());\n supplier.setAddress(dto.getAddress());\n supplier.setTaxNumber(dto.getTaxNumber());\n supplier.setTaxPercent(dto.getTaxPercent());\n supplier.setBankName(dto.getBankName());\n supplier.setBankAccount(dto.getBankAccount());\n supplier.setBankAddress(dto.getBankAddress());\n supplier.setSort(dto.getSort());\n supplier.setStatus(dto.getStatus() != null ? dto.getStatus() : 0);\n supplier.setRemark(dto.getRemark());\n this.save(supplier);\n return supplier.getId();\n }\n\n @Override\n @Transactional(rollbackFor = Exception.class)\n public void updateSupplier(SupplierSaveReqDTO dto) {\n Supplier supplier = new Supplier();\n supplier.setId(dto.getId());\n supplier.setSupplierCode(dto.getSupplierCode());\n supplier.setName(dto.getName());\n supplier.setContact(dto.getContact());\n supplier.setMobile(dto.getMobile());\n supplier.setTelephone(dto.getTelephone());\n supplier.setPhone(dto.getPhone());\n supplier.setEmail(dto.getEmail());\n supplier.setFax(dto.getFax());\n supplier.setAddress(dto.getAddress());\n supplier.setTaxNumber(dto.getTaxNumber());\n supplier.setTaxPercent(dto.getTaxPercent());\n supplier.setBankName(dto.getBankName());\n supplier.setBankAccount(dto.getBankAccount());\n supplier.setBankAddress(dto.getBankAddress());\n supplier.setSort(dto.getSort());\n supplier.setStatus(dto.getStatus());\n supplier.setRemark(dto.getRemark());\n this.updateById(supplier);\n }\n\n @Override\n public List searchByNameLike(String keyword) {\n if (keyword == null || keyword.trim().isEmpty()) {\n return List.of();\n }\n LambdaQueryWrapper wrapper = new LambdaQueryWrapper<>();\n wrapper.like(Supplier::getName, keyword.trim());\n wrapper.last(\"LIMIT 10\");\n return list(wrapper);\n }\n\n}"}],"isError":false,"timestamp":1780018688178}}
{"type":"message","id":"db85b959","parentId":"64cf063f","timestamp":"2026-05-29T01:38:08.179Z","message":{"role":"toolResult","toolCallId":"call_3fcd46f4543f4da8b7f2d651","toolName":"read","content":[{"type":"text","text":"package com.witsoft.erp.module.md.customer.service.impl;\r\n\r\nimport com.baomidou.mybatisplus.core.conditions.query.LambdaQueryWrapper;\r\nimport com.baomidou.mybatisplus.extension.plugins.pagination.Page;\r\nimport com.baomidou.mybatisplus.extension.service.impl.ServiceImpl;\r\nimport com.witsoft.erp.common.util.CodeGeneratorUtil;\r\nimport com.witsoft.erp.module.md.customer.dto.CustomerSaveReqDTO;\r\nimport com.witsoft.erp.module.md.customer.entity.Customer;\r\nimport com.witsoft.erp.module.md.customer.mapper.CustomerMapper;\r\nimport com.witsoft.erp.module.md.customer.service.CustomerService;\r\nimport lombok.RequiredArgsConstructor;\r\nimport org.springframework.jdbc.core.JdbcTemplate;\r\nimport org.springframework.stereotype.Service;\r\nimport org.springframework.transaction.annotation.Transactional;\r\n\r\nimport java.util.List;\r\n\r\n/**\r\n * 客户 Service 实现类\r\n */\r\n@Service\r\n@RequiredArgsConstructor\r\npublic class CustomerServiceImpl extends ServiceImpl implements CustomerService {\r\n\r\n private final JdbcTemplate jdbcTemplate;\r\n\r\n @Override\r\n public Page pageList(Customer customer, int pageNum, int pageSize) {\r\n LambdaQueryWrapper wrapper = new LambdaQueryWrapper();\r\n \r\n if (customer.getName() != null) {\r\n wrapper.like(Customer::getName, customer.getName());\r\n }\r\n if (customer.getCustomerCode() != null) {\r\n wrapper.like(Customer::getCustomerCode, customer.getCustomerCode());\r\n }\r\n \r\n wrapper.orderByDesc(Customer::getCreateTime);\r\n \r\n return this.page(new Page<>(pageNum, pageSize), wrapper);\r\n }\r\n\r\n @Override\r\n @Transactional(rollbackFor = Exception.class)\r\n public Long saveCustomer(CustomerSaveReqDTO dto) {\r\n Customer customer = new Customer();\r\n \r\n // 如果编码为空，自动生成\r\n if (dto.getCustomerCode() == null || dto.getCustomerCode().trim().isEmpty()) {\r\n // 从序列获取下一个值\r\n Long sequenceValue = jdbcTemplate.queryForObject(\"SELECT nextval('seq_md_customer_code')\", Long.class);\r\n customer.setCustomerCode(CodeGeneratorUtil.generateCustomerCode(sequenceValue));\r\n } else {\r\n customer.setCustomerCode(dto.getCustomerCode());\r\n }\r\n \r\n customer.setName(dto.getName());\r\n this.save(customer);\r\n return customer.getId();\r\n }\r\n\r\n @Override\r\n @Transactional(rollbackFor = Exception.class)\r\n public void updateCustomer(CustomerSaveReqDTO dto) {\r\n Customer customer = new Customer();\r\n customer.setId(dto.getId());\r\n customer.setCustomerCode(dto.getCustomerCode());\r\n customer.setName(dto.getName());\r\n this.updateById(customer);\r\n }\r\n\r\n @Override\r\n public List searchByNameLike(String keyword) {\r\n if (keyword == null || keyword.trim().isEmpty()) {\r\n return List.of();\r\n }\r\n LambdaQueryWrapper wrapper = new LambdaQueryWrapper<>();\r\n wrapper.like(Customer::getName, keyword.trim());\r\n wrapper.last(\"LIMIT 10\");\r\n return list(wrapper);\r\n }\r\n\r\n}\r\n"}],"isError":false,"timestamp":1780018688178}}
{"type":"message","id":"adab0f98","parentId":"db85b959","timestamp":"2026-05-29T01:38:42.576Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me analyze the current problem and the requested changes:\n\n1. **返回前端id 无法填充页面** — 前端收到的是 `materialId` 等 ID 字段，但需要名称、规格等显示字段来填充页面表单\n2. **客户供应商需要查询名称** — 当前 `Counterparty` 只返回了 `id` + `name`，但 `name` 是数据库中存储的，前端可能需要显示 AI 识别名 vs 实际匹配名。不过从前端角度看，已有 `name`（数据库中的名称）和 `recognizedName`（AI 识别名称），看起来够用？问题是前端要填充客户/供应商 **名称** 到输入框，而不是 ID。看当前 DTO 结构，`Counterparty` 有 `name` 字段（匹配到的数据库名称），前端用这个应该能填充。但需要确认是否真的把 `name` 返回了。\n\n3. **物料使用 MaterialFillService.getMaterialInfoMap 填充** — 已有 `materialInfo` 字段在 `RecognitionItem` 中有 `materialName/materialCode/spec/unit`，但现在这些字段是 inline 的而不是通过 `MaterialInfoDTO`。改动的方向是统一用 `MaterialInfoDTO` 来填充物料信息。\n\n4. **单据类型使用数据字典中类型** — 当前 `toTypeName()` 是硬编码的 map，应该从数据字典（`inbound_type` / `outbound_type`）中查询。\n\n5. **使用 local 库测试** — 配置 local 数据库连接进行测试。\n\n让我看看现有代码的更完整结构，然后做修改。\n\n让我看看整个 `AiRecognitionServiceImpl` 的完整内容：\n\n我看过了。当前流程：\n1. AI 返回 JSON，解析出 `supplier_name` 或 `customer_name` + `items`（含有 `material_name`, `spec`, `quantity`, `price`）\n2. `matchSupplier`/`matchCustomer` 用模糊匹配查到供应商/客户的 ID 和 name\n3. `matchMaterialItem` 用模糊匹配查到物料的 ID，然后用 `materialFillService.getMaterialInfo(best.getId())` 填充\n\n问题分析：\n\n**1. 物料填充现有问题：**\n当前 `RecognitionItem` 字段：`materialId, materialName, materialCode, spec, unit, recognizedName, quantity, price, exactMatch`\n这些都填了。看起来没什么问题？\n\n但是用户说 \"返回前端id无法填充页面\"，可能意味着前端需要的是*完整的物料信息*（含编码、名称、规格、单位），而且这些信息通过 `MaterialInfoDTO` 统一获取比较可靠。当前代码确实用了 `MaterialInfoDTO info = materialFillService.getMaterialInfo(best.getId())` 来填充，应该已经包含了所有需要的信息。\n\n**2. 客户/供应商：**\n当前 `Counterparty` 有 `id`, `name`, `recognizedName`, `exactMatch`。前端需要名称来填充。看起来也是有的。\n\n等等，让我重新看下代码...\n\n`Counterparty`:\n```java\npublic static class Counterparty {\n private Long id;\n private String name; // 数据库中名称\n private String recognizedName; // AI 识别名称\n private boolean exactMatch;\n}\n```\n\n这个结构应该够了。前端用 `name` 填充输入框。\n\n**3. 单据类型使用数据字典：**\n\n当前代码：\n```java\nprivate static final Map TYPE_MAPPING = Map.of(\n \"purchase_inbound\", 10,\n \"sales_outbound\", 10 // BUG！销售出库应该是不同类型\n);\n\nprivate static final Map TYPE_NAME_MAPPING = Map.of(\n 10, \"采购入库\",\n 20, \"退货入库\",\n 30, \"其他入库\"\n);\n```\n\n这有两个问题：\n- `sales_outbound` 映射到 10，但它的类型名称硬编码返回 \"采购入库\"？不，`toTypeName(10)` 返回 \"采购入库\"。这显然不对，销售出库应该从 `outbound_type` 字典取值\n- 类型名称应该从数据字典查询，而不是硬编码\n\n数据字典中定义了：\n- `inbound_type`: 10-采购入库, 20-退货入库, 30-其他入库\n- `outbound_type`: 10-销售出库, 20-领料出库, 30-其他出库\n\n所以应该使用 `DictService` 来查询字典标签。\n\n**4. 使用 local 库测试：**\n需要在 `application-local.yml` 或者测试配置中指向本地数据库。\n\n让我看看 `DictService` 的结构：\n\n我记得 MEMORY.md 提到：\n```\n字典优先用 DictService：字典取值优先使用 DictService 公共方法，不手写字典查询逻辑\n```\n\n所以应该注入 `DictService` 而不是在 `AiRecognitionServiceImpl` 里手写字典映射。\n\n让我看看 DictService 接口：\n\n已经看到 `MaterialFillServiceImpl` 直接使用 `DictDataMapper` 来查询 material_type 字典。但用户要求的是用 `DictService`。\n\n让我确认 DictService 有什么方法：","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_e09bfefdaad0450590dd4e5c","name":"read","arguments":{"path":"/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/system/dict/service/DictService.java"},"partialArgs":"{\"path\": \"/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/system/dict/service/DictService.java\"}"},{"type":"toolCall","id":"call_b03590fa911d4f91bbb74051","name":"exec","arguments":{"command":"find /home/yangxuan/Projects/IdeaProjects/tiny-erp/src -type f -name \"DictServiceImpl.java\" | grep -v target","workdir":"/home/yangxuan/Projects/IdeaProjects/tiny-erp"},"partialArgs":"{\"command\": \"find /home/yangxuan/Projects/IdeaProjects/tiny-erp/src -type f -name \\\"DictServiceImpl.java\\\" | grep -v target\", \"workdir\": \"/home/yangxuan/Projects/IdeaProjects/tiny-erp\"}"}],"api":"openai-completions","provider":"new-api","model":"deepseek-v4-flash","usage":{"input":2097,"output":1263,"cacheRead":49664,"cacheWrite":0,"reasoningTokens":1094,"totalTokens":53024,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1780018688495,"responseId":"chatcmpl-7bd31bb6-1963-4998-b3a9-e356e5d3ce00"}}
{"type":"message","id":"968216e1","parentId":"adab0f98","timestamp":"2026-05-29T01:38:43.891Z","message":{"role":"toolResult","toolCallId":"call_e09bfefdaad0450590dd4e5c","toolName":"read","content":[{"type":"text","text":"package com.witsoft.erp.module.system.dict.service;\n\nimport com.baomidou.mybatisplus.extension.service.IService;\nimport com.witsoft.erp.module.system.dict.entity.DictData;\nimport com.witsoft.erp.module.system.dict.entity.DictType;\n\nimport java.util.List;\nimport java.util.Map;\n\n/**\n * 字典服务接口\n */\npublic interface DictService extends IService {\n\n /**\n * 获取字典类型列表\n */\n List listTypes(DictType dictType);\n\n /**\n * 获取字典数据列表\n */\n List listData(String dictType);\n\n /**\n * 获取多个字典类型的数据（用于前端下拉框批量填充）\n * 例：传入 [\"inbound_type\", \"outbound_type\"] 返回对应的字典数据 Map\n *\n * @param dictTypes 字典类型标识列表\n * @return Map<字典类型，字典数据列表>\n */\n Map> listDataBatch(List dictTypes);\n\n /**\n * 获取字典数据的 label-value 列表（前端下拉框格式）\n *\n * @param dictType 字典类型标识\n * @return List>\n */\n List> listOptions(String dictType);\n\n /**\n * 批量获取多个字典类型的 options（前端下拉框格式）\n *\n * @param dictTypes 字典类型标识列表\n * @return Map<字典类型，List>>\n */\n Map>> listOptionsBatch(List dictTypes);\n\n}\n"}],"isError":false,"timestamp":1780018723891}}
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{"type":"message","id":"24dab807","parentId":"3e535f8d","timestamp":"2026-05-29T01:39:47.590Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me understand the full picture now. The user wants to improve the AI recognition result DTO so that the frontend can actually use it to populate forms. The key issues are:\n\n1. **返回前端id但无法填充** - The `RecognitionItem` has `materialId` but the frontend needs the full material info populated. The user wants us to use `MaterialFillService.getMaterialInfoMap()` for the material items, and query names for customers/suppliers.\n\n2. **客户供应商需要查询名称** - For the counterparty, we already return `name` from the database when there's a match. Need to ensure we're returning full name info.\n\n3. **物料使用 MaterialFillService.getMaterialInfoMap() 填充** - Already partially done (currently calls `getMaterialInfo()` for single item), but should be batch filled for efficiency.\n\n4. **单据类型使用数据字典中类型** - Currently the code has hardcoded `TYPE_MAPPING` and `TYPE_NAME_MAPPING`. Should use the DictService to look up from the database.\n\n5. **使用local库测试** - Run tests against local database.\n\nLet me re-read the current AiRecognitionResultDTO to see what's missing for frontend fill.\n\nLooking at the current `RecognitionItem`:\n```java\npublic static class RecognitionItem {\n private Long materialId;\n private String materialName;\n private String materialCode;\n private String spec;\n private String unit;\n private String recognizedName;\n private BigDecimal quantity;\n private BigDecimal price;\n private boolean exactMatch;\n}\n```\n\nIt already has materialCode, materialName, spec, unit. The issue might be that the frontend expects these fields in a specific way. Actually, looking at the issue description more carefully:\n\n\"返回前端id但无法填充页面\" - The front end receives IDs but can't fill the form fields. This means the front end expects to receive the full data (name, code, spec, unit) back, not just IDs.\n\nLooking at the code, it already does fill using `MaterialFillService`. So the question is - is there actually a missing piece?\n\nWait, let me re-read. The user says the frontend returns IDs but can't fill the form. So the DTO is already being populated with IDs. But the frontend expects the full data. Let me check if there's an issue with how the data is populated.\n\nActually, looking again at the current code flow:\n1. For materials - it calls `materialFillService.getMaterialInfo(best.getId())` for each item, but this is inefficient since it's one-by-one. It should batch.\n2. The `Counterparty` DTO has `name` field that should be the DB name. It already returns it.\n3. The `typeName` is hardcoded instead of using DictService.\n\nLet me also check if the issue is more about the frontend expecting certain structures. Let me look at the test to understand expected behavior...\n\nThe test expects:\n- `cp.getName()` equals \"老兵水晶店\"\n- Items have materialId, materialName, materialCode, spec, unit, quantity, price\n\nSo the DTO seems to already have the right fields. But the user is saying the frontend can't fill the page with just IDs.\n\nOK, I think the key improvements needed are:\n1. **Batch fill materials** - Currently uses `getMaterialInfo()` one-by-one in the item loop, should batch all items at once\n2. **Use DictService for type name** - Replace hardcoded TYPE_NAME_MAPPING with DictService lookup\n3. **Ensure counterparty returns full name info** - Already does this, but ensure it's consistent\n\nActually wait, I need to re-examine. The user said:\n- \"返回前端id但无法填充页面\" - maybe the current code doesn't return material_name at all when there's no match? No, it does return it.\n- \"客户供应商需要查询名称\" - maybe the counterparty is only returning ID but not the full name?\n\nLet me re-read the Counterparty:\n```java\n@Builder\nprivate Long id;\nprivate String name; // 数据库中名称\nprivate String recognizedName;\nprivate boolean exactMatch;\n```\n\nHmm, the builder pattern here is `CounterpartyBuilder`. When we do `mapper.apply(best).recognizedName().exactMatch().build()`, it should set id and name.\n\nLooking at `matchCounterparty`:\n```java\nreturn mapper.apply(best)\n .recognizedName(recognizedName)\n .exactMatch(true)\n .build();\n```\n\nAnd `matchSupplier`:\n```java\ns -> AiRecognitionResultDTO.Counterparty.builder()\n .id(s.getId()).name(s.getName())\n```\n\nSo it does return the DB name. The issue might be something else.\n\nLet me re-read the actual problem statement again: \"返回前端id但无法填充页面\" - \"returning front-end id but can't fill the page\"\n\nI think this means currently the code returns the ID from recognition, but the frontend needs additional data to populate the form (material name, code, spec, unit for each item). Looking at the current code, it does return those fields...\n\nWait, I need to look more carefully. The `AiRecognitionResultDTO` is returned as the response. Let me think about what the frontend expects:\n\nFor material items, the frontend probably has an autocomplete/selector component that needs:\n- materialId (to identify)\n- materialName (to display)\n- materialCode (for reference)\n- spec (for specification)\n- unit (for unit)\n\nThese already exist in `RecognitionItem`. So what's missing?\n\nActually, I think the user might be talking about a different scenario. Perhaps the previous version of this code (before my analysis) wasn't populating the material info properly, and the user wants me to ensure the improvements are made. Let me re-read the user's message:\n\n\"## 交互优化\n- 返回前端id但无法填充页面\n- 客户供应商需要查询名称\n- 物料使用com.witsoft.erp.module.md.material.service.MaterialFillService.getMaterialInfoMap填充\n- 单据类型使用 数据字典中类型\n- 使用local 库测试\n- [02-物料近似查询方案.md](yangxuan/设计/02-物料近似查询方案.md)\"\n\nOK so the user wants me to actually implement these improvements:\n\n1. Currently, the frontend receives IDs but can't fill forms because they lack the display data. Need to ensure full material/customer/supplier info is returned.\n2. Customer/Supplier should return the name from database when matched.\n3. Use `MaterialFillService.getMaterialInfoMap()` for batch filling of material info.\n4. Use DictService for the bill type (not hardcoded).\n5. Test with local database.\n\nLet me plan the implementation:\n\n### Changes needed:\n\n#### 1. `AiRecognitionResultDTO.java`\n- No structural changes needed, it already has the right fields.\n\n#### 2. `AiRecognitionServiceImpl.java` - Major refactoring:\n\na) **Batch material fill**: Instead of calling `materialFillService.getMaterialInfo()` per item, collect all matched IDs and batch fill.\n\nb) **Use DictService for type name**: Replace hardcoded TYPE_NAME_MAPPING with DictService lookup.\n\nc) **Return counterparty with full name info**: Already done for matched ones, but the \"unknown\" fallback could be improved.\n\nd) **Use searchSimilar from the design doc**: Apply the priority-based matching instead of the current fallback chain.\n\nLet me implement this step by step.\n\nActually, wait. Let me re-think the scope. The user said:\n\n- \"返回前端id但无法填充页面\" - The front end gets IDs but can't fill. This implies the current code does return IDs but doesn't return the associated display data that the frontend needs.\n\nLooking at the current code again for `RecognitionItem`:\n```java\n.materialId(best.getId())\n.materialName(info != null ? info.getMaterialName() : best.getMaterialName())\n.materialCode(info != null ? info.getMaterialCode() : null)\n.spec(info != null ? info.getSpec() : best.getSpec())\n.unit(info != null ? info.getUnit() : best.getUnit())\n```\n\nThis looks correct. Unless the `materialFillService.getMaterialInfo()` returns null, in which case materialCode would be null but materialName and spec would still come from the Material entity.\n\nHmm, maybe the issue is simpler. Let me look at what the frontend expects...\n\nActually I think I'm overthinking this. The user is telling me what needs to be improved, and I should just implement it. Let me code the changes:\n\n1. **Refactor `matchMaterialItem` to batch fill** - Instead of calling `getMaterialInfo` per item, collect all matched material IDs first, batch query, then fill.\n\n2. **Replace hardcoded type mapping with DictService** - Query `inbound_type` dict for the labels.\n\n3. **Refactor the fuzzy matching to use the priority-based approach** from the design doc.\n\n4. **Verify supplier/customer name return**.\n\nLet me start coding.\n\nFirst, let me understand the full current `AiRecognitionServiceImpl.java`:\n\nThe current implementation:\n- `recognize()` - Main entry point\n- Calls `matchSupplier` / `matchCustomer` for counterparty\n- Calls `matchMaterialItem` per item\n\nThe material matching flow:\n1. Normalize name (remove spaces, half-width)\n2. `materialService.searchByNameLike(normalized)` - LIKE search\n3. If no results and spec exists: search `normalized + \" \" + spec` \n4. If still none: tokenize and try each token\n5. If still none: extract Chinese parts\n6. If still none: return unmatched item\n7. If matched: fill with `materialFillService.getMaterialInfo(best.getId())`\n\nThe counterparty matching flow:\n1. Normalize name\n2. `searchByNameLike(normalized)` - LIKE search\n3. If none: tokenize, try longest token\n4. If still none: try combined tokens\n5. If still none: return unmatched\n\nOK, let me now implement the changes. I'll refactor `AiRecognitionServiceImpl.java` to:\n\n1. **Use the priority-based `searchSimilar()` matching** (from design doc) for materials\n2. **Batch fill all matched materials** using `MaterialFillService.getMaterialInfoMap()`\n3. **Use DictService** for type name lookup instead of hardcoded values\n4. **Ensure counterparty returns proper names** (should already work)\n\nI also need to:\n- Add `searchSimilar()` to `MaterialService` and `MaterialServiceImpl`\n- Add `MatchPriority` enum and `MaterialMatchResult` DTO\n\nLet me check what files I need to create/modify:\n\n**New files:**\n1. `MatchPriority.java` - enum\n2. `MaterialMatchResult.java` - DTO\n\n**Modified files:**\n1. `MaterialService.java` - add `searchSimilar()` method\n2. `MaterialServiceImpl.java` - implement `searchSimilar()` with priority-based search\n3. `AiRecognitionServiceImpl.java` - major refactor\n4. `AiRecognitionResultDTO.java` - maybe add matchType to RecognitionItem\n\nActually, let me check if MaterialServiceImpl already uses `LikeEscapeUtil.escapeLike` - yes it does.\n\nLet me think about the `searchSimilar` implementation more carefully.\n\n```java\n@Override\npublic List searchSimilar(String keyword) {\n if (!StringUtils.hasText(keyword) || keyword.trim().length() < 2) {\n return Collections.emptyList();\n }\n \n String escaped = escapeLike(keyword.trim());\n List results = new ArrayList<>();\n Set addedIds = new HashSet<>();\n \n // Step 1: 名称精确匹配\n addIfAbsent(results, addedIds, \n wrapper -> wrapper.eq(Material::getMaterialName, keyword.trim()), \n MatchPriority.EXACT_NAME);\n \n // Step 2: 名称前缀匹配\n addIfAbsent(results, addedIds, \n wrapper -> wrapper.apply(\"material_name LIKE {0}\", escaped + \"%\"), \n MatchPriority.PREFIX_NAME);\n \n // Step 3: 名称模糊匹配\n addIfAbsent(results, addedIds, \n wrapper -> wrapper.apply(\"material_name LIKE {0}\", \"%\" + escaped + \"%\"), \n MatchPriority.FUZZY_NAME);\n \n // Step 4: 规格模糊匹配\n addIfAbsent(results, addedIds, \n wrapper -> wrapper.apply(\"spec LIKE {0}\", \"%\" + escaped + \"%\"), \n MatchPriority.FUZZY_SPEC);\n \n // Step 5: 编码模糊匹配\n addIfAbsent(results, addedIds, \n wrapper -> wrapper.apply(\"material_code LIKE {0}\", \"%\" + escaped + \"%\"), \n MatchPriority.FUZZY_CODE);\n \n // Limit total results\n if (results.size() > 50) {\n results = results.subList(0, 50);\n }\n \n // Fill with material info\n List materialIds = results.stream().map(MaterialMatchResult::getId).toList();\n Map infoMap = materialFillService.getMaterialInfoMap(materialIds);\n for (MaterialMatchResult r : results) {\n r.setMaterialInfo(infoMap.get(r.getId()));\n }\n \n return results;\n}\n\nprivate void addIfAbsent(List results, Set addedIds,\n Consumer> condition, MatchPriority priority) {\n LambdaQueryWrapper wrapper = new LambdaQueryWrapper<>();\n condition.accept(wrapper);\n wrapper.eq(Material::getStatus, 0);\n wrapper.orderByDesc(Material::getCreateTime);\n wrapper.last(\"LIMIT 20\");\n \n List list = baseMapper.selectList(wrapper);\n for (Material m : list) {\n if (addedIds.add(m.getId())) {\n MaterialMatchResult r = new MaterialMatchResult();\n r.setId(m.getId());\n r.setMaterialCode(m.getMaterialCode());\n r.setMaterialName(m.getMaterialName());\n r.setSpec(m.getSpec());\n r.setUnit(m.getUnit());\n r.setMatchType(priority);\n r.setMatchRank(priority.rank);\n results.add(r);\n }\n }\n}\n```\n\nWait, but `MaterialServiceImpl` extends `ServiceImpl`, so it has access to `baseMapper`. For the `searchSimilar` method, I'll use `baseMapper` directly.\n\nActually, there's a concern: `eq(Material::getStatus, 0)` - this assumes status 0 is \"有效\". Let me check the entity - `private Integer status;` with comment `物料状态（0-有效，1-无效）`. Yes, 0 is valid.\n\nNow, I also need to think about the AI recognition service refactoring. The current code has a lot of fallback logic that tries to find materials. Let me simplify it to use `searchSimilar()` instead.\n\nFor `matchMaterialItem()`:\n1. Normalize name\n2. Call `materialService.searchSimilar(normalized)`\n3. If no results and spec exists: call `materialService.searchSimilar(normalized + specNoSpace)`\n4. If still none: use the existing tokenize/fallback searchSimilar\n5. Take the best match\n6. Return RecognitionItem with full info (already filled by searchSimilar's MaterialFillService)\n\nActually, `searchSimilar` already fills with MaterialFillService, so the returned MaterialMatchResult already has the material info. I just need the best match.\n\nLet me now also think about DictService. I need to replace:\n```java\nprivate static final Map TYPE_MAPPING = ...\nprivate static final Map TYPE_NAME_MAPPING = ...\n```\n\nWith DictService:\n```java\nprivate final DictService dictService;\n\n// In recognize():\n// 查询 inbound_type 字典\nList inboundTypes = dictService.listData(\"inbound_type\");\nMap typeNameMap = inboundTypes.stream()\n .collect(Collectors.toMap(DictData::getDictValue, DictData::getDictLabel));\n```\n\nBut wait, the AI recognition currently just returns typeValue and typeName. The typeValue comes from the AI's classification. The `TYPE_MAPPING` is just `{\"purchase_inbound\": 10, \"sales_outbound\": 10}` - which seems wrong (both point to 10?). Let me check...\n\nActually looking at the current code:\n```java\nprivate static final Map TYPE_MAPPING = Map.of(\n \"purchase_inbound\", 10,\n \"sales_outbound\", 10\n);\n```\n\nBoth map to 10?? Then in `toTypeName`:\n```java\nprivate String toTypeName(Integer typeValue) {\n if (typeValue == null) return null;\n if (typeValue == 10) return \"采购入库\";\n if (typeValue == 20) return \"退货入库\";\n if (typeValue == 30) return \"其他入库\";\n return null;\n}\n```\n\nThis always returns \"采购入库\" regardless of whether it's purchase_inbound or sales_outbound! That's a bug. The AI should differentiate.\n\nOK so the proper fix would be:\n- `purchase_inbound` → inbound_type = 10 → \"采购入库\"\n- `sales_outbound` → outbound_type = 10 → \"销售出库\"\n\nBut the current code seems incomplete. Let me check the AI prompt again:\n\n```\n\"如果是采购入库单/到货单，格式如下...\"\n\"如果是销售出库单，格式如下...\"\n```\n\nAnd the TYPE_MAPPING:\n```java\nMap.of(\"purchase_inbound\", 10, \"sales_outbound\", 10)\n```\n\nBoth point to value 10? That can't be right for the typeValue. Maybe the intent is that both inbound and outbound use value 10 for their first subtype. But then the typeName mapping only returns inbound names...\n\nThis looks like a bug/incomplete implementation. The user wants me to use DictService, so let me fix it properly:\n\nFor purchase_inbound:\n- typeValue = 10 (采购入库)\n- Use dict `inbound_type` to get label\n\nFor sales_outbound:\n- typeValue = 10 (销售出库) \n- Use dict `outbound_type` to get label\n\nBut wait, the DictService approach means I need to know the dict type and lookup the label. Let me simplify:\n\nFor `purchase_inbound`: dict type = `inbound_type`, dict value = `10`\nFor `sales_outbound`: dict type = `outbound_type`, dict value = `10`\n\nActually, I think the better approach is:\n1. Keep the TYPE_MAPPING from AI string → (dictType, dictValue)\n2. Use DictService to look up the label from dictType + dictValue\n\nLet me refactor:\n\n```java\nprivate static final Map TYPE_MAPPING = Map.of(\n \"purchase_inbound\", new BillTypeInfo(\"inbound_type\", \"10\"),\n \"sales_outbound\", new BillTypeInfo(\"outbound_type\", \"10\")\n);\n\n@Value\nprivate static class BillTypeInfo {\n String dictType;\n String dictValue;\n}\n```\n\nThen in the recognize method:\n```java\nBillTypeInfo typeInfo = TYPE_MAPPING.get(type);\nif (typeInfo != null) {\n builder.typeValue(Integer.valueOf(typeInfo.getDictValue()));\n // 从字典查询类型名称\n DictData dictData = dictService.listData(typeInfo.getDictType()).stream()\n .filter(d -> d.getDictValue().equals(typeInfo.getDictValue()))\n .findFirst().orElse(null);\n builder.typeName(dictData != null ? dictData.getDictLabel() : typeInfo.getDictValue());\n}\n```\n\nHmm, but DictService already returns a list, and I'd need to find the matching value. Actually, DictData already has DictType.Data, dictValue, dictLabel fields. Let me check...\n\nLooking at DictServiceImpl:\n```java\nList listData(String dictType);\n```\n\nI can just use this.\n\nNow let me think about the full plan and start coding.\n\nLet me also check if DictData has getDictValue, getDictLabel methods.\n\nLet me check the DictData entity:\nActually, I saw earlier that MaterialFillServiceImpl already uses `DictDataMapper` directly. It doesn't use DictService. The DictService is the interface used by controllers.\n\nI think using DictService directly in AiRecognitionServiceImpl is fine since it's a service layer.\n\nLet me plan the files I need to create/modify:\n\n**Create:**\n1. `material/dto/MaterialMatchResult.java`\n2. `material/enums/MatchPriority.java`\n\n**Modify:**\n1. `MaterialService.java` - add `searchSimilar()`\n2. `MaterialServiceImpl.java` - implement `searchSimilar()`\n3. `AiRecognitionResultDTO.java` - add `DictService`, refactor\n4. `AiRecognitionServiceImpl.java` - major refactor\n5. `AiControllerTest.java` - update if needed\n\nActually wait, let me check if the `AiRecognitionServiceImpl.java` needs DictService. Yes it does for type naming.\n\nLet me also think about the package structure. `MatchPriority` and `MaterialMatchResult` - where to put them?\n\n`MatchPriority` is an enum specific to material search. Put it in `com.witsoft.erp.module.md.material.enums`.\n`MaterialMatchResult` is a DTO. Put it in `com.witsoft.erp.module.md.material.dto`.\n\nLet me check if there's an enums package already:\nActually, I'll just create the files and see.\n\nLet me start coding now. I'll do it step by step.\n\nFirst, let me check the directory structure:\n- `com.witsoft.erp.module.md.material.enums` - probably doesn't exist yet\n- `com.witsoft.erp.module.md.material.dto` - exists (MaterialInfoDTO, MaterialPageDTO, etc.)\n\nLet me code each file now.\n\nFor the `searchSimilar` method, since `MaterialServiceImpl` extends `ServiceImpl`, I can use `baseMapper` directly. But I need to be careful - the status filter should match existing convention.\n\nActually, looking at the existing code, `searchByNameLike` doesn't filter by status:\n```java\npublic List searchByNameLike(String keyword) {\n ...\n wrapper.and(w -> w\n .apply(\"material_code LIKE {0}\", \"%\" + escaped + \"%\")\n .or()\n .apply(\"material_name LIKE {0}\", \"%\" + escaped + \"%\")\n .or()\n .apply(\"spec LIKE {0}\", \"%\" + escaped + \"%\")\n );\n wrapper.last(\"LIMIT 20\");\n return list(wrapper);\n}\n```\n\nFor `searchSimilar`, I'll add a status filter for valid materials only (status = 0).\n\nOK let me start implementing. I'll create the files and make the modifications.\n\nAlso, for the test - the user wants to use the local library for testing. I need to check if the test has `@SpringBootTest` which should use the application context. As long as the test runs against the local database config, it should work.\n\nLet me check the application-local config to ensure test will connect to local:\nActually I shouldn't touch config files. The test uses `@SpringBootTest` which loads the application context. As long as the active profile is set correctly, it should use local.\n\nLet me just code it now.\n\nLet me also re-examine: The user said \"使用local 库测试\". Maybe they mean I should run the test to verify? But I can't really run tests without a database connection. Let me focus on the code changes and tell the user to test.\n\nActually, let me check - can I run tests?\n\nLet me first set up the plan, then code everything.\n\nPlan:\n1. Create `MatchPriority` enum\n2. Create `MaterialMatchResult` DTO\n3. Add `searchSimilar` to `MaterialService` + `MaterialServiceImpl`\n4. Refactor `AiRecognitionServiceImpl.java`:\n a. Inject DictService\n b. Use DictService for type name lookup\n c. Use searchSimilar for material matching\n d. Batch fill materials using MaterialFillService.getMaterialInfoMap()\n5. 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{"type":"message","id":"6badc248","parentId":"9bc9ddad","timestamp":"2026-05-29T01:40:19.720Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"The user wants me to:\n\n1. Return supplier/customer names alongside their IDs (for front-end form filling)\n2. Use `MaterialFillService.getMaterialInfoMap()` to fill material info (name, code, spec, unit)\n3. Use data dictionary for the bill type (inbound_type/outbound_type)\n4. Use local database for testing (`test_png.png` which is a test image for AI recognition)\n\nLet me look at the current AiRecognitionServiceImpl in more detail to understand exactly what needs to change.\n\nThe current `AiRecognitionResultDTO` structure:\n- `Counterparty` already has `id`, `name`, `recognizedName`, `exactMatch` ✓\n- `RecognitionItem` already has `materialId`, `materialName`, `materialCode`, `spec`, `unit` ✓\n- Bill type - currently hardcoded to typeValue=10 for both purchase_inbound and sales_outbound, using simple map. Should use DictService instead.\n\nWait, the user says:\n- \"返回前端id无法填充页面\" - returning just id doesn't let the front-end fill the form. They need names too.\n- \"客户供应商需要查询名称\" - customer/supplier need name queries (already done)\n- \"物料使用MaterialFillService.getMaterialInfoMap填充\" - already using it per-item, but not using the batch method\n- \"单据类型使用数据字典中类型\" - use dict system\n- \"使用local库测试\" - use local database for testing\n\nLet me re-read the AiRecognitionServiceImpl:\n\n1. **Type mapping**: Currently hardcoded in `TYPE_MAPPING` and `TYPE_NAME_MAPPING`. The `toTypeName` method doesn't even properly map `sales_outbound`. Should use DictService `listData(\"inbound_type\")` or `listData(\"outbound_type\")`.\n\n2. **Match material**: Currently finds best match then calls `materialFillService.getMaterialInfo(best.getId())` individually. Could use batch but it's fine.\n\n3. **Match counterparty**: Returns `name` already from the database match result. But when no match, it returns `name=recognizedName` without id. That's correct.\n\nSo the main issues to fix:\n1. **Type mapping**: Use DictService instead of hardcoded mapping\n2. **Material matching**: Improve to use `searchSimilar()` approach from the design doc\n3. **Testing**: Make tests work with local DB (test_png.png)\n\nLet me look at the type mapping more carefully. The current code:\n```java\nprivate static final Map TYPE_MAPPING = Map.of(\n \"purchase_inbound\", 10,\n \"sales_outbound\", 10\n);\n```\n\nThis maps both to 10, which is wrong. `purchase_inbound` should map to inbound_type=10 (采购入库), and `sales_outbound` should map to outbound_type=10 (销售出库). But the DTO only has `typeValue` without distinguishing whether it's inbound_type or outbound_type.\n\nLooking at the DTO and controller, `typeValue` and `typeName` are returned. But 10 could be both inbound_type and outbound_type. I think we need a `billType` field or similar to differentiate.\n\nActually wait, looking at the test, it checks `data.getTypeValue()` without checking what dict type it is. But this seems wrong. Let me look more carefully...\n\nActually, the problem is more nuanced. When the front-end gets typeValue=10, it doesn't know whether that's an inbound or outbound type. We need a way to distinguish.\n\nBut the user said to use DictService. Let me check what dict data is available.\n\nFrom 99-data.sql:\n- `inbound_type`: 10-采购入库, 20-退货入库, 30-其他入库\n- `outbound_type`: 10-销售出库, 20-领料出库, 30-其他出库\n\nSo we need to return both the dict type and the value. The current DTO has `typeValue` and `typeName`. I think we should add a `dictType` or `billType` field.\n\nLet me now also look at what the MaterialSelectorReqDTO / matchMaterialItem does more carefully. Actually, I think the main thing the user wants is this interaction optimization:\n\nCurrent problem: After AI recognition, the result is returned to the frontend with IDs, but the front-end can't use just IDs to populate the form fields because the form needs names, codes, specs, etc.\n\nLet me trace through the current flow:\n1. AI recognizes the image, gets text\n2. Text is parsed into material names, specs, quantities, prices\n3. For each material name, fuzzy match against the database\n4. Return `materialId`, `materialName`, `materialCode`, `spec`, `unit`, `quantity`, `price`\n\nThe `materialName` and `spec` returned are from the database (via MaterialInfoDTO), so they're already correct for populating the front-end form. So what's the issue?\n\nWait, let me re-read more carefully. The user said:\n- \"返回前端id无法填充页面\" - returning just IDs can't fill the page\n\nMaybe the issue is in another part of the system? Not specifically in AI recognition? Or maybe the issue is that the current implementation doesn't properly use MaterialFillService's batch method (getMaterialInfoMap) but instead calls getMaterialInfo one-by-one per item.\n\nActually, looking at the AiRecognitionServiceImpl.matchMaterialItem more carefully, it does:\n```java\nMaterialInfoDTO info = materialFillService.getMaterialInfo(best.getId());\n```\n\nThis calls the single-item version. For multiple items, this results in N+1 queries. We should batch all matched materials together. But that's a performance optimization, not a correctness issue.\n\nLet me re-read user's requirements again:\n1. ✅ Customer/supplier names are already returned as `name` field \n2. ❓ MaterialFillService - currently using per-item call, should use batch\n3. ❓ Bill type using dict - currently hardcoded\n4. ✅ Local DB test\n\nHmm, maybe the user wants me to:\n1. Refactor the AI recognition to properly use DictService for type transitions\n2. Use MaterialFillService batch approach\n3. Make the test work against local DB\n\nLet me also look at what the MaterialSelectorReqDTO interaction is. The user mentioned returning id not filling the page. Maybe the problem is in a different flow, like the material selector dialog - the user selects from available materials but it doesn't populate the form properly.\n\nActually wait - I need to re-read the user's message more carefully. They said:\n\n### 交互优化\n- 返回前端id无法填充页面\n- 客户供应商需要查询名称\n- 物料使用MaterialFillService.getMaterialInfoMap填充\n- 单据类型使用数据字典中类型\n- 使用local库测试\n- [02-物料近似查询方案.md](yangxuan/设计/02-物料近似查询方案.md)\n\nThese are **requirements**. They want me to:\n1. The current implementation returns IDs, but the front-end needs names to fill the page\n2. Customer/supplier need name lookups\n3. Material uses MaterialFillService.getMaterialInfoMap for filling\n4. Bill type uses data dictionary\n5. Test against local DB\n\nWait, I think there might be an issue with the CurrentCounterparty matching - when it returns `recognizedName` as the name but without a proper id, the front-end might not be able to fill the form. Let me re-check...\n\nActually looking at the matchCounterparty method:\n- `name` = database name when matched, `recognizedName` = AI recognized name, `id` = database id\n- When no match: `name` = recognizedName, `id` = null\n\nSo the front-end gets the supplier/customer name. That seems fine.\n\nBut the user says \"返回前端id无法填充页面\" - returning just IDs can't fill the page. Maybe the issue is that some parts of the system return just IDs without names.\n\nHmm, I think I might be overthinking this. Let me just look at the current state and fix the obvious issues:\n\n1. **Bill type**: Replace hardcoded type mapping with DictService\n2. **Material matching**: Use batch MaterialFillService where possible\n3. **Test**: Make it work against local DB\n\nActually, I realize I should look at the test carefully. The test file `AiControllerTest.java` uses `test_png.png` from classpath. Let me check if that file exists.\n\nActually, let me look at what the AiRecognitionServiceImpl does wrong vs what the front-end expects. Let me just look at the full service first since I only read part of it.\n\nLet me read the full AiRecognitionServiceImpl to understand the complete flow.","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_641e69f491cc4343890a720e","name":"read","arguments":{"path":"/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/ai/service/impl/AiRecognitionServiceImpl.java"},"partialArgs":"{\"path\": \"/home/yangxuan/Projects/IdeaProjects/tiny-erp/src/main/java/com/witsoft/erp/module/ai/service/impl/AiRecognitionServiceImpl.java\"}"}],"api":"openai-completions","provider":"new-api","model":"deepseek-v4-flash","usage":{"input":551,"output":1963,"cacheRead":52224,"cacheWrite":0,"reasoningTokens":1882,"totalTokens":54738,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1780018788674,"responseId":"chatcmpl-1b6dc390-f281-45ae-af01-6197c823d049"}}
{"type":"message","id":"956f2d62","parentId":"6badc248","timestamp":"2026-05-29T01:40:20.207Z","message":{"role":"toolResult","toolCallId":"call_641e69f491cc4343890a720e","toolName":"read","content":[{"type":"text","text":"package com.witsoft.erp.module.ai.service.impl;\n\nimport com.witsoft.erp.common.core.BusinessException;\nimport com.witsoft.erp.module.ai.dto.*;\nimport com.witsoft.erp.module.ai.service.AiRecognitionService;\nimport com.witsoft.erp.module.ai.service.AiService;\nimport com.witsoft.erp.module.md.customer.entity.Customer;\nimport com.witsoft.erp.module.md.customer.service.CustomerService;\nimport com.witsoft.erp.module.md.material.dto.MaterialInfoDTO;\nimport com.witsoft.erp.module.md.material.entity.Material;\nimport com.witsoft.erp.module.md.material.service.MaterialFillService;\nimport com.witsoft.erp.module.md.material.service.MaterialService;\nimport com.witsoft.erp.module.md.supplier.entity.Supplier;\nimport com.witsoft.erp.module.md.supplier.service.SupplierService;\nimport lombok.RequiredArgsConstructor;\nimport lombok.extern.slf4j.Slf4j;\nimport org.springframework.stereotype.Service;\nimport org.springframework.web.multipart.MultipartFile;\n\nimport java.math.BigDecimal;\nimport java.util.*;\nimport java.util.stream.Collectors;\n\n/**\n * AI 单据识别服务实现\n *\n * 流程：\n * 1. 调用 AI 识别单据图片\n * 2. 解析 AI 返回的 JSON\n * 3. 单据类型：purchase_inbound → typeValue=10, typeName=采购入库\n * 4. 模糊匹配供应商/客户（去空格 + 分词片段 + 降级匹配）\n * 5. 模糊匹配物料（去空格 + 分词 + MaterialFillService 填充）\n * 6. 返回前端可用的 id + name\n */\n@Slf4j\n@Service\n@RequiredArgsConstructor\npublic class AiRecognitionServiceImpl implements AiRecognitionService {\n\n private final AiService aiService;\n private final SupplierService supplierService;\n private final CustomerService customerService;\n private final MaterialService materialService;\n private final MaterialFillService materialFillService;\n\n private static final String RECOGNITION_PROMPT =\n \"请识别图片中的单据内容，严格按照 JSON 格式返回，不要包含 Markdown 代码块标记，不要有其他文字。\\n\" +\n \"\\n\" +\n \"如果是采购入库单/到货单，格式如下：\\n\" +\n \"{\\n\" +\n \" \\\"type\\\": \\\"purchase_inbound\\\",\\n\" +\n \" \\\"supplier_name\\\": \\\"供应商名称\\\",\\n\" +\n \" \\\"warehouse_name\\\": \\\"仓库名称（可选）\\\",\\n\" +\n \" \\\"date\\\": \\\"日期\\\",\\n\" +\n \" \\\"total_amount\\\": 总金额,\\n\" +\n \" \\\"remark\\\": \\\"备注\\\",\\n\" +\n \" \\\"items\\\": [\\n\" +\n \" {\\n\" +\n \" \\\"material_name\\\": \\\"物料名称\\\",\\n\" +\n \" \\\"spec\\\": \\\"规格（可选）\\\",\\n\" +\n \" \\\"quantity\\\": 数量,\\n\" +\n \" \\\"unit\\\": \\\"单位（可选）\\\",\\n\" +\n \" \\\"price\\\": 单价\\n\" +\n \" }\\n\" +\n \" ]\\n\" +\n \"}\\n\" +\n \"\\n\" +\n \"如果是销售出库单，格式如下：\\n\" +\n \"{\\n\" +\n \" \\\"type\\\": \\\"sales_outbound\\\",\\n\" +\n \" \\\"customer_name\\\": \\\"客户名称\\\",\\n\" +\n \" \\\"warehouse_name\\\": \\\"仓库名称（可选）\\\",\\n\" +\n \" \\\"date\\\": \\\"日期\\\",\\n\" +\n \" \\\"total_amount\\\": 总金额,\\n\" +\n \" \\\"remark\\\": \\\"备注\\\",\\n\" +\n \" \\\"items\\\": [\\n\" +\n \" {\\n\" +\n \" \\\"material_name\\\": \\\"物料名称\\\",\\n\" +\n \" \\\"spec\\\": \\\"规格（可选）\\\",\\n\" +\n \" \\\"quantity\\\": 数量,\\n\" +\n \" \\\"unit\\\": \\\"单位（可选）\\\",\\n\" +\n \" \\\"price\\\": 单价\\n\" +\n \" }\\n\" +\n \" ]\\n\" +\n \"}\";\n\n @Override\n public AiRecognitionResultDTO recognize(MultipartFile file) {\n // 1. 调用 AI 识别图片\n AiChatResponseDTO aiResponse = aiService.chatWithImages(RECOGNITION_PROMPT, Collections.singletonList(file));\n String rawReply = aiResponse.getReply();\n log.info(\"AI 识别原始回复：{}\", rawReply);\n\n // 2. 提取 JSON\n String json = extractJson(rawReply);\n if (json == null) {\n throw new BusinessException(500, \"AI 回复格式异常，无法解析为 JSON\");\n }\n\n // 3. 解析 JSON\n Map parsed = parseSimpleJson(json);\n String type = (String) parsed.get(\"type\");\n if (type == null) {\n throw new BusinessException(400, \"AI 未能识别单据类型\");\n }\n\n // 4. 转换单据类型 → 字典值\n Integer typeValue = toTypeValue(type);\n String typeName = toTypeName(typeValue);\n\n // 5. 构建返回结果\n AiRecognitionResultDTO.AiRecognitionResultDTOBuilder builder = AiRecognitionResultDTO.builder()\n .typeValue(typeValue)\n .typeName(typeName)\n .warehouseName((String) parsed.get(\"warehouse_name\"))\n .remark((String) parsed.get(\"remark\"));\n\n // 6. 模糊匹配供应商或客户（去空格 + 分词片段匹配）\n if (\"purchase_inbound\".equals(type)) {\n String supplierName = (String) parsed.get(\"supplier_name\");\n if (supplierName != null) {\n builder.counterparty(matchSupplier(supplierName));\n }\n } else if (\"sales_outbound\".equals(type)) {\n String customerName = (String) parsed.get(\"customer_name\");\n if (customerName != null) {\n builder.counterparty(matchCustomer(customerName));\n }\n }\n\n // 7. 模糊匹配物料明细（去空格 + 分词 + MaterialFillService 填充）\n @SuppressWarnings(\"unchecked\")\n List> rawItems = (List>) parsed.get(\"items\");\n if (rawItems != null && !rawItems.isEmpty()) {\n List items = rawItems.stream()\n .map(this::matchMaterialItem)\n .filter(Objects::nonNull)\n .collect(Collectors.toList());\n builder.items(items);\n }\n\n return builder.build();\n }\n\n // ========== 单据类型转换 ==========\n\n private static final Map TYPE_MAPPING = Map.of(\n \"purchase_inbound\", 10,\n \"sales_outbound\", 10\n );\n\n private static final Map TYPE_NAME_MAPPING = Map.of(\n 10, \"采购入库\",\n 20, \"退货入库\",\n 30, \"其他入库\"\n );\n\n private Integer toTypeValue(String type) {\n return TYPE_MAPPING.getOrDefault(type, 10);\n }\n\n private String toTypeName(Integer typeValue) {\n if (typeValue == null) return null;\n // 入库类型\n if (typeValue == 10) return \"采购入库\";\n if (typeValue == 20) return \"退货入库\";\n if (typeValue == 30) return \"其他入库\";\n return null;\n }\n\n // ========== 模糊匹配供应商/客户（去空格 + 分词） ==========\n\n private AiRecognitionResultDTO.Counterparty matchSupplier(String recognizedName) {\n return matchCounterparty(recognizedName,\n keyword -> supplierService.searchByNameLike(keyword),\n s -> AiRecognitionResultDTO.Counterparty.builder()\n .id(s.getId()).name(s.getName()));\n }\n\n private AiRecognitionResultDTO.Counterparty matchCustomer(String recognizedName) {\n return matchCounterparty(recognizedName,\n keyword -> customerService.searchByNameLike(keyword),\n c -> AiRecognitionResultDTO.Counterparty.builder()\n .id(c.getId()).name(c.getName()));\n }\n\n /**\n * 通用模糊匹配：\n * 1. 直接 LIKE 搜索（AI 识别的名称与原版去空格后 LIKE）\n * 2. 如果无结果，分词后每个片段单独 LIKE\n * 3. 如果仍无结果，返回原始名称（exactMatch=false）\n */\n private AiRecognitionResultDTO.Counterparty matchCounterparty(\n String recognizedName,\n FuzzySearch searcher,\n java.util.function.Function mapper) {\n\n // 归一化：去空格\n String normalized = normalizeName(recognizedName);\n log.debug(\"供应商/客户模糊匹配：原始='{}', 归一='{}'\", recognizedName, normalized);\n\n // 1. 直接 LIKE 搜索（使用归一化后的名称）\n List candidates = searcher.search(normalized);\n if (!candidates.isEmpty()) {\n T best = candidates.get(0);\n return mapper.apply(best)\n .recognizedName(recognizedName)\n .exactMatch(true)\n .build();\n }\n\n // 2. 分词后每个片段单独搜索\n List tokens = tokenize(normalized);\n if (tokens.size() >= 2) {\n // 用最长分段词搜索\n String longestToken = tokens.stream()\n .max(Comparator.comparingInt(String::length))\n .orElse(\"\");\n if (longestToken.length() >= 2) {\n candidates = searcher.search(longestToken);\n if (!candidates.isEmpty()) {\n T best = candidates.get(0);\n return mapper.apply(best)\n .recognizedName(recognizedName)\n .exactMatch(false)\n .build();\n }\n }\n\n // 两个词组合搜索\n for (int i = 0; i < tokens.size() && candidates.isEmpty(); i++) {\n for (int j = i + 1; j < tokens.size(); j++) {\n String combined = tokens.get(i) + tokens.get(j).replace(tokens.get(i), \"\");\n if (combined.length() >= 2) {\n candidates = searcher.search(combined);\n if (!candidates.isEmpty()) break;\n }\n }\n }\n if (!candidates.isEmpty()) {\n T best = candidates.get(0);\n return mapper.apply(best)\n .recognizedName(recognizedName)\n .exactMatch(false)\n .build();\n }\n }\n\n // 3. 完全无法匹配，返回识别到的原始名称\n return AiRecognitionResultDTO.Counterparty.builder()\n .name(recognizedName)\n .recognizedName(recognizedName)\n .exactMatch(false)\n .build();\n }\n\n @FunctionalInterface\n private interface FuzzySearch {\n List search(String keyword);\n }\n\n // ========== 模糊匹配物料（去空格 + 分词 + MaterialFillService 填充） ==========\n\n private AiRecognitionResultDTO.RecognitionItem matchMaterialItem(Map