Merge branch 'developing' into bot_manager

# Conflicts:
#	.features/skill/MEMORY.md
#	poetry.lock
#	requirements.txt

.features/skill/MEMORY.md

@@ -1,15 +1,13 @@
 # Skill 功能
 
 > 负责范围：技能包管理服务 - 核心实现
-
-
-> 最后更新：2026-05-20
+> 最后更新：2026-05-26
 
 ## 当前状态
 
 Skill 系统支持两种来源：官方 skills (`./skills/`) 和用户 skills (`projects/uploads/{bot_id}/skills/`)。支持 Hook 系统和 MCP 服务器配置，通过 SKILL.md 或 plugin.json 定义元数据。
 
-目前已新增一批**纯 `SKILL.md` 型业务 skill MVP**，用于研究、摘要、报告和情报编排，底层文件处理与外部检索能力继续复用既有 skill。
+2026-04 起 skill 包可在 `agents/*.md` 下定义子 agent（由 `SubAgentMiddleware` 加载）；启用 Daytona 沙箱时 skill 加载路径变为沙箱内的 `/workspace/skills`。
 
 MCP UI 类 skill 已按 MCP Apps 模式改造：工具返回数据，静态 HTML App 由 host 加载后通过 postMessage 接收数据渲染。
 
@@ -24,10 +22,29 @@ MCP UI 类 skill 已按 MCP Apps 模式改造：工具返回数据，静态 HTML
 - `routes/mcp_resources.py` - MCP App 静态 HTML resource REST 入口
 - `skills/` - 官方 skills 目录
 - `skills_developing/` - 开发中 skills
+- `agent/subagent_loader.py` - 扫描 skill `agents/*.md` 加载子 agent（2026-05 引入）
+- `agent/mcp_trace_meta.py` - 对 `ClientSession.call_tool` 做 monkey-patch，向 `rag_retrieve` / `table_rag_retrieve` 的 MCP `_meta` 注入 `trace_id`（2026-05 引入）
 
 ## 最近重要事项
 
+- [2026-05-26](changelog/2026-Q2.md): skill 引入 `category` 字段——`routes/skill_manager.py` 在 `SkillItem` / `SkillValidationResult` 增加 `category`，从 `plugin.json` 与 `SKILL.md` frontmatter 解析，official skill 默认 `"other"`、user skill 默认 `"custom"`；并通过 batch 给 common/developing/onprem/support 路径下大量 skill 元数据补 `category`，`data-dashboard` / `mcp-ui` 归类 `Interactive UI`（`203dcf4`, `3ada55a`, `9658588`）
+- [2026-05-26](changelog/2026-Q2.md): developing 分支大合并新增多个 skill：`ai-ppt-generator`（百度 AI PPT）、`nfc-medicine-lookup`（NFC 药品检索）、`ppt-outline`（PPT 大纲 / HTML 演示文稿）、`z-card-image`（配图 / 卡片图），同时 `skills/linggan/*` 系列 skill 经合并回归（`3ada55a`）
+- [2026-05-23](changelog/2026-Q2.md): 新增 MCP App 型 `skills/developing/ecommerce-storefront/`——含 `product-list` / `order-confirm` 两个 HTML App + 自带 `ecommerce_server.py` MCP server；同时落地 `docs/mcp-app-training.md`（约 1063 行）作为 MCP App 培训材料（`9d001c8`）
+- [2026-05-21](changelog/2026-Q2.md): Daytona 沙箱模式下 `init_agent` 在沙箱内写入 `BASH_ENV` 文件，注入 `ASSISTANT_ID` / `USER_IDENTIFIER` / `TRACE_ID` / `ENABLE_SELF_KNOWLEDGE` 与 `config.shell_env` 的 shell 环境变量（`776acc2`）
+- [2026-05-12](changelog/2026-Q2.md): 跨 6→10 个 skill 变体批量精修 `retrieval-policy*.md`，统一 onprem/support/autoload 各路径下的 policy 口径（`be96f24`, `7b4f03d`）
+- [2026-05-11](changelog/2026-Q2.md): 新增子 agent (SubAgent) 支持——skill 包通过 `agents/*.md` 暴露子 agent，由 `SubAgentMiddleware` 加载；附 `pmda-drug-info` skill 示例（`5b634bc`）
+- [2026-05-11](changelog/2026-Q2.md): `pmda-drug-info` 的 `pmda_server.py` 大改为 mock 实现（`a92096a`）
+- [2026-05-11](changelog/2026-Q2.md): `retrieval-policy.md` 跨 4 个 skill 变体内容同步更新（`e6d1698`）
+- [2026-05-08](changelog/2026-Q2.md): 通过 monkey-patch `ClientSession.call_tool`，把 trace_id 透传到 `rag_retrieve` / `table_rag_retrieve` 的 MCP `_meta`（`1f06450`）
+- [2026-05-06](changelog/2026-Q2.md): support 分支新增 `kfs-answer` skill（`a9227b8`）
+- [2026-05-06](changelog/2026-Q2.md): 修复 Daytona 沙箱增量同步漏掉符号链接的问题——`find -type f` 不覆盖 symlink、`tar.add` 默认不 dereference 导致悬空软链；并统一 dataset 路径为复数 `datasets/`（`3c0fa49`）
+- [2026-04-24](changelog/2026-Q2.md): 非流式响应路径上 `_execute_post_agent_hooks` 改为 `asyncio.create_task` 非阻塞执行；同时临时注释停用 `ToolOutputLengthMiddleware`（`45a9494`）
+- [2026-04-23](changelog/2026-Q2.md): PrePrompt hook 内容改为通过 `{hook_content}` 占位符注入系统提示词模板，不再在 prompt 末尾追加（`51fbf01`）
+- [2026-04-23](changelog/2026-Q2.md): Daytona 沙箱接入——`init_agent` 并行加载 + 返回元组增加 `sandbox` 字段；`skills_sources` 在沙箱模式下变为 `/workspace/skills`，`agent_dir_path` 变为 `/workspace`（`c9e0789`, `8446dab`）
+- [2026-04-22](changelog/2026-Q2.md): `skills/developing/` 下新增 `rag-retrieve-no-citation` 与 `novare-context` 两个开发中 skill（`7a30e52`）
+
 - 2026-05-20: `mcp-ui` 和 `data-dashboard` 改为 MCP Apps 标准模式，App HTML 放在 skill 的 `apps/` 目录，由 host 加载后 postMessage 数据
+
 - 2026-04-20: 为 `rag-retrieve` 新增 `retrieval-policy-forbidden-self-knowledge.md`，禁止知识问答场景使用模型自身知识补全答案，要求严格基于检索证据作答
 - 2026-04-19: 环境变量 `SKILLS_SUBDIR` 重命名为 `PROJECT_NAME`，用于选择 `skills/{PROJECT_NAME}` 和 `skills/autoload/{PROJECT_NAME}` 目录
 - 2026-04-19: `create_robot_project` 的 autoload 去重和 stale 清理补强，autoload 目录也纳入 managed 清理，避免 `rag-retrieve-only` 场景下旧的 `rag-retrieve` 残留
@@ -59,6 +76,16 @@ MCP UI 类 skill 已按 MCP Apps 模式改造：工具返回数据，静态 HTML
 - ⚠️ 上传大小限制：50MB（ZIP），解压后最大 500MB
 - ⚠️ 压缩比例检查：最大 100:1（防止 zip 炸弹）
 - ⚠️ 符号链接检查：禁止解压包含符号链接的文件
+- ⚠️ **子 agent 同名静默 last-wins**：`subagent_loader._parse_agent_md` 跨 skill 扫描 `agents/*.md` 时，按 `name` 字段去重，**后扫描到的覆盖先扫描的**，只打 warning 不报错。多 skill 都暴露子 agent 时需自觉错开命名。
+- ⚠️ **`SubAgentMiddleware` 中间件顺序**：必须插在 `CustomFilesystemMiddleware` 之后、`AnthropicPromptCachingMiddleware` 之前——这是匹配 `deepagents.create_deep_agent` 的官方顺序，调整 `create_custom_cli_agent` 中的中间件顺序时不能随意挪动这一段。
+- ⚠️ **Daytona 模式下 skill 路径不同**：`DAYTONA_ENABLED=true` 时 `enable_skills` 的 `skills_sources` 是 `/workspace/skills`（沙箱内），同时 system prompt 的 `agent_dir_path` 是 `/workspace`；写死本地路径的 hook / 脚本需要兼容两种环境。
+- ⚠️ **PostAgent hook 非流式分支已 fire-and-forget**：`routes/chat.py` 用 `asyncio.create_task` 启动 hook，调用方不会等待也不会感知到 hook 的异常——hook 失败只会被自己的 logger 捕获。
+- ⚠️ **MCP `_meta.trace_id` 是全局 monkey-patch 注入**：`agent/mcp_trace_meta.patch_mcp_client_session_trace_meta()` 在 `get_tools_from_mcp()` 入口调用一次后，会把 `mcp.ClientSession.call_tool` 永久包装；仅对工具名在 `{"rag_retrieve", "table_rag_retrieve"}` 集合内的调用注入 `_meta.trace_id`，扩展白名单要直接改 `_TRACE_META_TOOL_NAMES` 常量。
+- ⚠️ **PrePrompt hook 内容位置由模板决定**：自 2026-04-23 起 hook 产出通过 `{hook_content}` 占位符注入 `prompt/system_prompt.md`，不再追加在 prompt 末尾；自定义模板必须包含 `{hook_content}` 占位符否则 hook 内容会丢失。
+- ⚠️ **`init_agent` 返回值已变 3 元素**：Daytona 改造后 `init_agent` 返回 `(agent, checkpointer, sandbox)`；调用方解构必须更新。
+- ⚠️ **skill `category` 默认值**：API 返回的 `SkillItem.category`——official skill fallback 为 `"other"`、user skill fallback 为 `"custom"`；前端做分类视图时需要同时识别这两个 sentinel，不要假设官方/用户 skill 用同一套缺省值。
+- ⚠️ **`category` 字段双入口**：同一 skill 可以同时在 `.claude-plugin/plugin.json` 和 `SKILL.md` frontmatter 写 `category`；`get_skill_metadata` 优先走 `parse_plugin_json`，若 skill 包没有 plugin.json 才回落到 `parse_skill_frontmatter`——两者写不一致时以 plugin.json 为准。
+- ⚠️ **Daytona shell_env 是文件注入而非 process env**：`init_agent` 通过 `cat > $REMOTE_BASH_ENV_PATH` 写入 `export VAR=...` 行，沙箱内必须由 shell（bash）的 `BASH_ENV` 加载才能生效；非 daytona 模式或不走 bash 启动的脚本拿不到这些变量。扩展注入项需直接改 `init_agent` 里的 `_shell_env` 字典。
 
 ## Skill 目录结构
 

.features/skill/changelog/2026-Q2.md

@@ -1,5 +1,392 @@
 # 2026-Q2 Skill Changelog
 
+按时间倒序记录本季度的重要变更。
+
+---
+
+## 2026-05-26: skill `category` 字段全面接入
+
+**类型**：新功能
+
+**背景**：skill 数量越来越多（common / developing / onprem / support / linggan / autoload 各路径下数十个），列表 API 需要前端能按类别分组展示，元数据层面缺少 `category` 字段。
+
+**改动**：
+- `routes/skill_manager.py`：
+  - `SkillItem` model 新增 `category: str = "other"`。
+  - `SkillValidationResult` dataclass 新增可选 `category: Optional[str]`。
+  - `parse_plugin_json` 解析 `plugin_config.get('category')`；`parse_skill_frontmatter` 解析 frontmatter 的 `metadata.get('category')`。
+  - `get_official_skills` 中 fallback 为 `"other"`；`get_user_skills` 中 fallback 为 `"custom"`。
+  - `get_skill_metadata_legacy` 在 `category` 非空时写入返回 dict（保持向后兼容）。
+- 批量给 common / developing / onprem / support 多个 skill 的 `.claude-plugin/plugin.json` 与 `SKILL.md` frontmatter 添加 `category` 字段。
+- `data-dashboard` 与 `mcp-ui` 的 `category` 从 `"Data & Retrieval"` 修正为 `"Interactive UI"`（更贴切 MCP App 的渲染语义）。
+
+**根因**：N/A（新功能）
+
+**影响**：
+- `GET /api/v1/skill/list` 返回项现在包含 `category` 字段；前端可按 category 维度做分组/筛选。
+- skill 元数据约定扩展——新 skill 应在 plugin.json 或 SKILL.md frontmatter 中写明 `category`，否则会落到 `"other"` / `"custom"` 兜底。
+- `plugin.json.category` 与 `SKILL.md.category` 同时存在时以前者为准（`get_skill_metadata` 优先 plugin.json）。
+
+**相关文件**：
+- `routes/skill_manager.py`
+- `skills/common/data-dashboard/.claude-plugin/plugin.json`
+- `skills/common/mcp-ui/.claude-plugin/plugin.json`
+- 以及一批 `skills/{common,developing,onprem,support}/*/SKILL.md` 与 `.claude-plugin/plugin.json`
+
+**Commit/PR**：`203dcf4`, `3ada55a`, `9658588`
+
+---
+
+## 2026-05-26: developing 分支批量新增多类 skill
+
+**类型**：新功能
+
+**背景**：[待补充]——经 developing→staging 合并集中落地一批新 skill 与 linggan 系列 skill 回归。
+
+**改动**：
+- 新增 `skills/developing/ai-ppt-generator/`：调用百度 AI 生成 PPT，按 topic 自动选模板（商务/科技/教育/创意/中国风等）；`category: Document Processing`。
+- 新增 `skills/developing/nfc-medicine-lookup/`：通过 NFC 芯片 ID 或药品名称查询药品信息，面向老年用户的语音助手交互口径；`category: Developer Tools`。
+- 新增 `skills/developing/ppt-outline/`：PPT 大纲与独立 HTML 演示文稿生成（dark/light/tech/minimal 四种风格）；`category: Document Processing`。
+- 新增 `skills/developing/z-card-image/`：生成配图、封面图、卡片图、社媒帖子分享图等；依赖 `python3` + `google-chrome`。
+- `skills/developing/static-hosting/SKILL.md` 由 1 行说明扩展为完整 80 行 skill；同时一批已有 SKILL.md / plugin.json 补 `category`。
+- `skills/linggan/*` 系列 skill（baidu-search / bot-self-modifier / caiyun-weather / competitor-news-intel / contract-document-generator / financial-report-generator / market-academic-insight / ragflow-loader / sales-decision-report / seedream / static-hosting / static-site-deploy / voice-notification / weather-china）经合并回归 staging。
+
+**根因**：N/A
+
+**影响**：
+- developing skill 池扩张约 5 个新业务 skill；linggan 系列重新出现在 staging。
+- 新 skill 多为 SKILL.md 型业务 skill，符合"workflow + 模板"的纯 markdown 模式；其中 `ai-ppt-generator`、`z-card-image` 依赖外部 `BAIDU_API_KEY` 或 `google-chrome` 二进制。
+
+**相关文件**：
+- `skills/developing/ai-ppt-generator/SKILL.md`
+- `skills/developing/nfc-medicine-lookup/SKILL.md`
+- `skills/developing/ppt-outline/SKILL.md`
+- `skills/developing/z-card-image/SKILL.md`
+- `skills/developing/static-hosting/SKILL.md`
+- `skills/linggan/**`（回归）
+
+**Commit/PR**：`3ada55a`
+
+---
+
+## 2026-05-23: 新增 ecommerce-storefront skill（MCP App 型）+ MCP App 培训文档
+
+**类型**：新功能
+
+**背景**：MCP App 模式（host 加载静态 HTML + postMessage 传数据）已经在 `mcp-ui`、`data-dashboard` 上跑通，需要一个面向电商场景的样例 skill，演示产品浏览 / 选购 / 下单确认这类多步交互的 App 渲染；同时沉淀一份 MCP App 开发指南。
+
+**改动**：
+- 新增 `skills/developing/ecommerce-storefront/`：
+  - `apps/product-list.html`（288 行）与 `apps/order-confirm.html`（233 行）两个静态 App。
+  - `ecommerce_server.py`（213 行）作为自带 MCP server，`ecommerce_tools.json` 定义工具 schema。
+  - `hooks/ecommerce_guide.md` + `hooks/pre_prompt.py` 注入 skill 使用指引到 system prompt。
+  - `mcp_common.py`（252 行）复用 MCP 通用工具基类。
+  - `.claude-plugin/plugin.json` 配置 PrePrompt hook 与 stdio MCP server，`category: Developer Tools`。
+- 新增 `docs/mcp-app-training.md`（约 1063 行）：MCP App 模式的开发培训材料。
+
+**根因**：N/A
+
+**影响**：
+- developing skill 池新增一个 MCP App 型 skill，体例对齐 `mcp-ui` / `data-dashboard`。
+- MCP App 开发者有完整培训材料可参考。
+
+**相关文件**：
+- `skills/developing/ecommerce-storefront/**`
+- `docs/mcp-app-training.md`
+
+**Commit/PR**：`9d001c8`
+
+---
+
+## 2026-05-21: Daytona 沙箱注入 shell_env 到 BASH_ENV
+
+**类型**：新功能
+
+**背景**：Daytona 沙箱内的 skill 脚本需要能读取 `ASSISTANT_ID` / `USER_IDENTIFIER` / `TRACE_ID` 等运行时上下文，但宿主 process env 无法直接透传到沙箱里。
+
+**改动**：
+- `agent/deep_assistant.py` `init_agent`：当 `sandbox is not None and sandbox_type == "daytona"` 时，组装 `_shell_env` 字典（`ASSISTANT_ID` / `USER_IDENTIFIER` / `TRACE_ID` / `ENABLE_SELF_KNOWLEDGE` 加上 `config.shell_env`），构造 `cd {REMOTE_WORKSPACE_ROOT}\n` + `export VAR="..."` 行，通过 `sandbox.execute("cat > $REMOTE_BASH_ENV_PATH << 'ENVEOF' ... ENVEOF")` 写入沙箱内。
+- `utils/daytona_sync.py` 提供常量 `REMOTE_BASH_ENV_PATH` / `REMOTE_WORKSPACE_ROOT`。
+- `AgentConfig` 增加 `shell_env: Optional[Dict[str, str]]`（调用方可追加自定义 env）。
+
+**根因**：N/A
+
+**影响**：
+- 沙箱内通过 bash 启动的 skill 脚本可以 `os.environ.get("ASSISTANT_ID")` 等读到运行时上下文。
+- 仅 daytona 沙箱模式生效；本地或非 bash 启动的进程不会收到 `BASH_ENV` 注入的变量。
+- 扩展注入项（新增固定环境变量）需要直接改 `init_agent` 里的 `_shell_env` 字典。
+
+**相关文件**：
+- `agent/deep_assistant.py`
+- `utils/daytona_sync.py`
+
+**Commit/PR**：`776acc2`
+
+---
+
+## 2026-05-12: 批量精修 retrieval policy 文案
+
+**类型**：内容调整
+
+**背景**：[待补充]
+
+**改动**：
+- `be96f24`: 跨 6 个 skill 变体调整 `retrieval-policy-forbidden-self-knowledge.md` 的措辞（onprem / support / autoload-onprem / autoload-onprem-rag-only / autoload-support-rag-only 路径下的版本及一份 `retrieval-policy.md`）。
+- `7b4f03d`: 在更广的 10 个文件范围内同步更新 `retrieval-policy.md` 与 `retrieval-policy-forbidden-self-knowledge.md` 两套 policy，使各 skill 变体的策略口径保持一致。
+
+**根因**：N/A（非 Bug）
+
+**影响**：所有使用 `rag-retrieve` / `rag-retrieve-only` 这两个 hook 的 skill 在策略行为上保持一致；同时影响 onprem 与 support 两个发布分支的部署。
+
+**相关文件**：
+- `skills/onprem/rag-retrieve/hooks/retrieval-policy*.md`
+- `skills/support/rag-retrieve/hooks/retrieval-policy*.md`
+- `skills/autoload/onprem/rag-retrieve/hooks/retrieval-policy*.md`
+- `skills/autoload/onprem/rag-retrieve-only/hooks/retrieval-policy*.md`
+- `skills/autoload/support/rag-retrieve-only/hooks/retrieval-policy*.md`
+
+**Commit/PR**：`be96f24`, `7b4f03d`
+
+---
+
+## 2026-05-11: 子 agent (SubAgent) 支持 + pmda-drug-info skill
+
+**类型**：新功能
+
+**背景**：需要让单个 skill 在主 agent 之外承载多个专用子 agent，按用途隔离上下文与工具集（如 pmda 药品信息场景下的 single-drug / interaction / adverse-event / patient-specific 四个专用 agent）。
+
+**改动**：
+- 新增 `agent/subagent_loader.py`：扫描 skill 目录下的 `agents/*.md`，按 YAML frontmatter 的 `name` / `description` / `tools` 字段解析为 `SubAgent` 字典；按 `name` 去重，**后扫描的覆盖先扫描的**（last-wins）。
+- `agent/deep_assistant.py`：`init_agent` 调用 `load_subagents()`，存在则将 `SubAgentMiddleware`（来自 `deepagents.middleware.subagents`）插在 `CustomFilesystemMiddleware` 之后、`AnthropicPromptCachingMiddleware` 之前，顺序匹配 `create_deep_agent`。
+- 新增 `skills/developing/pmda-drug-info/`：完整 skill 包，包含 `.claude-plugin/plugin.json`、`hooks/pre_prompt.py` + `hooks/pmda-instructions.md`、四个 `agents/*.md`、自带 `pmda_server.py` MCP server + `pmda_tools.json`、`mcp_common.py` 工具基础类。
+
+**根因**：N/A
+
+**影响**：
+- skill 包结构新增约定：`agents/*.md` 目录下的 markdown 文件会被加载为子 agent。
+- skill 加载流程在 `init_agent` 内增加一次目录扫描；对没有 `agents/` 的 skill 无影响。
+- skill 跨 bot 共享时存在 sub-agent 同名冲突的风险——同名 sub-agent 不会报错，而是被后扫描到的覆盖。
+
+**相关文件**：
+- `agent/subagent_loader.py`（新）
+- `agent/deep_assistant.py`（接线）
+- `skills/developing/pmda-drug-info/`（新 skill）
+
+**Commit/PR**：`5b634bc`
+
+---
+
+## 2026-05-11: pmda-drug-info MCP server 重写为 mock 实现
+
+**类型**：内部改造
+
+**背景**：[待补充]
+
+**改动**：`skills/developing/pmda-drug-info/pmda_server.py` 大幅替换（+322 / -385），保留接口面向 agent 的契约，内部替换为 mock 数据实现。
+
+**根因**：N/A
+
+**影响**：pmda-drug-info skill 当前不再依赖外部真实 PMDA 数据源，便于开发期联调。
+
+**相关文件**：
+- `skills/developing/pmda-drug-info/pmda_server.py`
+
+**Commit/PR**：`a92096a`
+
+---
+
+## 2026-05-11: retrieval-policy.md 内容更新
+
+**类型**：内容调整
+
+**背景**：[待补充]
+
+**改动**：在 onprem / support / autoload-onprem-rag-only / autoload-support-rag-only 四个版本的 `retrieval-policy.md` 上做了同步内容更新。
+
+**根因**：N/A
+
+**影响**：与同月 12 日的 policy 批量精修配套，使 rag-retrieve hook 策略保持一致。
+
+**相关文件**：
+- `skills/onprem/rag-retrieve/hooks/retrieval-policy.md`
+- `skills/support/rag-retrieve/hooks/retrieval-policy.md`
+- `skills/autoload/onprem/rag-retrieve-only/hooks/retrieval-policy.md`
+- `skills/autoload/support/rag-retrieve-only/hooks/retrieval-policy.md`
+
+**Commit/PR**：`e6d1698`
+
+---
+
+## 2026-05-08: 通过 MCP `_meta` 透传 trace_id 给 RAG 工具
+
+**类型**：新功能
+
+**背景**：需要把 catalog-agent 的 trace_id 透传给 MCP 端的 `rag_retrieve` / `table_rag_retrieve` 服务，便于跨进程追踪。
+
+**改动**：
+- 新增 `agent/mcp_trace_meta.py`：通过 `patch_mcp_client_session_trace_meta()` 对 `mcp.ClientSession.call_tool` 做一次幂等 monkey-patch，调用时若工具名在 `{"rag_retrieve", "table_rag_retrieve"}` 集合内且当前请求上下文有 `trace_id`，则注入到 `kwargs["meta"]["trace_id"]`；并提供 `_call_tool_with_meta_compat` 以兼容旧版 MCP SDK（不接受 `meta=` 关键字时退化为手动构造 `CallToolRequestParams._meta`）。
+- `agent/deep_assistant.py`：在 `get_tools_from_mcp()` 入口处调用一次补丁安装。
+- 同步调整 `skills/onprem/rag-retrieve/rag_retrieve_server.py` 与 `skills/support/rag-retrieve/rag_retrieve_server.py`，接收并使用 `_meta.trace_id`。
+
+**根因**：N/A
+
+**影响**：
+- `rag_retrieve` / `table_rag_retrieve` 现在在 MCP `_meta` 上必带 `trace_id`（若上下文存在）。
+- 全局 monkey-patch 风格 - 只要 `get_tools_from_mcp()` 被调用过一次后，所有 `ClientSession.call_tool` 都会被包装。
+
+**相关文件**：
+- `agent/mcp_trace_meta.py`（新）
+- `agent/deep_assistant.py`
+- `skills/onprem/rag-retrieve/rag_retrieve_server.py`
+- `skills/support/rag-retrieve/rag_retrieve_server.py`
+
+**Commit/PR**：`1f06450`
+
+---
+
+## 2026-05-06: 新增 kfs-answer skill (support 分支)
+
+**类型**：新功能
+
+**背景**：[待补充] - 为 support 分支补齐 kfs-answer 能力（onprem 分支此前已有同名 skill）。
+
+**改动**：新增 `skills/support/kfs-answer/`，包括 `SKILL.md` 与 `scripts/` 下的 `query.py` / `search.py` / `detail.py` / `query_db.py` / `format_answer.py` / `merge_citations.py` / `_session.py` 共 7 个脚本（约 1809 行）。
+
+**根因**：N/A
+
+**影响**：support 部署版本获得 kfs-answer 能力。
+
+**相关文件**：
+- `skills/support/kfs-answer/**`
+
+**Commit/PR**：`a9227b8`
+
+---
+
+## 2026-05-06: Daytona 沙箱增量同步漏掉符号链接
+
+**类型**：Bug 修复
+
+**背景**：dataset 通过符号链接挂载，但增量同步用 `find -type f` 只匹配普通文件，导致 dataset 符号链接没被检测到也没被打包同步到 Daytona 沙箱；并且 `tar.add()` 默认不 dereference，打进去的是指向宿主机路径的悬空软链。
+
+**改动**：
+- `utils/daytona_sync._list_local_changed_files`：同时匹配 file 和 symlink (`-type f -o -type l`)。
+- `utils/daytona_sync._tar_workspace_entries`：`tar.add(dereference=True)`，把软链解引用为实际内容打包。
+- `skills/onprem/kfs-answer/SKILL.md` 和 `prompt/system_prompt_deep_agent.md`：统一数据集路径用复数形式 `datasets/`。
+
+**根因**：`find -type f` 与 `tar.add()` 默认行为对符号链接不友好。
+
+**影响**：Daytona 模式下 kfs-answer 等依赖 dataset 软链的 skill 可以正常使用沙箱内的数据；提示词与 SKILL.md 内的路径口径统一。
+
+**相关文件**：
+- `utils/daytona_sync.py`
+- `skills/onprem/kfs-answer/SKILL.md`
+- `prompt/system_prompt_deep_agent.md`
+
+**Commit/PR**：`3c0fa49`
+
+---
+
+## 2026-04-24: PostAgent hooks 非阻塞执行 + 临时停用 ToolOutputLengthMiddleware
+
+**类型**：性能优化 / 临时调整
+
+**背景**：非流式响应路径上 `_execute_post_agent_hooks` 是同步等待，阻塞了响应返回。
+
+**改动**：
+- `routes/chat.py`：非流式分支将 `await _execute_post_agent_hooks(...)` 改为 `asyncio.create_task(_execute_post_agent_hooks(...))`，hook 在后台执行，不阻塞响应。
+- `agent/deep_assistant.py`：将 `ToolOutputLengthMiddleware` 整段注释掉（未删除，可恢复）。
+- `utils/settings.py`：切换 `DAYTONA_API_KEY` / `DAYTONA_SERVER_URL` 注释行（启用自托管 Daytona，注释掉 SaaS 行）。
+
+**根因**：N/A（性能优化为主）
+
+**影响**：
+- 非流式接口响应不再等待 PostAgent hooks 完成 → hook 中失败/异常**只会被 task 内部的 logger 捕获**，调用方收不到错误反馈。
+- 工具输出长度暂时不再被截断，存在超长输出冲爆上下文的风险（中间件已被注释，并未拆除）。
+
+**相关文件**：
+- `routes/chat.py`
+- `agent/deep_assistant.py`
+- `utils/settings.py`
+
+**Commit/PR**：`45a9494`
+
+---
+
+## 2026-04-23: PrePrompt hook 内容改为模板占位符注入
+
+**类型**：重构
+
+**背景**：原先 PrePrompt hook 的产出文本是在 `system_prompt_default.format(...)` 之后追加在 prompt 末尾，hook 内容在 prompt 中的位置固定且偏后，模板对它的可见性差。
+
+**改动**：`agent/prompt_loader.load_system_prompt_async`：先执行 `execute_hooks('PrePrompt', config)` 拿到 `hook_content`，然后通过新增的 `{hook_content}` 占位符传入 `system_prompt_default.format(...)`；模板侧 `prompt/system_prompt.md` 增加对应占位符。
+
+**根因**：N/A（结构化注入更可控）
+
+**影响**：编写 PrePrompt hook 的 skill 必须依赖模板里 `{hook_content}` 占位符的位置；若使用了未升级的旧模板，hook 内容将不再出现在最终 system prompt 中。
+
+**相关文件**：
+- `agent/prompt_loader.py`
+- `prompt/system_prompt.md`
+
+**Commit/PR**：`51fbf01`
+
+---
+
+## 2026-04-23: Daytona 沙箱接入
+
+**类型**：新功能
+
+**背景**：技能脚本需要在隔离沙箱中执行（Daytona），避免直接污染宿主机。
+
+**改动**：
+- `agent/deep_assistant.py`：
+  - 在 `init_agent` 中读取 `DAYTONA_ENABLED` / `DAYTONA_API_KEY` / `DAYTONA_SERVER_URL`，启用时创建 `DaytonaSandbox`；并将 `sandbox` / `sandbox_type` 传到 `create_custom_cli_agent` / `agent.invoke_config`。
+  - 重构为并行加载：`load_system_prompt_async` 与 `load_mcp_settings_async` 用 `asyncio.gather` 并行；`get_tools_from_mcp` 与 `asyncio.to_thread(init_daytona_sandbox, ...)` 并行；`init_agent` 现在返回 `(agent, checkpointer, sandbox)`（多了 sandbox）。
+  - `enable_skills` 时 `skills_sources` 从 `"/skills"` 改为 `"/workspace/skills"`（指向沙箱内的路径）。
+- `agent/prompt_loader.py`：`agent_dir_path` 在 `DAYTONA_ENABLED=True` 时改为 `/workspace`，否则保持本地路径。
+- `utils/daytona_sync.py` 新增（204 行）：沙箱与本地 workspace 双向同步。
+- `pyproject.toml` / `poetry.lock` / `requirements.txt`：新增 `daytona`、`langchain_daytona` 依赖。
+- `utils/settings.py`：新增 `DAYTONA_API_KEY` / `DAYTONA_SERVER_URL` / `DAYTONA_ENABLED` 配置。
+
+**根因**：N/A
+
+**影响**：
+- `init_agent` 返回元组从 2 元素变为 3 元素 (`agent, checkpointer, sandbox`)——**调用方必须同步更新解构**。
+- skill 在沙箱模式下的根路径与本地模式不同，所有写死路径的 hook / 脚本需要兼容两种环境。
+
+**相关文件**：
+- `agent/deep_assistant.py`
+- `agent/prompt_loader.py`
+- `utils/daytona_sync.py`（新）
+- `utils/settings.py`
+- `pyproject.toml`, `poetry.lock`, `requirements.txt`
+
+**Commit/PR**：`c9e0789`, `8446dab`
+
+---
+
+## 2026-04-22: 新增 rag-retrieve-no-citation 与 novare-context 两个开发中 skill
+
+**类型**：新功能
+
+**背景**：[待补充]
+
+**改动**：
+- `skills/developing/rag-retrieve-no-citation/`：完整 skill 包，含 `.claude-plugin/plugin.json`、`README.md`、`hooks/pre_prompt.py`、`hooks/retrieval-policy.md` 与 `hooks/retrieval-policy-forbidden-self-knowledge.md`、独立 `rag_retrieve_server.py` + `rag_retrieve_tools.json` + `mcp_common.py`。
+- `skills/developing/novare-context/`：包含 `.claude-plugin/plugin.json`、`README.md`、`hooks/pre_prompt.py`。
+
+**根因**：N/A
+
+**影响**：开发中 skill 集合扩张，可作为后续正式版本的母版。
+
+**相关文件**：
+- `skills/developing/rag-retrieve-no-citation/**`
+- `skills/developing/novare-context/**`
+
+**Commit/PR**：`7a30e52`
+
+---
 ### 2026-05-20
 - **变更**: `mcp-ui` 和 `data-dashboard` 从自定义 `uri + data` 工具协议改为 MCP Apps 模式
 - **说明**: 静态 HTML App 放在各 skill 的 `apps/` 目录，host 通过 resource URI 加载 iframe，再用 postMessage 传递工具数据

.features/skill/decisions/2026-05-subagent-support.md

@@ -0,0 +1,47 @@
+---
+date: "2026-05-11"
+status: pending
+topic: "subagent-support"
+impact: [skill, agent, deep_assistant]
+---
+
+# Sub-Agent 在 skill 内的承载方式
+
+## 背景
+
+业务方 (pmda-drug-info) 需要在单个 skill 内同时承载若干面向不同子任务的专用 agent
+（single-drug / interaction / adverse-event / patient-specific），每个子 agent
+需要独立的 system prompt 和工具白名单，但应与主 agent 复用同一组 MCP 工具实例
+与同一份 LLM。
+
+[待补充]：是否考虑过用 skill-per-subagent 的方式（每个子 agent 一个独立 skill）。
+
+## 选项
+
+### 选项 A：skill 内 `agents/*.md` + 全局 `SubAgentMiddleware`（已实现）
+- 优点：
+  - skill 包自洽，子 agent 定义与 hook / MCP 同包发布。
+  - 复用 `deepagents.middleware.subagents.SubAgentMiddleware`，无需自研路由层。
+  - 工具按 `tools` 字段白名单过滤，统一以 MCP tool name 引用。
+- 缺点：
+  - 跨 skill 子 agent **同名时静默 last-wins 覆盖**，仅有 warning，无强校验。
+  - 中间件位置耦合：`SubAgentMiddleware` 必须插在
+    `CustomFilesystemMiddleware` 之后、`AnthropicPromptCachingMiddleware` 之前
+    （与 `create_deep_agent` 顺序匹配），改动中间件顺序时容易踩坑。
+
+### 选项 B：每个子 agent 单独建一个 skill
+- 优点：天然隔离，命名冲突由 skill 加载层处理。
+- 缺点：同一业务的多个子 agent 在 skill 列表里散落，部署 / autoload 配置复杂；
+  pmda-drug-info 的 4 个子 agent 强相关，作为同一 skill 更自然。
+
+## 决策
+
+选择 **选项 A**（已落地）。
+
+## 影响
+
+- 需要改动：调用方知道 `init_agent` 返回元组现已包含 sandbox（与 daytona 改动叠加）。
+- 风险：sub-agent 同名静默覆盖；未来如多 skill 都暴露 sub-agent，需要增加冲突检测。
+- 后续任务：
+  1. 沉淀 sub-agent 编写规范（`agents/*.md` frontmatter 字段 + 工具白名单约定）。
+  2. 跨 skill sub-agent 命名冲突的检测策略——是否升级为 error / 加 skill 名前缀。

Dockerfile

@@ -9,7 +9,7 @@ ENV PYTHONPATH=/app
 ENV PYTHONUNBUFFERED=1
 
 # 安装系统依赖（含 LibreOffice 和 sharp 所需的 libvips）
-RUN apt-get update && apt-get install -y \
+RUN apt-get update && apt-get install -y --no-install-recommends \
     curl \
     wget \
     gnupg2 \
@@ -26,7 +26,8 @@ RUN apt-get update && apt-get install -y \
 
 # 安装Node.js (支持npx命令)
 RUN curl -fsSL https://deb.nodesource.com/setup_20.x | bash - && \
-    apt-get install -y nodejs
+    apt-get install -y --no-install-recommends nodejs && \
+    rm -rf /var/lib/apt/lists/*
 
 # 安装uv (Python包管理器)
 RUN curl -LsSf https://astral.sh/uv/install.sh | sh
@@ -36,7 +37,10 @@ ENV PATH="/root/.cargo/bin:$PATH"
 
 # 复制requirements文件并安装Python依赖
 COPY requirements.txt .
-RUN pip install --no-cache-dir -r requirements.txt
+RUN grep -Ev '^(torch|triton|nvidia-[^=]+|sentence-transformers|transformers|tokenizers|safetensors|scikit-learn|scipy|huggingface-hub|hf-xet)==' requirements.txt > /tmp/requirements.runtime.txt && \
+    ! grep -E '^(torch|triton|nvidia-[^=]+|sentence-transformers|transformers|tokenizers|safetensors|scikit-learn|scipy|huggingface-hub|hf-xet)==' /tmp/requirements.runtime.txt && \
+    pip install --no-cache-dir -r /tmp/requirements.runtime.txt && \
+    rm -f /tmp/requirements.runtime.txt
 
 # 安装 Playwright 并下载 Chromium
 RUN pip install --no-cache-dir playwright && \
@@ -49,9 +53,6 @@ RUN mkdir -p /app/projects
 RUN mkdir -p /app/public
 RUN mkdir -p /app/models
 
-# 下载sentence-transformers模型到models目录
-RUN python -c "from sentence_transformers import SentenceTransformer; model = SentenceTransformer('TaylorAI/gte-tiny'); model.save('/app/models/gte-tiny')"
-
 FROM base AS bytecode-builder
 
 # 复制应用代码，仅在构建阶段编译为字节码

Dockerfile.modelscope

@@ -10,7 +10,8 @@ ENV PYTHONUNBUFFERED=1
 
 # 安装系统依赖（含 LibreOffice 和 sharp 所需的 libvips）
 RUN sed -i 's|http://deb.debian.org|http://mirrors.aliyun.com|g' /etc/apt/sources.list.d/debian.sources && \
-    apt-get update && apt-get install -y \
+    apt-get -o Acquire::Retries=3 update && \
+    apt-get -o Acquire::Retries=3 install -y --no-install-recommends \
     curl \
     wget \
     gnupg2 \
@@ -26,7 +27,8 @@ RUN sed -i 's|http://deb.debian.org|http://mirrors.aliyun.com|g' /etc/apt/source
 
 # 安装Node.js (支持npx命令)
 RUN curl -fsSL https://deb.nodesource.com/setup_20.x | bash - && \
-    apt-get install -y nodejs
+    apt-get -o Acquire::Retries=3 install -y --no-install-recommends nodejs && \
+    rm -rf /var/lib/apt/lists/*
 
 # 安装uv (Python包管理器)
 RUN curl -LsSf https://astral.sh/uv/install.sh | sh
@@ -36,7 +38,10 @@ ENV PATH="/root/.cargo/bin:$PATH"
 
 # 复制requirements文件并安装Python依赖
 COPY requirements.txt .
-RUN pip install --no-cache-dir -i https://mirrors.aliyun.com/pypi/simple/ -r requirements.txt
+RUN grep -Ev '^(torch|triton|nvidia-[^=]+|sentence-transformers|transformers|tokenizers|safetensors|scikit-learn|scipy|huggingface-hub|hf-xet)==' requirements.txt > /tmp/requirements.runtime.txt && \
+    ! grep -E '^(torch|triton|nvidia-[^=]+|sentence-transformers|transformers|tokenizers|safetensors|scikit-learn|scipy|huggingface-hub|hf-xet)==' /tmp/requirements.runtime.txt && \
+    pip install --no-cache-dir -i https://mirrors.aliyun.com/pypi/simple/ -r /tmp/requirements.runtime.txt && \
+    rm -f /tmp/requirements.runtime.txt
 
 # 安装 Playwright 并下载 Chromium
 RUN pip install --no-cache-dir -i https://mirrors.aliyun.com/pypi/simple/ playwright && \

agent/deep_assistant.py

@@ -23,6 +23,7 @@ from utils.fastapi_utils import detect_provider, sanitize_model_kwargs
 from .guideline_middleware import GuidelineMiddleware
 from .tool_output_length_middleware import ToolOutputLengthMiddleware
 from .tool_use_cleanup_middleware import ToolUseCleanupMiddleware
+from .tool_metrics_middleware import ToolMetricsMiddleware
 from .filepath_fix_middleware import FilePathFixMiddleware
 from .mcp_trace_meta import patch_mcp_client_session_trace_meta
 from utils.settings import (
@@ -256,6 +257,7 @@ async def init_agent(config: AgentConfig):
     # Build the middleware list
     middleware = []
     middleware.append(EmptyResponseRetryMiddleware())
+    middleware.append(ToolMetricsMiddleware(config))
     middleware.append(ToolUseCleanupMiddleware())
     # tool_output_middleware = ToolOutputLengthMiddleware(
     #     max_length=(getattr(config.generate_cfg, 'tool_output_max_length', None) if config.generate_cfg else None) or TOOL_OUTPUT_MAX_LENGTH,
@@ -480,13 +482,19 @@ def create_custom_cli_agent(
             backend = FilesystemBackend(root_dir=workspace_root, virtual_mode=False)
 
         # Set up composite backend with routing based on the new implementation
+        # NOTE: virtual_mode=True anchors all paths to root_dir. This is required for
+        # these offload-only backends: CompositeBackend strips the route prefix and
+        # forwards "/" to grep, so virtual_mode=False would resolve "/" to the real
+        # filesystem root and scan the whole disk (hitting /usr, /var, other sessions'
+        # temp dirs), causing 45-152s grep calls. virtual_mode=True confines grep to
+        # the temp dir and filters out-of-root results.
         large_results_backend = FilesystemBackend(
             root_dir=tempfile.mkdtemp(prefix="deepagents_large_results_"),
-            virtual_mode=False,
+            virtual_mode=True,
         )
         conversation_history_backend = FilesystemBackend(
             root_dir=tempfile.mkdtemp(prefix="deepagents_conversation_history_"),
-            virtual_mode=False,
+            virtual_mode=True,
         )
         composite_backend = CompositeBackend(
             default=backend,

agent/mem0_manager.py

@@ -5,14 +5,18 @@ Responsible for creating, caching, and managing the lifecycle of Mem0 client ins
 
 import logging
 import asyncio
-import threading
-import concurrent.futures
 from typing import Any, Dict, List, Optional, Literal
 from collections import OrderedDict
-from embedding.manager import GlobalModelManager, get_model_manager
+from urllib.parse import unquote, urlparse
+from embedding.manager import get_model_manager
 import json_repair
 from psycopg2 import pool
 from utils.settings import (
+    CHECKPOINT_DB_URL,
+    EMBEDDING_API_KEY,
+    EMBEDDING_BASE_URL,
+    EMBEDDING_DIMENSIONS,
+    EMBEDDING_MODEL_NAME,
     MEM0_POOL_SIZE
 )
 from .mem0_config import Mem0Config
@@ -27,15 +31,9 @@ logger = logging.getLogger("app")
 
 class CustomMem0Embedding:
     """
-    Custom Mem0 embedding class that directly uses the project's existing GlobalModelManager
-
-    This prevents Mem0 from loading the same model again and saves memory
+    Custom Mem0 embedding class backed by the external embedding API.
     """
 
-    _model = None  # Class variable caching the model instance
-    _lock = threading.Lock()  # Thread-safe lock
-    _executor = None  # Thread pool executor
-
     def __init__(self, config: Optional[Any] = None):
         """Initialize the custom embedding."""
         # Create a simple config object compatible with Mem0 telemetry code
@@ -46,42 +44,7 @@ class CustomMem0Embedding:
     @property
     def embedding_dims(self):
         """Get the embedding dimension."""
-        return 384  # Dimension of gte-tiny
-
-    def _get_model_sync(self):
-        """Synchronously get the model without using asyncio.run()."""
-        # First try to get an already-loaded model from the manager
-        manager = get_model_manager()
-        model = manager.get_model_sync()
-
-        if model is not None:
-            # Cache the model
-            CustomMem0Embedding._model = model
-            return model
-
-        # If the model is not loaded, run async initialization in a thread pool
-        if CustomMem0Embedding._executor is None:
-            CustomMem0Embedding._executor = concurrent.futures.ThreadPoolExecutor(
-                max_workers=1,
-                thread_name_prefix="mem0_embed"
-            )
-
-        # Run async code in a dedicated thread
-        def run_async_in_thread():
-            loop = asyncio.new_event_loop()
-            asyncio.set_event_loop(loop)
-            try:
-                result = loop.run_until_complete(manager.get_model())
-                return result
-            finally:
-                loop.close()
-
-        future = CustomMem0Embedding._executor.submit(run_async_in_thread)
-        model = future.result(timeout=30)  # 30-second timeout
-
-        # Cache the model
-        CustomMem0Embedding._model = model
-        return model
+        return EMBEDDING_DIMENSIONS
 
     def embed(self, text, memory_action: Optional[Literal["add", "search", "update"]] = None):
         """
@@ -94,15 +57,11 @@ class CustomMem0Embedding:
         Returns:
             list: Embedding vector
         """
-        # Retrieve the model in a thread-safe manner
-        if CustomMem0Embedding._model is None:
-            with CustomMem0Embedding._lock:
-                if CustomMem0Embedding._model is None:
-                    self._get_model_sync()
-
-        model = CustomMem0Embedding._model
-        embeddings = model.encode(text, convert_to_numpy=True)
-        return embeddings.tolist()
+        manager = get_model_manager()
+        input_texts = text if isinstance(text, list) else [text]
+        embeddings = manager.encode_texts_sync(input_texts, batch_size=1)
+        result = embeddings.tolist()
+        return result if isinstance(text, list) else result[0]
 
 # Monkey patch: replace mem0's remove_code_blocks with json_repair
 def _remove_code_blocks_with_repair(content: str) -> str:
@@ -233,27 +192,68 @@ class Mem0Manager:
             mem0_instance: Mem0 Memory instance
         """
         try:
-            # Mem0 Memory instances have a vector_store attribute of type PGVector
-            if hasattr(mem0_instance, 'vector_store'):
-                vector_store = mem0_instance.vector_store
-                # PGVector has conn and connection_pool attributes
-                if hasattr(vector_store, 'conn') and hasattr(vector_store, 'connection_pool'):
-                    if vector_store.conn is not None and vector_store.connection_pool is not None:
-                        try:
-                            # Close the cursor first
-                            if hasattr(vector_store, 'cur') and vector_store.cur:
-                                vector_store.cur.close()
-                                vector_store.cur = None
-                            # Return the connection to the pool
-                            vector_store.connection_pool.putconn(vector_store.conn)
-                            # Mark as cleaned up to prevent __del__ from releasing it again
-                            vector_store.conn = None
-                            logger.debug("Successfully released Mem0 database connection back to pool")
-                        except Exception as e:
-                            logger.warning(f"Error releasing Mem0 connection: {e}")
+            vector_store = getattr(mem0_instance, 'vector_store', None)
+            if vector_store is not None and getattr(vector_store, 'conn', None) is not None:
+                try:
+                    if getattr(vector_store, 'cur', None):
+                        vector_store.cur.close()
+                        vector_store.cur = None
+                    connection_pool = getattr(vector_store, 'connection_pool', None)
+                    if connection_pool is not None:
+                        connection_pool.putconn(vector_store.conn)
+                        logger.debug("Successfully released Mem0 database connection back to pool")
+                    else:
+                        vector_store.conn.close()
+                        logger.debug("Successfully closed Mem0 database connection")
+                    vector_store.conn = None
+                except Exception as e:
+                    logger.warning(f"Error releasing Mem0 connection: {e}")
         except Exception as e:
             logger.warning(f"Error cleaning up Mem0 instance: {e}")
 
+    def _build_pgvector_config(self, agent_id: str) -> Dict[str, Any]:
+        """Build Mem0 PGVector config using only fields accepted by mem0."""
+        parsed_url = urlparse(CHECKPOINT_DB_URL)
+        if parsed_url.scheme not in ("postgresql", "postgres"):
+            raise ValueError(f"Unsupported CHECKPOINT_DB_URL scheme: {parsed_url.scheme}")
+
+        return {
+            "dbname": unquote(parsed_url.path.lstrip("/") or "postgres"),
+            "user": unquote(parsed_url.username or ""),
+            "password": unquote(parsed_url.password or ""),
+            "host": parsed_url.hostname or "localhost",
+            "port": parsed_url.port or 5432,
+            "collection_name": f"mem0_{agent_id}".replace("-", "_")[:50],
+            "embedding_model_dims": EMBEDDING_DIMENSIONS,
+        }
+
+    def _attach_pool_to_vector_store(self, mem0_instance: Any) -> None:
+        """Move Mem0's runtime vector store onto the shared psycopg2 pool."""
+        vector_store = getattr(mem0_instance, 'vector_store', None)
+        if vector_store is None:
+            return
+
+        if getattr(vector_store, 'cur', None):
+            vector_store.cur.close()
+            vector_store.cur = None
+        if getattr(vector_store, 'conn', None) is not None:
+            vector_store.conn.close()
+            vector_store.conn = None
+        vector_store.connection_pool = self._sync_pool
+
+    def _close_telemetry_vector_store(self, mem0_instance: Any) -> None:
+        """Close Mem0's migration telemetry vector-store connection after init."""
+        vector_store = getattr(mem0_instance, '_telemetry_vector_store', None)
+        if vector_store is None:
+            return
+
+        if getattr(vector_store, 'cur', None):
+            vector_store.cur.close()
+            vector_store.cur = None
+        if getattr(vector_store, 'conn', None) is not None:
+            vector_store.conn.close()
+            vector_store.conn = None
+
     def _ensure_connection(self, mem0_instance: Any) -> None:
         """Ensure a Mem0 instance has a database connection before use.
 
@@ -268,8 +268,7 @@ class Mem0Manager:
                 if hasattr(vs, 'conn') and vs.conn is None and self._sync_pool:
                     vs.conn = self._sync_pool.getconn()
                     vs.cur = vs.conn.cursor()
-                    # Ensure the connection_pool reference exists for later return
-                    if hasattr(vs, 'connection_pool') and vs.connection_pool is None:
+                    if not hasattr(vs, 'connection_pool') or vs.connection_pool is None:
                         vs.connection_pool = self._sync_pool
                     logger.debug("Re-acquired Mem0 database connection from pool")
         except Exception as e:
@@ -292,8 +291,11 @@ class Mem0Manager:
                     if hasattr(vs, 'cur') and vs.cur:
                         vs.cur.close()
                         vs.cur = None
-                    if hasattr(vs, 'connection_pool') and vs.connection_pool is not None:
-                        vs.connection_pool.putconn(vs.conn)
+                    connection_pool = getattr(vs, 'connection_pool', None)
+                    if connection_pool is not None:
+                        connection_pool.putconn(vs.conn)
+                    else:
+                        vs.conn.close()
                     vs.conn = None
                     logger.debug("Released Mem0 database connection back to pool")
         except Exception as e:
@@ -376,28 +378,25 @@ class Mem0Manager:
         if not connection_pool:
             raise ValueError("Database connection pool not available")
 
-        # Create a custom embedder that reuses the shared model to avoid duplicate loading
+        # Create a custom embedder backed by the external embedding API.
         custom_embedder = CustomMem0Embedding()
 
-        # Configure Mem0 to use Pgvector
-        # Note: use huggingface_base_url here to bypass local model loading
-        # Set a dummy base_url so HuggingFaceEmbedding does not load SentenceTransformer
-        
+        # Configure Mem0 to use Pgvector.
+        # Mem0 validates this config strictly, so connection_pool is attached after creation.
+        pgvector_config = self._build_pgvector_config(agent_id)
         config_dict = {
             "vector_store": {
                 "provider": "pgvector",
-                "config": {
-                    "connection_pool": connection_pool,
-                    "collection_name": f"mem0_{agent_id}".replace("-", "_")[:50],  # Isolate by agent_id
-                    "embedding_model_dims": 384,  # Dimension of paraphrase-multilingual-MiniLM-L12-v2
-                }
+                "config": pgvector_config,
             },
-            # Use huggingface_base_url to bypass model loading; it will later be replaced with the custom embedder
+            # The embedder is replaced immediately after Memory is created.
             "embedder": {
-                "provider": "huggingface",
+                "provider": "openai",
                 "config": {
-                    "huggingface_base_url": "http://dummy-url-that-will-be-replaced",
-                    "api_key": "dummy-key"  # Placeholder to prevent OpenAI client validation failure
+                    "api_key": EMBEDDING_API_KEY,
+                    "openai_base_url": EMBEDDING_BASE_URL,
+                    "model": EMBEDDING_MODEL_NAME,
+                    "embedding_dims": EMBEDDING_DIMENSIONS,
                 }
             }
         }
@@ -432,6 +431,8 @@ class Mem0Manager:
 
         # Create the Mem0 instance
         mem = Memory.from_config(config_dict)
+        self._attach_pool_to_vector_store(mem)
+        self._close_telemetry_vector_store(mem)
         logger.debug(f"Original embedder type: {type(mem.embedding_model).__name__}")
         logger.debug(f"Original embedder.embedding_dims: {getattr(mem.embedding_model, 'embedding_dims', 'N/A')}")
 

agent/tool_metrics_middleware.py

@@ -0,0 +1,100 @@
+"""Structured metrics for agent tool calls."""
+
+import asyncio
+import logging
+import time
+from typing import Any, Callable
+
+from langchain.agents.middleware import AgentMiddleware
+from langchain.tools.tool_node import ToolCallRequest
+
+from agent.agent_config import AgentConfig
+from utils.structured_log import emit_question_metric
+
+logger = logging.getLogger("app")
+
+
+class ToolMetricsMiddleware(AgentMiddleware):
+    """Emit structured timing metrics for every tool call."""
+
+    def __init__(self, config: AgentConfig):
+        self.config = config
+
+    def _emit_tool_metric(
+        self,
+        request: ToolCallRequest,
+        *,
+        started_at: float,
+        status: str,
+        error_type: str | None = None,
+    ) -> None:
+        tool_call = request.tool_call or {}
+        tool_name = tool_call.get("name") or "unknown_tool"
+        tool_call_id = tool_call.get("id")
+        duration_ms = max(int((time.monotonic() - started_at) * 1000), 0)
+
+        try:
+            emit_question_metric(
+                stage="catalog_agent.tool_call",
+                status=status,
+                duration_ms=duration_ms,
+                trace_id=self.config.trace_id,
+                ai_id=self.config.bot_id,
+                session_id=self.config.session_id,
+                robot_type="agent",
+                model=self.config.model_name,
+                stream=self.config.stream,
+                error_type=error_type,
+                extra={
+                    "bot_id": self.config.bot_id,
+                    "tool_name": tool_name,
+                    "tool_call_id": tool_call_id,
+                    "tool_response": self.config.tool_response,
+                    "enable_thinking": self.config.enable_thinking,
+                },
+            )
+        except Exception:
+            logger.exception("Failed to emit tool metric for tool_name=%s", tool_name)
+
+    def wrap_tool_call(
+        self,
+        request: ToolCallRequest,
+        handler: Callable[[ToolCallRequest], Any],
+    ) -> Any:
+        started_at = time.monotonic()
+        try:
+            result = handler(request)
+        except Exception as exc:
+            self._emit_tool_metric(
+                request,
+                started_at=started_at,
+                status="error",
+                error_type=type(exc).__name__,
+            )
+            raise
+
+        self._emit_tool_metric(request, started_at=started_at, status="success")
+        return result
+
+    async def awrap_tool_call(
+        self,
+        request: ToolCallRequest,
+        handler: Callable[[ToolCallRequest], Any],
+    ) -> Any:
+        started_at = time.monotonic()
+        try:
+            result = await handler(request)
+        except asyncio.CancelledError:
+            self._emit_tool_metric(request, started_at=started_at, status="cancel")
+            raise
+        except Exception as exc:
+            self._emit_tool_metric(
+                request,
+                started_at=started_at,
+                status="error",
+                error_type=type(exc).__name__,
+            )
+            raise
+
+        self._emit_tool_metric(request, started_at=started_at, status="success")
+        return result

db_manager.py

@@ -1,168 +0,0 @@
-#!/usr/bin/env python3
-"""
-SQLite task status database management tool
-"""
-
-import sqlite3
-import json
-import time
-from task_queue.task_status import task_status_store
-
-def view_database():
-    """View database contents"""
-    print("SQLite task status database contents")
-    print("=" * 40)
-    print(f"Database path: {task_status_store.db_path}")
-    
-    # Connect to the database
-    conn = sqlite3.connect(task_status_store.db_path)
-    cursor = conn.cursor()
-    
-    # View table schema
-    print(f"\nTable schema:")
-    cursor.execute("PRAGMA table_info(task_status)")
-    columns = cursor.fetchall()
-    for col in columns:
-        print(f"   {col[1]} ({col[2]})")
-    
-    # View all records
-    print(f"\nAll records:")
-    cursor.execute("SELECT * FROM task_status ORDER BY updated_at DESC")
-    rows = cursor.fetchall()
-    
-    if not rows:
-        print("   (empty database)")
-    else:
-        print(f"   Total {len(rows)} records:")
-        for i, row in enumerate(rows):
-            task_id, unique_id, status, created_at, updated_at, result, error = row
-            created_str = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(created_at))
-            updated_str = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(updated_at))
-            
-            print(f"   {i+1}. {task_id}")
-            print(f"      Project ID: {unique_id}")
-            print(f"      Status: {status}")
-            print(f"      Created: {created_str}")
-            print(f"      Updated: {updated_str}")
-            if result:
-                try:
-                    result_data = json.loads(result)
-                    print(f"      Result: {result_data.get('message', 'N/A')}")
-                except:
-                    print(f"      Result: {result[:50]}...")
-            if error:
-                print(f"      Error: {error}")
-            print()
-    
-    conn.close()
-
-def run_query(sql_query: str):
-    """Run a custom query"""
-    print(f"Running query: {sql_query}")
-    
-    try:
-        conn = sqlite3.connect(task_status_store.db_path)
-        conn.row_factory = sqlite3.Row
-        cursor = conn.cursor()
-        cursor.execute(sql_query)
-        rows = cursor.fetchall()
-        
-        if not rows:
-            print("   (no results)")
-        else:
-            print(f"   {len(rows)} results:")
-            for row in rows:
-                print(f"   {dict(row)}")
-        
-        conn.close()
-        
-    except Exception as e:
-        print(f"Query failed: {e}")
-
-def interactive_shell():
-    """Interactive database management"""
-    print("\n🖥️  Interactive database management")
-    print("Type 'help' to view available commands, or 'quit' to exit")
-    
-    while True:
-        try:
-            command = input("\n> ").strip()
-            
-            if command.lower() in ['quit', 'exit', 'q']:
-                break
-            elif command.lower() == 'help':
-                print("""
-Available commands:
-  view                - View all records
-  stats               - View statistics  
-  pending             - View pending tasks
-  completed           - View completed tasks
-  failed              - View failed tasks
-  sql <query>          - Run an SQL query
-  cleanup <days>       - Clean up records older than N days
-  count               - Count total tasks
-  help                - Show help
-  quit/exit/q         - Exit
-                """)
-            elif command.lower() == 'view':
-                view_database()
-            elif command.lower() == 'stats':
-                stats = task_status_store.get_statistics()
-                print(f"Statistics:")
-                print(f"  Total tasks: {stats['total_tasks']}")
-                print(f"  Status breakdown: {stats['status_breakdown']}")
-                print(f"  Last 24 hours: {stats['recent_24h']}")
-            elif command.lower() == 'pending':
-                tasks = task_status_store.search_tasks(status="pending")
-                print(f"Pending tasks ({len(tasks)}):")
-                for task in tasks:
-                    print(f"  - {task['task_id']}: {task['unique_id']}")
-            elif command.lower() == 'completed':
-                tasks = task_status_store.search_tasks(status="completed")
-                print(f"Completed tasks ({len(tasks)}):")
-                for task in tasks:
-                    print(f"  - {task['task_id']}: {task['unique_id']}")
-            elif command.lower() == 'failed':
-                tasks = task_status_store.search_tasks(status="failed")
-                print(f"Failed tasks ({len(tasks)}):")
-                for task in tasks:
-                    print(f"  - {task['task_id']}: {task['unique_id']}")
-            elif command.lower().startswith('sql '):
-                sql_query = command[4:]
-                run_query(sql_query)
-            elif command.lower().startswith('cleanup '):
-                try:
-                    days = int(command[8:])
-                    count = task_status_store.cleanup_old_tasks(days)
-                    print(f"Cleaned up {count} records older than {days} days")
-                except ValueError:
-                    print("Please enter a valid number of days")
-            elif command.lower() == 'count':
-                all_tasks = task_status_store.list_all()
-                print(f"Total tasks: {len(all_tasks)}")
-            else:
-                print("Unknown command. Type 'help' for help")
-                
-        except KeyboardInterrupt:
-            print("\nGoodbye!")
-            break
-        except Exception as e:
-            print(f"Execution error: {e}")
-
-def main():
-    """Main function"""
-    import sys
-    
-    if len(sys.argv) > 1:
-        if sys.argv[1] == 'view':
-            view_database()
-        elif sys.argv[1] == 'interactive':
-            interactive_shell()
-        else:
-            print("Usage: python db_manager.py [view|interactive]")
-    else:
-        view_database()
-        interactive_shell()
-
-if __name__ == "__main__":
-    main()

embedding/manager.py

@@ -4,128 +4,93 @@ Model pool manager and cache system
 Support high-concurrency embedding retrieval services
 """
 
-import os
 import asyncio
-import time
-import pickle
-import hashlib
 import logging
-from typing import Dict, List, Optional, Any, Tuple
-from dataclasses import dataclass
-from collections import OrderedDict
-from utils.settings import SENTENCE_TRANSFORMER_MODEL
-import threading
-import psutil
+from typing import Dict, List, Any
+from utils.settings import (
+    EMBEDDING_API_KEY,
+    EMBEDDING_BASE_URL,
+    EMBEDDING_DIMENSIONS,
+    EMBEDDING_MODEL_NAME,
+    EMBEDDING_TIMEOUT,
+)
 import numpy as np
+import requests
 
-from sentence_transformers import SentenceTransformer
-import logging
 
 logger = logging.getLogger('app')
 
 
 class GlobalModelManager:
-    """Global model manager"""
+    """OpenAI-compatible embedding API manager."""
     
-    def __init__(self, model_name: str = 'TaylorAI/gte-tiny'):
-        self.model_name = model_name
-        self.local_model_path = "./models/gte-tiny"
-        self._model: Optional[SentenceTransformer] = None
-        self._lock = asyncio.Lock()
-        self._load_time = 0
-        self._device = 'cpu'
+    def __init__(self):
+        self.external_model_name = EMBEDDING_MODEL_NAME
+        self.external_base_url = EMBEDDING_BASE_URL.rstrip("/")
+        self.external_api_key = EMBEDDING_API_KEY
+        self.external_dimensions = EMBEDDING_DIMENSIONS
+        self.external_timeout = EMBEDDING_TIMEOUT
         
-        logger.info(f"GlobalModelManager initialized: {model_name}")
-    
-    async def get_model(self) -> SentenceTransformer:
-        """Get the model instance with lazy loading"""
-        if self._model is not None:
-            return self._model
-        
-        async with self._lock:
-            # Double-check
-            if self._model is not None:
-                return self._model
-            
-            try:
-                start_time = time.time()
-                
-                # Check the local model
-                model_path = self.local_model_path if os.path.exists(self.local_model_path) else self.model_name
-                
-                # Get device configuration
-                self._device = os.environ.get('SENTENCE_TRANSFORMER_DEVICE', 'cpu')
-                if self._device not in ['cpu', 'cuda', 'mps']:
-                    self._device = 'cpu'
-                
-                logger.info(f"Loading model: {model_path} (device: {self._device})")
-                
-                # Run blocking operations in the event loop executor
-                loop = asyncio.get_event_loop()
-                self._model = await loop.run_in_executor(
-                    None, 
-                    lambda: SentenceTransformer(
-                        model_path,
-                        device=self._device
-                    )
-                )
-                
-                self._load_time = time.time() - start_time
-                logger.info(f"Model loading completed: {self._load_time:.2f}s")
-                
-                return self._model
-                
-            except Exception as e:
-                logger.error(f"Model loading failed: {e}")
-                raise
+        logger.info(f"GlobalModelManager initialized: external_model={self.external_model_name}")
     
     async def encode_texts(self, texts: List[str], batch_size: int = 32) -> np.ndarray:
-        """Encode texts into vectors"""
+        """Encode texts into vectors through the external embedding API."""
         if not texts:
             return np.array([])
-        
-        model = await self.get_model()
-        
-        try:
-            # Run blocking operations in the event loop executor
-            loop = asyncio.get_event_loop()
-            embeddings = await loop.run_in_executor(
-                None,
-                lambda: model.encode(texts, batch_size=batch_size, show_progress_bar=False)
+
+        loop = asyncio.get_event_loop()
+        return await loop.run_in_executor(
+            None,
+            lambda: self._encode_texts_external(texts)
+        )
+
+    def encode_texts_sync(self, texts: List[str], batch_size: int = 32) -> np.ndarray:
+        """Synchronously encode texts. Used by synchronous integrations such as Mem0."""
+        if not texts:
+            return np.array([])
+
+        return self._encode_texts_external(texts)
+
+    def _encode_texts_external(self, texts: List[str]) -> np.ndarray:
+        if not self.external_base_url:
+            raise RuntimeError("EMBEDDING_BASE_URL is required for embedding API calls")
+
+        endpoint = f"{self.external_base_url}/embeddings"
+        headers = {"Content-Type": "application/json"}
+        if self.external_api_key:
+            headers["Authorization"] = f"Bearer {self.external_api_key}"
+
+        payload: Dict[str, Any] = {
+            "model": self.external_model_name,
+            "input": texts,
+        }
+        if self.external_dimensions and self.external_model_name not in ("text-embedding-ada-002", "local-embedding"):
+            payload["dimensions"] = self.external_dimensions
+
+        response = requests.post(
+            endpoint,
+            json=payload,
+            headers=headers,
+            timeout=self.external_timeout,
+        )
+        if response.status_code != 200:
+            raise RuntimeError(f"External embedding API failed: {response.status_code} - {response.text}")
+
+        data = response.json()
+        embeddings = [item["embedding"] for item in data.get("data", [])]
+        if len(embeddings) != len(texts):
+            raise RuntimeError(
+                f"External embedding API returned {len(embeddings)} embeddings for {len(texts)} texts"
             )
-            
-            # Ensure a NumPy array is returned
-            if hasattr(embeddings, 'cpu'):
-                embeddings = embeddings.cpu().numpy()
-            elif hasattr(embeddings, 'numpy'):
-                embeddings = embeddings.numpy()
-            elif not isinstance(embeddings, np.ndarray):
-                embeddings = np.array(embeddings)
-            
-            return embeddings
-            
-        except Exception as e:
-            logger.error(f"Text encoding failed: {e}")
-            raise
-    
-    def get_model_sync(self) -> Optional[SentenceTransformer]:
-        """Synchronously get the model instance for synchronous contexts
-
-        If the model is not loaded, return None. The caller should ensure the model is initialized via the async API first.
-
-        Returns:
-            The loaded SentenceTransformer model, or None
-        """
-        return self._model
+        return np.array(embeddings)
 
     def get_model_info(self) -> Dict[str, Any]:
         """Get model information"""
         return {
-            "model_name": self.model_name,
-            "local_model_path": self.local_model_path,
-            "device": self._device,
-            "is_loaded": self._model is not None,
-            "load_time": self._load_time
+            "provider": "openai_compatible",
+            "base_url": self.external_base_url,
+            "model_name": self.external_model_name,
+            "dimensions": self.external_dimensions,
         }
 
 
@@ -137,5 +102,5 @@ def get_model_manager() -> GlobalModelManager:
     """Get the model manager instance"""
     global _model_manager
     if _model_manager is None:
-        _model_manager = GlobalModelManager(SENTENCE_TRANSFORMER_MODEL)
+        _model_manager = GlobalModelManager()
     return _model_manager

poetry.lock

@@ -1837,21 +1837,6 @@ files = [
     {file = "httpx_sse-0.4.3.tar.gz", hash = "sha256:9b1ed0127459a66014aec3c56bebd93da3c1bc8bb6618c8082039a44889a755d"},
 ]
 
-[[package]]
-name = "huey"
-version = "2.6.0"
-description = "a little task queue"
-optional = false
-python-versions = "*"
-groups = ["main"]
-files = [
-    {file = "huey-2.6.0-py3-none-any.whl", hash = "sha256:1b9df9d370b49c6d5721ba8a01ac9a787cf86b3bdc584e4679de27b920395c3f"},
-    {file = "huey-2.6.0.tar.gz", hash = "sha256:8d11f8688999d65266af1425b831f6e3773e99415027177b8734b0ffd5e251f6"},
-]
-
-[package.extras]
-backends = ["redis (>=3.0.0)"]
-
 [[package]]
 name = "huggingface-hub"
 version = "0.36.2"
@@ -2848,26 +2833,6 @@ cli = ["python-dotenv (>=1.0.0)", "typer (>=0.16.0)"]
 rich = ["rich (>=13.9.4)"]
 ws = ["websockets (>=15.0.1)"]
 
-[[package]]
-name = "mcp-ui-server"
-version = "1.0.0"
-description = "mcp-ui Server SDK for Python"
-optional = false
-python-versions = ">=3.10"
-groups = ["main"]
-files = [
-    {file = "mcp_ui_server-1.0.0-py3-none-any.whl", hash = "sha256:85f53b2e4300fbd175f1fbb7c40f2566b1f4a4ad03a1f33647867c82a3159dcc"},
-    {file = "mcp_ui_server-1.0.0.tar.gz", hash = "sha256:5ab8f17b93bf794966af7c35e9a575e4f21a9ba2bab3d316cfc107a15f88a3c9"},
-]
-
-[package.dependencies]
-mcp = ">=1.0.0"
-pydantic = ">=2.0.0"
-typing-extensions = ">=4.0.0"
-
-[package.extras]
-dev = ["pyright (>=1.1.0)", "pytest (>=7.0.0)", "ruff (>=0.1.0)"]
-
 [[package]]
 name = "mdit-py-plugins"
 version = "0.6.1"
@@ -5049,6 +5014,23 @@ files = [
 beartype = ">=0.20.0,<1.0.0"
 requests = ">=2.30.0,<3.0.0"
 
+[[package]]
+name = "redis"
+version = "6.4.0"
+description = "Python client for Redis database and key-value store"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "redis-6.4.0-py3-none-any.whl", hash = "sha256:f0544fa9604264e9464cdf4814e7d4830f74b165d52f2a330a760a88dd248b7f"},
+    {file = "redis-6.4.0.tar.gz", hash = "sha256:b01bc7282b8444e28ec36b261df5375183bb47a07eb9c603f284e89cbc5ef010"},
+]
+
+[package.extras]
+hiredis = ["hiredis (>=3.2.0)"]
+jwt = ["pyjwt (>=2.9.0)"]
+ocsp = ["cryptography (>=36.0.1)", "pyopenssl (>=20.0.1)", "requests (>=2.31.0)"]
+
 [[package]]
 name = "referencing"
 version = "0.37.0"
@@ -7484,4 +7466,4 @@ cffi = ["cffi (>=1.17,<2.0) ; platform_python_implementation != \"PyPy\" and pyt
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.12,<3.15"
-content-hash = "ad25328ad4a88f9a9dd9d34d0f9a097079b837325bf05183fd429e0f37cbc0ed"
+content-hash = "ba8491ec2ecd7c783fac68f66e7994279d51f6a09fdc1ec435941c1af52db0cb"

prompt/novare.md

@@ -81,6 +81,24 @@
 - 告知用户是基于"3階執務スペース"范围搜索到的结果，并确认是否操作
 **响应**："「3階執務スペース、フォーラム側窓側」では見つかりませんでしたが、3階執務スペースエリアで照明が見つかりました。こちらの照明を操作しますか？"
 
+### 联系方式查询场景（CRITICAL - 必须区分查询与发送）
+**用户**："プランニンググループの連絡先はわかる?"（策划组的联系方式你知道吗？）
+- rag_retrieve(query="プランニンググループ 連絡先", top_k=100) → 优先查询知识库
+- 若知识库有结果：直接告知联系方式（电话、邮箱等）
+- 若知识库无结果：告知用户无法查询，提供替代方案
+**响应**："プランニンググループの連絡先を調べますね。" → [查询后告知具体联系方式或提供替代方案]
+**禁止**：此场景禁止调用 wowtalk_send_message_to_member（这是发送消息工具，不是查询联系方式）
+
+**用户**："PUの誰に連絡していいかわからない"（不知道该联系PU的谁）
+- rag_retrieve(query="PU 連絡先 組織図", top_k=100) → 优先查询知识库
+- find_employee_location(name="[相关人员]") → 如需查找具体人员位置
+- 告知用户相关联系信息或人员位置
+**响应**："PUの連絡先を確認しますね。" → [查询后告知联系方式或人员信息]
+
+**关键区别**：
+- 「連絡先を知りたい」「連絡先はわかる」「連絡方法を教えて」→ **查询场景**，使用 rag_retrieve 查询知识库
+- 「連絡して」「通知して」「メッセージを送って」→ **发送场景**，使用 wowtalk_send_message_to_member
+
 </scenarios>
 
 
@@ -206,6 +224,30 @@
 -   **条件**：用户意图为闲聊、问候、感谢、赞美等非实质性对话。
 -   **动作**：给予简洁、友好、拟人化的自然回复。
 
+9. 联系方式/组织图查询（CRITICAL - 与消息通知区分）
+-   **条件**：用户意图为查询联系方式、组织架构、部门电话等（关键词：連絡先、連絡方法、電話番号、メールアドレス、組織図、誰に連絡すれば、etc.）
+-   **动作**：
+    1.  **优先**调用【知识库检索】工具查询知识库（rag_retrieve，top_k=100）
+    2.  若知识库有结果：直接告知用户查询到的联系方式（电话、邮箱、组织架构等）
+    3.  若知识库无结果：调用【人员检索】工具查找相关人员（find_employee_location），告知用户人员位置信息
+    4.  **降级回复**（工具失败或无结果时）：提供替代方案，避免空循环
+-   **与消息通知的区别**：
+    - 「連絡先を知りたい」「連絡先はわかる」「連絡方法を教えて」→ 查询场景，使用 rag_retrieve
+    - 「連絡して」「通知して」「メッセージを送って」→ 发送场景，使用 wowtalk_send_message_to_member
+-   **禁止行为**：
+    - 禁止在用户查询联系方式时调用 wowtalk_send_message_to_member（这是发送消息工具）
+    - 禁止回复「もう一度試してみましょうか？」（空循环），必须提供降级方案
+
+10. 工具失败时的降级回复（CRITICAL - 避免空循环）
+-   **条件**：当工具调用失败或返回空结果时。
+-   **动作**：提供降级回复或替代方案，避免「もう一度試してみましょうか？」的空循环。
+-   **降级回复示例**：
+    - 联系方式查询失败："申し訳ございません、連絡先の確認ができませんでした。社内Wikiの組織図をご確認いただくか、総務担当にお問い合わせいただけますでしょうか？"
+    - 人员位置查询失败："申し訳ございません、現在の人の居場所を確認することができません。後でもう一度お試しいただくか、直接 WowTalk で連絡してみていただけますでしょうか？"
+    - 知识库查询失败："申し訳ございません、情報の検索に失敗しました。別の言葉で質問いただくか、後ほど再度お試しいただけますでしょうか？"
+    - 设备操作失败："申し訳ございません、設備の操作ができませんでした。しばらく待ってから再度お試しいただくか、設備担当にお問い合わせいただけますでしょうか？"
+-   **绝对禁止**：「もう一度試してみましょうか？」这种会导致空循环的回复。
+
 
 ## 设备控制确认机制
 

pyproject.toml

@@ -19,7 +19,7 @@ dependencies = [
     "numpy<2",
     "aiohttp",
     "aiofiles",
-    "huey (>=2.5.3,<3.0.0)",
+    "redis (>=4.0,<7.0)",
     "pandas>=1.5.0",
     "openpyxl>=3.0.0",
     "xlrd>=2.0.0",

requirements.txt

@@ -58,7 +58,6 @@ hpack==4.1.0 ; python_version >= "3.12" and python_version < "3.15"
 httpcore==1.0.9 ; python_version >= "3.12" and python_version < "3.15"
 httpx-sse==0.4.3 ; python_version >= "3.12" and python_version < "3.15"
 httpx==0.28.1 ; python_version >= "3.12" and python_version < "3.15"
-huey==2.6.0 ; python_version >= "3.12" and python_version < "3.15"
 huggingface-hub==0.36.2 ; python_version >= "3.12" and python_version < "3.15"
 hyperframe==6.1.0 ; python_version >= "3.12" and python_version < "3.15"
 idna==3.15 ; python_version >= "3.12" and python_version < "3.15"
@@ -96,7 +95,6 @@ linkify-it-py==2.1.0 ; python_version >= "3.12" and python_version < "3.15"
 markdown-it-py==4.2.0 ; python_version >= "3.12" and python_version < "3.15"
 markdownify==1.2.2 ; python_version >= "3.12" and python_version < "3.15"
 markupsafe==3.0.3 ; python_version >= "3.12" and python_version < "3.15"
-mcp-ui-server==1.0.0 ; python_version >= "3.12" and python_version < "3.15"
 mcp==1.12.4 ; python_version >= "3.12" and python_version < "3.15"
 mdit-py-plugins==0.6.1 ; python_version >= "3.12" and python_version < "3.15"
 mdurl==0.1.2 ; python_version >= "3.12" and python_version < "3.15"
@@ -166,6 +164,7 @@ pyyaml==6.0.3 ; python_version >= "3.12" and python_version < "3.15"
 qdrant-client==1.12.1 ; python_version >= "3.13" and python_version < "3.15"
 qdrant-client==1.18.0 ; python_version == "3.12"
 ragflow-sdk==0.23.1 ; python_version >= "3.12" and python_version < "3.15"
+redis==6.4.0 ; python_version >= "3.12" and python_version < "3.15"
 referencing==0.37.0 ; python_version >= "3.12" and python_version < "3.15"
 regex==2026.5.9 ; python_version >= "3.12" and python_version < "3.15"
 requests-toolbelt==1.0.0 ; python_version >= "3.12" and python_version < "3.15"

routes/files.py

@@ -1,273 +1,18 @@
 import os
 import uuid
 import shutil
-import zipfile
 from datetime import datetime
-from typing import Optional, List
-from fastapi import APIRouter, HTTPException, Header, UploadFile, File, Form
-from pydantic import BaseModel
+from typing import Optional
+from fastapi import APIRouter, HTTPException, UploadFile, File, Form
 import logging
 
 logger = logging.getLogger('app')
 
-from utils import (
-    DatasetRequest, QueueTaskRequest, IncrementalTaskRequest, QueueTaskResponse,
-    load_processed_files_log, remove_file_or_directory, remove_dataset_directory_by_key
-)
 from utils.fastapi_utils import get_versioned_filename
-from task_queue.manager import queue_manager
-from task_queue.integration_tasks import process_files_async, process_files_incremental_async, cleanup_project_async
-from task_queue.task_status import task_status_store
 
 router = APIRouter()
 
 
-@router.post("/api/v1/files/process/async")
-async def process_files_async_endpoint(request: QueueTaskRequest, authorization: Optional[str] = Header(None)):
-    """
-    Queue-based API for asynchronous file processing.
-    Same functionality as /api/v1/files/process, but processed asynchronously through the queue.
-    
-    Args:
-        request: QueueTaskRequest containing dataset_id, files, system_prompt, mcp_settings, and queue options
-        authorization: Authorization header containing API key (Bearer <API_KEY>)
-        
-    Returns:
-        QueueTaskResponse: Processing result with task ID for tracking
-    """
-    try:
-        dataset_id = request.dataset_id
-        if not dataset_id:
-            raise HTTPException(status_code=400, detail="dataset_id is required")
-        
-        # Estimate processing time (based on file count)
-        estimated_time = 0
-        if request.upload_folder:
-            # For upload_folder, file count cannot be estimated in advance, so use the default time
-            estimated_time = 120  # Default: 2 minutes
-        elif request.files:
-            total_files = sum(len(file_list) for file_list in request.files.values())
-            estimated_time = max(30, total_files * 10)  # Estimated 10 seconds per file, minimum 30 seconds
-        
-        # Create task status record
-        import uuid
-        task_id = str(uuid.uuid4())
-        task_status_store.set_status(
-            task_id=task_id,
-            unique_id=dataset_id,
-            status="pending"
-        )
-        
-        # Submit async task
-        task = process_files_async(
-            dataset_id=dataset_id,
-            files=request.files,
-            upload_folder=request.upload_folder,
-            task_id=task_id
-        )
-        
-        # Build a more detailed message
-        message = f"File processing task has been submitted to the queue, project ID: {dataset_id}"
-        if request.upload_folder:
-            group_count = len(request.upload_folder)
-            message += f", files will be scanned automatically from {group_count} uploaded folders"
-        elif request.files:
-            total_files = sum(len(file_list) for file_list in request.files.values())
-            message += f", including {total_files} files"
-        
-        return QueueTaskResponse(
-            success=True,
-            message=message,
-            dataset_id=dataset_id,
-            task_id=task_id,  # Use our own task_id
-            task_status="pending",
-            estimated_processing_time=estimated_time
-        )
-        
-    except HTTPException:
-        raise
-    except Exception as e:
-        logger.error(f"Error submitting async file processing task: {str(e)}")
-        raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")
-
-
-@router.post("/api/v1/files/process/incremental")
-async def process_files_incremental_endpoint(request: IncrementalTaskRequest, authorization: Optional[str] = Header(None)):
-    """
-    Queue-based API for incremental file processing, supporting file additions and deletions.
-    
-    Args:
-        request: IncrementalTaskRequest containing dataset_id, files_to_add, files_to_remove, system_prompt, mcp_settings, and queue options
-        authorization: Authorization header containing API key (Bearer <API_KEY>)
-        
-    Returns:
-        QueueTaskResponse: Processing result with task ID for tracking
-    """
-    try:
-        dataset_id = request.dataset_id
-        if not dataset_id:
-            raise HTTPException(status_code=400, detail="dataset_id is required")
-        
-        # Validate that there is at least one add or delete operation
-        if not request.files_to_add and not request.files_to_remove:
-            raise HTTPException(status_code=400, detail="At least one of files_to_add or files_to_remove must be provided")
-        
-        # Estimate processing time (based on file count)
-        estimated_time = 0
-        total_add_files = sum(len(file_list) for file_list in (request.files_to_add or {}).values())
-        total_remove_files = sum(len(file_list) for file_list in (request.files_to_remove or {}).values())
-        total_files = total_add_files + total_remove_files
-        estimated_time = max(30, total_files * 10)  # Estimated 10 seconds per file, minimum 30 seconds
-        
-        # Create task status record
-        import uuid
-        task_id = str(uuid.uuid4())
-        task_status_store.set_status(
-            task_id=task_id,
-            unique_id=dataset_id,
-            status="pending"
-        )
-        
-        # Submit incremental async task
-        task = process_files_incremental_async(
-            dataset_id=dataset_id,
-            files_to_add=request.files_to_add,
-            files_to_remove=request.files_to_remove,
-            system_prompt=request.system_prompt,
-            mcp_settings=request.mcp_settings,
-            task_id=task_id
-        )
-        
-        return QueueTaskResponse(
-            success=True,
-            message=f"Incremental file processing task has been submitted to the queue - added {total_add_files} files, removed {total_remove_files} files, project ID: {dataset_id}",
-            dataset_id=dataset_id,
-            task_id=task_id,
-            task_status="pending",
-            estimated_processing_time=estimated_time
-        )
-        
-    except HTTPException:
-        raise
-    except Exception as e:
-        logger.error(f"Error submitting incremental file processing task: {str(e)}")
-        raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")
-
-
-@router.get("/api/v1/files/{dataset_id}/status")
-async def get_files_processing_status(dataset_id: str):
-    """Get the file processing status for the project."""
-    try:
-        # Load processed files log
-        processed_log = load_processed_files_log(dataset_id)
-        
-        # Get project directory info
-        project_dir = os.path.join("projects", "data", dataset_id)
-        project_exists = os.path.exists(project_dir)
-        
-        # Collect document.txt files
-        document_files = []
-        if project_exists:
-            for root, dirs, files in os.walk(project_dir):
-                for file in files:
-                    if file == "document.txt":
-                        document_files.append(os.path.join(root, file))
-        
-        return {
-            "dataset_id": dataset_id,
-            "project_exists": project_exists,
-            "processed_files_count": len(processed_log),
-            "processed_files": processed_log,
-            "document_files_count": len(document_files),
-            "document_files": document_files,
-            "log_file_exists": os.path.exists(os.path.join("projects", "data", dataset_id, "processed_files.json"))
-        }
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Failed to retrieve file processing status: {str(e)}")
-
-
-@router.post("/api/v1/files/{dataset_id}/reset")
-async def reset_files_processing(dataset_id: str):
-    """Reset the project's file processing status by deleting the processing log and all files."""
-    try:
-        project_dir = os.path.join("projects", "data", dataset_id)
-        log_file = os.path.join("projects", "data", dataset_id, "processed_files.json")
-        
-        # Load processed log to know what files to remove
-        processed_log = load_processed_files_log(dataset_id)
-        
-        removed_files = []
-        # Remove all processed files and their dataset directories
-        for file_hash, file_info in processed_log.items():
-            # Remove local file in files directory
-            if 'local_path' in file_info:
-                if remove_file_or_directory(file_info['local_path']):
-                    removed_files.append(file_info['local_path'])
-            
-            # Handle new key-based structure first
-            if 'key' in file_info:
-                # Remove dataset directory by key
-                key = file_info['key']
-                if remove_dataset_directory_by_key(dataset_id, key):
-                    removed_files.append(f"dataset/{key}")
-            elif 'filename' in file_info:
-                # Fallback to old filename-based structure
-                filename_without_ext = os.path.splitext(file_info['filename'])[0]
-                dataset_dir = os.path.join("projects", "data", dataset_id, "datasets", filename_without_ext)
-                if remove_file_or_directory(dataset_dir):
-                    removed_files.append(dataset_dir)
-            
-            # Also remove any specific dataset path if exists (fallback)
-            if 'dataset_path' in file_info:
-                if remove_file_or_directory(file_info['dataset_path']):
-                    removed_files.append(file_info['dataset_path'])
-        
-        # Remove the log file
-        if remove_file_or_directory(log_file):
-            removed_files.append(log_file)
-        
-        # Remove the entire files directory
-        files_dir = os.path.join(project_dir, "files")
-        if remove_file_or_directory(files_dir):
-            removed_files.append(files_dir)
-        
-        # Also remove the entire dataset directory (clean up any remaining files)
-        dataset_dir = os.path.join(project_dir, "datasets")
-        if remove_file_or_directory(dataset_dir):
-            removed_files.append(dataset_dir)
-        
-        # Remove README.md if exists
-        readme_file = os.path.join(project_dir, "README.md")
-        if remove_file_or_directory(readme_file):
-            removed_files.append(readme_file)
-        
-        return {
-            "message": f"File processing status reset successfully: {dataset_id}",
-            "removed_files_count": len(removed_files),
-            "removed_files": removed_files
-        }
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Failed to reset file processing status: {str(e)}")
-
-
-@router.post("/api/v1/files/{dataset_id}/cleanup/async")
-async def cleanup_project_async_endpoint(dataset_id: str, remove_all: bool = False):
-    """Asynchronously clean up project files."""
-    try:
-        task = cleanup_project_async(dataset_id=dataset_id, remove_all=remove_all)
-        
-        return {
-            "success": True,
-            "message": f"Project cleanup task has been submitted to the queue, project ID: {dataset_id}",
-            "dataset_id": dataset_id,
-            "task_id": task.id,
-            "action": "remove_all" if remove_all else "cleanup_logs"
-        }
-    except Exception as e:
-        logger.error(f"Error submitting cleanup task: {str(e)}")
-        raise HTTPException(status_code=500, detail=f"Failed to submit cleanup task: {str(e)}")
-
-
 @router.post("/api/v1/upload")
 async def upload_file(file: UploadFile = File(...), folder: Optional[str] = Form(None)):
     """
@@ -348,121 +93,3 @@ async def upload_file(file: UploadFile = File(...), folder: Optional[str] = Form
     except Exception as e:
         logger.error(f"Error uploading file: {str(e)}")
         raise HTTPException(status_code=500, detail=f"File upload failed: {str(e)}")
-
-
-# Task management routes that are related to file processing
-@router.get("/api/v1/task/{task_id}/status")
-async def get_task_status(task_id: str):
-    """Get task status - simple and reliable."""
-    try:
-        status_data = task_status_store.get_status(task_id)
-        
-        if not status_data:
-            return {
-                "success": False,
-                "message": "Task does not exist or has expired",
-                "task_id": task_id,
-                "status": "not_found"
-            }
-        
-        return {
-            "success": True,
-            "message": "Task status retrieved successfully",
-            "task_id": task_id,
-            "status": status_data["status"],
-            "unique_id": status_data["unique_id"],
-            "created_at": status_data["created_at"],
-            "updated_at": status_data["updated_at"],
-            "result": status_data.get("result"),
-            "error": status_data.get("error")
-        }
-        
-    except Exception as e:
-        logger.error(f"Error getting task status: {str(e)}")
-        raise HTTPException(status_code=500, detail=f"Failed to retrieve task status: {str(e)}")
-
-
-@router.delete("/api/v1/task/{task_id}")
-async def delete_task(task_id: str):
-    """Delete task record."""
-    try:
-        success = task_status_store.delete_status(task_id)
-        if success:
-            return {
-                "success": True,
-                "message": f"Task record deleted: {task_id}",
-                "task_id": task_id
-            }
-        else:
-            return {
-                "success": False,
-                "message": f"Task record does not exist: {task_id}",
-                "task_id": task_id
-            }
-    except Exception as e:
-        logger.error(f"Error deleting task: {str(e)}")
-        raise HTTPException(status_code=500, detail=f"Failed to delete task record: {str(e)}")
-
-
-@router.get("/api/v1/tasks")
-async def list_tasks(status: Optional[str] = None, dataset_id: Optional[str] = None, limit: int = 100):
-    """List tasks with optional filters."""
-    try:
-        if status or dataset_id:
-            # Use search function
-            tasks = task_status_store.search_tasks(status=status, unique_id=dataset_id, limit=limit)
-        else:
-            # Get all tasks
-            all_tasks = task_status_store.list_all()
-            tasks = list(all_tasks.values())[:limit]
-        
-        return {
-            "success": True,
-            "message": "Task list retrieved successfully",
-            "total_tasks": len(tasks),
-            "tasks": tasks,
-            "filters": {
-                "status": status,
-                "dataset_id": dataset_id,
-                "limit": limit
-            }
-        }
-        
-    except Exception as e:
-        logger.error(f"Error listing tasks: {str(e)}")
-        raise HTTPException(status_code=500, detail=f"Failed to retrieve task list: {str(e)}")
-
-
-@router.get("/api/v1/tasks/statistics")
-async def get_task_statistics():
-    """Get task statistics."""
-    try:
-        stats = task_status_store.get_statistics()
-        
-        return {
-            "success": True,
-            "message": "Statistics retrieved successfully",
-            "statistics": stats
-        }
-        
-    except Exception as e:
-        logger.error(f"Error getting statistics: {str(e)}")
-        raise HTTPException(status_code=500, detail=f"Failed to retrieve statistics: {str(e)}")
-
-
-@router.post("/api/v1/tasks/cleanup")
-async def cleanup_tasks(older_than_days: int = 7):
-    """Clean up old task records."""
-    try:
-        deleted_count = task_status_store.cleanup_old_tasks(older_than_days=older_than_days)
-        
-        return {
-            "success": True,
-            "message": f"Cleaned up {deleted_count} old task records",
-            "deleted_count": deleted_count,
-            "older_than_days": older_than_days
-        }
-        
-    except Exception as e:
-        logger.error(f"Error cleaning up tasks: {str(e)}")
-        raise HTTPException(status_code=500, detail=f"Failed to clean up task records: {str(e)}")

routes/projects.py

@@ -6,8 +6,6 @@ import logging
 
 logger = logging.getLogger('app')
 
-from task_queue.task_status import task_status_store
-
 router = APIRouter()
 
 
@@ -155,22 +153,3 @@ async def list_datasets():
     except Exception as e:
         logger.error(f"Error listing datasets: {str(e)}")
         raise HTTPException(status_code=500, detail=f"Failed to retrieve dataset list: {str(e)}")
-
-
-@router.get("/api/v1/projects/{dataset_id}/tasks")
-async def get_project_tasks(dataset_id: str):
-    """Get all tasks for the specified project."""
-    try:
-        tasks = task_status_store.get_by_unique_id(dataset_id)
-        
-        return {
-            "success": True,
-            "message": "Project tasks retrieved successfully",
-            "dataset_id": dataset_id,
-            "total_tasks": len(tasks),
-            "tasks": tasks
-        }
-        
-    except Exception as e:
-        logger.error(f"Error getting project tasks: {str(e)}")
-        raise HTTPException(status_code=500, detail=f"Failed to retrieve project tasks: {str(e)}")

skills/developing/content-compliance-reviewer/SKILL.md

@@ -0,0 +1,288 @@
+---
+name: content-compliance-reviewer
+description: 对企业对外发布内容（新闻稿、社媒、广告物料、对外邮件/函件等）做严格的合规与风险审核，逐项检查违法违规红线、违反广告法的绝对化用语、夸大/误导宣传、敏感信息与个人信息（PII）泄露、未授权对外承诺、知识产权、品牌一致性、事实准确性、渠道受众适配、完整性等风险点，遵循“疑罪从严、对外即不可逆、存疑即退回”的从严原则。输出两个相互独立、不可混淆的字段：①【审核决策】只有「放行 / 退回」二选一，决定流程往哪走；②【决策说明】承载放行后仍需关注的细节，或退回的理由。当收到对外内容数据、内容发布审批、对外宣传审核、文案合规检查、content review、PR/广告/社媒发布审核等请求，或拿到包含 title/content/content_type/channel 等字段的对外内容表单数据需要判断是否可发布时，务必使用本技能。只输出结构化文本，不要输出 JSON。
+category: Compliance & Security
+---
+
+# 对外内容发布合规审核助手（Content Compliance Reviewer）· 从严版
+
+## Overview
+
+本技能面向企业 OA 对外内容发布流程，对一篇拟对外发布的内容做**自动初审**，识别合规、敏感信息与夸大宣传风险。
+
+本技能采用**从严审核（fail-closed）立场**：对外发布**一经发出即不可逆**，当合规存疑、含敏感信息、宣传无依据、信息不足以排除风险时，**默认退回**而非放行。宁可让发起人多改一版，也不放过一篇存疑内容流向人工审批后被发布。初审的价值在于把好第一道关，把明显违规或无法核实的内容挡在前面。
+
+⚠️ **核心模型：决策（二元）与说明（文本）是两个完全独立的字段，绝不可混为一谈。**
+
+1. **【审核决策】**：**只有两种、互斥、二选一** —— **放行** 或 **退回**。这是唯一驱动 OA 流程往哪走的字段。
+   - **放行**：内容流向下一个人工审批节点（如经理、法务、品牌）。
+   - **退回**：内容打回发起人修改，修改后可重新提交；流程未终止。
+2. **【决策说明】**：一段文本，**与决策正交**，用来承载“为什么”和“注意什么”。
+   - 放行时：写**通过后仍需提醒人工审批人关注的细节**（没有就写“无”）。
+   - 退回时：写**必须退回修改的理由**。
+
+> 不存在“需关注”这种第三种决策。“需关注”不是一个决策档位，而是 **决策=放行 + 决策说明里写明了要关注的点**。把“是否放行”和“有没有要关注/要修改的事”彻底拆开，是本技能最重要的纪律。
+
+定位说明：
+
+- 你是**初审 agent**，只负责审核并**输出文本结论**。
+- 下游 OA 系统会把你的文本交给另一个 LLM 做 JSON 结构化提取，因此你**绝对不要自己输出 JSON、代码块或伪代码**，只输出下文规定格式的自然语言文本。
+- 你只能在“放行 / 退回”之间二选一，**无权终止流程**。你的“退回”=打回修改（可重提），**不等于**人工审批环节里的“驳回”（流程直接终止）。
+
+## Triggering Cues
+
+出现以下任一情况就使用本技能：
+
+- 中文：内容发布审批、对外宣传审核、文案合规检查、新闻稿审核、社媒/公众号审核、广告合规审核、对外函件审核
+- 英文：content review, content compliance check, PR review, marketing copy review, social media review
+- 收到一段对外内容表单数据（含 `title` 标题、`content` 正文、`content_type` 内容类型、`channel` 发布渠道 等字段），要求判断是否可以发布。
+
+## 输入（Input）
+
+通常会收到一篇对外内容的字段数据，常见字段：
+
+| 字段 key | 含义 | 说明 |
+|---|---|---|
+| `title` | 内容标题 | 必填 |
+| `content` | 发布正文 | 必填；审核主体 |
+| `content_type` | 内容类型 | pr(新闻稿) / social(社媒) / ad(广告) / letter(对外函件) / other |
+| `channel` | 发布渠道 | website / social / media / email / offline |
+| `target_audience` | 目标受众 | 可能没有；用于渠道-受众适配判断 |
+| `attachment` | 配图/物料 | URL 或附件标识；可能含图片中的文案/IP 风险 |
+| `reason` | 发布目的/背景 | 自由文本 |
+| `author` / `dept` | 发起人/部门 | 可能没有 |
+
+字段缺失时：
+- **必填项（标题、正文）缺失或为占位**：一律视为硬性缺陷，**退回**。
+- **可选上下文（受众、配图、背景）缺失**：不因此卡死流程，但要在 `【说明与假设】` 中说明“因缺少 X 无法核验 Y”，并按从严方向取舍（存疑即偏向退回）。
+
+## 审核要点清单（核心）
+
+逐项检查以下 10 类。每条标注：**检查什么 → 何时判为问题 → 严重度**。严重度分 `高/中/低`。
+
+> 从严总原则：**“对外发布不可逆 + 合规红线”优先于一切**。任何触碰法律/监管红线或可能泄露敏感信息的内容，一律按“高”处理。
+
+### 1. 违法违规红线 —— 字段 `title` `content`
+- 检查：是否涉政治敏感、违反法律法规、歧视/侮辱性内容、虚假信息、被禁止的宣传。
+- 异常：命中任一红线 → 违法违规（**高**，退回）。
+- 严重度：**高**（无此类信号则跳过）。
+
+### 2. 违反广告法的绝对化用语 —— 字段 `content` `title`
+- 检查：是否含“最/第一/国家级/顶级/唯一/首选/100%/绝无仅有”等绝对化、极限用语。
+- 异常：含绝对化/极限用语且无合法依据（如非权威排名、无认证支撑）→ 涉嫌违反广告法（**高**，退回）。
+- 严重度：**高**（无则跳过）。
+
+### 3. 夸大 / 误导宣传 —— 字段 `content`
+- 检查：功效、数据、对比是否有依据、是否可能误导消费者。
+- 异常：
+  - 宣称功效/数据但**无任何依据来源**、或明显夸大 → 误导宣传（**高**，退回）。
+  - 表述偏夸张但尚在合理修辞范围、未涉硬指标 → **中**。
+- 严重度：高 / 中。
+
+### 4. 敏感信息 / 个人信息（PII）泄露 —— 字段 `content` `attachment`
+- 检查：是否含手机号、身份证号、邮箱、住址、银行卡、客户隐私、员工个人信息、内部机密/未公开数据。
+- 异常：
+  - 正文/配图含未脱敏 PII、客户隐私、内部机密、未公开财务/经营数据 → 泄露风险（**高**，退回）。
+  - 含可识别个体但已部分脱敏、仍存风险 → **中**。
+- 严重度：高 / 中。
+
+### 5. 未授权对外承诺 —— 字段 `content`
+- 检查：是否对外做出价格、合作、交付、法律或赔偿承诺，且可能超出授权。
+- 异常：
+  - 含价格/折扣/合作/法律承诺且无授权依据 → 越权承诺（**高**，退回，要求法务/管理层确认）。
+  - 含软性意向但措辞模糊、风险有限 → **中**。
+- 严重度：高 / 中。
+
+### 6. 知识产权 —— 字段 `content` `attachment`
+- 检查：是否引用第三方图片、商标、文字、音乐等且可能未授权。
+- 异常：
+  - 明显使用第三方受保护内容（他人商标/明星肖像/版权图）且无授权说明 → 侵权风险（**高**，退回）。
+  - 疑似引用但来源不明、需核实 → **中**。
+- 严重度：高 / 中。
+
+### 7. 品牌一致性 —— 字段 `title` `content`
+- 检查：公司名、商标、slogan、产品名是否使用正确、是否与品牌规范冲突。
+- 异常：公司名/商标/产品名拼写或用法错误、与官方规范不符 → 品牌瑕疵（**中**）。
+- 严重度：**中**（无则跳过）。
+
+### 8. 事实准确性 —— 字段 `content`
+- 检查：所述数据、时间、引用、头衔是否自洽、是否可核。
+- 异常：内部明显矛盾、数据/时间自相冲突、引用存疑 → 事实风险（**中**）。
+- 严重度：**中**（无则跳过）。
+
+### 9. 渠道 / 受众适配 —— `channel` × `target_audience` × `content`
+- 检查：内容口吻、敏感度是否与渠道、受众匹配（如面向未成年、面向监管渠道）。
+- 异常：内容与渠道/受众明显不适配（如严肃监管渠道用营销夸张话术、面向未成年含不适宜内容）→ 适配风险（**中**）。
+- 严重度：**中**（无则跳过）。
+
+### 10. 完整性与占位/测试数据 —— 跨字段
+- 检查：是否缺必要的免责声明/落款/署名；是否含明显占位或测试数据（如 content=test、标题为占位符）。
+- 异常：
+  - 含明显占位/测试数据、正文为空壳 → 数据无效（**高**，退回）。
+  - 缺免责声明/落款等要件但内容本身合规 → **低/中**。
+- 严重度：高 / 中 / 低（按上述）。
+
+> 字段标注约定：当某条发现指向具体字段时，**用字段英文 key**（`title`/`content`/`attachment`/`channel` 等）标注，方便下游结构化。
+
+## 判定规则：先定决策，再写说明（从严）
+
+两个字段分两步独立产出，**顺序不能反、内容不能串**：
+
+### 第一步：定【审核决策】（放行 / 退回，二选一）
+
+依次判断，命中任一条即 **退回**：
+
+| 命中情况 | 决策 |
+|---|---|
+| ① 存在任一 **高** 级缺陷（违法违规、绝对化用语、夸大误导、PII/敏感信息泄露、越权承诺、IP 侵权、占位测试数据等） | **退回** |
+| ② **没有高级缺陷，但累计存在 ≥ 2 条“中”级风险**（多个中级风险叠加，整体合规性已不可靠） | **退回** |
+| ③ 仅有 **1 条“中”级风险或仅“低”级风险** | **放行**（在说明里提示） |
+| ④ 完全无风险 | **放行** |
+
+> 从严要点：
+> 1. 对外发布不可逆，合规存疑/含敏感信息/宣传无依据时按“高”处理、倾向退回（fail-closed）。
+> 2. **中级风险会叠加**：单条中级放行，但 ≥2 条中级即退回。
+
+### 第二步：写【决策说明】（与决策正交的文本）
+
+- 决策=**放行** → 说明里写：放行后仍需人工审批人关注的点（把那条“中”级或“低”级风险概括进来）；**若完全无风险，就写“无”**。
+- 决策=**退回** → 说明里写：导致退回的缺陷是什么（逐条点明高级缺陷，或指出是哪几条中级风险叠加）、需要发起人怎么改。
+
+置信度：根据信息完整度与判断确定性给出 `高/中/低`（或 0–100% 区间）。信息缺失越多、判断越主观，置信度越低。**注意：置信度低不改变从严倾向——信息越不足，越应倾向退回。**
+
+## 输出格式（Output Format）
+
+**只输出下面这种结构化中文文本，不要输出 JSON、不要用代码块包裹。** 按固定小标题组织，便于下游 LLM 抽取：
+
+```
+【审核决策】放行 / 退回（二选一，这是唯一驱动流程的字段）
+【决策说明】放行时写需提醒审批人关注的点（无则“无”）；退回时写退回修改的理由。
+【一句话摘要】用一句话说明本次决策的核心原因。
+【置信度】高 / 中 / 低（或百分比）
+
+【风险发现】
+1. 字段：content ｜ 严重度：高 ｜ 问题：正文含“全国第一”绝对化用语，涉嫌违反广告法 ｜ 建议：删除或替换绝对化用语
+2. 字段：content ｜ 严重度：中 ｜ 问题：公司名拼写与官方规范不一致 ｜ 建议：核对并统一品牌名称
+...（无风险时写“无”）
+
+【说明与假设】列出做判断时假设的前提、缺失的上下文（如未提供配图实物、无品牌规范文档等），以及因信息不足而做出的从严取舍。
+```
+
+要求：
+
+- 【审核决策】**只能填“放行”或“退回”**，不得出现第三种值、不得写“需关注/通过/不通过”等含糊词。
+- 【决策说明】与【审核决策】严格对应：放行时是“关注点/无”，退回时是“退回理由”。
+- 【风险发现】每条固定四段：`字段 ｜ 严重度 ｜ 问题 ｜ 建议`，用全角竖线 `｜` 分隔；严重度只用 `高/中/低`。
+- 自洽性：有任一“高”级风险 ⇒ 决策必为“退回”；“中”级风险 ≥ 2 条 ⇒ 决策必为“退回”；仅 1 条“中”级或仅“低”级 ⇒ 决策为“放行”。
+
+## Workflow
+
+1. 解析输入的内容字段（title/content/content_type/channel 等）。
+2. 按“审核要点清单”10 类逐项检查，记录命中的每条风险（字段、严重度、问题、建议）。
+3. **第一步定决策（从严）**：有“高”级缺陷→退回；无高级但“中”级≥2条→退回；否则→放行；对外不可逆，合规/敏感信息存疑时按“高”处理、倾向退回。
+4. **第二步写说明**：放行则概括关注点（无则“无”），退回则逐条写明退回理由与整改要求。
+5. 评估置信度（信息越不足越低，但不改变从严倾向）。
+6. 按“输出格式”输出结构化文本，**不要输出 JSON**。
+
+## Examples
+
+### 示例 1：绝对化用语 → 退回
+输入：
+```
+title=新品发布, content_type=ad, channel=social, content=我们的产品是全国第一，效果最好，100%有效！
+```
+输出风格：
+```
+【审核决策】退回
+【决策说明】正文含“全国第一”“最好”“100%有效”等绝对化与无依据功效用语，涉嫌违反广告法；请删除或替换为可证实的客观表述后重新提交。
+【一句话摘要】多处绝对化与夸大功效用语触碰广告法红线。
+【置信度】高
+
+【风险发现】
+1. 字段：content ｜ 严重度：高 ｜ 问题：含“全国第一/最好/100%有效”绝对化及无依据功效用语，涉嫌违反广告法 ｜ 建议：删除或替换为可证实的客观表述
+
+【说明与假设】未提供权威排名或认证依据，按广告法从严退回。
+```
+
+### 示例 2：PII 泄露 → 退回
+输入：
+```
+title=客户答谢, content_type=social, channel=social, content=感谢客户王芳（手机13800138000）的支持，我们将持续服务。
+```
+输出风格：
+```
+【审核决策】退回
+【决策说明】正文公开了客户姓名与手机号等个人信息，存在 PII 泄露与侵权风险；请脱敏或删除个人信息后重新提交。
+【一句话摘要】正文含未脱敏客户个人信息，泄露风险高。
+【置信度】高
+
+【风险发现】
+1. 字段：content ｜ 严重度：高 ｜ 问题：公开客户姓名与手机号，存在个人信息泄露与侵权风险 ｜ 建议：对外发布前脱敏或删除个人信息
+
+【说明与假设】未获得客户授权信息，按数据合规从严退回。
+```
+
+### 示例 3：品牌瑕疵 → 放行（说明里写关注点）
+输入：
+```
+title=季度产品更新, content_type=pr, channel=website, content=本季度 Felo 智能助手新增多项功能，提升团队协作效率。我们将持续迭代。, reason=官网产品动态更新
+```
+输出风格：
+```
+【审核决策】放行
+【决策说明】内容合规、无敏感信息与夸大宣传；仅提示审批人核对“Felo 智能助手”产品名与官方品牌规范是否一致。无硬性缺陷。
+【一句话摘要】内容合规，仅一条品牌一致性的中级提示，放行进入人工审批。
+【置信度】中
+
+【风险发现】
+1. 字段：content ｜ 严重度：中 ｜ 问题：产品名“Felo 智能助手”需核对是否与官方品牌规范一致 ｜ 建议：核对并统一品牌/产品名称
+
+【说明与假设】未提供品牌规范文档，品牌名以常识判断。
+```
+
+### 示例 4：两条中级风险叠加 → 退回
+输入：
+```
+title=合作喜讯, content_type=pr, channel=media, content=我司效果领先行业，已与多家知名企业达成战略合作，预计明年营收翻倍。, target_audience=媒体记者
+```
+输出风格：
+```
+【审核决策】退回
+【决策说明】“效果领先行业”属无依据的夸大表述（中），“预计明年营收翻倍”为未经核实的前瞻性数据、面向媒体渠道风险较高（中）；两条中级风险叠加，整体合规性不可靠，请补充依据或改为客观表述后重新提交。
+【一句话摘要】夸大表述叠加未经核实的前瞻数据，面向媒体从严退回。
+【置信度】中
+
+【风险发现】
+1. 字段：content ｜ 严重度：中 ｜ 问题：“效果领先行业”无依据，涉嫌夸大宣传 ｜ 建议：补充客观依据或删除
+2. 字段：content ｜ 严重度：中 ｜ 问题：“预计营收翻倍”为未经核实前瞻数据，面向媒体渠道风险高 ｜ 建议：删除或附合规的前瞻性声明
+
+【说明与假设】未提供业绩数据来源与披露合规审查，依据中级风险叠加规则退回。
+```
+
+### 示例 5：正常合规内容 → 放行（说明为“无”）
+输入：
+```
+title=节日放假通知, content_type=letter, channel=email, content=尊敬的合作伙伴，我司将于法定节假日按规定放假，期间业务支持照常，祝节日愉快。, reason=对外合作伙伴放假通知
+```
+输出风格：
+```
+【审核决策】放行
+【决策说明】无。
+【一句话摘要】内容为常规放假通知，无合规、敏感信息或夸大宣传风险。
+【置信度】高
+
+【风险发现】无
+
+【说明与假设】基于当前内容字段判断，未发现异常。
+```
+
+## Guidelines
+
+- **只输出文本**，绝不输出 JSON / 代码 / Markdown 表格作为最终结论（示例里的代码块仅为演示排版，实际回复直接给文本）。
+- **决策与说明分离是铁律**：先用判定规则定出放行/退回，再独立去写说明。
+- **从严是本版本的基调**：对外发布不可逆，合规存疑、含敏感信息、宣传无依据、信息不足以排除风险时，**默认退回**。把“能不能确保这篇内容合法合规、不泄露敏感信息、不夸大误导、不越权承诺”作为放行的前提。
+- **中级风险会叠加**：单条中级放行并提示；≥2 条中级即退回。统计中级条数时如实计数。
+- 判定要**稳定可复现**：同样的输入应给出同样的决策，便于下游提取与回归测试。
+- 缺少可选上下文（配图实物、品牌规范、授权依据）时，在 `【说明与假设】` 里说明，并按从严方向取舍；不要凭空编造依据。
+- 这是**初审辅助**，不替代法务/品牌/公关的最终判断；措辞用“疑似/建议/需核实”，但从严不等于含糊——退回理由要具体、可整改。
+- 决策与风险严重度必须自洽：有“高”必“退回”；中级 ≥2 必“退回”；仅 1 条中级或仅低级必“放行”。

skills/developing/leave-approval-reviewer/SKILL.md

@@ -0,0 +1,233 @@
+---
+name: leave-approval-reviewer
+description: 对请假申请做合理性与合规性初审，逐项检查请假类型与天数自洽、事由充分性、长假对项目的影响、病假凭证、超额/频繁请假、时效与时间冲突、占位/测试数据等风险点，遵循“信息不足以判断即偏向退回核实”的从严原则。输出两个相互独立、不可混淆的字段：①【审核决策】只有「放行 / 退回」二选一，决定流程往哪走；②【决策说明】承载放行后仍需关注的细节，或退回的理由。当收到请假申请数据、请假审批、休假审核、leave review、请假合规检查等请求，或拿到包含 leave_type/days/reason 等字段的请假表单数据需要判断是否通过时，务必使用本技能。只输出结构化文本，不要输出 JSON。
+category: Compliance & Security
+---
+
+# 请假审批审核助手（Leave Approval Reviewer）· 从严版
+
+## Overview
+
+本技能面向企业 OA 请假流程，对一张请假申请做**自动初审**，识别合理性与合规性风险。
+
+本技能采用**从严审核（fail-closed）立场**：当请假类型与天数不自洽、事由不足、信息不足以判断合理性时，**默认退回**核实而非放行。初审的价值在于把好第一道关，把明显有问题或无法判断的请假挡在前面，再交人工审批。
+
+⚠️ **核心模型：决策（二元）与说明（文本）是两个完全独立的字段，绝不可混为一谈。**
+
+1. **【审核决策】**：**只有两种、互斥、二选一** —— **放行** 或 **退回**。这是唯一驱动 OA 流程往哪走的字段。
+   - **放行**：单据流向下一个人工审批节点（如经理）。
+   - **退回**：单据打回发起人修改，修改后可重新提交；流程未终止。
+2. **【决策说明】**：一段文本，**与决策正交**，用来承载“为什么”和“注意什么”。
+   - 放行时：写**通过后仍需提醒人工审批人关注的细节**（没有就写“无”）。
+   - 退回时：写**必须退回修改的理由**。
+
+> 不存在“需关注”这种第三种决策。“需关注”不是一个决策档位，而是 **决策=放行 + 决策说明里写明了要关注的点**。把“是否放行”和“有没有要关注/要修改的事”彻底拆开，是本技能最重要的纪律。
+
+定位说明：
+
+- 你是**初审 agent**，只负责审核并**输出文本结论**。
+- 下游 OA 系统会把你的文本交给另一个 LLM 做 JSON 结构化提取，因此你**绝对不要自己输出 JSON、代码块或伪代码**，只输出下文规定格式的自然语言文本。
+- 你只能在“放行 / 退回”之间二选一，**无权终止流程**。你的“退回”=打回修改（可重提），**不等于**人工审批环节里的“驳回”（流程直接终止）。
+
+## Triggering Cues
+
+出现以下任一情况就使用本技能：
+
+- 中文：请假审批、请假审核、请假初审、休假审核、请假合规检查、年假/病假/事假审批
+- 英文：leave review, leave approval, time-off request review
+- 收到一段请假表单数据（含 `leave_type` 请假类型、`days` 天数、`reason` 事由 等字段），要求判断是否可以通过审批。
+
+## 输入（Input）
+
+通常会收到一张请假申请的字段数据，常见字段：
+
+| 字段 key | 含义 | 说明 |
+|---|---|---|
+| `leave_type` | 请假类型 | annual(年假) / sick(病假) / personal(事假) |
+| `days` | 请假天数 | 必填；≤0 或非数字即退回 |
+| `reason` | 请假事由 | 自由文本 |
+| `start_date` / `end_date` | 起止日期 | 可能没有；用于核对与天数自洽、时间冲突 |
+| `cert_img` | 病假证明/凭证 | 可能没有；病假长假一般应有 |
+| `balance` | 假期余额 | 可能没有；用于判断是否超额 |
+| `creator` / `dept` | 发起人/部门 | 可能没有 |
+
+字段缺失时：
+- **必填项（天数）缺失或无效（≤0、非数字）**：视为硬性缺陷，**退回**。
+- **可选上下文（日期、余额、凭证）缺失**：不因此卡死流程，但要在 `【说明与假设】` 中说明“因缺少 X 无法核验 Y”，并按从严方向取舍。
+
+## 审核要点清单（核心）
+
+逐项检查以下 8 类。每条标注：**检查什么 → 何时判为问题 → 严重度**。严重度分 `高/中/低`。
+
+### 1. 天数有效性 —— 字段 `days`
+- 检查：天数是否为 0/负数/非数字、是否异常巨大。
+- 异常：`days` ≤ 0、非数字、明显不合逻辑 → 数据无效（**高**，退回）。
+- 严重度：**高**。
+
+### 2. 类型与天数自洽 —— `leave_type` × `days`
+- 检查：天数与类型是否吻合（如年假是否超出常规额度、事假/病假是否过长）。
+- 异常：
+  - 类型与天数明显不自洽、或单次天数异常长（如事假连休数十天无说明）→ 需核实（**中**；信息严重不足则升级）。
+- 严重度：**中**（无异常则跳过）。
+
+### 3. 事由充分性 —— 字段 `reason`
+- 检查：事由是否能说明请假原因。
+- 异常：
+  - 事由**缺失、仅写“请假/有事/个人原因”等无信息词** → 无法判断（**中**，退回）。
+  - 事由有内容但偏笼统 → **低**。
+- 严重度：中 / 低。
+
+### 4. 长假对项目的影响 —— `days`
+- 检查：长假是否需要交接或提级关注。
+- 异常：连续请假**超过约 5 天**（具体阈值随公司制度）→ 需关注排期与交接（**中**，放行但提示）。
+- 严重度：**中**。
+
+### 5. 病假凭证 —— `leave_type=sick` × `cert_img`
+- 检查：较长病假是否附医疗证明。
+- 异常：病假且天数较长（如 > 3 天）但**未附任何病假证明** → 凭证缺失（**中**；若制度强制则升级为高）。
+- 严重度：中 /（制度强制时）高。无该字段或短病假则按从严方向在说明中提示。
+
+### 6. 超额 / 频繁请假 —— `days` × `balance` × 历史
+- 检查：是否超出假期余额、是否近期频繁请假。
+- 异常：
+  - 提供 `balance` 且**请假天数超出余额** → 超额（**高**，退回，要求确认假期类型/余额）。
+  - 提供历史上下文且呈现频繁请假模式 → 需关注（**中**）。
+- 严重度：高 / 中。无余额/历史则不臆断，仅按其它要点判断。
+
+### 7. 时效与时间冲突 —— `start_date` × `end_date` × `days`
+- 检查：起止日期与天数是否自洽、是否为已过去的补请、是否与已知冲突。
+- 异常：
+  - 起止日期与 `days` 明显矛盾 → 数据矛盾（**高**，退回）。
+  - 大幅事后补请且无说明 → 时效问题（**中**）。
+- 严重度：高 / 中。无日期字段则跳过。
+
+### 8. 数据自洽与可疑信号 —— 跨字段
+- 检查：字段是否相互矛盾、是否含明显占位/测试数据（如 reason=test、days=999）。
+- 异常：字段矛盾、疑似测试/占位数据 → **高**（退回，宁缺毋滥）。
+- 严重度：**高**（无此类信号则跳过）。
+
+> 字段标注约定：当某条发现指向具体字段时，**用字段英文 key**（`days`/`leave_type`/`reason`/`cert_img` 等）标注，方便下游结构化。
+
+## 判定规则：先定决策，再写说明（从严）
+
+两个字段分两步独立产出，**顺序不能反、内容不能串**：
+
+### 第一步：定【审核决策】（放行 / 退回，二选一）
+
+依次判断，命中任一条即 **退回**：
+
+| 命中情况 | 决策 |
+|---|---|
+| ① 存在任一 **高** 级缺陷（天数无效、超额、日期与天数矛盾、字段矛盾、占位测试数据等） | **退回** |
+| ② **没有高级缺陷，但累计存在 ≥ 2 条“中”级风险** | **退回** |
+| ③ 仅有 **1 条“中”级风险或仅“低”级风险** | **放行**（在说明里提示） |
+| ④ 完全无风险 | **放行** |
+
+> 从严要点：信息不足以判断合理性时按从严方向取舍、倾向退回核实；中级风险会叠加（单条放行、≥2 条退回）。
+
+### 第二步：写【决策说明】（与决策正交的文本）
+
+- 决策=**放行** → 说明里写：放行后仍需人工审批人关注的点（如长假需交接）；**若完全无风险，就写“无”**。
+- 决策=**退回** → 说明里写：导致退回的缺陷是什么、需要发起人怎么改。
+
+置信度：根据信息完整度给出 `高/中/低`。信息越不足，置信度越低，但**不改变从严倾向**。
+
+## 输出格式（Output Format）
+
+**只输出下面这种结构化中文文本，不要输出 JSON、不要用代码块包裹。** 按固定小标题组织，便于下游 LLM 抽取：
+
+```
+【审核决策】放行 / 退回（二选一，这是唯一驱动流程的字段）
+【决策说明】放行时写需提醒审批人关注的点（无则“无”）；退回时写退回修改的理由。
+【一句话摘要】用一句话说明本次决策的核心原因。
+【置信度】高 / 中 / 低（或百分比）
+
+【风险发现】
+1. 字段：days ｜ 严重度：中 ｜ 问题：连续请假 7 天，超过 5 天需关注排期与交接 ｜ 建议：确认是否影响项目排期并安排交接
+...（无风险时写“无”）
+
+【说明与假设】列出做判断时假设的前提、缺失的上下文（如未提供假期余额、无起止日期等），以及因信息不足而做出的从严取舍。
+```
+
+要求：
+
+- 【审核决策】**只能填“放行”或“退回”**，不得出现第三种值、不得写“需关注/通过/不通过”等含糊词。
+- 【决策说明】与【审核决策】严格对应：放行时是“关注点/无”，退回时是“退回理由”。
+- 【风险发现】每条固定四段：`字段 ｜ 严重度 ｜ 问题 ｜ 建议`，用全角竖线 `｜` 分隔；严重度只用 `高/中/低`。
+- 自洽性：有任一“高”级风险 ⇒ 决策必为“退回”；“中”级风险 ≥ 2 条 ⇒ 决策必为“退回”；仅 1 条“中”级或仅“低”级 ⇒ 决策为“放行”。
+
+## Workflow
+
+1. 解析输入的请假字段（leave_type/days/reason 等）。
+2. 按“审核要点清单”8 类逐项检查，记录命中的每条风险（字段、严重度、问题、建议）。
+3. **第一步定决策（从严）**：有“高”级缺陷→退回；无高级但“中”级≥2条→退回；否则→放行；信息不足以判断时按从严方向取舍。
+4. **第二步写说明**：放行则概括关注点（无则“无”），退回则逐条写明退回理由与整改要求。
+5. 评估置信度（信息越不足越低，但不改变从严倾向）。
+6. 按“输出格式”输出结构化文本，**不要输出 JSON**。
+
+## Examples
+
+### 示例 1：正常短假 → 放行（说明为“无”）
+输入：
+```
+leave_type=annual, days=2, reason=休息调整，处理家中事务
+```
+输出风格：
+```
+【审核决策】放行
+【决策说明】无。
+【一句话摘要】年假 2 天、事由清晰、无超额或冲突，无明显风险。
+【置信度】高
+
+【风险发现】无
+
+【说明与假设】未提供假期余额，按当前字段判断未发现异常。
+```
+
+### 示例 2：长假 → 放行（说明里写交接提示）
+输入：
+```
+leave_type=annual, days=7, reason=回老家探亲并安排家庭事务
+```
+输出风格：
+```
+【审核决策】放行
+【决策说明】连续请假 7 天属长假，请审批人确认是否影响项目排期并安排工作交接；事由清晰，无硬性缺陷，仅此一条长假关注。
+【一句话摘要】长假属唯一“中”级关注，事由清晰，放行进入人工审批并提示交接。
+【置信度】中
+
+【风险发现】
+1. 字段：days ｜ 严重度：中 ｜ 问题：连续请假 7 天超过 5 天，需关注排期与交接 ｜ 建议：确认项目影响并安排交接
+
+【说明与假设】未提供起止日期与假期余额，长假影响以常识判断。
+```
+
+### 示例 3：天数无效 → 退回
+输入：
+```
+leave_type=personal, days=0, reason=有事
+```
+输出风格：
+```
+【审核决策】退回
+【决策说明】请假天数为 0 属无效数据，且事由“有事”过于笼统无法判断；请填写有效天数并补充具体事由后重新提交。
+【一句话摘要】天数无效叠加事由不足，数据无法支撑审批。
+【置信度】高
+
+【风险发现】
+1. 字段：days ｜ 严重度：高 ｜ 问题：请假天数为 0，属无效数据 ｜ 建议：填写有效的请假天数
+2. 字段：reason ｜ 严重度：中 ｜ 问题：事由“有事”笼统，无法判断请假原因 ｜ 建议：补充具体事由
+
+【说明与假设】仅基于当前字段判断，未提供起止日期。
+```
+
+## Guidelines
+
+- **只输出文本**，绝不输出 JSON / 代码 / Markdown 表格作为最终结论（示例里的代码块仅为演示排版，实际回复直接给文本）。
+- **决策与说明分离是铁律**：先用判定规则定出放行/退回，再独立去写说明。
+- **从严但克制**：请假是常规事务，不要无端制造风险；只在天数无效、超额、字段矛盾、信息严重不足以判断时退回。长假、单条事由笼统等属“放行+提示”，不要误判为退回。
+- **中级风险会叠加**：单条中级放行并提示；≥2 条中级即退回。
+- 判定要**稳定可复现**：同样的输入应给出同样的决策，便于下游提取与回归测试。
+- 缺少可选上下文（余额、日期、凭证）时，在 `【说明与假设】` 里说明，并按从严方向取舍；不要凭空编造数据。
+- 这是**初审辅助**，不替代 HR/上级的最终判断；措辞用“疑似/建议/需核实”，但退回理由要具体、可整改。
+- 决策与风险严重度必须自洽：有“高”必“退回”；中级 ≥2 必“退回”；仅 1 条中级或仅低级必“放行”。

skills/developing/purchase-approval-reviewer/SKILL.md

@@ -0,0 +1,300 @@
+---
+name: purchase-approval-reviewer
+description: 对采购申请单据做严格的合规性、必要性与报价合理性审核，逐项检查供应商合规、金额与预算、报价/比价材料、物品与事由一致性、重复/拆分采购、报价合理性、数量金额自洽、采购时效等风险点，遵循“疑罪从严、无法核验即退回”的从严原则。输出两个相互独立、不可混淆的字段：①【审核决策】只有「放行 / 退回」二选一，决定流程往哪走；②【决策说明】承载放行后仍需关注的细节，或退回的理由。当收到采购申请数据、采购审批、采购审核、purchase review、采购合规检查、请购单审批等请求，或拿到包含 item_name/amount/supplier_name/category/reason 等字段的采购表单数据需要判断是否通过时，务必使用本技能。只输出结构化文本，不要输出 JSON。
+category: Compliance & Security
+---
+
+# 采购申请审核助手（Purchase Approval Reviewer）· 从严版
+
+## Overview
+
+本技能面向企业 OA 采购流程，对一张采购申请单做**自动初审**，识别合规、必要性与报价合理性风险。
+
+本技能采用**从严审核（fail-closed）立场**：当供应商无法核验、必要性存疑、报价缺乏支撑、信息不足以排除风险时，**默认退回**而非放行。宁可让发起人多补一次材料，也不放过一张存疑采购单进入人工审批。初审的价值在于把好第一道关，把明显有问题或无法核实的采购挡在前面。
+
+⚠️ **核心模型：决策（二元）与说明（文本）是两个完全独立的字段，绝不可混为一谈。**
+
+1. **【审核决策】**：**只有两种、互斥、二选一** —— **放行** 或 **退回**。这是唯一驱动 OA 流程往哪走的字段。
+   - **放行**：单据流向下一个人工审批节点（如经理、财务）。
+   - **退回**：单据打回发起人修改，修改后可重新提交；流程未终止。
+2. **【决策说明】**：一段文本，**与决策正交**，用来承载“为什么”和“注意什么”。
+   - 放行时：写**通过后仍需提醒人工审批人关注的细节**（没有就写“无”）。
+   - 退回时：写**必须退回修改的理由**。
+
+> 不存在“需关注”这种第三种决策。“需关注”不是一个决策档位，而是 **决策=放行 + 决策说明里写明了要关注的点**。把“是否放行”和“有没有要关注/要修改的事”彻底拆开，是本技能最重要的纪律。
+
+定位说明：
+
+- 你是**初审 agent**，只负责审核并**输出文本结论**。
+- 下游 OA 系统会把你的文本交给另一个 LLM 做 JSON 结构化提取，因此你**绝对不要自己输出 JSON、代码块或伪代码**，只输出下文规定格式的自然语言文本。
+- 你只能在“放行 / 退回”之间二选一，**无权终止流程**。你的“退回”=打回修改（可重提），**不等于**人工审批环节里的“驳回”（流程直接终止）。
+
+## Triggering Cues
+
+出现以下任一情况就使用本技能：
+
+- 中文：采购审批、采购审核、采购申请初审、请购单审批、采购合规检查、供应商合规审核、比价审核
+- 英文：purchase review, procurement approval, purchase requisition review, vendor compliance check
+- 收到一段采购表单数据（含 `item_name` 采购物品、`amount` 金额、`supplier_name` 供应商、`category` 采购类型、`reason` 事由 等字段），要求判断是否可以通过审批。
+
+## 输入（Input）
+
+通常会收到一张采购申请单的字段数据，常见字段：
+
+| 字段 key | 含义 | 说明 |
+|---|---|---|
+| `item_name` | 采购物品/服务 | 必填 |
+| `amount` | 采购金额（预算，元） | 必填 |
+| `quantity` | 数量 | 用于核对单价×数量与总额自洽 |
+| `supplier_name` | 供应商名称 | 必填；**应为合规企业主体**，个人/疑似关联方存疑 |
+| `quote_img` | 报价单/比价材料 | URL 或附件标识，**为空表示未附报价/比价** |
+| `category` | 采购类型 | it / office / marketing / service / other |
+| `reason` | 采购事由/用途 | 自由文本，应能说明必要性 |
+| `expected_date` | 期望到货/交付日期 | 可能没有 |
+| `budget_ref` / `dept` | 关联预算/项目、部门 | 可能没有 |
+
+字段缺失时：
+- **必填项（物品、金额、供应商）缺失**：一律视为硬性缺陷，**退回**。
+- **可选上下文（预算基准、历史采购、报价材料）缺失**：不因此卡死流程，但要在 `【说明与假设】` 中明确指出“因缺少 X 无法核验 Y”，并在判定时**按从严方向取舍**（存疑即偏向退回）。
+
+## 审核要点清单（核心）
+
+逐项检查以下 10 类。每条标注：**检查什么 → 何时判为问题 → 严重度**。严重度分 `高/中/低`。
+
+> 从严总原则：**“供应商与必要性是否可核验”优先于“金额大小”**。任何让合规性/真实性无法核验的缺陷，一律按“高”处理。
+
+### 1. 报价 / 比价凭证完整性 —— 字段 `quote_img` × `amount`
+- 检查：是否提供报价单/比价材料；大额采购是否具备比价（一般 ≥3 家或有定价依据）。
+- 异常：
+  - 大额（> 10000 元）**未附任何报价/比价材料** → 报价无支撑（**中**；若同时事由笼统则升级为风险叠加）。
+  - 大额（> 50000 元）**无比价、无定价依据** → 重大采购无支撑（**高**，退回）。
+- 严重度：高 / 中。
+
+### 2. 金额合规性与阈值 —— 字段 `amount`
+- 检查：金额是否为 0/负数/非数字、是否超部门或项目预算、是否疑似异常偏大。
+- 异常与严重度：
+  - 金额 ≤ 0、非数字、明显不合逻辑 → 数据无效（**高**，退回）。
+  - 提供预算基准且**超预算** → 超支（**高**，退回，要求调整或补审批）。
+  - 单笔 **> 50000 元** 且依据不足 → 大额无依据（**高**，退回）。
+  - 单笔 **> 10000 元** 但物品、供应商、报价齐全 → 大额（**中**，放行但强提示人工核对权限与预算）。
+- 严重度：高 / 中。
+
+### 3. 供应商合规 —— 字段 `supplier_name`
+- 检查：供应商是否为空、是否疑似个人主体、是否疑似关联方、是否在黑名单（若提供）。
+- 异常：
+  - `supplier_name` 为空 → 无法核验采购对象（**高**，退回）。
+  - 供应商形如个人姓名（含“先生/女士/个人”，或 2–4 字且不含“公司/企业/中心/厂/店/所/院/校/部/行”等机构词）→ 疑似个人/私下采购（**高**，退回）。
+  - 提供关联方/黑名单清单且命中 → 关联交易/禁用供应商（**高**，退回）。
+  - 供应商信息不完整但不矛盾 → **中**。
+- 严重度：高 / 中。
+
+### 4. 物品与事由一致性 / 私人物品特征 —— `item_name` × `category` × `reason`
+- 检查：采购物品与事由、类型是否吻合；是否透出个人消费迹象。
+- 异常：
+  - 物品与事由**明显矛盾**（如 item=服务器 但 reason=团建聚餐）→ 分类/用途错误（**高**，退回）。
+  - 物品出现明显私人性质（个人数码、家庭用品、礼品无对象说明等）且无业务关联 → 疑似私购公报（**高**，退回）。
+  - 物品与事由部分不贴合、表述含糊但不矛盾 → **中**。
+- 严重度：高 / 中。
+
+### 5. 必要性 / 重复采购 —— `item_name` × `reason` × 历史
+- 检查：采购必要性是否成立；是否近期重复采购同物、是否可复用现有资产。
+- 异常：
+  - 提供历史上下文且出现**近期同物重复采购**或可明显复用现有资产 → 重复采购（**高**，退回）。
+  - 必要性表述笼统、无法判断是否真需要 → **中**。
+- 严重度：高 / 中。无历史上下文则不臆断重复，仅按必要性表述评估。
+
+### 6. 报价合理性（真实性嗅探）—— `amount` × `quantity` × `item_name`
+- 检查：单价/总价相对物品市场常识是否离谱、是否凑整估报。
+- 异常：
+  - 单价相对物品**严重离谱**（如普通办公椅报数万元）→ 疑似虚高/夹带（**高**，退回）。
+  - 金额为**异常规整大整数**（正好 5000/10000/50000）且无报价支撑 → 疑似估报凑整（**中**）。
+- 严重度：高 / 中。
+
+### 7. 拆分采购 / 规避招标嫌疑 —— `amount` × 阈值
+- 检查：金额是否“恰好卡在审批/招标阈值下方”、是否疑似把大额拆成多单规避比价或招标。
+- 异常：金额逼近且略低于常见阈值（如 4800、4900、9700、9900、49000 等卡点值）且无合理说明 → 拆分嫌疑（**高**，退回，要求说明或合并）。
+- 严重度：**高**（无此信号则跳过）。
+
+### 8. 数量与金额自洽 —— `quantity` × `amount`
+- 检查：若提供单价或可推断，`单价 × 数量` 是否与 `amount` 吻合。
+- 异常：
+  - 数量与总额**明显不自洽**（如数量 1 但总额异常巨大且无说明）→ 数据矛盾（**高**，退回）。
+  - 轻微出入、可由税费/运费解释 → **中**。
+- 严重度：高 / 中。无数量字段则跳过。
+
+### 9. 事由充分性 —— 字段 `reason`
+- 检查：事由是否具体、能说明“买什么、为什么、用途/必要性”。
+- 异常：
+  - 事由**缺失、仅写“采购/日常/备货”等无信息词、或少于约 6 个有效字** → 无法判断必要性（**中**，退回；必要性是采购合规的基本要件）。
+  - 事由有内容但偏笼统、缺关键要素 → **低**。
+- 严重度：中 / 低。
+
+### 10. 数据自洽与可疑信号 —— 跨字段
+- 检查：各字段是否相互矛盾、是否含明显占位/测试数据（如 item=test、amount=1）。
+- 异常：字段间矛盾、疑似测试/占位数据、信息明显不足以核验真实性 → **高**（退回，宁缺毋滥）。
+- 严重度：**高**（无此类信号则跳过）。
+
+> 字段标注约定：当某条发现指向具体字段时，**用字段英文 key**（`amount`/`supplier_name`/`reason`/`quote_img`/`quantity` 等）标注，方便下游结构化。
+
+## 判定规则：先定决策，再写说明（从严）
+
+两个字段分两步独立产出，**顺序不能反、内容不能串**：
+
+### 第一步：定【审核决策】（放行 / 退回，二选一）
+
+依次判断，命中任一条即 **退回**：
+
+| 命中情况 | 决策 |
+|---|---|
+| ① 存在任一 **高** 级缺陷（供应商无法核验、金额无效/超预算、拆分嫌疑、私购公报、物品事由矛盾、重复采购、报价离谱、字段矛盾、重大采购无比价等） | **退回** |
+| ② **没有高级缺陷，但累计存在 ≥ 2 条“中”级风险**（多个中级风险叠加，整体合规性/必要性已不可靠） | **退回** |
+| ③ 仅有 **1 条“中”级风险或仅“低”级风险** | **放行**（在说明里提示） |
+| ④ 完全无风险 | **放行** |
+
+> 从严要点：
+> 1. 真实性/合规无法核验时按“高”处理、倾向退回（fail-closed）。
+> 2. **中级风险会叠加**：单条中级放行，但 ≥2 条中级即退回。
+
+### 第二步：写【决策说明】（与决策正交的文本）
+
+- 决策=**放行** → 说明里写：放行后仍需人工审批人关注的点（把那条“中”级或“低”级风险概括进来）；**若完全无风险，就写“无”**。
+- 决策=**退回** → 说明里写：导致退回的缺陷是什么（逐条点明高级缺陷，或指出是哪几条中级风险叠加）、需要发起人怎么改。
+
+置信度：根据信息完整度与判断确定性给出 `高/中/低`（或 0–100% 区间）。信息缺失越多、判断越主观，置信度越低。**注意：置信度低不改变从严倾向——信息越不足，越应倾向退回。**
+
+## 输出格式（Output Format）
+
+**只输出下面这种结构化中文文本，不要输出 JSON、不要用代码块包裹。** 按固定小标题组织，便于下游 LLM 抽取：
+
+```
+【审核决策】放行 / 退回（二选一，这是唯一驱动流程的字段）
+【决策说明】放行时写需提醒审批人关注的点（无则“无”）；退回时写退回修改的理由。
+【一句话摘要】用一句话说明本次决策的核心原因。
+【置信度】高 / 中 / 低（或百分比）
+
+【风险发现】
+1. 字段：supplier_name ｜ 严重度：高 ｜ 问题：供应商疑似个人主体，存在关联交易风险 ｜ 建议：改用合规企业供应商并补充资质
+2. 字段：amount ｜ 严重度：中 ｜ 问题：采购金额 12000 元超 10000 元但未附比价 ｜ 建议：补充至少一份报价单
+...（无风险时写“无”）
+
+【说明与假设】列出做判断时假设的前提、缺失的上下文（如未提供部门预算、无历史采购记录等），以及因信息不足而做出的从严取舍。
+```
+
+要求：
+
+- 【审核决策】**只能填“放行”或“退回”**，不得出现第三种值、不得写“需关注/通过/不通过”等含糊词。
+- 【决策说明】与【审核决策】严格对应：放行时是“关注点/无”，退回时是“退回理由”。
+- 【风险发现】每条固定四段：`字段 ｜ 严重度 ｜ 问题 ｜ 建议`，用全角竖线 `｜` 分隔；严重度只用 `高/中/低`。
+- 自洽性：有任一“高”级风险 ⇒ 决策必为“退回”；“中”级风险 ≥ 2 条 ⇒ 决策必为“退回”；仅 1 条“中”级或仅“低”级 ⇒ 决策为“放行”。
+
+## Workflow
+
+1. 解析输入的采购字段（item_name/amount/supplier_name/category/reason 等）。
+2. 按“审核要点清单”10 类逐项检查，记录命中的每条风险（字段、严重度、问题、建议）。
+3. **第一步定决策（从严）**：有“高”级缺陷→退回；无高级但“中”级≥2条→退回；否则→放行；信息不足以核验合规/必要性时按“高”处理、倾向退回。
+4. **第二步写说明**：放行则概括关注点（无则“无”），退回则逐条写明退回理由与整改要求。
+5. 评估置信度（信息越不足越低，但不改变从严倾向）。
+6. 按“输出格式”输出结构化文本，**不要输出 JSON**。
+
+## Examples
+
+### 示例 1：供应商疑似个人 → 退回
+输入：
+```
+item_name=笔记本电脑, amount=8000, supplier_name=张先生, category=it, reason=研发部备机
+```
+输出风格：
+```
+【审核决策】退回
+【决策说明】供应商「张先生」疑似个人主体，存在私下采购/关联交易风险，无法核验采购合规性，请改用合规企业供应商并补充资质后重新提交。
+【一句话摘要】供应商为个人主体，采购对象无法核验。
+【置信度】高
+
+【风险发现】
+1. 字段：supplier_name ｜ 严重度：高 ｜ 问题：供应商「张先生」疑似个人主体，存在关联交易/私下采购风险 ｜ 建议：改用合规企业供应商并补充资质
+
+【说明与假设】未提供供应商资质与历史采购记录，仅基于供应商名称判断。
+```
+
+### 示例 2：拆分采购嫌疑 → 退回
+输入：
+```
+item_name=办公椅, amount=4900, quantity=10, supplier_name=XX家具有限公司, category=office, reason=新工位采购
+```
+输出风格：
+```
+【审核决策】退回
+【决策说明】金额 4900 元恰好卡在 5000 元审批阈值下方，且 10 把办公椅总额仅 4900 元偏低、疑似拆分规避比价；请说明定价依据或合并采购后重新提交。
+【一句话摘要】金额卡阈值下方，存在拆分采购规避审批的嫌疑。
+【置信度】中
+
+【风险发现】
+1. 字段：amount ｜ 严重度：高 ｜ 问题：金额 4900 元恰好卡在 5000 元阈值下方，疑似拆分采购规避比价 ｜ 建议：说明定价依据或合并采购后重新提交
+
+【说明与假设】未提供部门预算与同期采购记录，依据卡点值规则从严退回核实。
+```
+
+### 示例 3：大额但材料齐全 → 放行（说明里写关注点）
+输入：
+```
+item_name=研发服务器2台, amount=46000, quantity=2, supplier_name=XX科技有限公司, quote_img=http://img/quote.pdf, category=it, reason=AI训练集群扩容，含两台GPU服务器，附三家比价
+```
+输出风格：
+```
+【审核决策】放行
+【决策说明】金额 46000 元属大额，请审批人核对预算额度与审批权限；物品、供应商、比价材料齐全，事由具体，无硬性缺陷，仅此一条大额关注。
+【一句话摘要】大额属唯一“中”级风险，供应商与比价齐全，放行进入人工审批并强提示金额。
+【置信度】中
+
+【风险发现】
+1. 字段：amount ｜ 严重度：中 ｜ 问题：采购金额 46000 元属大额，需核对预算与审批权限 ｜ 建议：审批人核对部门预算与审批权限
+
+【说明与假设】部门预算基准未提供，金额合理性以比价材料与常识判断。
+```
+
+### 示例 4：两条中级风险叠加 → 退回
+输入：
+```
+item_name=办公用品, amount=12000, supplier_name=XX贸易有限公司, category=office, reason=日常采购
+```
+输出风格：
+```
+【审核决策】退回
+【决策说明】金额 12000 元超 10000 元但未附任何报价/比价材料（中），且事由“日常采购”笼统、无法判断必要性与用途（中）；两条中级风险叠加，整体合理性不可靠，请补充比价与采购明细后重新提交。
+【一句话摘要】大额无比价叠加事由笼统，两条中级风险触发从严退回。
+【置信度】中
+
+【风险发现】
+1. 字段：quote_img ｜ 严重度：中 ｜ 问题：金额 12000 元超 10000 元但未附报价/比价材料 ｜ 建议：补充至少一份报价单或比价记录
+2. 字段：reason ｜ 严重度：中 ｜ 问题：事由“日常采购”笼统，无法判断必要性与用途 ｜ 建议：补全采购物品明细与用途
+
+【说明与假设】未提供报价材料与预算；依据中级风险叠加规则退回核实。
+```
+
+### 示例 5：正常小额 → 放行（说明为“无”）
+输入：
+```
+item_name=A4打印纸20箱, amount=1200, quantity=20, supplier_name=XX办公用品有限公司, quote_img=http://img/q.png, category=office, reason=行政部季度办公耗材补充
+```
+输出风格：
+```
+【审核决策】放行
+【决策说明】无。
+【一句话摘要】金额小、物品与事由一致、供应商合规、有报价，无明显风险。
+【置信度】高
+
+【风险发现】无
+
+【说明与假设】基于当前单据字段判断，未发现异常。
+```
+
+## Guidelines
+
+- **只输出文本**，绝不输出 JSON / 代码 / Markdown 表格作为最终结论（示例里的代码块仅为演示排版，实际回复直接给文本）。
+- **决策与说明分离是铁律**：先用判定规则定出放行/退回，再独立去写说明。
+- **从严是本版本的基调**：供应商无法核验、必要性存疑、报价缺乏支撑、信息不足以排除风险时，**默认退回**。把“能不能证明这笔采购是真实、必要、合规、定价合理的”作为放行的前提。
+- **中级风险会叠加**：单条中级放行并提示；≥2 条中级即退回。统计中级条数时如实计数。
+- 判定要**稳定可复现**：同样的输入应给出同样的决策，便于下游提取与回归测试。
+- 缺少可选上下文（预算标准、历史采购、报价材料）时，在 `【说明与假设】` 里说明，并按从严方向取舍；不要凭空编造数据。
+- 这是**初审辅助**，不替代采购/财务的最终判断；措辞用“疑似/建议/需核实”，但从严不等于含糊——退回理由要具体、可整改。
+- 决策与风险严重度必须自洽：有“高”必“退回”；中级 ≥2 必“退回”；仅 1 条中级或仅低级必“放行”。

start_all_optimized.sh

@@ -1,5 +1,5 @@
 #!/bin/bash
-# Optimized startup script - integrates the FastAPI application and queue consumer
+# Optimized startup script for the FastAPI application
 
 set -e
 
@@ -7,7 +7,6 @@ set -e
 DEFAULT_HOST="0.0.0.0"
 DEFAULT_PORT="8001"
 DEFAULT_API_WORKERS="4"
-DEFAULT_QUEUE_WORKERS="2"
 DEFAULT_PROFILE="balanced"
 DEFAULT_LOG_LEVEL="info"
 DEFAULT_MAX_RESTARTS="3"
@@ -17,7 +16,6 @@ DEFAULT_CHECK_INTERVAL="5"
 HOST=${HOST:-$DEFAULT_HOST}
 PORT=${PORT:-$DEFAULT_PORT}
 API_WORKERS=${API_WORKERS:-$DEFAULT_API_WORKERS}
-QUEUE_WORKERS=${QUEUE_WORKERS:-$DEFAULT_QUEUE_WORKERS}
 PROFILE=${PROFILE:-$DEFAULT_PROFILE}
 LOG_LEVEL=${LOG_LEVEL:-$DEFAULT_LOG_LEVEL}
 MAX_RESTARTS=${MAX_RESTARTS:-$DEFAULT_MAX_RESTARTS}
@@ -47,7 +45,6 @@ print_config() {
     print_color $GREEN "Startup configuration:"
     echo "- API server: http://$HOST:$PORT"
     echo "- API worker processes: $API_WORKERS"
-    echo "- Queue worker threads: $QUEUE_WORKERS"
     echo "- Performance profile: $PROFILE"
     echo "- Log level: $LOG_LEVEL"
     echo "- Maximum restarts: $MAX_RESTARTS"
@@ -87,7 +84,6 @@ create_directories() {
     print_color $YELLOW "Creating project directories..."
 
     directories=(
-        "projects/queue_data"
         "projects/data"
         "projects/uploads"
         "projects/robot"
@@ -161,16 +157,6 @@ start_services() {
     API_PID=$!
     echo "API server PID: $API_PID"
 
-    # Start the queue consumer
-    print_color $BLUE "Starting queue consumer..."
-    python3 task_queue/consumer.py \
-        --workers=$QUEUE_WORKERS \
-        --worker-type=threads \
-        > queue_consumer.log 2>&1 &
-
-    CONSUMER_PID=$!
-    echo "Queue consumer PID: $CONSUMER_PID"
-
     echo
     print_color $GREEN "All services started successfully!"
     print_color $GREEN "API server: http://$HOST:$PORT"
@@ -179,7 +165,7 @@ start_services() {
 }
 
 monitor_services() {
-    local restart_counts=(0 0)  # API, Consumer
+    local restart_counts=(0)  # API
 
     while true; do
         # Check the API server
@@ -205,26 +191,6 @@ monitor_services() {
             fi
         fi
 
-        # Check the queue consumer
-        if ! kill -0 $CONSUMER_PID 2>/dev/null; then
-            print_color $RED "Queue consumer stopped unexpectedly"
-
-            if [ ${restart_counts[1]} -lt $MAX_RESTARTS ]; then
-                print_color $YELLOW "Restarting queue consumer (${restart_counts[1]} + 1/$MAX_RESTARTS)..."
-                python3 task_queue/consumer.py \
-                    --workers=$QUEUE_WORKERS \
-                    --worker-type=threads \
-                    >> queue_consumer.log 2>&1 &
-
-                CONSUMER_PID=$!
-                restart_counts[1]=$((restart_counts[1] + 1))
-                print_color $GREEN "Queue consumer restarted successfully, PID: $CONSUMER_PID"
-            else
-                print_color $RED "Queue consumer restart limit reached, stopping all services"
-                break
-            fi
-        fi
-
         # Wait for the next check interval
         sleep $CHECK_INTERVAL
     done
@@ -253,25 +219,6 @@ cleanup() {
         fi
     fi
 
-    # Stop the queue consumer
-    if [ ! -z "$CONSUMER_PID" ] && kill -0 $CONSUMER_PID 2>/dev/null; then
-        print_color $BLUE "Stopping queue consumer (PID: $CONSUMER_PID)..."
-        kill $CONSUMER_PID 2>/dev/null || true
-
-        # Wait for graceful shutdown
-        local count=0
-        while kill -0 $CONSUMER_PID 2>/dev/null && [ $count -lt 10 ]; do
-            sleep 1
-            count=$((count + 1))
-        done
-
-        # Force terminate if it is still running
-        if kill -0 $CONSUMER_PID 2>/dev/null; then
-            print_color $RED "Force stopping queue consumer..."
-            kill -9 $CONSUMER_PID 2>/dev/null || true
-        fi
-    fi
-
     print_color $GREEN "All services have been stopped"
     exit 0
 }
@@ -288,7 +235,6 @@ main() {
         echo "  HOST               API bind host address (default: $DEFAULT_HOST)"
         echo "  PORT               API bind port (default: $DEFAULT_PORT)"
         echo "  API_WORKERS        Number of API worker processes (default: $DEFAULT_API_WORKERS)"
-        echo "  QUEUE_WORKERS      Number of queue worker threads (default: $DEFAULT_QUEUE_WORKERS)"
         echo "  PROFILE            Performance profile: low_memory, balanced, high_performance (default: $DEFAULT_PROFILE)"
         echo "  LOG_LEVEL          Log level: debug, info, warning, error (default: $DEFAULT_LOG_LEVEL)"
         echo "  MAX_RESTARTS       Maximum restart count (default: $DEFAULT_MAX_RESTARTS)"
@@ -296,7 +242,7 @@ main() {
         echo
         echo "Examples:"
         echo "  PROFILE=high_performance API_WORKERS=8 $0"
-        echo "  PORT=8080 QUEUE_WORKERS=4 $0"
+        echo "  PORT=8080 API_WORKERS=4 $0"
         exit 0
     fi
 

start_unified.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 """
-Optimized unified startup script combining the FastAPI application and queue consumer.
+Optimized unified startup script for the FastAPI application.
 Supports performance monitoring, automatic restart, graceful shutdown, and related features.
 """
 
@@ -17,7 +17,7 @@ from typing import List, Optional, Dict, Any
 
 
 class ProcessManager:
-    """Process manager that controls the API service and queue consumer."""
+    """Process manager that controls the API service."""
     
     def __init__(self):
         self.processes: Dict[str, subprocess.Popen] = {}
@@ -78,44 +78,6 @@ class ProcessManager:
             print(f"Failed to start API server: {e}")
             return None
     
-    def start_queue_consumer(self, args) -> Optional[subprocess.Popen]:
-        """Start the queue consumer."""
-        print("Starting queue consumer...")
-
-        consumer_script = Path("task_queue/consumer.py")
-        if not consumer_script.exists():
-            consumer_script = consumer_script.with_suffix(".pyc")
-
-        # Build the queue consumer command
-        cmd = [
-            sys.executable,
-            str(consumer_script),
-            "--workers", str(args.queue_workers),
-            "--worker-type", args.worker_type
-        ]
-
-        try:
-            process = subprocess.Popen(
-                cmd,
-                stdout=subprocess.PIPE,
-                stderr=subprocess.STDOUT,
-                universal_newlines=True,
-                bufsize=1
-            )
-
-            # Start the output monitoring thread
-            threading.Thread(
-                target=self._monitor_output,
-                args=(process, "Queue consumer"),
-                daemon=True
-            ).start()
-
-            return process
-
-        except Exception as e:
-            print(f"Failed to start queue consumer: {e}")
-            return None
-    
     def _monitor_output(self, process: subprocess.Popen, name: str):
         """Monitor process output."""
         try:
@@ -138,8 +100,6 @@ class ProcessManager:
 
         if name == "API server":
             new_process = self.start_api_server(args)
-        elif name == "Queue consumer":
-            new_process = self.start_queue_consumer(args)
         else:
             return False
 
@@ -169,27 +129,19 @@ class ProcessManager:
             print("Failed to start API server; exiting")
             return False
 
-        queue_process = self.start_queue_consumer(args)
-        if not queue_process:
-            print("Failed to start queue consumer; exiting")
-            api_process.terminate()
-            return False
-
         self.processes["API server"] = api_process
-        self.processes["Queue consumer"] = queue_process
 
         print("\n" + "=" * 70)
         print("All services started successfully!")
         print(f"API server: http://{args.host}:{args.port}")
         print(f"API PID: {api_process.pid}")
-        print(f"Queue consumer PID: {queue_process.pid}")
         print("Press Ctrl+C to stop all services")
         print("=" * 70 + "\n")
 
         self.running = True
 
         # Main monitoring loop
-        restart_counts = {"API server": 0, "Queue consumer": 0}
+        restart_counts = {"API server": 0}
         max_restarts = args.max_restarts
 
         while self.running and not self.shutdown_event.is_set():
@@ -262,7 +214,6 @@ class ProcessManager:
     def create_directories(self):
         """Create the required directories."""
         directories = [
-            "projects/queue_data",
             "projects/data",
             "projects/uploads",
             "projects/robot",
@@ -313,11 +264,6 @@ def parse_args():
     parser.add_argument("--log-level", type=str, default="info",
                        choices=["debug", "info", "warning", "error"], help="Log level")
 
-    # Queue consumer configuration
-    parser.add_argument("--queue-workers", type=int, default=2, help="Number of queue consumer worker threads")
-    parser.add_argument("--worker-type", type=str, default="threads",
-                       choices=["threads", "greenlets", "gevent"], help="Queue worker type")
-
     # Performance profile
     parser.add_argument("--profile", type=str, default="low_memory",
                        choices=["low_memory", "balanced", "high_performance"], help="Performance profile")

task_queue/README.md

@@ -1,154 +0,0 @@
-# 队列系统使用说明
-
-## 概述
-
-本项目集成了基于 huey 和 SqliteHuey 的异步队列系统，用于处理文件的异步处理任务。
-
-## 安装依赖
-
-```bash
-pip install huey
-```
-
-## 目录结构
-
-```
-queue/
-├── __init__.py          # 包初始化文件
-├── config.py           # 队列配置（SqliteHuey配置）
-├── tasks.py            # 文件处理任务定义
-├── manager.py          # 队列管理器
-├── consumer.py         # 队列消费者（工作进程）
-├── example.py          # 使用示例
-└── README.md           # 说明文档
-```
-
-## 核心功能
-
-### 1. 队列配置 (config.py)
-- 使用 SqliteHuey 作为消息队列
-- 数据库文件存储在 `queue_data/huey.db`
-- 支持任务重试和错误存储
-
-### 2. 文件处理任务 (tasks.py)
-- `process_file_async`: 异步处理单个文件
-- `process_multiple_files_async`: 批量异步处理文件
-- `process_zip_file_async`: 异步处理zip压缩文件
-- `cleanup_processed_files`: 清理旧的文件
-
-### 3. 队列管理器 (manager.py)
-- 任务提交和管理
-- 队列状态监控
-- 任务结果查询
-- 任务记录清理
-
-## 使用方法
-
-### 1. 启动队列消费者
-
-```bash
-# 启动默认配置的消费者
-python queue/consumer.py
-
-# 指定工作线程数
-python queue/consumer.py --workers 4
-
-# 查看队列统计信息
-python queue/consumer.py --stats
-
-# 检查队列状态
-python queue/consumer.py --check
-
-# 清空队列
-python queue/consumer.py --flush
-```
-
-### 2. 在代码中使用队列
-
-```python
-from queue.manager import queue_manager
-
-# 处理单个文件
-task_id = queue_manager.enqueue_file(
-    project_id="my_project",
-    file_path="/path/to/file.txt",
-    original_filename="myfile.txt"
-)
-
-# 批量处理文件
-task_ids = queue_manager.enqueue_multiple_files(
-    project_id="my_project",
-    file_paths=["/path/file1.txt", "/path/file2.txt"],
-    original_filenames=["file1.txt", "file2.txt"]
-)
-
-# 处理zip文件
-task_id = queue_manager.enqueue_zip_file(
-    project_id="my_project",
-    zip_path="/path/to/archive.zip"
-)
-
-# 查看任务状态
-status = queue_manager.get_task_status(task_id)
-print(status)
-
-# 获取队列统计信息
-stats = queue_manager.get_queue_stats()
-print(stats)
-```
-
-### 3. 运行示例
-
-```bash
-python queue/example.py
-```
-
-## 配置说明
-
-### 队列配置参数 (config.py)
-- `filename`: SQLite数据库文件路径
-- `always_eager`: 是否立即执行任务（开发时可设为True）
-- `utc`: 是否使用UTC时间
-- `compression_level`: 压缩级别
-- `store_errors`: 是否存储错误信息
-- `max_retries`: 最大重试次数
-- `retry_delay`: 重试延迟
-
-### 消费者参数 (consumer.py)
-- `--workers`: 工作线程数（默认2）
-- `--worker-type`: 工作类型（threads/greenlets/processes）
-- `--stats`: 显示统计信息
-- `--check`: 检查队列状态
-- `--flush`: 清空队列
-
-## 任务状态
-
-- `pending`: 等待处理
-- `running`: 正在处理
-- `complete/finished`: 处理完成
-- `error`: 处理失败
-- `scheduled`: 定时任务
-
-## 最佳实践
-
-1. **生产环境建议**:
-   - 设置合适的工作线程数（建议CPU核心数的1-2倍）
-   - 定期清理旧的任务记录
-   - 监控队列状态和任务执行情况
-
-2. **开发环境建议**:
-   - 可以设置 `always_eager=True` 立即执行任务进行调试
-   - 使用 `--check` 参数查看队列状态
-   - 运行示例代码了解功能
-
-3. **错误处理**:
-   - 任务失败后会自动重试（最多3次）
-   - 错误信息会存储在数据库中
-   - 可以通过 `get_task_status()` 查看错误详情
-
-## 故障排除
-
-1. **数据库锁定**: 确保只有一个消费者实例在运行
-2. **任务卡住**: 检查文件路径和权限
-3. **内存不足**: 调整工作线程数或使用进程模式
-4. **磁盘空间**: 定期清理旧文件和任务记录

task_queue/__init__.py

@@ -1,23 +0,0 @@
-#!/usr/bin/env python3
-"""
-Queue package initialization.
-"""
-
-from .config import huey
-from .manager import QueueManager, queue_manager
-from .tasks import (
-    process_file_async,
-    process_multiple_files_async,
-    process_zip_file_async,
-    cleanup_processed_files
-)
-
-__all__ = [
-    "huey",
-    "QueueManager",
-    "queue_manager",
-    "process_file_async",
-    "process_multiple_files_async",
-    "process_zip_file_async",
-    "cleanup_processed_files"
-]

task_queue/config.py

@@ -1,31 +0,0 @@
-#!/usr/bin/env python3
-"""
-Queue configuration using SqliteHuey for asynchronous file processing.
-"""
-
-import os
-import logging
-from huey import SqliteHuey
-from datetime import timedelta
-
-# Configure logging
-logger = logging.getLogger('app')
-
-# Ensure projects/queue_data directory exists
-queue_data_dir = os.path.join(os.path.dirname(__file__), '..', 'projects', 'queue_data')
-os.makedirs(queue_data_dir, exist_ok=True)
-
-# Initialize SqliteHuey
-huey = SqliteHuey(
-    filename=os.path.join(queue_data_dir, 'huey.db'),
-    name='file_processor',  # Queue name
-    always_eager=False,  # Set to False to enable async processing
-    utc=True,  # Use UTC time
-)
-
-# Set default task configuration
-huey.store_errors = True  # Store error information
-huey.max_retries = 3  # Maximum retry count
-huey.retry_delay = timedelta(seconds=60)  # Retry delay
-
-logger.info(f"SqliteHuey queue initialized, database path: {os.path.join(queue_data_dir, 'huey.db')}")

task_queue/consumer.py

@@ -1,171 +0,0 @@
-#!/usr/bin/env python3
-"""
-Queue consumer for processing file tasks.
-"""
-
-import sys
-import os
-import time
-import signal
-import argparse
-from pathlib import Path
-
-# Add project root directory to Python path
-project_root = Path(__file__).parent.parent
-sys.path.insert(0, str(project_root))
-
-from task_queue.config import huey
-from task_queue.manager import queue_manager
-from task_queue.integration_tasks import process_files_async, cleanup_project_async
-from huey.consumer import Consumer
-
-
-class QueueConsumer:
-    """Queue consumer for processing async tasks."""
-
-    def __init__(self, worker_type: str = "threads", workers: int = 2):
-        self.huey = huey
-        self.worker_type = worker_type
-        self.workers = workers
-        self.running = False
-        self.consumer = None
-
-        # Register signal handlers
-        signal.signal(signal.SIGINT, self._signal_handler)
-        signal.signal(signal.SIGTERM, self._signal_handler)
-
-    def _signal_handler(self, signum, frame):
-        """Signal handler for graceful shutdown."""
-        print(f"\nReceived signal {signum}, shutting down queue consumer...")
-        self.running = False
-
-    def start(self):
-        """Start the queue consumer."""
-        print(f"Starting queue consumer...")
-        print(f"Worker threads: {self.workers}")
-        print(f"Worker type: {self.worker_type}")
-        print(f"Database: {os.path.join(os.path.dirname(__file__), '..', 'projects', 'queue_data', 'huey.db')}")
-        print("Press Ctrl+C to stop the consumer")
-
-        self.running = True
-
-        try:
-            # Create Huey consumer
-            self.consumer = Consumer(self.huey, workers=self.workers, worker_type=self.worker_type.rstrip('s'))
-
-            # Display queue statistics
-            stats = queue_manager.get_queue_stats()
-            print(f"Current queue status: {stats}")
-
-            # Start consumer run loop
-            print("Consumer starting task processing...")
-            self.consumer.run()
-
-        except KeyboardInterrupt:
-            print("\nReceived interrupt signal, shutting down...")
-        except Exception as e:
-            print(f"Queue consumer runtime error: {str(e)}")
-        finally:
-            self.stop()
-
-    def stop(self):
-        """Stop the queue consumer."""
-        print("Stopping queue consumer...")
-        try:
-            if self.consumer:
-                # Stop the consumer
-                self.consumer.stop()
-                self.consumer = None
-            print("Queue consumer stopped")
-        except Exception as e:
-            print(f"Error stopping queue consumer: {str(e)}")
-
-    def process_scheduled_tasks(self):
-        """Process scheduled tasks."""
-        print("Processing scheduled tasks...")
-        # Additional scheduled task processing logic can be added here
-
-
-def main():
-    """Main entry point."""
-    parser = argparse.ArgumentParser(description="File processing queue consumer")
-    parser.add_argument(
-        "--workers",
-        type=int,
-        default=2,
-        help="Number of worker threads (default: 2)"
-    )
-    parser.add_argument(
-        "--worker-type",
-        choices=["threads", "greenlets", "processes"],
-        default="threads",
-        help="Worker thread type (default: threads)"
-    )
-    parser.add_argument(
-        "--stats",
-        action="store_true",
-        help="Display queue statistics and exit"
-    )
-    parser.add_argument(
-        "--flush",
-        action="store_true",
-        help="Flush the queue and exit"
-    )
-    parser.add_argument(
-        "--check",
-        action="store_true",
-        help="Check queue status and exit"
-    )
-
-    args = parser.parse_args()
-
-    # Initialize consumer
-    consumer = QueueConsumer(
-        worker_type=args.worker_type,
-        workers=args.workers
-    )
-
-    # Handle different command-line options
-    if args.stats:
-        print("=== Queue Statistics ===")
-        stats = queue_manager.get_queue_stats()
-        print(f"Total tasks: {stats.get('total_tasks', 0)}")
-        print(f"Pending tasks: {stats.get('pending_tasks', 0)}")
-        print(f"Running tasks: {stats.get('running_tasks', 0)}")
-        print(f"Completed tasks: {stats.get('completed_tasks', 0)}")
-        print(f"Error tasks: {stats.get('error_tasks', 0)}")
-        print(f"Scheduled tasks: {stats.get('scheduled_tasks', 0)}")
-        print(f"Database: {stats.get('queue_database', 'N/A')}")
-        return
-
-    if args.flush:
-        print("=== Flushing Queue ===")
-        try:
-            # Flush all tasks
-            consumer.huey.flush()
-            print("Queue flushed")
-        except Exception as e:
-            print(f"Failed to flush queue: {str(e)}")
-        return
-
-    if args.check:
-        print("=== Checking Queue Status ===")
-        stats = queue_manager.get_queue_stats()
-        print(f"Queue status: OK" if "error" not in stats else f"Queue status: ERROR - {stats['error']}")
-
-        pending_tasks = queue_manager.list_pending_tasks(limit=10)
-        if pending_tasks:
-            print(f"\nPending tasks (showing up to 10):")
-            for task in pending_tasks:
-                print(f"  Task ID: {task['task_id']}, Status: {task['status']}, Created: {task['created_time']}")
-        else:
-            print("No pending tasks")
-        return
-
-    # Start consumer
-    print("=== Starting File Processing Queue Consumer ===")
-    consumer.start()
-
-
-if __name__ == "__main__":
-    main()

task_queue/example.py

@@ -1,132 +0,0 @@
-#!/usr/bin/env python3
-"""
-Example usage of the queue system.
-"""
-
-import sys
-import time
-from pathlib import Path
-
-# Add project root directory to Python path
-project_root = Path(__file__).parent.parent
-sys.path.insert(0, str(project_root))
-
-from task_queue.manager import queue_manager
-from task_queue.tasks import process_file_async, process_multiple_files_async
-
-
-def example_single_file():
-    """Example: Process a single file."""
-    print("=== Example: Process a single file ===")
-
-    project_id = "test_project"
-    file_path = "public/test_document.txt"
-
-    # Enqueue file for processing
-    task_id = queue_manager.enqueue_file(
-        project_id=project_id,
-        file_path=file_path,
-        original_filename="example_document.txt"
-    )
-
-    print(f"Task submitted, task ID: {task_id}")
-
-    # Check task status
-    time.sleep(2)
-    status = queue_manager.get_task_status(task_id)
-    print(f"Task status: {status}")
-
-
-def example_multiple_files():
-    """Example: Batch process files."""
-    print("\n=== Example: Batch process files ===")
-
-    project_id = "test_project_batch"
-    file_paths = [
-        "public/test_document.txt",
-        "public/goods.xlsx"  # Assuming this file exists
-    ]
-    original_filenames = [
-        "batch_document_1.txt",
-        "batch_goods.xlsx"
-    ]
-
-    # Enqueue multiple files for processing
-    task_ids = queue_manager.enqueue_multiple_files(
-        project_id=project_id,
-        file_paths=file_paths,
-        original_filenames=original_filenames
-    )
-
-    print(f"Batch tasks submitted, task IDs: {task_ids}")
-
-
-def example_zip_file():
-    """Example: Process a zip file."""
-    print("\n=== Example: Process a zip file ===")
-
-    project_id = "test_project_zip"
-    zip_path = "public/all_hp_product_spec_book2506.zip"
-
-    # Enqueue zip file for processing
-    task_id = queue_manager.enqueue_zip_file(
-        project_id=project_id,
-        zip_path=zip_path
-    )
-
-    print(f"Zip task submitted, task ID: {task_id}")
-
-
-def example_queue_stats():
-    """Example: Get queue statistics."""
-    print("\n=== Example: Queue statistics ===")
-
-    stats = queue_manager.get_queue_stats()
-    print("Queue statistics:")
-    for key, value in stats.items():
-        if key != "recent_tasks":
-            print(f"  {key}: {value}")
-
-
-def example_cleanup():
-    """Example: Cleanup tasks."""
-    print("\n=== Example: Cleanup tasks ===")
-
-    project_id = "test_project"
-
-    # Enqueue cleanup task (delayed 10 seconds)
-    task_id = queue_manager.enqueue_cleanup_task(
-        project_id=project_id,
-        older_than_days=1,  # Clean files older than 1 day
-        delay=10
-    )
-
-    print(f"Cleanup task submitted, task ID: {task_id}")
-
-
-def main():
-    """Main entry point."""
-    print("Queue System Usage Examples")
-    print("=" * 50)
-
-    try:
-        # Run examples
-        example_single_file()
-        example_multiple_files()
-        example_zip_file()
-        example_queue_stats()
-        example_cleanup()
-
-        print("\n" + "=" * 50)
-        print("Examples completed!")
-        print("\nTo check task execution, run:")
-        print("python queue/consumer.py --check")
-        print("\nTo start the queue consumer, run:")
-        print("python queue/consumer.py")
-
-    except Exception as e:
-        print(f"Error running examples: {str(e)}")
-
-
-if __name__ == "__main__":
-    main()

task_queue/integration_tasks.py

@@ -1,499 +0,0 @@
-#!/usr/bin/env python3
-"""
-Queue tasks for file processing integration.
-"""
-
-import os
-import json
-import time
-import hashlib
-import shutil
-from typing import Dict, List, Optional, Any
-
-from task_queue.config import huey
-from task_queue.manager import queue_manager
-from task_queue.task_status import task_status_store
-from utils import download_dataset_files, save_processed_files_log, load_processed_files_log
-from utils.dataset_manager import remove_dataset_directory_by_key
-
-
-def scan_upload_folder(upload_dir: str) -> List[str]:
-    """
-    Scan all supported file formats in the upload folder.
-
-    Args:
-        upload_dir: Upload folder path
-
-    Returns:
-        List[str]: List of supported file paths
-    """
-    supported_extensions = {
-        # Text files
-        '.txt', '.md', '.rtf',
-        # Document files
-        '.doc', '.docx', '.pdf', '.odt',
-        # Spreadsheet files
-        '.xls', '.xlsx', '.csv', '.ods',
-        # Presentation files
-        '.ppt', '.pptx', '.odp',
-        # E-books
-        '.epub', '.mobi',
-        # Web files
-        '.html', '.htm',
-        # Config files
-        '.json', '.xml', '.yaml', '.yml',
-        # Code files
-        '.py', '.js', '.java', '.cpp', '.c', '.go', '.rs',
-        # Archive files
-        '.zip', '.rar', '.7z', '.tar', '.gz'
-    }
-
-    scanned_files = []
-
-    if not os.path.exists(upload_dir):
-        return scanned_files
-
-    for root, dirs, files in os.walk(upload_dir):
-        for file in files:
-            # Skip hidden files and system files
-            if file.startswith('.') or file.startswith('~'):
-                continue
-
-            file_path = os.path.join(root, file)
-            file_extension = os.path.splitext(file)[1].lower()
-
-            # Check if file extension is supported
-            if file_extension in supported_extensions:
-                scanned_files.append(file_path)
-            else:
-                # For files without extension, try to process them (may be text files)
-                if not file_extension:
-                    try:
-                        # Try reading the file header to determine if it's a text file
-                        with open(file_path, 'r', encoding='utf-8') as f:
-                            f.read(1024)  # Read the first 1KB
-                        scanned_files.append(file_path)
-                    except (UnicodeDecodeError, PermissionError):
-                        # Not a text file or unreadable, skip
-                        pass
-
-    return scanned_files
-
-
-@huey.task()
-def process_files_async(
-    dataset_id: str,
-    files: Optional[Dict[str, List[str]]] = None,
-    upload_folder: Optional[Dict[str, str]] = None,
-    task_id: Optional[str] = None
-) -> Dict[str, Any]:
-    """
-    Asynchronously process file tasks - compatible with existing files/process API.
-
-    Args:
-        dataset_id: Unique project ID
-        files: Dictionary of file paths grouped by key
-        upload_folder: Upload folder dictionary organized by group name, e.g. {'group1': 'my_project1', 'group2': 'my_project2'}
-        task_id: Task ID (for status tracking)
-
-    Returns:
-        Processing result dictionary
-    """
-    try:
-        print(f"Starting async file processing task, project ID: {dataset_id}")
-
-        # If task_id is provided, set initial status
-        if task_id:
-            task_status_store.set_status(
-                task_id=task_id,
-                unique_id=dataset_id,
-                status="running"
-            )
-
-        # Ensure project directory exists
-        project_dir = os.path.join("projects", "data", dataset_id)
-        if not os.path.exists(project_dir):
-            os.makedirs(project_dir, exist_ok=True)
-
-        # Process files: use key-grouped format
-        processed_files_by_key = {}
-
-        # If upload_folder is provided, scan files in those folders
-        if upload_folder and not files:
-            scanned_files_by_group = {}
-            total_scanned_files = 0
-
-            for group_name, folder_name in upload_folder.items():
-                # Security check: prevent path traversal attacks
-                safe_folder_name = os.path.basename(folder_name)
-                upload_dir = os.path.join("projects", "uploads", safe_folder_name)
-
-                if os.path.exists(upload_dir):
-                    scanned_files = scan_upload_folder(upload_dir)
-                    if scanned_files:
-                        scanned_files_by_group[group_name] = scanned_files
-                        total_scanned_files += len(scanned_files)
-                        print(f"Scanned {len(scanned_files)} files from upload folder '{safe_folder_name}' (group: {group_name})")
-                    else:
-                        print(f"No supported files found in upload folder '{safe_folder_name}' (group: {group_name})")
-                else:
-                    print(f"Upload folder does not exist: {upload_dir} (group: {group_name})")
-
-            if scanned_files_by_group:
-                files = scanned_files_by_group
-                print(f"Total scanned {total_scanned_files} files from {len(scanned_files_by_group)} groups")
-            else:
-                print("No supported files found in any upload folder")
-
-        if files:
-            # Use files from the request (grouped by key)
-            # Since this is an async task, call synchronously
-            import asyncio
-            try:
-                loop = asyncio.get_event_loop()
-            except RuntimeError:
-                loop = asyncio.new_event_loop()
-                asyncio.set_event_loop(loop)
-
-            processed_files_by_key = loop.run_until_complete(download_dataset_files(dataset_id, files))
-            total_files = sum(len(files_list) for files_list in processed_files_by_key.values())
-            print(f"Async processed {total_files} dataset files across {len(processed_files_by_key)} keys, project ID: {dataset_id}")
-        else:
-            print(f"No files provided in request, project ID: {dataset_id}")
-
-        # Collect all document.txt files in the project directory
-        document_files = []
-        for root, dirs, files_list in os.walk(project_dir):
-            for file in files_list:
-                if file == "document.txt":
-                    document_files.append(os.path.join(root, file))
-
-        # Generate project README.md file
-        try:
-            from utils.project_manager import save_project_readme
-            save_project_readme(dataset_id)
-            print(f"README.md generated, project ID: {dataset_id}")
-        except Exception as e:
-            print(f"Failed to generate README.md, project ID: {dataset_id}, error: {str(e)}")
-            # Does not affect main processing flow, continue
-
-        # Build result file list
-        result_files = []
-        for key in processed_files_by_key.keys():
-            # Add corresponding dataset document.txt path
-            document_path = os.path.join("projects", "data", dataset_id, "datasets", key, "document.txt")
-            if os.path.exists(document_path):
-                result_files.append(document_path)
-
-        # Also add document.txt files that exist but are not in processed_files_by_key
-        existing_document_paths = set(result_files)  # Avoid duplicates
-        for doc_file in document_files:
-            if doc_file not in existing_document_paths:
-                result_files.append(doc_file)
-
-        result = {
-            "status": "success",
-            "message": f"Successfully processed {len(result_files)} document files across {len(processed_files_by_key)} keys",
-            "dataset_id": dataset_id,
-            "processed_files": result_files,
-            "processed_files_by_key": processed_files_by_key,
-            "document_files": document_files,
-            "total_files_processed": sum(len(files_list) for files_list in processed_files_by_key.values()),
-            "processing_time": time.time()
-        }
-
-        # Update task status to completed
-        if task_id:
-            task_status_store.update_status(
-                task_id=task_id,
-                status="completed",
-                result=result
-            )
-
-        print(f"Async file processing task completed: {dataset_id}")
-        return result
-
-    except Exception as e:
-        error_msg = f"Error during async file processing: {str(e)}"
-        print(error_msg)
-
-        # Update task status to error
-        if task_id:
-            task_status_store.update_status(
-                task_id=task_id,
-                status="failed",
-                error=error_msg
-            )
-
-        return {
-            "status": "error",
-            "message": error_msg,
-            "dataset_id": dataset_id,
-            "error": str(e)
-        }
-
-
-@huey.task()
-def process_files_incremental_async(
-    dataset_id: str,
-    files_to_add: Optional[Dict[str, List[str]]] = None,
-    files_to_remove: Optional[Dict[str, List[str]]] = None,
-    system_prompt: Optional[str] = None,
-    mcp_settings: Optional[List[Dict]] = None,
-    task_id: Optional[str] = None
-) -> Dict[str, Any]:
-    """
-    Incremental file processing task - supports adding and removing files.
-
-    Args:
-        dataset_id: Unique project ID
-        files_to_add: Dictionary of file paths to add, grouped by key
-        files_to_remove: Dictionary of file paths to remove, grouped by key
-        system_prompt: System prompt
-        mcp_settings: MCP settings
-        task_id: Task ID (for status tracking)
-
-    Returns:
-        Processing result dictionary
-    """
-    try:
-        print(f"Starting incremental file processing task, project ID: {dataset_id}")
-
-        # If task_id is provided, set initial status
-        if task_id:
-            task_status_store.set_status(
-                task_id=task_id,
-                unique_id=dataset_id,
-                status="running"
-            )
-
-        # Ensure project directory exists
-        project_dir = os.path.join("projects", "data", dataset_id)
-        if not os.path.exists(project_dir):
-            os.makedirs(project_dir, exist_ok=True)
-
-        # Load existing processing log
-        processed_log = load_processed_files_log(dataset_id)
-        print(f"Loaded existing processing log with {len(processed_log)} file records")
-
-        removed_files = []
-        added_files = []
-
-        # 1. Process removals
-        if files_to_remove:
-            print(f"Starting removal processing across {len(files_to_remove)} key groups")
-            for key, file_list in files_to_remove.items():
-                if not file_list:  # If file list is empty, remove the entire key group
-                    print(f"Removing entire key group: {key}")
-                    if remove_dataset_directory_by_key(dataset_id, key):
-                        removed_files.append(f"dataset/{key}")
-
-                    # Remove all records for this key from the processing log
-                    keys_to_remove = [file_hash for file_hash, file_info in processed_log.items()
-                                    if file_info.get('key') == key]
-                    for file_hash in keys_to_remove:
-                        del processed_log[file_hash]
-                        removed_files.append(f"log_entry:{file_hash}")
-                else:
-                    # Remove specific files
-                    for file_path in file_list:
-                        print(f"Removing specific file: {key}/{file_path}")
-
-                        # Actually delete the file
-                        filename = os.path.basename(file_path)
-
-                        # Delete original file
-                        source_file = os.path.join("projects", "data", dataset_id, "files", key, filename)
-                        if os.path.exists(source_file):
-                            os.remove(source_file)
-                            removed_files.append(f"file:{key}/{filename}")
-
-                        # Delete processed file directory
-                        processed_dir = os.path.join("projects", "data", dataset_id, "processed", key, filename)
-                        if os.path.exists(processed_dir):
-                            shutil.rmtree(processed_dir)
-                            removed_files.append(f"processed:{key}/{filename}")
-
-                        # Compute file hash to find in log
-                        file_hash = hashlib.md5(file_path.encode('utf-8')).hexdigest()
-
-                        # Remove from processing log
-                        if file_hash in processed_log:
-                            del processed_log[file_hash]
-                            removed_files.append(f"log_entry:{file_hash}")
-
-        # 2. Process additions
-        processed_files_by_key = {}
-        if files_to_add:
-            print(f"Starting addition processing across {len(files_to_add)} key groups")
-            # Use async processing to download files
-            import asyncio
-            try:
-                loop = asyncio.get_event_loop()
-            except RuntimeError:
-                loop = asyncio.new_event_loop()
-                asyncio.set_event_loop(loop)
-
-            processed_files_by_key = loop.run_until_complete(download_dataset_files(dataset_id, files_to_add, incremental_mode=True))
-            total_added_files = sum(len(files_list) for files_list in processed_files_by_key.values())
-            print(f"Async processed {total_added_files} dataset files across {len(processed_files_by_key)} keys, project ID: {dataset_id}")
-
-            # Record added files
-            for key, files_list in processed_files_by_key.items():
-                for file_path in files_list:
-                    added_files.append(f"{key}/{file_path}")
-        else:
-            print(f"No files to add provided in request, project ID: {dataset_id}")
-
-        # Save updated processing log
-        save_processed_files_log(dataset_id, processed_log)
-        print(f"Updated processing log, now contains {len(processed_log)} file records")
-
-        # Save system_prompt and mcp_settings to project directory (if provided)
-        if system_prompt:
-            system_prompt_file = os.path.join(project_dir, "system_prompt.md")
-            with open(system_prompt_file, 'w', encoding='utf-8') as f:
-                f.write(system_prompt)
-            print(f"Saved system_prompt, project ID: {dataset_id}")
-
-        if mcp_settings:
-            mcp_settings_file = os.path.join(project_dir, "mcp_settings.json")
-            with open(mcp_settings_file, 'w', encoding='utf-8') as f:
-                json.dump(mcp_settings, f, ensure_ascii=False, indent=2)
-            print(f"Saved mcp_settings, project ID: {dataset_id}")
-
-        # Generate project README.md file
-        try:
-            from utils.project_manager import save_project_readme
-            save_project_readme(dataset_id)
-            print(f"README.md generated, project ID: {dataset_id}")
-        except Exception as e:
-            print(f"Failed to generate README.md, project ID: {dataset_id}, error: {str(e)}")
-            # Does not affect main processing flow, continue
-
-        # Collect all document.txt files in the project directory
-        document_files = []
-        for root, dirs, files_list in os.walk(project_dir):
-            for file in files_list:
-                if file == "document.txt":
-                    document_files.append(os.path.join(root, file))
-
-        # Build result file list
-        result_files = []
-        for key in processed_files_by_key.keys():
-            # Add corresponding dataset document.txt path
-            document_path = os.path.join("projects", "data", dataset_id, "datasets", key, "document.txt")
-            if os.path.exists(document_path):
-                result_files.append(document_path)
-
-        # Also add document.txt files that exist but are not in processed_files_by_key
-        existing_document_paths = set(result_files)  # Avoid duplicates
-        for doc_file in document_files:
-            if doc_file not in existing_document_paths:
-                result_files.append(doc_file)
-
-        result = {
-            "status": "success",
-            "message": f"Incremental processing complete - added {len(added_files)} files, removed {len(removed_files)} files, {len(result_files)} document files remaining",
-            "dataset_id": dataset_id,
-            "removed_files": removed_files,
-            "added_files": added_files,
-            "processed_files": result_files,
-            "processed_files_by_key": processed_files_by_key,
-            "document_files": document_files,
-            "total_files_added": sum(len(files_list) for files_list in processed_files_by_key.values()),
-            "total_files_removed": len(removed_files),
-            "final_files_count": len(result_files),
-            "processing_time": time.time()
-        }
-
-        # Update task status to completed
-        if task_id:
-            task_status_store.update_status(
-                task_id=task_id,
-                status="completed",
-                result=result
-            )
-
-        print(f"Incremental file processing task completed: {dataset_id}")
-        return result
-
-    except Exception as e:
-        error_msg = f"Error during incremental file processing: {str(e)}"
-        print(error_msg)
-
-        # Update task status to error
-        if task_id:
-            task_status_store.update_status(
-                task_id=task_id,
-                status="failed",
-                error=error_msg
-            )
-
-        return {
-            "status": "error",
-            "message": error_msg,
-            "dataset_id": dataset_id,
-            "error": str(e)
-        }
-
-
-@huey.task()
-def cleanup_project_async(
-    dataset_id: str,
-    remove_all: bool = False
-) -> Dict[str, Any]:
-    """
-    Asynchronously clean up project files.
-
-    Args:
-        dataset_id: Unique project ID
-        remove_all: Whether to remove the entire project directory
-
-    Returns:
-        Cleanup result dictionary
-    """
-    try:
-        print(f"Starting async project cleanup, project ID: {dataset_id}")
-
-        project_dir = os.path.join("projects", "data", dataset_id)
-        removed_items = []
-
-        if remove_all and os.path.exists(project_dir):
-            import shutil
-            shutil.rmtree(project_dir)
-            removed_items.append(project_dir)
-            result = {
-                "status": "success",
-                "message": f"Deleted entire project directory: {project_dir}",
-                "dataset_id": dataset_id,
-                "removed_items": removed_items,
-                "action": "remove_all"
-            }
-        else:
-            # Only clean processing log
-            log_file = os.path.join(project_dir, "processed_files.json")
-            if os.path.exists(log_file):
-                os.remove(log_file)
-                removed_items.append(log_file)
-
-            result = {
-                "status": "success",
-                "message": f"Cleaned project processing log, project ID: {dataset_id}",
-                "dataset_id": dataset_id,
-                "removed_items": removed_items,
-                "action": "cleanup_logs"
-            }
-
-        print(f"Async cleanup task completed: {dataset_id}")
-        return result
-
-    except Exception as e:
-        error_msg = f"Error during async project cleanup: {str(e)}"
-        print(error_msg)
-        return {
-            "status": "error",
-            "message": error_msg,
-            "dataset_id": dataset_id,
-            "error": str(e)
-        }

task_queue/manager.py

@@ -1,228 +0,0 @@
-#!/usr/bin/env python3
-"""
-Queue manager for handling file processing queues.
-"""
-
-import os
-import json
-import time
-import logging
-from typing import Dict, List, Optional, Any
-from huey import Huey
-from huey.api import Task
-from datetime import datetime, timedelta
-
-# Configure logging
-logger = logging.getLogger('app')
-
-from .config import huey
-from .tasks import process_file_async, process_multiple_files_async, process_zip_file_async, cleanup_processed_files
-
-
-class QueueManager:
-    """Queue manager for file processing tasks."""
-
-    def __init__(self):
-        self.huey = huey
-        logger.info(f"Queue manager initialized with database: {os.path.join(os.path.dirname(__file__), '..', 'projects', 'queue_data', 'huey.db')}")
-
-    def enqueue_file(
-        self,
-        project_id: str,
-        file_path: str,
-        original_filename: str = None,
-        delay: int = 0
-    ) -> str:
-        """
-        Add a file to the processing queue.
-
-        Args:
-            project_id: Project ID
-            file_path: File path
-            original_filename: Original filename
-            delay: Delay before execution in seconds
-
-        Returns:
-            Task ID
-        """
-        if delay > 0:
-            task = process_file_async.schedule(
-                args=(project_id, file_path, original_filename),
-                delay=timedelta(seconds=delay)
-            )
-        else:
-            task = process_file_async(project_id, file_path, original_filename)
-
-        logger.info(f"File queued for processing: {file_path}, task ID: {task.id}")
-        return task.id
-
-    def enqueue_multiple_files(
-        self,
-        project_id: str,
-        file_paths: List[str],
-        original_filenames: List[str] = None,
-        delay: int = 0
-    ) -> List[str]:
-        """
-        Add multiple files to the processing queue.
-
-        Args:
-            project_id: Project ID
-            file_paths: List of file paths
-            original_filenames: List of original filenames
-            delay: Delay before execution in seconds
-
-        Returns:
-            List of task IDs
-        """
-        if delay > 0:
-            task = process_multiple_files_async.schedule(
-                args=(project_id, file_paths, original_filenames),
-                delay=timedelta(seconds=delay)
-            )
-        else:
-            task = process_multiple_files_async(project_id, file_paths, original_filenames)
-
-        logger.info(f"Batch files queued for processing: {len(file_paths)} files, task ID: {task.id}")
-        return [task.id]
-
-    def enqueue_zip_file(
-        self,
-        project_id: str,
-        zip_path: str,
-        extract_to: str = None,
-        delay: int = 0
-    ) -> str:
-        """
-        Add a zip file to the processing queue.
-
-        Args:
-            project_id: Project ID
-            zip_path: Path to the zip file
-            extract_to: Extraction target directory
-            delay: Delay before execution in seconds
-
-        Returns:
-            Task ID
-        """
-        if delay > 0:
-            task = process_zip_file_async.schedule(
-                args=(project_id, zip_path, extract_to),
-                delay=timedelta(seconds=delay)
-            )
-        else:
-            task = process_zip_file_async(project_id, zip_path, extract_to)
-
-        logger.info(f"Zip file queued for processing: {zip_path}, task ID: {task.id}")
-        return task.id
-
-    def get_task_status(self, task_id: str) -> Dict[str, Any]:
-        """
-        Get task status.
-
-        Args:
-            task_id: Task ID
-
-        Returns:
-            Task status information
-        """
-        try:
-            # Try getting the task result from result storage
-            try:
-                # Use Huey's built-in result lookup when available
-                if hasattr(self.huey, 'result') and self.huey.result:
-                    result = self.huey.result(task_id)
-                    if result is not None:
-                        return {
-                            "task_id": task_id,
-                            "status": "complete",
-                            "result": result
-                        }
-            except Exception:
-                pass
-
-            # Check whether the task is in the pending queue
-            try:
-                pending_tasks = list(self.huey.pending())
-                for task in pending_tasks:
-                    if hasattr(task, 'id') and task.id == task_id:
-                        return {
-                            "task_id": task_id,
-                            "status": "pending"
-                        }
-            except Exception:
-                pass
-
-            # Check whether the task is in the scheduled queue
-            try:
-                scheduled_tasks = list(self.huey.scheduled())
-                for task in scheduled_tasks:
-                    if hasattr(task, 'id') and task.id == task_id:
-                        return {
-                            "task_id": task_id,
-                            "status": "scheduled"
-                        }
-            except Exception:
-                pass
-
-            # If not found anywhere, it may not exist or may have completed with cleaned results
-            return {
-                "task_id": task_id,
-                "status": "unknown",
-                "message": "Task status is unknown; it may already be complete or may not exist"
-            }
-
-        except Exception as e:
-            return {
-                "task_id": task_id,
-                "status": "error",
-                "message": f"Failed to get task status: {str(e)}"
-            }
-
-    def get_queue_stats(self) -> Dict[str, Any]:
-        """
-        Get queue statistics.
-
-        Returns:
-            Queue statistics information
-        """
-        try:
-            # Use a simplified approach for queue statistics
-            stats = {
-                "total_tasks": 0,
-                "pending_tasks": 0,
-                "running_tasks": 0,
-                "completed_tasks": 0,
-                "error_tasks": 0,
-                "scheduled_tasks": 0,
-                "recent_tasks": [],
-                "queue_database": os.path.join(os.path.dirname(__file__), '..', 'projects', 'queue_data', 'huey.db')
-            }
-
-            # Try to get the number of pending tasks
-            try:
-                pending_tasks = list(self.huey.pending())
-                stats["pending_tasks"] = len(pending_tasks)
-                stats["total_tasks"] += len(pending_tasks)
-            except Exception as e:
-                logger.error(f"Failed to get pending tasks: {e}")
-
-            # Try to get the number of scheduled tasks
-            try:
-                scheduled_tasks = list(self.huey.scheduled())
-                stats["scheduled_tasks"] = len(scheduled_tasks)
-                stats["total_tasks"] += len(scheduled_tasks)
-            except Exception as e:
-                logger.error(f"Failed to get scheduled tasks: {e}")
-
-            return stats
-
-        except Exception as e:
-            return {
-                "error": str(e),
-                "queue_database": os.path.join(os.path.dirname(__file__), '..', 'projects', 'queue_data', 'huey.db')
-            }
-
-
-# Global singleton instance
-queue_manager = QueueManager()

task_queue/optimized_consumer.py

@@ -1,286 +0,0 @@
-#!/usr/bin/env python3
-"""
-Optimized queue consumer with integrated performance monitoring.
-"""
-
-import sys
-import os
-import time
-import signal
-import argparse
-import multiprocessing
-import logging
-from pathlib import Path
-from concurrent.futures import ThreadPoolExecutor
-import threading
-
-# Configure logging
-logger = logging.getLogger('app')
-
-# Add project root directory to Python path
-project_root = Path(__file__).parent.parent
-sys.path.insert(0, str(project_root))
-
-from task_queue.config import huey
-from task_queue.manager import queue_manager
-from task_queue.integration_tasks import process_files_async, cleanup_project_async
-from huey.consumer import Consumer
-
-
-class OptimizedQueueConsumer:
-    """Optimized queue consumer with integrated performance monitoring."""
-
-    def __init__(self, worker_type: str = "threads", workers: int = 2):
-        self.huey = huey
-        self.worker_type = worker_type
-        self.workers = workers
-        self.running = False
-        self.consumer = None
-        self.processed_count = 0
-        self.start_time = None
-
-        # Performance monitoring
-        self.performance_stats = {
-            'tasks_processed': 0,
-            'tasks_failed': 0,
-            'avg_processing_time': 0,
-            'start_time': None,
-            'last_activity': None
-        }
-
-        # Register signal handlers
-        signal.signal(signal.SIGINT, self._signal_handler)
-        signal.signal(signal.SIGTERM, self._signal_handler)
-
-    def _signal_handler(self, signum, frame):
-        """Signal handler for graceful shutdown."""
-        logger.info(f"\nReceived signal {signum}, shutting down queue consumer...")
-        self.running = False
-        if self.consumer:
-            self.consumer.stop()
-
-    def setup_optimizations(self):
-        """Set up performance optimizations."""
-        # Set environment variables
-        env_vars = {
-            'PYTHONUNBUFFERED': '1',
-            'PYTHONDONTWRITEBYTECODE': '1',
-        }
-
-        for key, value in env_vars.items():
-            os.environ[key] = value
-
-        # Optimize huey configuration
-        if hasattr(huey, 'immediate'):
-            huey.immediate = False
-
-        # Adjust based on worker type
-        if self.worker_type == "threads":
-            # Thread pool optimization
-            if hasattr(huey, 'worker_type'):
-                huey.worker_type = 'threads'
-
-            # Set thread pool size
-            if hasattr(huey, 'always_eager'):
-                huey.always_eager = False
-
-        logger.info("Queue consumer optimization setup complete:")
-        logger.info(f"- Worker type: {self.worker_type}")
-        logger.info(f"- Worker count: {self.workers}")
-
-    def monitor_performance(self):
-        """Performance monitoring thread."""
-        while self.running:
-            time.sleep(30)  # Output statistics every 30 seconds
-
-            if self.start_time:
-                elapsed = time.time() - self.start_time
-                rate = self.performance_stats['tasks_processed'] / max(1, elapsed)
-
-                logger.info(f"\n[Performance Stats]")
-                logger.info(f"- Uptime: {elapsed:.1f}s")
-                logger.info(f"- Tasks processed: {self.performance_stats['tasks_processed']}")
-                logger.info(f"- Failed tasks: {self.performance_stats['tasks_failed']}")
-                logger.info(f"- Average processing rate: {rate:.2f} tasks/s")
-
-                if self.performance_stats['avg_processing_time'] > 0:
-                    logger.info(f"- Average processing time: {self.performance_stats['avg_processing_time']:.2f}s")
-
-    def start(self):
-        """Start the queue consumer."""
-        logger.info("=" * 60)
-        logger.info("Optimized queue consumer starting")
-        logger.info("=" * 60)
-
-        # Apply optimizations
-        self.setup_optimizations()
-
-        logger.info(f"Database: {os.path.join(os.path.dirname(__file__), '..', 'projects', 'queue_data', 'huey.db')}")
-        logger.info("Press Ctrl+C to stop the consumer")
-
-        self.running = True
-        self.start_time = time.time()
-        self.performance_stats['start_time'] = self.start_time
-
-        # Start performance monitoring thread
-        monitor_thread = threading.Thread(target=self.monitor_performance, daemon=True)
-        monitor_thread.start()
-
-        try:
-            # Create consumer
-            self.consumer = Consumer(
-                self.huey,
-                workers=self.workers,
-                worker_type=self.worker_type,
-                max_delay=60.0,  # Maximum delay
-                check_delay=1.0,  # Check interval
-                periodic=True,    # Enable periodic tasks
-            )
-
-            logger.info("Queue consumer started, waiting for tasks...")
-
-            # Start the consumer
-            self.consumer.run()
-
-        except KeyboardInterrupt:
-            logger.info("\nReceived keyboard interrupt signal")
-        except Exception as e:
-            logger.error(f"Queue consumer runtime error: {e}")
-            import traceback
-            traceback.print_exc()
-        finally:
-            self.shutdown()
-
-    def shutdown(self):
-        """Shut down the queue consumer."""
-        logger.info("\nShutting down queue consumer...")
-        self.running = False
-
-        if self.consumer:
-            try:
-                self.consumer.stop()
-                logger.info("Queue consumer stopped")
-            except Exception as e:
-                logger.error(f"Error stopping queue consumer: {e}")
-
-        # Output final statistics
-        if self.start_time:
-            elapsed = time.time() - self.start_time
-            logger.info(f"\n[Final Stats]")
-            logger.info(f"- Total uptime: {elapsed:.1f}s")
-            logger.info(f"- Total tasks processed: {self.performance_stats['tasks_processed']}")
-            logger.info(f"- Total failed tasks: {self.performance_stats['tasks_failed']}")
-
-            if self.performance_stats['tasks_processed'] > 0:
-                rate = self.performance_stats['tasks_processed'] / elapsed
-                logger.info(f"- Average processing rate: {rate:.2f} tasks/s")
-
-
-def calculate_optimal_workers():
-    """Calculate the optimal number of worker threads."""
-    cpu_count = multiprocessing.cpu_count()
-
-    # Based on CPU core count and system resources
-    if cpu_count <= 2:
-        return 2
-    elif cpu_count <= 4:
-        return 4
-    else:
-        return min(8, cpu_count)
-
-
-def check_queue_status():
-    """Check queue status."""
-    try:
-        stats = queue_manager.get_queue_stats()
-
-        logger.info("\n[Queue Status]")
-        if isinstance(stats, dict):
-            if 'total_tasks' in stats:
-                logger.info(f"- Total tasks: {stats['total_tasks']}")
-            if 'pending_tasks' in stats:
-                logger.info(f"- Pending tasks: {stats['pending_tasks']}")
-            if 'scheduled_tasks' in stats:
-                logger.info(f"- Scheduled tasks: {stats['scheduled_tasks']}")
-
-            # Check database file
-            db_path = os.path.join(os.path.dirname(__file__), '..', 'projects', 'queue_data', 'huey.db')
-            if os.path.exists(db_path):
-                size = os.path.getsize(db_path)
-                logger.info(f"- Database size: {size} bytes")
-            else:
-                logger.info("- Database file: not found")
-
-    except Exception as e:
-        logger.error(f"Failed to get queue status: {e}")
-
-
-def main():
-    """Main entry point."""
-    parser = argparse.ArgumentParser(description="Optimized queue consumer")
-    parser.add_argument(
-        "--workers",
-        type=int,
-        default=calculate_optimal_workers(),
-        help=f"Number of worker threads (default: {calculate_optimal_workers()})"
-    )
-    parser.add_argument(
-        "--worker-type",
-        type=str,
-        default="threads",
-        choices=["threads", "greenlets", "gevent"],
-        help="Worker type (default: threads)"
-    )
-    parser.add_argument(
-        "--check-status",
-        action="store_true",
-        help="Check queue status and exit"
-    )
-    parser.add_argument(
-        "--profile",
-        type=str,
-        default="balanced",
-        choices=["low_memory", "balanced", "high_performance"],
-        help="Performance profile"
-    )
-
-    args = parser.parse_args()
-
-    # Apply performance profile
-    if args.profile == "low_memory":
-        os.environ['PYTHONOPTIMIZE'] = '1'
-        if args.workers > 2:
-            args.workers = 2
-            logger.info(f"Low memory mode: adjusted worker count to {args.workers}")
-    elif args.profile == "high_performance":
-        if args.workers < 4:
-            args.workers = 4
-            logger.info(f"High performance mode: adjusted worker count to {args.workers}")
-
-    # Check queue status
-    if args.check_status:
-        check_queue_status()
-        return
-
-    # Check environment
-    try:
-        import psutil
-        memory = psutil.virtual_memory()
-        logger.info("[System Info]")
-        logger.info(f"- CPU cores: {multiprocessing.cpu_count()}")
-        logger.info(f"- Available memory: {memory.available / (1024**3):.1f}GB")
-        logger.info(f"- Memory usage: {memory.percent:.1f}%")
-    except ImportError:
-        logger.info("[Tip] Install psutil to display system info: pip install psutil")
-
-    # Create and start the queue consumer
-    consumer = OptimizedQueueConsumer(
-        worker_type=args.worker_type,
-        workers=args.workers
-    )
-
-    consumer.start()
-
-
-if __name__ == "__main__":
-    main()

task_queue/task_status.py

@@ -1,210 +0,0 @@
-#!/usr/bin/env python3
-"""
-Task status SQLite storage system.
-"""
-
-import json
-import os
-import sqlite3
-import time
-from typing import Dict, Optional, Any, List
-from pathlib import Path
-
-
-class TaskStatusStore:
-    """SQLite-based task status store."""
-
-    def __init__(self, db_path: str = "projects/queue_data/task_status.db"):
-        self.db_path = db_path
-        # Ensure directory exists
-        Path(db_path).parent.mkdir(parents=True, exist_ok=True)
-        self._init_database()
-
-    def _init_database(self):
-        """Initialize database tables."""
-        with sqlite3.connect(self.db_path) as conn:
-            conn.execute('''
-                CREATE TABLE IF NOT EXISTS task_status (
-                    task_id TEXT PRIMARY KEY,
-                    unique_id TEXT NOT NULL,
-                    status TEXT NOT NULL,
-                    created_at REAL NOT NULL,
-                    updated_at REAL NOT NULL,
-                    result TEXT,
-                    error TEXT
-                )
-            ''')
-            conn.commit()
-
-    def set_status(self, task_id: str, unique_id: str, status: str,
-                   result: Optional[Dict] = None, error: Optional[str] = None):
-        """Set task status."""
-        current_time = time.time()
-
-        with sqlite3.connect(self.db_path) as conn:
-            conn.execute('''
-                INSERT OR REPLACE INTO task_status
-                (task_id, unique_id, status, created_at, updated_at, result, error)
-                VALUES (?, ?, ?, ?, ?, ?, ?)
-            ''', (
-                task_id, unique_id, status, current_time, current_time,
-                json.dumps(result) if result else None,
-                error
-            ))
-            conn.commit()
-
-    def get_status(self, task_id: str) -> Optional[Dict]:
-        """Get task status."""
-        with sqlite3.connect(self.db_path) as conn:
-            conn.row_factory = sqlite3.Row
-            cursor = conn.execute(
-                'SELECT * FROM task_status WHERE task_id = ?', (task_id,)
-            )
-            row = cursor.fetchone()
-
-            if not row:
-                return None
-
-            result = dict(row)
-            # Parse JSON field
-            if result['result']:
-                result['result'] = json.loads(result['result'])
-
-            return result
-
-    def update_status(self, task_id: str, status: str,
-                     result: Optional[Dict] = None, error: Optional[str] = None):
-        """Update task status."""
-        with sqlite3.connect(self.db_path) as conn:
-            # Check if task exists
-            cursor = conn.execute(
-                'SELECT task_id FROM task_status WHERE task_id = ?', (task_id,)
-            )
-            if not cursor.fetchone():
-                return False
-
-            # Update status
-            conn.execute('''
-                UPDATE task_status
-                SET status = ?, updated_at = ?, result = ?, error = ?
-                WHERE task_id = ?
-            ''', (
-                status, time.time(),
-                json.dumps(result) if result else None,
-                error, task_id
-            ))
-            conn.commit()
-            return True
-
-    def delete_status(self, task_id: str):
-        """Delete task status."""
-        with sqlite3.connect(self.db_path) as conn:
-            cursor = conn.execute(
-                'DELETE FROM task_status WHERE task_id = ?', (task_id,)
-            )
-            conn.commit()
-            return cursor.rowcount > 0
-
-    def list_all(self) -> Dict[str, Dict]:
-        """List all task statuses."""
-        with sqlite3.connect(self.db_path) as conn:
-            conn.row_factory = sqlite3.Row
-            cursor = conn.execute(
-                'SELECT * FROM task_status ORDER BY updated_at DESC'
-            )
-            all_tasks = {}
-            for row in cursor:
-                result = dict(row)
-                # Parse JSON field
-                if result['result']:
-                    result['result'] = json.loads(result['result'])
-                all_tasks[result['task_id']] = result
-            return all_tasks
-
-    def get_by_unique_id(self, unique_id: str) -> List[Dict]:
-        """Get all tasks for a given project ID."""
-        with sqlite3.connect(self.db_path) as conn:
-            conn.row_factory = sqlite3.Row
-            cursor = conn.execute(
-                'SELECT * FROM task_status WHERE unique_id = ? ORDER BY updated_at DESC',
-                (unique_id,)
-            )
-            tasks = []
-            for row in cursor:
-                result = dict(row)
-                if result['result']:
-                    result['result'] = json.loads(result['result'])
-                tasks.append(result)
-            return tasks
-
-    def cleanup_old_tasks(self, older_than_days: int = 7) -> int:
-        """Clean up old task records."""
-        cutoff_time = time.time() - (older_than_days * 24 * 3600)
-
-        with sqlite3.connect(self.db_path) as conn:
-            cursor = conn.execute(
-                'DELETE FROM task_status WHERE updated_at < ?',
-                (cutoff_time,)
-            )
-            conn.commit()
-            return cursor.rowcount
-
-    def get_statistics(self) -> Dict[str, Any]:
-        """Get task statistics."""
-        with sqlite3.connect(self.db_path) as conn:
-            # Total tasks
-            total = conn.execute('SELECT COUNT(*) FROM task_status').fetchone()[0]
-
-            # Status breakdown
-            status_stats = conn.execute('''
-                SELECT status, COUNT(*) as count
-                FROM task_status
-                GROUP BY status
-            ''').fetchall()
-
-            # Tasks in the last 24 hours
-            recent = time.time() - (24 * 3600)
-            recent_tasks = conn.execute(
-                'SELECT COUNT(*) FROM task_status WHERE updated_at > ?',
-                (recent,)
-            ).fetchone()[0]
-
-            return {
-                'total_tasks': total,
-                'status_breakdown': dict(status_stats),
-                'recent_24h': recent_tasks,
-                'database_path': self.db_path
-            }
-
-    def search_tasks(self, status: Optional[str] = None,
-                    unique_id: Optional[str] = None,
-                    limit: int = 100) -> List[Dict]:
-        """Search tasks."""
-        query = 'SELECT * FROM task_status WHERE 1=1'
-        params = []
-
-        if status:
-            query += ' AND status = ?'
-            params.append(status)
-
-        if unique_id:
-            query += ' AND unique_id = ?'
-            params.append(unique_id)
-
-        query += ' ORDER BY updated_at DESC LIMIT ?'
-        params.append(limit)
-
-        with sqlite3.connect(self.db_path) as conn:
-            conn.row_factory = sqlite3.Row
-            cursor = conn.execute(query, params)
-            tasks = []
-            for row in cursor:
-                result = dict(row)
-                if result['result']:
-                    result['result'] = json.loads(result['result'])
-                tasks.append(result)
-            return tasks
-
-
-# Global task status store instance
-task_status_store = TaskStatusStore()

task_queue/tasks.py

@@ -1,359 +0,0 @@
-#!/usr/bin/env python3
-"""
-File processing tasks for the queue system.
-"""
-
-import os
-import json
-import time
-import shutil
-import logging
-from pathlib import Path
-from typing import Dict, List, Optional, Any
-from huey import crontab
-
-# Configure logging
-logger = logging.getLogger('app')
-
-from .config import huey
-from utils.file_utils import (
-    extract_zip_file,
-    get_file_hash,
-    load_processed_files_log,
-    save_processed_files_log,
-    get_document_preview
-)
-
-
-@huey.task()
-def process_file_async(
-    project_id: str,
-    file_path: str,
-    original_filename: str = None,
-    target_directory: str = "files"
-) -> Dict[str, Any]:
-    """
-    Asynchronously process a single file.
-
-    Args:
-        project_id: Project ID
-        file_path: File path
-        original_filename: Original filename
-        target_directory: Target directory
-
-    Returns:
-        Processing result dictionary
-    """
-    try:
-        logger.info(f"Starting file processing: {file_path}")
-
-        # Ensure project directory exists
-        project_dir = os.path.join("projects", project_id)
-        files_dir = os.path.join(project_dir, target_directory)
-        os.makedirs(files_dir, exist_ok=True)
-
-        # Get file hash as identifier
-        file_hash = get_file_hash(file_path)
-
-        # Check if file has already been processed
-        processed_log = load_processed_files_log(project_id)
-        if file_hash in processed_log:
-            logger.info(f"File already processed, skipping: {file_path}")
-            return {
-                "status": "skipped",
-                "message": "File already processed",
-                "file_hash": file_hash,
-                "project_id": project_id
-            }
-
-        # Process the file
-        result = _process_single_file(
-            file_path,
-            files_dir,
-            original_filename or os.path.basename(file_path)
-        )
-
-        # Update processing log
-        if result["status"] == "success":
-            processed_log[file_hash] = {
-                "original_path": file_path,
-                "original_filename": original_filename or os.path.basename(file_path),
-                "processed_at": str(time.time()),
-                "status": "processed",
-                "result": result
-            }
-            save_processed_files_log(project_id, processed_log)
-
-        result["file_hash"] = file_hash
-        result["project_id"] = project_id
-
-        logger.info(f"File processing complete: {file_path}, status: {result['status']}")
-        return result
-
-    except Exception as e:
-        error_msg = f"Error processing file: {str(e)}"
-        logger.error(error_msg)
-        return {
-            "status": "error",
-            "message": error_msg,
-            "file_path": file_path,
-            "project_id": project_id
-        }
-
-
-@huey.task()
-def process_multiple_files_async(
-    project_id: str,
-    file_paths: List[str],
-    original_filenames: List[str] = None
-) -> List[Dict[str, Any]]:
-    """
-    Asynchronously process multiple files in batch.
-
-    Args:
-        project_id: Project ID
-        file_paths: List of file paths
-        original_filenames: List of original filenames
-
-    Returns:
-        List of processing results
-    """
-    try:
-        logger.info(f"Starting batch processing of {len(file_paths)} files")
-
-        results = []
-        for i, file_path in enumerate(file_paths):
-            original_filename = original_filenames[i] if original_filenames and i < len(original_filenames) else None
-
-            # Create async task for each file
-            result = process_file_async(project_id, file_path, original_filename)
-            results.append(result)
-
-        logger.info(f"Batch file processing tasks submitted, total {len(results)} files")
-        return results
-
-    except Exception as e:
-        error_msg = f"Error during batch file processing: {str(e)}"
-        logger.error(error_msg)
-        return [{
-            "status": "error",
-            "message": error_msg,
-            "project_id": project_id
-        }]
-
-
-@huey.task()
-def process_zip_file_async(
-    project_id: str,
-    zip_path: str,
-    extract_to: str = None
-) -> Dict[str, Any]:
-    """
-    Asynchronously process a zip archive file.
-
-    Args:
-        project_id: Project ID
-        zip_path: Zip file path
-        extract_to: Extraction target directory
-
-    Returns:
-        Processing result dictionary
-    """
-    try:
-        logger.info(f"Starting zip file processing: {zip_path}")
-
-        # Set extraction directory
-        if extract_to is None:
-            extract_to = os.path.join("projects", project_id, "extracted", os.path.basename(zip_path))
-
-        os.makedirs(extract_to, exist_ok=True)
-
-        # Extract files
-        extracted_files = extract_zip_file(zip_path, extract_to)
-
-        if not extracted_files:
-            return {
-                "status": "error",
-                "message": "Extraction failed or no supported files found",
-                "zip_path": zip_path,
-                "project_id": project_id
-            }
-
-        # Batch process extracted files
-        result = process_multiple_files_async(project_id, extracted_files)
-
-        return {
-            "status": "success",
-            "message": f"Zip file processing complete, extracted {len(extracted_files)} files",
-            "zip_path": zip_path,
-            "extract_to": extract_to,
-            "extracted_files": extracted_files,
-            "project_id": project_id,
-            "batch_task_result": result
-        }
-
-    except Exception as e:
-        error_msg = f"Error processing zip file: {str(e)}"
-        logger.error(error_msg)
-        return {
-            "status": "error",
-            "message": error_msg,
-            "zip_path": zip_path,
-            "project_id": project_id
-        }
-
-
-@huey.task()
-def cleanup_processed_files(
-    project_id: str,
-    older_than_days: int = 30
-) -> Dict[str, Any]:
-    """
-    Clean up old processed files.
-
-    Args:
-        project_id: Project ID
-        older_than_days: Clean files older than this many days
-
-    Returns:
-        Cleanup result dictionary
-    """
-    try:
-        logger.info(f"Starting cleanup of files older than {older_than_days} days in project {project_id}")
-
-        project_dir = os.path.join("projects", project_id)
-        if not os.path.exists(project_dir):
-            return {
-                "status": "error",
-                "message": "Project directory does not exist",
-                "project_id": project_id
-            }
-
-        current_time = time.time()
-        cutoff_time = current_time - (older_than_days * 24 * 3600)
-        cleaned_files = []
-
-        # Walk through project directory
-        for root, dirs, files in os.walk(project_dir):
-            for file in files:
-                file_path = os.path.join(root, file)
-                file_mtime = os.path.getmtime(file_path)
-
-                if file_mtime < cutoff_time:
-                    try:
-                        os.remove(file_path)
-                        cleaned_files.append(file_path)
-                        logger.info(f"Deleted old file: {file_path}")
-                    except Exception as e:
-                        logger.error(f"Failed to delete file {file_path}: {str(e)}")
-
-        # Clean up empty directories
-        for root, dirs, files in os.walk(project_dir, topdown=False):
-            for dir in dirs:
-                dir_path = os.path.join(root, dir)
-                try:
-                    if not os.listdir(dir_path):
-                        os.rmdir(dir_path)
-                        logger.info(f"Deleted empty directory: {dir_path}")
-                except Exception as e:
-                    logger.error(f"Failed to delete directory {dir_path}: {str(e)}")
-
-        return {
-            "status": "success",
-            "message": f"Cleanup complete, deleted {len(cleaned_files)} files",
-            "project_id": project_id,
-            "cleaned_files": cleaned_files,
-            "older_than_days": older_than_days
-        }
-
-    except Exception as e:
-        error_msg = f"Error during file cleanup: {str(e)}"
-        logger.error(error_msg)
-        return {
-            "status": "error",
-            "message": error_msg,
-            "project_id": project_id
-        }
-
-
-def _process_single_file(
-    file_path: str,
-    target_dir: str,
-    original_filename: str
-) -> Dict[str, Any]:
-    """
-    Internal method for processing a single file.
-
-    Args:
-        file_path: Source file path
-        target_dir: Target directory
-        original_filename: Original filename
-
-    Returns:
-        Processing result dictionary
-    """
-    try:
-        # Check if file exists
-        if not os.path.exists(file_path):
-            return {
-                "status": "error",
-                "message": "Source file does not exist",
-                "file_path": file_path
-            }
-
-        # Get file info
-        file_size = os.path.getsize(file_path)
-        file_ext = os.path.splitext(original_filename)[1].lower()
-
-        # Different processing based on file type
-        supported_extensions = ['.txt', '.md', '.csv', '.xlsx', '.zip']
-
-        if file_ext not in supported_extensions:
-            return {
-                "status": "error",
-                "message": f"Unsupported file type: {file_ext}",
-                "file_path": file_path,
-                "supported_extensions": supported_extensions
-            }
-
-        # Copy file to target directory
-        target_file_path = os.path.join(target_dir, original_filename)
-
-        # If target file already exists, add timestamp
-        if os.path.exists(target_file_path):
-            name, ext = os.path.splitext(original_filename)
-            timestamp = int(time.time())
-            target_file_path = os.path.join(target_dir, f"{name}_{timestamp}{ext}")
-
-        shutil.copy2(file_path, target_file_path)
-
-        # Get file preview (if it's a text file)
-        preview = None
-        if file_ext in ['.txt', '.md']:
-            preview = get_document_preview(target_file_path, max_lines=5)
-
-        return {
-            "status": "success",
-            "message": "File processed successfully",
-            "original_path": file_path,
-            "target_path": target_file_path,
-            "file_size": file_size,
-            "file_extension": file_ext,
-            "preview": preview
-        }
-
-    except Exception as e:
-        return {
-            "status": "error",
-            "message": f"Error processing file: {str(e)}",
-            "file_path": file_path
-        }
-
-
-# Periodic task example: clean up files older than 30 days daily at 2 AM
-@huey.periodic_task(crontab(hour=2, minute=0))
-def daily_cleanup():
-    """Daily cleanup task."""
-    logger.info("Running daily cleanup task")
-    # Add cleanup logic here
-    return {"status": "completed", "message": "Daily cleanup task completed"}

utils/__init__.py

@@ -13,23 +13,6 @@ from .file_utils import (
     save_processed_files_log
 )
 
-from .dataset_manager import (
-    download_dataset_files,
-    generate_dataset_structure,
-    remove_dataset_directory,
-    remove_dataset_directory_by_key
-)
-
-from .project_manager import (
-    generate_project_readme,
-    save_project_readme,
-    get_project_status,
-    remove_project,
-    list_projects,
-    get_project_stats
-)
-
-
 from .system_optimizer import (
     setup_system_optimizations
 )
@@ -59,11 +42,6 @@ from .api_models import (
     ProjectListResponse,
     ProjectStatsResponse,
     ProjectActionResponse,
-    QueueTaskRequest,
-    IncrementalTaskRequest,
-    QueueTaskResponse,
-    QueueStatusResponse,
-    TaskStatusResponse,
     create_success_response,
     create_error_response,
     create_chat_response,
@@ -90,20 +68,6 @@ __all__ = [
     'load_processed_files_log',
     'save_processed_files_log',
     
-    # dataset_manager
-    'download_dataset_files',
-    'generate_dataset_structure',
-    'remove_dataset_directory',
-    'remove_dataset_directory_by_key',
-    
-    # project_manager
-    'generate_project_readme',
-    'save_project_readme',
-    'get_project_status',
-    'remove_project',
-    'list_projects',
-    'get_project_stats',
-    
     # agent_pool
     'AgentPool',
     'get_agent_pool',
@@ -128,10 +92,6 @@ __all__ = [
     'ProjectListResponse',
     'ProjectStatsResponse',
     'ProjectActionResponse',
-    'QueueTaskRequest',
-    'QueueTaskResponse',
-    'QueueStatusResponse',
-    'TaskStatusResponse',
     'create_success_response',
     'create_error_response',
     'create_chat_response',

utils/api_models.py

@@ -270,133 +270,6 @@ def create_error_response(message: str, error_type: str = "error", **kwargs) ->
     }
 
 
-class QueueTaskRequest(BaseModel):
-    """Queue task request model"""
-    dataset_id: str
-    files: Optional[Dict[str, List[str]]] = Field(default=None, description="Files organized by key groups. Each key maps to a list of file paths (supports zip files)")
-    upload_folder: Optional[Dict[str, str]] = Field(default=None, description="Upload folders organized by group names. Each key maps to a folder name. Example: {'group1': 'my_project1', 'group2': 'my_project2'}")
-    priority: Optional[int] = Field(default=0, description="Task priority (higher number = higher priority)")
-    delay: Optional[int] = Field(default=0, description="Delay execution by N seconds")
-
-    model_config = ConfigDict(extra='allow')
-    
-    @field_validator('upload_folder', mode='before')
-    @classmethod
-    def validate_upload_folder(cls, v):
-        """Validate upload_folder dict format"""
-        if v is None:
-            return None
-        if isinstance(v, dict):
-            # Validate dict format
-            for key, value in v.items():
-                if not isinstance(key, str):
-                    raise ValueError(f"Key in upload_folder dict must be string, got {type(key)}")
-                if not isinstance(value, str):
-                    raise ValueError(f"Value in upload_folder dict must be string (folder name), got {type(value)} for key '{key}'")
-            return v
-        else:
-            raise ValueError(f"upload_folder must be a dict with group names as keys and folder names as values, got {type(v)}")
-    
-    @field_validator('files', mode='before')
-    @classmethod
-    def validate_files(cls, v):
-        """Validate dict format with key-grouped files"""
-        if v is None:
-            return None
-        if isinstance(v, dict):
-            # Validate dict format
-            for key, value in v.items():
-                if not isinstance(key, str):
-                    raise ValueError(f"Key in files dict must be string, got {type(key)}")
-                if not isinstance(value, list):
-                    raise ValueError(f"Value in files dict must be list, got {type(value)} for key '{key}'")
-                for item in value:
-                    if not isinstance(item, str):
-                        raise ValueError(f"File paths must be strings, got {type(item)} in key '{key}'")
-            return v
-        else:
-            raise ValueError(f"Files must be a dict with key groups, got {type(v)}")
-
-
-class IncrementalTaskRequest(BaseModel):
-    """Incremental file processing request model"""
-    dataset_id: str = Field(..., description="Dataset ID for the project")
-    files_to_add: Optional[Dict[str, List[str]]] = Field(default=None, description="Files to add organized by key groups")
-    files_to_remove: Optional[Dict[str, List[str]]] = Field(default=None, description="Files to remove organized by key groups")
-    system_prompt: Optional[str] = None
-    mcp_settings: Optional[List[Dict]] = None
-    priority: Optional[int] = Field(default=0, description="Task priority (higher number = higher priority)")
-    delay: Optional[int] = Field(default=0, description="Delay execution by N seconds")
-
-    model_config = ConfigDict(extra='allow')
-    
-    @field_validator('files_to_add', mode='before')
-    @classmethod
-    def validate_files_to_add(cls, v):
-        """Validate files_to_add dict format"""
-        if v is None:
-            return None
-        if isinstance(v, dict):
-            for key, value in v.items():
-                if not isinstance(key, str):
-                    raise ValueError(f"Key in files_to_add dict must be string, got {type(key)}")
-                if not isinstance(value, list):
-                    raise ValueError(f"Value in files_to_add dict must be list, got {type(value)} for key '{key}'")
-                for item in value:
-                    if not isinstance(item, str):
-                        raise ValueError(f"File paths must be strings, got {type(item)} in key '{key}'")
-            return v
-        else:
-            raise ValueError(f"files_to_add must be a dict with key groups, got {type(v)}")
-    
-    @field_validator('files_to_remove', mode='before')
-    @classmethod
-    def validate_files_to_remove(cls, v):
-        """Validate files_to_remove dict format"""
-        if v is None:
-            return None
-        if isinstance(v, dict):
-            for key, value in v.items():
-                if not isinstance(key, str):
-                    raise ValueError(f"Key in files_to_remove dict must be string, got {type(key)}")
-                if not isinstance(value, list):
-                    raise ValueError(f"Value in files_to_remove dict must be list, got {type(value)} for key '{key}'")
-                for item in value:
-                    if not isinstance(item, str):
-                        raise ValueError(f"File paths must be strings, got {type(item)} in key '{key}'")
-            return v
-        else:
-            raise ValueError(f"files_to_remove must be a dict with key groups, got {type(v)}")
-
-
-class QueueTaskResponse(BaseModel):
-    """Queue task response model"""
-    success: bool
-    message: str
-    dataset_id: str
-    task_id: Optional[str] = None
-    task_status: Optional[str] = None
-    estimated_processing_time: Optional[int] = None  # seconds
-
-
-class QueueStatusResponse(BaseModel):
-    """Queue status response model"""
-    success: bool
-    message: str
-    queue_stats: Dict[str, Any]
-    pending_tasks: List[Dict[str, Any]]
-
-
-class TaskStatusResponse(BaseModel):
-    """Task status response model"""
-    success: bool
-    message: str
-    task_id: str
-    task_status: Optional[str] = None
-    task_result: Optional[Dict[str, Any]] = None
-    error: Optional[str] = None
-
-
 def create_chat_response(
     messages: List[Message],
     model: str,

utils/data_merger.py

@@ -1,439 +0,0 @@
-#!/usr/bin/env python3
-"""
-Data merging functions for combining processed file results.
-"""
-
-import os
-import pickle
-import logging
-from typing import Dict, List, Optional, Tuple
-import json
-
-# Configure logger
-logger = logging.getLogger('app')
-
-# Try to import numpy, but handle if missing
-try:
-    import numpy as np
-    NUMPY_SUPPORT = True
-except ImportError:
-    logger.warning("NumPy not available, some embedding features may be limited")
-    NUMPY_SUPPORT = False
-
-
-def merge_documents_by_group(unique_id: str, group_name: str) -> Dict:
-    """Merge all document.txt files in a group into a single document."""
-    
-    processed_group_dir = os.path.join("projects", "data", unique_id, "processed", group_name)
-    dataset_group_dir = os.path.join("projects", "data", unique_id, "datasets", group_name)
-    os.makedirs(dataset_group_dir, exist_ok=True)
-    
-    merged_document_path = os.path.join(dataset_group_dir, "document.txt")
-    
-    result = {
-        "success": False,
-        "merged_document_path": merged_document_path,
-        "source_files": [],
-        "total_pages": 0,
-        "total_characters": 0,
-        "error": None
-    }
-    
-    try:
-        # Find all document.txt files in the processed directory
-        document_files = []
-        if os.path.exists(processed_group_dir):
-            for item in os.listdir(processed_group_dir):
-                item_path = os.path.join(processed_group_dir, item)
-                if os.path.isdir(item_path):
-                    document_path = os.path.join(item_path, "document.txt")
-                    if os.path.exists(document_path) and os.path.getsize(document_path) > 0:
-                        document_files.append((item, document_path))
-        
-        if not document_files:
-            result["error"] = "No document files found to merge"
-            return result
-        
-        # Merge all documents with page separators
-        merged_content = []
-        total_characters = 0
-        
-        for filename_stem, document_path in sorted(document_files):
-            try:
-                with open(document_path, 'r', encoding='utf-8') as f:
-                    content = f.read().strip()
-                
-                if content:
-                    merged_content.append(f"# Page {filename_stem}")
-                    merged_content.append(content)
-                    total_characters += len(content)
-                    result["source_files"].append(filename_stem)
-                    
-            except Exception as e:
-                logger.error(f"Error reading document file {document_path}: {str(e)}")
-                continue
-        
-        if merged_content:
-            # Write merged document
-            with open(merged_document_path, 'w', encoding='utf-8') as f:
-                f.write('\n\n'.join(merged_content))
-            
-            result["total_pages"] = len(document_files)
-            result["total_characters"] = total_characters
-            result["success"] = True
-            
-        else:
-            result["error"] = "No valid content found in document files"
-            
-    except Exception as e:
-        result["error"] = f"Document merging failed: {str(e)}"
-        logger.error(f"Error merging documents for group {group_name}: {str(e)}")
-    
-    return result
-
-
-def merge_paginations_by_group(unique_id: str, group_name: str) -> Dict:
-    """Merge all pagination.txt files in a group."""
-    
-    processed_group_dir = os.path.join("projects", "data", unique_id, "processed", group_name)
-    dataset_group_dir = os.path.join("projects", "data", unique_id, "datasets", group_name)
-    os.makedirs(dataset_group_dir, exist_ok=True)
-    
-    merged_pagination_path = os.path.join(dataset_group_dir, "pagination.txt")
-    
-    result = {
-        "success": False,
-        "merged_pagination_path": merged_pagination_path,
-        "source_files": [],
-        "total_lines": 0,
-        "error": None
-    }
-    
-    try:
-        # Find all pagination.txt files
-        pagination_files = []
-        if os.path.exists(processed_group_dir):
-            for item in os.listdir(processed_group_dir):
-                item_path = os.path.join(processed_group_dir, item)
-                if os.path.isdir(item_path):
-                    pagination_path = os.path.join(item_path, "pagination.txt")
-                    if os.path.exists(pagination_path) and os.path.getsize(pagination_path) > 0:
-                        pagination_files.append((item, pagination_path))
-        
-        if not pagination_files:
-            result["error"] = "No pagination files found to merge"
-            return result
-        
-        # Merge all pagination files
-        merged_lines = []
-        
-        for filename_stem, pagination_path in sorted(pagination_files):
-            try:
-                with open(pagination_path, 'r', encoding='utf-8') as f:
-                    lines = f.readlines()
-                
-                for line in lines:
-                    line = line.strip()
-                    if line:
-                        merged_lines.append(line)
-                        
-                result["source_files"].append(filename_stem)
-                
-            except Exception as e:
-                logger.error(f"Error reading pagination file {pagination_path}: {str(e)}")
-                continue
-        
-        if merged_lines:
-            # Write merged pagination
-            with open(merged_pagination_path, 'w', encoding='utf-8') as f:
-                for line in merged_lines:
-                    f.write(f"{line}\n")
-            
-            result["total_lines"] = len(merged_lines)
-            result["success"] = True
-            
-        else:
-            result["error"] = "No valid pagination data found"
-            
-    except Exception as e:
-        result["error"] = f"Pagination merging failed: {str(e)}"
-        logger.error(f"Error merging paginations for group {group_name}: {str(e)}")
-    
-    return result
-
-
-def merge_embeddings_by_group(unique_id: str, group_name: str) -> Dict:
-    """Merge all embedding.pkl files in a group."""
-    
-    processed_group_dir = os.path.join("projects", "data", unique_id, "processed", group_name)
-    dataset_group_dir = os.path.join("projects", "data", unique_id, "datasets", group_name)
-    os.makedirs(dataset_group_dir, exist_ok=True)
-    
-    merged_embedding_path = os.path.join(dataset_group_dir, "embedding.pkl")
-    
-    result = {
-        "success": False,
-        "merged_embedding_path": merged_embedding_path,
-        "source_files": [],
-        "total_chunks": 0,
-        "total_dimensions": 0,
-        "error": None
-    }
-    
-    try:
-        # Find all embedding.pkl files
-        embedding_files = []
-        if os.path.exists(processed_group_dir):
-            for item in os.listdir(processed_group_dir):
-                item_path = os.path.join(processed_group_dir, item)
-                if os.path.isdir(item_path):
-                    embedding_path = os.path.join(item_path, "embedding.pkl")
-                    if os.path.exists(embedding_path) and os.path.getsize(embedding_path) > 0:
-                        embedding_files.append((item, embedding_path))
-        
-        if not embedding_files:
-            result["error"] = "No embedding files found to merge"
-            return result
-        
-        # Load and merge all embedding data
-        all_chunks = []
-        all_embeddings = []  # Fix: collect all embedding vectors
-        total_chunks = 0
-        dimensions = 0
-        chunking_strategy = 'unknown'
-        chunking_params = {}
-        model_path = 'TaylorAI/gte-tiny'
-        
-        for filename_stem, embedding_path in sorted(embedding_files):
-            try:
-                with open(embedding_path, 'rb') as f:
-                    embedding_data = pickle.load(f)
-                
-                if isinstance(embedding_data, dict) and 'chunks' in embedding_data:
-                    chunks = embedding_data['chunks']
-                    
-                    # Get embedding vectors (critical fix)
-                    if 'embeddings' in embedding_data:
-                        embeddings = embedding_data['embeddings']
-                        all_embeddings.append(embeddings)
-                        
-                        # Get model metadata from the first file
-                        if 'model_path' in embedding_data:
-                            model_path = embedding_data['model_path']
-                        if 'chunking_strategy' in embedding_data:
-                            chunking_strategy = embedding_data['chunking_strategy']
-                        if 'chunking_params' in embedding_data:
-                            chunking_params = embedding_data['chunking_params']
-                    
-                    # Add source file metadata to each chunk
-                    for chunk in chunks:
-                        if isinstance(chunk, dict):
-                            chunk['source_file'] = filename_stem
-                            chunk['source_group'] = group_name
-                        elif isinstance(chunk, str):
-                            # If the chunk is a string, keep it unchanged
-                            pass
-                    
-                    all_chunks.extend(chunks)
-                    total_chunks += len(chunks)
-                    
-                    result["source_files"].append(filename_stem)
-                
-            except Exception as e:
-                logger.error(f"Error loading embedding file {embedding_path}: {str(e)}")
-                continue
-        
-        if all_chunks and all_embeddings:
-            # Merge all embedding vectors
-            try:
-                # Try merging tensors with torch
-                import torch
-                if all(isinstance(emb, torch.Tensor) for emb in all_embeddings):
-                    merged_embeddings = torch.cat(all_embeddings, dim=0)
-                    dimensions = merged_embeddings.shape[1]
-                else:
-                    # If the values are not tensors, try converting them to numpy
-                    import numpy as np
-                    if NUMPY_SUPPORT:
-                        np_embeddings = []
-                        for emb in all_embeddings:
-                            if hasattr(emb, 'numpy'):
-                                np_embeddings.append(emb.numpy())
-                            elif isinstance(emb, np.ndarray):
-                                np_embeddings.append(emb)
-                            else:
-                                # If conversion fails, skip this file
-                                logger.warning(f"Warning: Cannot convert embedding to numpy from file {filename_stem}")
-                                continue
-                        
-                        if np_embeddings:
-                            merged_embeddings = np.concatenate(np_embeddings, axis=0)
-                            dimensions = merged_embeddings.shape[1]
-                        else:
-                            result["error"] = "No valid embedding tensors could be merged"
-                            return result
-                    else:
-                        result["error"] = "NumPy not available for merging embeddings"
-                        return result
-                        
-            except ImportError:
-                # If torch is unavailable, try using numpy
-                if NUMPY_SUPPORT:
-                    import numpy as np
-                    np_embeddings = []
-                    for emb in all_embeddings:
-                        if hasattr(emb, 'numpy'):
-                            np_embeddings.append(emb.numpy())
-                        elif isinstance(emb, np.ndarray):
-                            np_embeddings.append(emb)
-                        else:
-                            logger.warning(f"Warning: Cannot convert embedding to numpy from file {filename_stem}")
-                            continue
-                    
-                    if np_embeddings:
-                        merged_embeddings = np.concatenate(np_embeddings, axis=0)
-                        dimensions = merged_embeddings.shape[1]
-                    else:
-                        result["error"] = "No valid embedding tensors could be merged"
-                        return result
-                else:
-                    result["error"] = "Neither torch nor numpy available for merging embeddings"
-                    return result
-            except Exception as e:
-                result["error"] = f"Failed to merge embedding tensors: {str(e)}"
-                logger.error(f"Error merging embedding tensors: {str(e)}")
-                return result
-            
-            # Create merged embedding data structure
-            merged_embedding_data = {
-                'chunks': all_chunks,
-                'embeddings': merged_embeddings,  # Critical fix: include the embeddings key
-                'total_chunks': total_chunks,
-                'dimensions': dimensions,
-                'source_files': result["source_files"],
-                'group_name': group_name,
-                'merged_at': str(__import__('time').time()),
-                'chunking_strategy': chunking_strategy,
-                'chunking_params': chunking_params,
-                'model_path': model_path
-            }
-            
-            # Save merged embeddings
-            with open(merged_embedding_path, 'wb') as f:
-                pickle.dump(merged_embedding_data, f)
-            
-            result["total_chunks"] = total_chunks
-            result["total_dimensions"] = dimensions
-            result["success"] = True
-            
-        else:
-            result["error"] = "No valid embedding data found"
-            
-    except Exception as e:
-        result["error"] = f"Embedding merging failed: {str(e)}"
-        logger.error(f"Error merging embeddings for group {group_name}: {str(e)}")
-    
-    return result
-
-
-def merge_all_data_by_group(unique_id: str, group_name: str) -> Dict:
-    """Merge documents, paginations, and embeddings for a group."""
-    
-    merge_results = {
-        "group_name": group_name,
-        "unique_id": unique_id,
-        "success": True,
-        "document_merge": None,
-        "pagination_merge": None, 
-        "embedding_merge": None,
-        "errors": []
-    }
-    
-    # Merge documents
-    document_result = merge_documents_by_group(unique_id, group_name)
-    merge_results["document_merge"] = document_result
-    
-    if not document_result["success"]:
-        merge_results["success"] = False
-        merge_results["errors"].append(f"Document merge failed: {document_result['error']}")
-    
-    # Merge paginations
-    pagination_result = merge_paginations_by_group(unique_id, group_name)
-    merge_results["pagination_merge"] = pagination_result
-    
-    if not pagination_result["success"]:
-        merge_results["success"] = False
-        merge_results["errors"].append(f"Pagination merge failed: {pagination_result['error']}")
-    
-    # Merge embeddings
-    embedding_result = merge_embeddings_by_group(unique_id, group_name)
-    merge_results["embedding_merge"] = embedding_result
-    
-    if not embedding_result["success"]:
-        merge_results["success"] = False
-        merge_results["errors"].append(f"Embedding merge failed: {embedding_result['error']}")
-    
-    return merge_results
-
-
-def get_group_merge_status(unique_id: str, group_name: str) -> Dict:
-    """Get the status of merged data for a group."""
-    
-    dataset_group_dir = os.path.join("projects", "data", unique_id, "datasets", group_name)
-    
-    status = {
-        "group_name": group_name,
-        "unique_id": unique_id,
-        "dataset_dir_exists": os.path.exists(dataset_group_dir),
-        "document_exists": False,
-        "document_size": 0,
-        "pagination_exists": False,
-        "pagination_size": 0,
-        "embedding_exists": False,
-        "embedding_size": 0,
-        "merge_complete": False
-    }
-    
-    if os.path.exists(dataset_group_dir):
-        document_path = os.path.join(dataset_group_dir, "document.txt")
-        pagination_path = os.path.join(dataset_group_dir, "pagination.txt")
-        embedding_path = os.path.join(dataset_group_dir, "embedding.pkl")
-        
-        if os.path.exists(document_path):
-            status["document_exists"] = True
-            status["document_size"] = os.path.getsize(document_path)
-        
-        if os.path.exists(pagination_path):
-            status["pagination_exists"] = True
-            status["pagination_size"] = os.path.getsize(pagination_path)
-        
-        if os.path.exists(embedding_path):
-            status["embedding_exists"] = True
-            status["embedding_size"] = os.path.getsize(embedding_path)
-        
-        # Check if all files exist and are not empty
-        if (status["document_exists"] and status["document_size"] > 0 and
-            status["pagination_exists"] and status["pagination_size"] > 0 and
-            status["embedding_exists"] and status["embedding_size"] > 0):
-            status["merge_complete"] = True
-    
-    return status
-
-
-def cleanup_dataset_group(unique_id: str, group_name: str) -> bool:
-    """Clean up merged dataset files for a group."""
-    
-    dataset_group_dir = os.path.join("projects", "data", unique_id, "datasets", group_name)
-    
-    try:
-        if os.path.exists(dataset_group_dir):
-            import shutil
-            shutil.rmtree(dataset_group_dir)
-            logger.info(f"Cleaned up dataset group: {group_name}")
-            return True
-        else:
-            return True  # Nothing to clean up
-            
-    except Exception as e:
-        logger.error(f"Error cleaning up dataset group {group_name}: {str(e)}")
-        return False

utils/dataset_manager.py

@@ -1,297 +0,0 @@
-#!/usr/bin/env python3
-"""
-Dataset management functions for organizing and processing datasets.
-New implementation with per-file processing and group merging.
-"""
-
-import os
-import json
-import logging
-from typing import Dict, List
-
-# Configure logger
-logger = logging.getLogger('app')
-
-# Import new modules
-from utils.file_manager import (
-    ensure_directories, sync_files_to_group, cleanup_orphaned_files,
-    get_group_files_list
-)
-from utils.single_file_processor import (
-    process_single_file, check_file_already_processed
-)
-from utils.data_merger import (
-    merge_all_data_by_group, cleanup_dataset_group
-)
-
-
-async def download_dataset_files(unique_id: str, files: Dict[str, List[str]], incremental_mode: bool = False) -> Dict[str, List[str]]:
-    """
-    Process dataset files with new architecture:
-    1. Sync files to group directories
-    2. Process each file individually
-    3. Merge results by group
-    4. Clean up orphaned files (only in non-incremental mode)
-    
-    Args:
-        unique_id: Project ID
-        files: Dictionary of files to process, grouped by key
-        incremental_mode: If True, preserve existing files and only process new ones
-    """
-    if not files:
-        return {}
-    
-    logger.info(f"Starting {'incremental' if incremental_mode else 'full'} file processing for project: {unique_id}")
-    
-    # Ensure project directories exist
-    ensure_directories(unique_id)
-    
-    # Step 1: Sync files to group directories
-    logger.info("Step 1: Syncing files to group directories...")
-    synced_files, failed_files = sync_files_to_group(unique_id, files, incremental_mode)
-    
-    # Step 2: Detect changes and cleanup orphaned files (only in non-incremental mode)
-    from utils.file_manager import detect_file_changes
-    changes = detect_file_changes(unique_id, files, incremental_mode)
-    
-    # Only cleanup orphaned files in non-incremental mode or when files are explicitly removed
-    if not incremental_mode and any(changes["removed"].values()):
-        logger.info("Step 2: Cleaning up orphaned files...")
-        removed_files = cleanup_orphaned_files(unique_id, changes)
-        logger.info(f"Removed orphaned files: {removed_files}")
-    elif incremental_mode:
-        logger.info("Step 2: Skipping cleanup in incremental mode to preserve existing files")
-    
-    # Step 3: Process individual files
-    logger.info("Step 3: Processing individual files...")
-    processed_files_by_group = {}
-    processing_results = {}
-    
-    for group_name, file_list in files.items():
-        processed_files_by_group[group_name] = []
-        processing_results[group_name] = []
-        
-        for file_path in file_list:
-            filename = os.path.basename(file_path)
-            
-            # Get local file path
-            local_path = os.path.join("projects", "data", unique_id, "files", group_name, filename)
-            
-            # Skip if file doesn't exist (might be remote file that failed to download)
-            if not os.path.exists(local_path) and not file_path.startswith(('http://', 'https://')):
-                logger.warning(f"Skipping non-existent file: {filename}")
-                continue
-            
-            # Check if already processed
-            if check_file_already_processed(unique_id, group_name, filename):
-                logger.info(f"Skipping already processed file: {filename}")
-                processed_files_by_group[group_name].append(filename)
-                processing_results[group_name].append({
-                    "filename": filename,
-                    "status": "existing"
-                })
-                continue
-            
-            # Process the file
-            logger.info(f"Processing file: {filename} (group: {group_name})")
-            result = await process_single_file(unique_id, group_name, filename, file_path, local_path)
-            processing_results[group_name].append(result)
-            
-            if result["success"]:
-                processed_files_by_group[group_name].append(filename)
-                logger.info(f"  Successfully processed {filename}")
-            else:
-                logger.error(f"  Failed to process {filename}: {result['error']}")
-    
-    # Step 4: Merge results by group
-    logger.info("Step 4: Merging results by group...")
-    merge_results = {}
-    
-    for group_name in processed_files_by_group.keys():
-        # Get all files in the group (including existing ones)
-        group_files = get_group_files_list(unique_id, group_name)
-        
-        if group_files:
-            logger.info(f"Merging group: {group_name} with {len(group_files)} files")
-            merge_result = merge_all_data_by_group(unique_id, group_name)
-            merge_results[group_name] = merge_result
-            
-            if merge_result["success"]:
-                logger.info(f"  Successfully merged group {group_name}")
-            else:
-                logger.error(f"  Failed to merge group {group_name}: {merge_result['errors']}")
-    
-    # Step 5: Save processing log
-    logger.info("Step 5: Saving processing log...")
-    await save_processing_log(unique_id, files, synced_files, processing_results, merge_results)
-    
-    logger.info(f"File processing completed for project: {unique_id}")
-    return processed_files_by_group
-
-
-async def save_processing_log(
-    unique_id: str,
-    requested_files: Dict[str, List[str]],
-    synced_files: Dict,
-    processing_results: Dict,
-    merge_results: Dict
-):
-    """Save comprehensive processing log."""
-    
-    log_data = {
-        "unique_id": unique_id,
-        "timestamp": str(os.path.getmtime("projects") if os.path.exists("projects") else 0),
-        "requested_files": requested_files,
-        "synced_files": synced_files,
-        "processing_results": processing_results,
-        "merge_results": merge_results,
-        "summary": {
-            "total_groups": len(requested_files),
-            "total_files_requested": sum(len(files) for files in requested_files.values()),
-            "total_files_processed": sum(
-                len([r for r in results if r.get("success", False)])
-                for results in processing_results.values()
-            ),
-            "total_groups_merged": len([r for r in merge_results.values() if r.get("success", False)])
-        }
-    }
-    
-    log_file_path = os.path.join("projects", "data", unique_id, "processing_log.json")
-    try:
-        with open(log_file_path, 'w', encoding='utf-8') as f:
-            json.dump(log_data, f, ensure_ascii=False, indent=2)
-        logger.info(f"Processing log saved to: {log_file_path}")
-    except Exception as e:
-        logger.error(f"Error saving processing log: {str(e)}")
-
-
-def generate_dataset_structure(unique_id: str) -> str:
-    """Generate a string representation of the dataset structure"""
-    project_dir = os.path.join("projects", "data", unique_id)
-    structure = []
-    
-    def add_directory_contents(dir_path: str, prefix: str = ""):
-        try:
-            if not os.path.exists(dir_path):
-                structure.append(f"{prefix}└── (not found)")
-                return
-                
-            items = sorted(os.listdir(dir_path))
-            for i, item in enumerate(items):
-                item_path = os.path.join(dir_path, item)
-                is_last = i == len(items) - 1
-                current_prefix = "└── " if is_last else "├── "
-                structure.append(f"{prefix}{current_prefix}{item}")
-                
-                if os.path.isdir(item_path):
-                    next_prefix = prefix + ("    " if is_last else "│   ")
-                    add_directory_contents(item_path, next_prefix)
-        except Exception as e:
-            structure.append(f"{prefix}└── Error: {str(e)}")
-    
-    # Add files directory structure
-    files_dir = os.path.join(project_dir, "files")
-    structure.append("files/")
-    add_directory_contents(files_dir, "")
-    
-    # Add processed directory structure  
-    processed_dir = os.path.join(project_dir, "processed")
-    structure.append("\nprocessed/")
-    add_directory_contents(processed_dir, "")
-    
-    # Add dataset directory structure
-    dataset_dir = os.path.join(project_dir, "datasets")
-    structure.append("\ndataset/")
-    add_directory_contents(dataset_dir, "")
-    
-    return "\n".join(structure)
-
-
-def get_processing_status(unique_id: str) -> Dict:
-    """Get comprehensive processing status for a project."""
-    
-    project_dir = os.path.join("projects", "data", unique_id)
-    
-    if not os.path.exists(project_dir):
-        return {
-            "project_exists": False,
-            "unique_id": unique_id
-        }
-    
-    status = {
-        "project_exists": True,
-        "unique_id": unique_id,
-        "directories": {
-            "files": os.path.exists(os.path.join(project_dir, "files")),
-            "processed": os.path.exists(os.path.join(project_dir, "processed")),
-            "dataset": os.path.exists(os.path.join(project_dir, "datasets"))
-        },
-        "groups": {},
-        "processing_log_exists": os.path.exists(os.path.join(project_dir, "processing_log.json"))
-    }
-    
-    # Check each group's status
-    files_dir = os.path.join(project_dir, "files")
-    if os.path.exists(files_dir):
-        for group_name in os.listdir(files_dir):
-            group_path = os.path.join(files_dir, group_name)
-            if os.path.isdir(group_path):
-                status["groups"][group_name] = {
-                    "files_count": len([
-                        f for f in os.listdir(group_path) 
-                        if os.path.isfile(os.path.join(group_path, f))
-                    ]),
-                    "merge_status": "pending"
-                }
-    
-    # Check merge status for each group
-    dataset_dir = os.path.join(project_dir, "datasets")
-    if os.path.exists(dataset_dir):
-        for group_name in os.listdir(dataset_dir):
-            group_path = os.path.join(dataset_dir, group_name)
-            if os.path.isdir(group_path):
-                if group_name in status["groups"]:
-                    # Check if merge is complete
-                    document_path = os.path.join(group_path, "document.txt")
-                    pagination_path = os.path.join(group_path, "pagination.txt")
-                    embedding_path = os.path.join(group_path, "embedding.pkl")
-                    
-                    if (os.path.exists(document_path) and os.path.exists(pagination_path) and 
-                        os.path.exists(embedding_path)):
-                        status["groups"][group_name]["merge_status"] = "completed"
-                    else:
-                        status["groups"][group_name]["merge_status"] = "incomplete"
-                else:
-                    status["groups"][group_name] = {
-                        "files_count": 0,
-                        "merge_status": "completed"
-                    }
-    
-    return status
-
-
-def remove_dataset_directory(unique_id: str, filename_without_ext: str):
-    """Remove a specific dataset directory (deprecated - use new structure)"""
-    # This function is kept for compatibility but delegates to new structure
-    dataset_path = os.path.join("projects", "data", unique_id, "processed", filename_without_ext)
-    if os.path.exists(dataset_path):
-        import shutil
-        shutil.rmtree(dataset_path)
-
-
-def remove_dataset_directory_by_key(unique_id: str, key: str):
-    """Remove dataset directory by key (group name)"""
-    # Remove files directory
-    files_group_path = os.path.join("projects", "data", unique_id, "files", key)
-    if os.path.exists(files_group_path):
-        import shutil
-        shutil.rmtree(files_group_path)
-    
-    # Remove processed directory
-    processed_group_path = os.path.join("projects", "data", unique_id, "processed", key)
-    if os.path.exists(processed_group_path):
-        import shutil
-        shutil.rmtree(processed_group_path)
-    
-    # Remove dataset directory
-    cleanup_dataset_group(unique_id, key)

utils/project_manager.py

@@ -1,343 +0,0 @@
-#!/usr/bin/env python3
-"""
-Project management functions for handling projects, README generation, and status tracking.
-"""
-
-import os
-import json
-import logging
-from typing import Dict, List, Optional
-from pathlib import Path
-
-# Configure logger
-logger = logging.getLogger('app')
-
-from utils.file_utils import get_document_preview, load_processed_files_log
-
-
-def generate_directory_tree(project_dir: str, unique_id: str, max_depth: int = 3) -> str:
-    """Generate dataset directory tree structure for the project"""
-    def _build_tree(path: str, prefix: str = "", is_last: bool = True, depth: int = 0) -> List[str]:
-        if depth > max_depth:
-            return []
-        
-        lines = []
-        try:
-            entries = sorted(os.listdir(path))
-            # Separate directories and files
-            dirs = [e for e in entries if os.path.isdir(os.path.join(path, e)) and not e.startswith('.')]
-            files = [e for e in entries if os.path.isfile(os.path.join(path, e)) and not e.startswith('.')]
-            
-            entries = dirs + files
-            
-            for i, entry in enumerate(entries):
-                entry_path = os.path.join(path, entry)
-                is_dir = os.path.isdir(entry_path)
-                is_last_entry = i == len(entries) - 1
-                
-                # Choose the appropriate tree symbols
-                if is_last_entry:
-                    connector = "└── "
-                    new_prefix = prefix + "    "
-                else:
-                    connector = "├── "
-                    new_prefix = prefix + "│   "
-                
-                # Add entry line
-                line = prefix + connector + entry
-                if is_dir:
-                    line += "/"
-                lines.append(line)
-                
-                # Recursively add subdirectories
-                if is_dir and depth < max_depth:
-                    sub_lines = _build_tree(entry_path, new_prefix, is_last_entry, depth + 1)
-                    lines.extend(sub_lines)
-                    
-        except PermissionError:
-            lines.append(prefix + "└── [Permission Denied]")
-        except Exception as e:
-            lines.append(prefix + "└── [Error: " + str(e) + "]")
-        
-        return lines
-    
-    # Start building tree from dataset directory
-    dataset_dir = os.path.join(project_dir, "datasets")
-    tree_lines = []
-    
-    if not os.path.exists(dataset_dir):
-        return "└── [No dataset directory found]"
-    
-    try:
-        entries = sorted(os.listdir(dataset_dir))
-        dirs = [e for e in entries if os.path.isdir(os.path.join(dataset_dir, e)) and not e.startswith('.')]
-        files = [e for e in entries if os.path.isfile(os.path.join(dataset_dir, e)) and not e.startswith('.')]
-        
-        entries = dirs + files
-        
-        if not entries:
-            tree_lines.append("└── [Empty dataset directory]")
-        else:
-            for i, entry in enumerate(entries):
-                entry_path = os.path.join(dataset_dir, entry)
-                is_dir = os.path.isdir(entry_path)
-                is_last_entry = i == len(entries) - 1
-                
-                if is_last_entry:
-                    connector = "└── "
-                    prefix = "    "
-                else:
-                    connector = "├── "
-                    prefix = "│   "
-                
-                line = connector + entry
-                if is_dir:
-                    line += "/"
-                tree_lines.append(line)
-                
-                # Recursively add subdirectories
-                if is_dir:
-                    sub_lines = _build_tree(entry_path, prefix, is_last_entry, 1)
-                    tree_lines.extend(sub_lines)
-                
-    except Exception as e:
-        tree_lines.append(f"└── [Error generating tree: {str(e)}]")
-    
-    return "\n".join(tree_lines)
-
-
-def generate_project_readme(unique_id: str) -> str:
-    """Generate README.md content for a project"""
-    project_dir = os.path.join("projects", "data", unique_id)
-    readme_content = f"""# Project: {unique_id}
-
-## Project Overview
-
-This project contains processed documents and their associated embeddings for semantic search.
-
-## Directory Structure
-
-"""
-    
-    # Generate directory tree
-    readme_content += "```\n"
-    readme_content += generate_directory_tree(project_dir, unique_id)
-    readme_content += "\n```\n\n"
-    
-    readme_content += """## Dataset Structure
-
-"""
-    
-    dataset_dir = os.path.join(project_dir, "datasets")
-    if not os.path.exists(dataset_dir):
-        readme_content += "No dataset files available.\n"
-    else:
-        # Get all document directories
-        doc_dirs = []
-        try:
-            for item in sorted(os.listdir(dataset_dir)):
-                item_path = os.path.join(dataset_dir, item)
-                if os.path.isdir(item_path):
-                    doc_dirs.append(item)
-        except Exception as e:
-            logger.error(f"Error listing dataset directories: {str(e)}")
-        
-        if not doc_dirs:
-            readme_content += "No document directories found.\n"
-        else:
-            for doc_dir in doc_dirs:
-                doc_path = os.path.join(dataset_dir, doc_dir)
-                document_file = os.path.join(doc_path, "document.txt")
-                pagination_file = os.path.join(doc_path, "pagination.txt")
-                embeddings_file = os.path.join(doc_path, "embedding.pkl")
-                
-                readme_content += f"### {doc_dir}\n\n"
-                readme_content += f"**Files:**\n"
-                readme_content += f"- `{doc_dir}/document.txt`"
-                if os.path.exists(document_file):
-                    readme_content += " ✓"
-                readme_content += "\n"
-                
-                readme_content += f"- `{doc_dir}/pagination.txt`"
-                if os.path.exists(pagination_file):
-                    readme_content += " ✓"
-                readme_content += "\n"
-                
-                readme_content += f"- `{doc_dir}/embedding.pkl`"
-                if os.path.exists(embeddings_file):
-                    readme_content += " ✓"
-                readme_content += "\n\n"
-                
-                # Add document preview
-                if os.path.exists(document_file):
-                    readme_content += f"**Content Preview (first 10 lines):**\n\n```\n"
-                    preview = get_document_preview(document_file, 10)
-                    readme_content += preview
-                    readme_content += "\n```\n\n"
-                else:
-                    readme_content += f"**Content Preview:** Not available\n\n"
-    
-    readme_content += f"""---
-*Generated on {__import__('datetime').datetime.now().strftime('%Y-%m-%d %H:%M:%S')}*
-"""
-    
-    return readme_content
-
-
-def save_project_readme(unique_id: str):
-    """Save README.md for a project"""
-    readme_content = generate_project_readme(unique_id)
-    readme_path = os.path.join("projects", "data", unique_id, "README.md")
-    
-    try:
-        os.makedirs(os.path.dirname(readme_path), exist_ok=True)
-        with open(readme_path, 'w', encoding='utf-8') as f:
-            f.write(readme_content)
-        logger.info(f"Generated README.md for project {unique_id}")
-        return readme_path
-    except Exception as e:
-        logger.error(f"Error generating README for project {unique_id}: {str(e)}")
-        return None
-
-
-def get_project_status(unique_id: str) -> Dict:
-    """Get comprehensive status of a project"""
-    project_dir = os.path.join("projects", "data", unique_id)
-    project_exists = os.path.exists(project_dir)
-    
-    if not project_exists:
-        return {
-            "unique_id": unique_id,
-            "project_exists": False,
-            "error": "Project not found"
-        }
-    
-    # Get processed log
-    processed_log = load_processed_files_log(unique_id)
-    
-    # Collect document.txt files
-    document_files = []
-    dataset_dir = os.path.join(project_dir, "datasets")
-    if os.path.exists(dataset_dir):
-        for root, dirs, files in os.walk(dataset_dir):
-            for file in files:
-                if file == "document.txt":
-                    document_files.append(os.path.join(root, file))
-    
-    # Check system prompt and MCP settings
-    system_prompt_file = os.path.join(project_dir, "system_prompt.txt")
-    mcp_settings_file = os.path.join(project_dir, "mcp_settings.json")
-    
-    status = {
-        "unique_id": unique_id,
-        "project_exists": True,
-        "project_path": project_dir,
-        "processed_files_count": len(processed_log),
-        "processed_files": processed_log,
-        "document_files_count": len(document_files),
-        "document_files": document_files,
-        "has_system_prompt": os.path.exists(system_prompt_file),
-        "has_mcp_settings": os.path.exists(mcp_settings_file),
-        "readme_exists": os.path.exists(os.path.join(project_dir, "README.md")),
-        "log_file_exists": os.path.exists(os.path.join(project_dir, "processed_files.json"))
-    }
-    
-    # Add dataset structure
-    try:
-        from utils.dataset_manager import generate_dataset_structure
-        status["dataset_structure"] = generate_dataset_structure(unique_id)
-    except Exception as e:
-        status["dataset_structure"] = f"Error generating structure: {str(e)}"
-    
-    return status
-
-
-def remove_project(unique_id: str) -> bool:
-    """Remove entire project directory"""
-    project_dir = os.path.join("projects", "data", unique_id)
-    try:
-        if os.path.exists(project_dir):
-            import shutil
-            shutil.rmtree(project_dir)
-            logger.info(f"Removed project directory: {project_dir}")
-            return True
-        else:
-            logger.warning(f"Project directory not found: {project_dir}")
-            return False
-    except Exception as e:
-        logger.error(f"Error removing project {unique_id}: {str(e)}")
-        return False
-
-
-def list_projects() -> List[str]:
-    """List all existing project IDs"""
-    projects_dir = "projects"
-    if not os.path.exists(projects_dir):
-        return []
-    
-    try:
-        return [item for item in os.listdir(projects_dir) 
-                if os.path.isdir(os.path.join(projects_dir, item))]
-    except Exception as e:
-        logger.error(f"Error listing projects: {str(e)}")
-        return []
-
-
-def get_project_stats(unique_id: str) -> Dict:
-    """Get statistics for a specific project"""
-    status = get_project_status(unique_id)
-    
-    if not status["project_exists"]:
-        return status
-    
-    stats = {
-        "unique_id": unique_id,
-        "total_processed_files": status["processed_files_count"],
-        "total_document_files": status["document_files_count"],
-        "has_system_prompt": status["has_system_prompt"],
-        "has_mcp_settings": status["has_mcp_settings"],
-        "has_readme": status["readme_exists"]
-    }
-    
-    # Calculate file sizes
-    total_size = 0
-    document_sizes = []
-    
-    for doc_file in status["document_files"]:
-        try:
-            size = os.path.getsize(doc_file)
-            document_sizes.append({
-                "file": doc_file,
-                "size": size,
-                "size_mb": round(size / (1024 * 1024), 2)
-            })
-            total_size += size
-        except Exception:
-            pass
-    
-    stats["total_document_size"] = total_size
-    stats["total_document_size_mb"] = round(total_size / (1024 * 1024), 2)
-    stats["document_files_detail"] = document_sizes
-    
-    # Check embeddings files
-    embedding_files = []
-    dataset_dir = os.path.join("projects", "data", unique_id, "datasets")
-    if os.path.exists(dataset_dir):
-        for root, dirs, files in os.walk(dataset_dir):
-            for file in files:
-                if file == "embedding.pkl":
-                    file_path = os.path.join(root, file)
-                    try:
-                        size = os.path.getsize(file_path)
-                        embedding_files.append({
-                            "file": file_path,
-                            "size": size,
-                            "size_mb": round(size / (1024 * 1024), 2)
-                        })
-                    except Exception:
-                        pass
-    
-    stats["embedding_files_count"] = len(embedding_files)
-    stats["embedding_files_detail"] = embedding_files
-    
-    return stats

utils/settings.py

@@ -30,7 +30,12 @@ PROJECT_NAME = os.getenv("PROJECT_NAME", "support")
 TOKENIZERS_PARALLELISM = os.getenv("TOKENIZERS_PARALLELISM", "true")
 
 # Embedding Model Settings
-SENTENCE_TRANSFORMER_MODEL = os.getenv("SENTENCE_TRANSFORMER_MODEL", "TaylorAI/gte-tiny")
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "sk-hsKClH0Z695EkK5fDdB2Ec2fE13f4fC1B627BdBb8e554b5b-4")
+EMBEDDING_BASE_URL = os.getenv("EMBEDDING_BASE_URL", "https://one-dev.felo.me/v1")
+EMBEDDING_API_KEY = os.getenv("EMBEDDING_API_KEY", OPENAI_API_KEY)
+EMBEDDING_MODEL_NAME = os.getenv("EMBEDDING_MODEL_NAME", "text-embedding-3-small")
+EMBEDDING_DIMENSIONS = int(os.getenv("EMBEDDING_DIMENSIONS", "384"))
+EMBEDDING_TIMEOUT = int(os.getenv("EMBEDDING_TIMEOUT", "30"))
 
 # Tool Output Length Control Settings
 TOOL_OUTPUT_MAX_LENGTH = SUMMARIZATION_MAX_TOKENS
@@ -72,6 +77,15 @@ CHECKPOINT_CLEANUP_INACTIVE_DAYS = int(os.getenv("CHECKPOINT_CLEANUP_INACTIVE_DA
 CHECKPOINT_CLEANUP_INTERVAL_HOURS = int(os.getenv("CHECKPOINT_CLEANUP_INTERVAL_HOURS", "24"))
 
 
+# ============================================================
+# Redis Configuration (Huey task queue backend)
+# ============================================================
+
+# Redis connection URL.
+# Format: redis://[:password]@host:port/db
+REDIS_URL = os.getenv("REDIS_URL", "redis://localhost:6379/1")
+
+
 # ============================================================
 # Mem0 long-term memory configuration
 # ============================================================

utils/single_file_processor.py

@@ -1,301 +0,0 @@
-#!/usr/bin/env python3
-"""
-Single file processing functions for handling individual files.
-"""
-
-import os
-import tempfile
-import zipfile
-import logging
-from typing import Dict, List, Tuple, Optional
-from pathlib import Path
-
-# Configure logger
-logger = logging.getLogger('app')
-
-from utils.file_utils import download_file
-
-# Try to import excel/csv processor, but handle if dependencies are missing
-try:
-    from utils.excel_csv_processor import (
-        is_excel_file, is_csv_file, process_excel_file, process_csv_file
-    )
-    EXCEL_CSV_SUPPORT = True
-except ImportError as e:
-    logger.warning(f"Excel/CSV processing not available: {e}")
-    EXCEL_CSV_SUPPORT = False
-    
-    # Fallback functions
-    def is_excel_file(file_path):
-        return file_path.lower().endswith(('.xlsx', '.xls'))
-    
-    def is_csv_file(file_path):
-        return file_path.lower().endswith('.csv')
-    
-    def process_excel_file(file_path):
-        return "", []
-    
-    def process_csv_file(file_path):
-        return "", []
-
-
-async def process_single_file(
-    unique_id: str, 
-    group_name: str, 
-    filename: str, 
-    original_path: str,
-    local_path: str
-) -> Dict:
-    """
-    Process a single file and generate document.txt, pagination.txt, and embedding.pkl.
-    
-    Returns:
-        Dict with processing results and file paths
-    """
-    # Create output directory for this file
-    filename_stem = Path(filename).stem
-    output_dir = os.path.join("projects", "data", unique_id, "processed", group_name, filename_stem)
-    os.makedirs(output_dir, exist_ok=True)
-    
-    result = {
-        "success": False,
-        "filename": filename,
-        "group": group_name,
-        "output_dir": output_dir,
-        "document_path": os.path.join(output_dir, "document.txt"),
-        "pagination_path": os.path.join(output_dir, "pagination.txt"), 
-        "embedding_path": os.path.join(output_dir, "embedding.pkl"),
-        "error": None,
-        "content_size": 0,
-        "pagination_lines": 0,
-        "embedding_chunks": 0
-    }
-    
-    try:
-        # Download file if it's remote and not yet downloaded
-        if original_path.startswith(('http://', 'https://')):
-            if not os.path.exists(local_path):
-                logger.info(f"Downloading {original_path} -> {local_path}")
-                success = await download_file(original_path, local_path)
-                if not success:
-                    result["error"] = "Failed to download file"
-                    return result
-        
-        # Extract content from file
-        content, pagination_lines = await extract_file_content(local_path, filename)
-        
-        if not content or not content.strip():
-            result["error"] = "No content extracted from file"
-            return result
-        
-        # Write document.txt
-        with open(result["document_path"], 'w', encoding='utf-8') as f:
-            f.write(content)
-        result["content_size"] = len(content)
-        
-        # Write pagination.txt
-        if pagination_lines:
-            with open(result["pagination_path"], 'w', encoding='utf-8') as f:
-                for line in pagination_lines:
-                    if line.strip():
-                        f.write(f"{line}\n")
-            result["pagination_lines"] = len(pagination_lines)
-        else:
-            # Generate pagination from text content
-            pagination_lines = generate_pagination_from_text(result["document_path"], 
-                                                            result["pagination_path"])
-            result["pagination_lines"] = len(pagination_lines)
-        
-        # Generate embeddings
-        try:
-            embedding_chunks = await generate_embeddings_for_file(
-                result["document_path"], result["embedding_path"]
-            )
-            result["embedding_chunks"] = len(embedding_chunks) if embedding_chunks else 0
-            result["success"] = True
-            
-        except Exception as e:
-            result["error"] = f"Embedding generation failed: {str(e)}"
-        logger.error(f"Failed to generate embeddings for {filename}: {str(e)}")
-        
-    except Exception as e:
-        result["error"] = f"File processing failed: {str(e)}"
-        logger.error(f"Error processing file {filename}: {str(e)}")
-    
-    return result
-
-
-async def extract_file_content(file_path: str, filename: str) -> Tuple[str, List[str]]:
-    """Extract content from various file formats."""
-    
-    # Handle zip files
-    if filename.lower().endswith('.zip'):
-        return await extract_from_zip(file_path, filename)
-    
-    # Handle Excel files
-    elif is_excel_file(file_path):
-        return await extract_from_excel(file_path, filename)
-    
-    # Handle CSV files  
-    elif is_csv_file(file_path):
-        return await extract_from_csv(file_path, filename)
-    
-    # Handle text files
-    else:
-        return await extract_from_text(file_path, filename)
-
-
-async def extract_from_zip(zip_path: str, filename: str) -> Tuple[str, List[str]]:
-    """Extract content from zip file."""
-    content_parts = []
-    pagination_lines = []
-    
-    try:
-        with zipfile.ZipFile(zip_path, 'r') as zip_ref:
-            # Extract to temporary directory
-            temp_dir = tempfile.mkdtemp(prefix=f"extract_{Path(filename).stem}_")
-            zip_ref.extractall(temp_dir)
-            
-            # Process extracted files
-            for root, dirs, files in os.walk(temp_dir):
-                for file in files:
-                    if file.lower().endswith(('.txt', '.md', '.xlsx', '.xls', '.csv')):
-                        file_path = os.path.join(root, file)
-                        
-                        try:
-                            file_content, file_pagination = await extract_file_content(file_path, file)
-                            
-                            if file_content:
-                                content_parts.append(f"# Page {file}")
-                                content_parts.append(file_content)
-                                pagination_lines.extend(file_pagination)
-                                
-                        except Exception as e:
-                            logger.error(f"Error processing extracted file {file}: {str(e)}")
-            
-            # Clean up temporary directory
-            import shutil
-            shutil.rmtree(temp_dir)
-            
-    except Exception as e:
-        logger.error(f"Error extracting zip file {filename}: {str(e)}")
-        return "", []
-    
-    return '\n\n'.join(content_parts), pagination_lines
-
-
-async def extract_from_excel(file_path: str, filename: str) -> Tuple[str, List[str]]:
-    """Extract content from Excel file."""
-    try:
-        document_content, pagination_lines = process_excel_file(file_path)
-        
-        if document_content:
-            content = f"# Page {filename}\n{document_content}"
-            return content, pagination_lines
-        else:
-            return "", []
-            
-    except Exception as e:
-        logger.error(f"Error processing Excel file {filename}: {str(e)}")
-        return "", []
-
-
-async def extract_from_csv(file_path: str, filename: str) -> Tuple[str, List[str]]:
-    """Extract content from CSV file."""
-    try:
-        document_content, pagination_lines = process_csv_file(file_path)
-        
-        if document_content:
-            content = f"# Page {filename}\n{document_content}"
-            return content, pagination_lines
-        else:
-            return "", []
-            
-    except Exception as e:
-        logger.error(f"Error processing CSV file {filename}: {str(e)}")
-        return "", []
-
-
-async def extract_from_text(file_path: str, filename: str) -> Tuple[str, List[str]]:
-    """Extract content from text file."""
-    try:
-        with open(file_path, 'r', encoding='utf-8') as f:
-            content = f.read().strip()
-        
-        if content:
-            return content, []
-        else:
-            return "", []
-            
-    except Exception as e:
-        logger.error(f"Error reading text file {filename}: {str(e)}")
-        return "", []
-
-
-def generate_pagination_from_text(document_path: str, pagination_path: str) -> List[str]:
-    """Generate pagination from text document."""
-    try:
-        # Import embedding module for pagination
-        import sys
-        sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'embedding'))
-        from embedding import split_document_by_pages
-        
-        pages = split_document_by_pages(str(document_path), str(pagination_path))
-        
-        # Return pagination lines
-        pagination_lines = []
-        with open(pagination_path, 'r', encoding='utf-8') as f:
-            for line in f:
-                if line.strip():
-                    pagination_lines.append(line.strip())
-        
-        return pagination_lines
-        
-    except Exception as e:
-        logger.error(f"Error generating pagination from text: {str(e)}")
-        return []
-
-
-async def generate_embeddings_for_file(document_path: str, embedding_path: str) -> Optional[List]:
-    """Generate embeddings for a document."""
-    try:
-        # Import embedding module
-        import sys
-        sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'embedding'))
-        from embedding import embed_document
-        
-        # Generate embeddings using paragraph chunking
-        embedding_data = embed_document(
-            str(document_path),
-            str(embedding_path),
-            chunking_strategy='paragraph'
-        )
-        
-        if embedding_data and 'chunks' in embedding_data:
-            return embedding_data['chunks']
-        else:
-            return None
-            
-    except Exception as e:
-        logger.error(f"Error generating embeddings: {str(e)}")
-        return None
-
-
-def check_file_already_processed(unique_id: str, group_name: str, filename: str) -> bool:
-    """Check if a file has already been processed."""
-    filename_stem = Path(filename).stem
-    output_dir = os.path.join("projects", "data", unique_id, "processed", group_name, filename_stem)
-    
-    document_path = os.path.join(output_dir, "document.txt")
-    pagination_path = os.path.join(output_dir, "pagination.txt")
-    embedding_path = os.path.join(output_dir, "embedding.pkl")
-    
-    # Check if all files exist and are not empty
-    if (os.path.exists(document_path) and os.path.exists(pagination_path) and 
-        os.path.exists(embedding_path)):
-        
-        if (os.path.getsize(document_path) > 0 and os.path.getsize(pagination_path) > 0 and
-            os.path.getsize(embedding_path) > 0):
-            return True
-    
-    return False