自动同步: 2026-03-20 02:10
This commit is contained in:
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|
||||
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||||
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|
||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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@@ -0,0 +1,164 @@
|
||||
# Session: 2026-03-19 15:00:36 UTC
|
||||
|
||||
- **Session Key**: agent:knowledge-base:main
|
||||
- **Session ID**: 731aff7c-67da-45ff-957f-7940849d0aae
|
||||
- **Source**: feishu
|
||||
|
||||
## Conversation Summary
|
||||
|
||||
user: Conversation info (untrusted metadata):
|
||||
```json
|
||||
{
|
||||
"message_id": "om_x100b548be4a02ca4b33d43c7a3208be",
|
||||
"sender_id": "ou_9351c287f42069fa30366ac641d24df7",
|
||||
"sender": "张青",
|
||||
"timestamp": "Thu 2026-03-19 21:31 GMT+8"
|
||||
}
|
||||
```
|
||||
|
||||
Sender (untrusted metadata):
|
||||
```json
|
||||
{
|
||||
"label": "张青 (ou_9351c287f42069fa30366ac641d24df7)",
|
||||
"id": "ou_9351c287f42069fa30366ac641d24df7",
|
||||
"name": "张青"
|
||||
}
|
||||
```
|
||||
|
||||
[message_id: om_x100b548be4a02ca4b33d43c7a3208be]
|
||||
张青: 记一下，可可想要一个里面有小人的公交车
|
||||
assistant: 已记录到速记本：**可可想要一个里面有小人的公交车** (2026-03-19 21:31)
|
||||
user: Conversation info (untrusted metadata):
|
||||
```json
|
||||
{
|
||||
"message_id": "om_x100b548be25df0b0b22cdf708250b00",
|
||||
"sender_id": "ou_9351c287f42069fa30366ac641d24df7",
|
||||
"sender": "张青",
|
||||
"timestamp": "Thu 2026-03-19 21:31 GMT+8"
|
||||
}
|
||||
```
|
||||
|
||||
Sender (untrusted metadata):
|
||||
```json
|
||||
{
|
||||
"label": "张青 (ou_9351c287f42069fa30366ac641d24df7)",
|
||||
"id": "ou_9351c287f42069fa30366ac641d24df7",
|
||||
"name": "张青"
|
||||
}
|
||||
```
|
||||
|
||||
[message_id: om_x100b548be25df0b0b22cdf708250b00]
|
||||
张青: 还要带语音控制的
|
||||
assistant: 已更新：**可可想要一个里面有小人的公交车（带语音控制）** (2026-03-19 21:31)
|
||||
user: Conversation info (untrusted metadata):
|
||||
```json
|
||||
{
|
||||
"message_id": "om_x100b548be03aaca4b393638a734446f",
|
||||
"sender_id": "ou_9351c287f42069fa30366ac641d24df7",
|
||||
"sender": "张青",
|
||||
"timestamp": "Thu 2026-03-19 21:32 GMT+8"
|
||||
}
|
||||
```
|
||||
|
||||
Sender (untrusted metadata):
|
||||
```json
|
||||
{
|
||||
"label": "张青 (ou_9351c287f42069fa30366ac641d24df7)",
|
||||
"id": "ou_9351c287f42069fa30366ac641d24df7",
|
||||
"name": "张青"
|
||||
}
|
||||
```
|
||||
|
||||
[message_id: om_x100b548be03aaca4b393638a734446f]
|
||||
张青: 还要能放音乐的
|
||||
assistant: 已更新：**可可想要一个里面有小人的公交车（带语音控制、能放音乐）** (2026-03-19 21:31)
|
||||
user: Conversation info (untrusted metadata):
|
||||
```json
|
||||
{
|
||||
"message_id": "om_x100b548bfc0824a4b364c8c0c8977d1",
|
||||
"sender_id": "ou_9351c287f42069fa30366ac641d24df7",
|
||||
"sender": "张青",
|
||||
"timestamp": "Thu 2026-03-19 21:33 GMT+8"
|
||||
}
|
||||
```
|
||||
|
||||
Sender (untrusted metadata):
|
||||
```json
|
||||
{
|
||||
"label": "张青 (ou_9351c287f42069fa30366ac641d24df7)",
|
||||
"id": "ou_9351c287f42069fa30366ac641d24df7",
|
||||
"name": "张青"
|
||||
}
|
||||
```
|
||||
|
||||
[message_id: om_x100b548bfc0824a4b364c8c0c8977d1]
|
||||
张青: 乐乐想要一个库洛米城堡，里面有很多摆件，这些摆件呢，可以动
|
||||
assistant: 已记录：**乐乐想要一个库洛米城堡，里面有很多摆件，这些摆件可以动** (2026-03-19 21:33)
|
||||
user: Conversation info (untrusted metadata):
|
||||
```json
|
||||
{
|
||||
"message_id": "om_x100b548bfd70e48cb22d0cbc00050f8",
|
||||
"sender_id": "ou_9351c287f42069fa30366ac641d24df7",
|
||||
"sender": "张青",
|
||||
"timestamp": "Thu 2026-03-19 21:33 GMT+8"
|
||||
}
|
||||
```
|
||||
|
||||
Sender (untrusted metadata):
|
||||
```json
|
||||
{
|
||||
"label": "张青 (ou_9351c287f42069fa30366ac641d24df7)",
|
||||
"id": "ou_9351c287f42069fa30366ac641d24df7",
|
||||
"name": "张青"
|
||||
}
|
||||
```
|
||||
|
||||
[message_id: om_x100b548bfd70e48cb22d0cbc00050f8]
|
||||
张青: 通过按按钮来控制里面的家具或者家电呃运动
|
||||
assistant: 已更新：**乐乐想要一个库洛米城堡，里面有很多摆件，通过按按钮控制家具或家电运动** (2026-03-19 21:33)
|
||||
user: Conversation info (untrusted metadata):
|
||||
```json
|
||||
{
|
||||
"message_id": "om_x100b548bf82dbca0b2c751a1b709843",
|
||||
"sender_id": "ou_9351c287f42069fa30366ac641d24df7",
|
||||
"sender": "张青",
|
||||
"timestamp": "Thu 2026-03-19 21:34 GMT+8"
|
||||
}
|
||||
```
|
||||
|
||||
Sender (untrusted metadata):
|
||||
```json
|
||||
{
|
||||
"label": "张青 (ou_9351c287f42069fa30366ac641d24df7)",
|
||||
"id": "ou_9351c287f42069fa30366ac641d24df7",
|
||||
"name": "张青"
|
||||
}
|
||||
```
|
||||
|
||||
[message_id: om_x100b548bf82dbca0b2c751a1b709843]
|
||||
张青: 可可还想要一个可以涂颜色的石膏娃娃
|
||||
assistant: 已记录：**可可想要一个可以涂颜色的石膏娃娃** (2026-03-19 21:34)
|
||||
user: Conversation info (untrusted metadata):
|
||||
```json
|
||||
{
|
||||
"message_id": "om_x100b548b8e22dcacb2d8d587bb7b14e",
|
||||
"sender_id": "ou_9351c287f42069fa30366ac641d24df7",
|
||||
"sender": "张青",
|
||||
"timestamp": "Thu 2026-03-19 21:37 GMT+8"
|
||||
}
|
||||
```
|
||||
|
||||
Sender (untrusted metadata):
|
||||
```json
|
||||
{
|
||||
"label": "张青 (ou_9351c287f42069fa30366ac641d24df7)",
|
||||
"id": "ou_9351c287f42069fa30366ac641d24df7",
|
||||
"name": "张青"
|
||||
}
|
||||
```
|
||||
|
||||
[message_id: om_x100b548b8e22dcacb2d8d587bb7b14e]
|
||||
张青: 嗯，可可想要一个空的城堡，里面有个绿色按钮。
|
||||
|
||||
绿色按钮按一下，城堡的顶上就会出现一个美乐蒂，再按一下按钮美乐蒂就会变成一朵花
|
||||
assistant: 已记录：**可可想要一个空的城堡，里面有个绿色按钮：按一下城堡顶上出现美乐蒂，再按一下美乐蒂变成一朵花** (2026-03-19 21:37)
|
||||
@@ -0,0 +1,67 @@
|
||||
# Session: 2026-03-19 16:12:12 UTC
|
||||
|
||||
- **Session Key**: agent:knowledge-base:main
|
||||
- **Session ID**: b11228d2-b3ca-444f-837c-c0a787d3a0cb
|
||||
- **Source**: feishu
|
||||
|
||||
## Conversation Summary
|
||||
|
||||
assistant: ✅ New session started · model: zhipu/glm-5
|
||||
user: A new session was started via /new or /reset. Execute your Session Startup sequence now - read the required files before responding to the user. Then greet the user in your configured persona, if one is provided. Be yourself - use your defined voice, mannerisms, and mood. Keep it to 1-3 sentences and ask what they want to do. If the runtime model differs from default_model in the system prompt, mention the default model. Do not mention internal steps, files, tools, or reasoning.
|
||||
assistant: 📚 蒋先生您好，我是达康书记。已为您准备好知识库与工作区维护服务。
|
||||
|
||||
今天有什么需要我帮您处理的？
|
||||
user: Conversation info (untrusted metadata):
|
||||
```json
|
||||
{
|
||||
"message_id": "om_x100b54f5454c14a4c42e9b6c6ea2cd3",
|
||||
"sender_id": "ou_9351c287f42069fa30366ac641d24df7",
|
||||
"sender": "张青",
|
||||
"timestamp": "Thu 2026-03-19 23:05 GMT+8"
|
||||
}
|
||||
```
|
||||
|
||||
Sender (untrusted metadata):
|
||||
```json
|
||||
{
|
||||
"label": "张青 (ou_9351c287f42069fa30366ac641d24df7)",
|
||||
"id": "ou_9351c287f42069fa30366ac641d24df7",
|
||||
"name": "张青"
|
||||
}
|
||||
```
|
||||
|
||||
[message_id: om_x100b54f5454c14a4c42e9b6c6ea2cd3]
|
||||
张青: https://www.bilibili.com/video/BV1NwwqzeE55/?t=6&spm_id_from=333.1007.tianma.1-1-1.click&vd_source=1deaab00c5de8885422a7a7690170eba 添加知识库
|
||||
assistant: 收到，蒋先生。我立即按照知识库工作流技能处理这个B站视频链接。
|
||||
assistant: 好的，蒋先生。我按照知识库工作流技能处理这个B站视频链接。
|
||||
|
||||
**步骤 1：检查环境变量**
|
||||
assistant: 好的，蒋先生。工作流已经开始处理这个B站视频。我看到它正在：
|
||||
|
||||
1. ✅ 解析 URL
|
||||
2. ⚠️ 字幕获取失败（可能视频没有字幕）
|
||||
3. 🔄 正在进行 AI 深度分析...
|
||||
|
||||
让我继续监控进度：
|
||||
assistant: ✅ **处理完成！**
|
||||
|
||||
**视频信息：**
|
||||
- **标题：** 爱因斯坦又对了！科学家首次观测到物质从真空中诞生，【科学快报】265期
|
||||
- **作者：** 科学透镜
|
||||
- **BV号：** BV1NwwqzeE55
|
||||
|
||||
**处理流程：**
|
||||
- ✅ Parse - URL 解析成功
|
||||
- ✅ Download - 下载音频和元数据
|
||||
- ✅ ASR - 语音识别完成（Whisper）
|
||||
- ✅ Analyze - AI 深度分析完成（耗时 110.78秒）
|
||||
- ✅ Archive - 归档成功
|
||||
|
||||
**核心内容摘要：**
|
||||
本视频报道了三项颠覆性科学发现：
|
||||
1. **物质从真空中诞生** - 布鲁克海文国家实验室首次观测到Lambda超子与反粒子的自旋纠缠，证实虚粒子可转化为真实物质
|
||||
2. **电子运动延迟** - ETH测得电子响应原子核运动存在30飞秒延迟，打破波恩-奥本海默近似
|
||||
3. **半莫比乌斯分子** - 人类首次合成并精准操控具有拓扑结构的分子
|
||||
|
||||
**归档位置：**
|
||||
`~/work/zq/30.资源/AI分析/bilibili/20260319_BV1NwwqzeE55_analysis.md`
|
||||
@@ -0,0 +1,8 @@
|
||||
# 2026-03-19 每日记录
|
||||
|
||||
## 定时任务执行
|
||||
|
||||
### 02:30 - 每日回顾整理（daily-review-cleanup）
|
||||
- 执行脚本：`~/.openclaw/cron/scripts/daily-review.sh`
|
||||
- 处理日期：2026-03-18
|
||||
- 结果：从 NAS 拉取更新完成，无已完成任务需要归档
|
||||
@@ -1,7 +1,7 @@
|
||||
{
|
||||
"agent:ui-designer:main": {
|
||||
"sessionId": "4caf7900-b5bc-4f76-a39d-80e506bcea84",
|
||||
"updatedAt": 1773758631721,
|
||||
"sessionId": "60827bbf-d985-4a29-bd62-1783159bb38e",
|
||||
"updatedAt": 1773937164358,
|
||||
"systemSent": true,
|
||||
"abortedLastRun": false,
|
||||
"chatType": "direct",
|
||||
@@ -22,7 +22,7 @@
|
||||
"to": "user:ou_9847ce84b960736c14554730b41e4957",
|
||||
"accountId": "feishu-art"
|
||||
},
|
||||
"sessionFile": "/home/starmen1265/.openclaw/agents/ui-designer/sessions/4caf7900-b5bc-4f76-a39d-80e506bcea84.jsonl",
|
||||
"sessionFile": "/home/starmen1265/.openclaw/agents/ui-designer/sessions/60827bbf-d985-4a29-bd62-1783159bb38e.jsonl",
|
||||
"compactionCount": 0,
|
||||
"skillsSnapshot": {
|
||||
"prompt": "\n\nThe following skills provide specialized instructions for specific tasks.\nUse the read tool to load a skill's file when the task matches its description.\nWhen a skill file references a relative path, resolve it against the skill directory (parent of SKILL.md / dirname of the path) and use that absolute path in tool commands.\n\n<available_skills>\n <skill>\n <name>capability-evolver</name>\n <description>A self-evolution engine for AI agents. Analyzes runtime history to identify improvements and applies protocol-constrained evolution.</description>\n <location>~/.openclaw/skills/capability-evolver/SKILL.md</location>\n </skill>\n</available_skills>",
|
||||
@@ -51,8 +51,8 @@
|
||||
"model": "glm-5",
|
||||
"systemPromptReport": {
|
||||
"source": "run",
|
||||
"generatedAt": 1773758614151,
|
||||
"sessionId": "4caf7900-b5bc-4f76-a39d-80e506bcea84",
|
||||
"generatedAt": 1773937122951,
|
||||
"sessionId": "60827bbf-d985-4a29-bd62-1783159bb38e",
|
||||
"sessionKey": "agent:ui-designer:main",
|
||||
"provider": "zhipu",
|
||||
"model": "glm-5",
|
||||
@@ -64,8 +64,8 @@
|
||||
"sandboxed": false
|
||||
},
|
||||
"systemPrompt": {
|
||||
"chars": 24499,
|
||||
"projectContextChars": 12014,
|
||||
"chars": 36876,
|
||||
"projectContextChars": 24391,
|
||||
"nonProjectContextChars": 12485
|
||||
},
|
||||
"injectedWorkspaceFiles": [
|
||||
@@ -73,8 +73,8 @@
|
||||
"name": "AGENTS.md",
|
||||
"path": "/home/starmen1265/.openclaw/agents/ui-designer/workspace/AGENTS.md",
|
||||
"missing": false,
|
||||
"rawChars": 2617,
|
||||
"injectedChars": 2617,
|
||||
"rawChars": 2663,
|
||||
"injectedChars": 2663,
|
||||
"truncated": false
|
||||
},
|
||||
{
|
||||
@@ -187,6 +187,120 @@
|
||||
"injectedChars": 616,
|
||||
"truncated": false
|
||||
},
|
||||
{
|
||||
"path": "SELF_IMPROVEMENT_REMINDER.md",
|
||||
"rawChars": 616,
|
||||
"injectedChars": 616,
|
||||
"truncated": false
|
||||
},
|
||||
{
|
||||
"path": "SELF_IMPROVEMENT_REMINDER.md",
|
||||
"rawChars": 616,
|
||||
"injectedChars": 616,
|
||||
"truncated": false
|
||||
},
|
||||
{
|
||||
"path": "SELF_IMPROVEMENT_REMINDER.md",
|
||||
"rawChars": 616,
|
||||
"injectedChars": 616,
|
||||
"truncated": false
|
||||
},
|
||||
{
|
||||
"path": "SELF_IMPROVEMENT_REMINDER.md",
|
||||
"rawChars": 616,
|
||||
"injectedChars": 616,
|
||||
"truncated": false
|
||||
},
|
||||
{
|
||||
"path": "SELF_IMPROVEMENT_REMINDER.md",
|
||||
"rawChars": 616,
|
||||
"injectedChars": 616,
|
||||
"truncated": false
|
||||
},
|
||||
{
|
||||
"path": "SELF_IMPROVEMENT_REMINDER.md",
|
||||
"rawChars": 616,
|
||||
"injectedChars": 616,
|
||||
"truncated": false
|
||||
},
|
||||
{
|
||||
"path": "SELF_IMPROVEMENT_REMINDER.md",
|
||||
"rawChars": 616,
|
||||
"injectedChars": 616,
|
||||
"truncated": false
|
||||
},
|
||||
{
|
||||
"path": "SELF_IMPROVEMENT_REMINDER.md",
|
||||
"rawChars": 616,
|
||||
"injectedChars": 616,
|
||||
"truncated": false
|
||||
},
|
||||
{
|
||||
"path": "SELF_IMPROVEMENT_REMINDER.md",
|
||||
"rawChars": 616,
|
||||
"injectedChars": 616,
|
||||
"truncated": false
|
||||
},
|
||||
{
|
||||
"path": "SELF_IMPROVEMENT_REMINDER.md",
|
||||
"rawChars": 616,
|
||||
"injectedChars": 616,
|
||||
"truncated": false
|
||||
},
|
||||
{
|
||||
"path": "SELF_IMPROVEMENT_REMINDER.md",
|
||||
"rawChars": 616,
|
||||
"injectedChars": 616,
|
||||
"truncated": false
|
||||
},
|
||||
{
|
||||
"path": "SELF_IMPROVEMENT_REMINDER.md",
|
||||
"rawChars": 616,
|
||||
"injectedChars": 616,
|
||||
"truncated": false
|
||||
},
|
||||
{
|
||||
"path": "SELF_IMPROVEMENT_REMINDER.md",
|
||||
"rawChars": 616,
|
||||
"injectedChars": 616,
|
||||
"truncated": false
|
||||
},
|
||||
{
|
||||
"path": "SELF_IMPROVEMENT_REMINDER.md",
|
||||
"rawChars": 616,
|
||||
"injectedChars": 616,
|
||||
"truncated": false
|
||||
},
|
||||
{
|
||||
"path": "SELF_IMPROVEMENT_REMINDER.md",
|
||||
"rawChars": 616,
|
||||
"injectedChars": 616,
|
||||
"truncated": false
|
||||
},
|
||||
{
|
||||
"path": "SELF_IMPROVEMENT_REMINDER.md",
|
||||
"rawChars": 616,
|
||||
"injectedChars": 616,
|
||||
"truncated": false
|
||||
},
|
||||
{
|
||||
"path": "SELF_IMPROVEMENT_REMINDER.md",
|
||||
"rawChars": 616,
|
||||
"injectedChars": 616,
|
||||
"truncated": false
|
||||
},
|
||||
{
|
||||
"path": "SELF_IMPROVEMENT_REMINDER.md",
|
||||
"rawChars": 616,
|
||||
"injectedChars": 616,
|
||||
"truncated": false
|
||||
},
|
||||
{
|
||||
"path": "SELF_IMPROVEMENT_REMINDER.md",
|
||||
"rawChars": 616,
|
||||
"injectedChars": 616,
|
||||
"truncated": false
|
||||
},
|
||||
{
|
||||
"path": "SELF_IMPROVEMENT_REMINDER.md",
|
||||
"rawChars": 616,
|
||||
@@ -414,13 +528,13 @@
|
||||
]
|
||||
}
|
||||
},
|
||||
"cacheRead": 9408,
|
||||
"cacheRead": 23872,
|
||||
"cacheWrite": 0,
|
||||
"totalTokensFresh": true,
|
||||
"contextTokens": 200000,
|
||||
"inputTokens": 7026,
|
||||
"outputTokens": 464,
|
||||
"totalTokens": 16434
|
||||
"inputTokens": 311770,
|
||||
"outputTokens": 332,
|
||||
"totalTokens": 170338
|
||||
},
|
||||
"agent:ui-designer:cron:78b64c2f-7f91-47ff-9250-d13f546eb21f": {
|
||||
"sessionId": "03e44b46-c707-4a9f-bf3d-1818aafdcde2",
|
||||
|
||||
+28
-27
@@ -7,7 +7,7 @@
|
||||
"name": "backup-openclaw-json",
|
||||
"enabled": true,
|
||||
"createdAtMs": 1772261581796,
|
||||
"updatedAtMs": 1773856817101,
|
||||
"updatedAtMs": 1773943214562,
|
||||
"schedule": {
|
||||
"kind": "cron",
|
||||
"expr": "0 2 * * *",
|
||||
@@ -23,10 +23,10 @@
|
||||
"mode": "announce"
|
||||
},
|
||||
"state": {
|
||||
"nextRunAtMs": 1773943200000,
|
||||
"lastRunAtMs": 1773856800004,
|
||||
"nextRunAtMs": 1774029600000,
|
||||
"lastRunAtMs": 1773943200004,
|
||||
"lastStatus": "ok",
|
||||
"lastDurationMs": 17097,
|
||||
"lastDurationMs": 14558,
|
||||
"consecutiveErrors": 0,
|
||||
"lastRunStatus": "ok",
|
||||
"lastDelivered": true,
|
||||
@@ -59,7 +59,8 @@
|
||||
"consecutiveErrors": 0,
|
||||
"lastRunStatus": "ok",
|
||||
"lastDeliveryStatus": "delivered",
|
||||
"lastDelivered": true
|
||||
"lastDelivered": true,
|
||||
"runningAtMs": 1773943800002
|
||||
},
|
||||
"delivery": {
|
||||
"mode": "announce"
|
||||
@@ -71,7 +72,7 @@
|
||||
"name": "capability-evolver-daily",
|
||||
"enabled": true,
|
||||
"createdAtMs": 1772550743528,
|
||||
"updatedAtMs": 1773771643135,
|
||||
"updatedAtMs": 1773858232095,
|
||||
"schedule": {
|
||||
"kind": "cron",
|
||||
"expr": "20 2 * * *",
|
||||
@@ -92,16 +93,16 @@
|
||||
"to": "feishu-pm"
|
||||
},
|
||||
"state": {
|
||||
"nextRunAtMs": 1773858000000,
|
||||
"lastRunAtMs": 1773771600014,
|
||||
"nextRunAtMs": 1773944400000,
|
||||
"lastRunAtMs": 1773858000004,
|
||||
"lastStatus": "error",
|
||||
"lastDurationMs": 43121,
|
||||
"consecutiveErrors": 5,
|
||||
"lastDurationMs": 232091,
|
||||
"consecutiveErrors": 6,
|
||||
"lastRunStatus": "error",
|
||||
"lastDeliveryStatus": "not-delivered",
|
||||
"lastDelivered": false,
|
||||
"lastDeliveryError": "⚠️ ✉️ Message: 蒋先生 failed",
|
||||
"runningAtMs": 1773858000003
|
||||
"lastDeliveryError": "⚠️ ✉️ Message failed",
|
||||
"lastError": "⚠️ ✉️ Message failed"
|
||||
}
|
||||
},
|
||||
{
|
||||
@@ -111,7 +112,7 @@
|
||||
"description": "每天凌晨2:30整理昨天的已完成任务到每日回顾",
|
||||
"enabled": true,
|
||||
"createdAtMs": 1741484880000,
|
||||
"updatedAtMs": 1773772248827,
|
||||
"updatedAtMs": 1773858626341,
|
||||
"schedule": {
|
||||
"kind": "cron",
|
||||
"expr": "30 2 * * *",
|
||||
@@ -129,11 +130,11 @@
|
||||
"channel": "last"
|
||||
},
|
||||
"state": {
|
||||
"nextRunAtMs": 1773858600000,
|
||||
"lastRunAtMs": 1773772200010,
|
||||
"nextRunAtMs": 1773945000000,
|
||||
"lastRunAtMs": 1773858600004,
|
||||
"lastRunStatus": "ok",
|
||||
"lastStatus": "ok",
|
||||
"lastDurationMs": 48817,
|
||||
"lastDurationMs": 26337,
|
||||
"lastDelivered": true,
|
||||
"lastDeliveryStatus": "delivered",
|
||||
"consecutiveErrors": 0
|
||||
@@ -145,7 +146,7 @@
|
||||
"name": "zq_workspace_sync",
|
||||
"enabled": true,
|
||||
"createdAtMs": 1772551807455,
|
||||
"updatedAtMs": 1773772800011,
|
||||
"updatedAtMs": 1773859200006,
|
||||
"schedule": {
|
||||
"expr": "40 2 * * *",
|
||||
"kind": "cron",
|
||||
@@ -158,8 +159,8 @@
|
||||
"text": "执行 zq 工作区同步：bash ~/.openclaw/cron/scripts/zq_workspace_sync.sh"
|
||||
},
|
||||
"state": {
|
||||
"nextRunAtMs": 1773859200000,
|
||||
"lastRunAtMs": 1773772800010,
|
||||
"nextRunAtMs": 1773945600000,
|
||||
"lastRunAtMs": 1773859200005,
|
||||
"lastStatus": "skipped",
|
||||
"lastDurationMs": 1,
|
||||
"consecutiveErrors": 0,
|
||||
@@ -175,7 +176,7 @@
|
||||
"description": "每天早上8点整理与各个 AI 代理的对话记录",
|
||||
"enabled": true,
|
||||
"createdAtMs": 1772776369645,
|
||||
"updatedAtMs": 1773792029087,
|
||||
"updatedAtMs": 1773878422623,
|
||||
"schedule": {
|
||||
"kind": "cron",
|
||||
"expr": "0 8 * * *",
|
||||
@@ -193,11 +194,11 @@
|
||||
"channel": "last"
|
||||
},
|
||||
"state": {
|
||||
"nextRunAtMs": 1773878400000,
|
||||
"lastRunAtMs": 1773792000007,
|
||||
"nextRunAtMs": 1773964800000,
|
||||
"lastRunAtMs": 1773878400004,
|
||||
"lastRunStatus": "ok",
|
||||
"lastStatus": "ok",
|
||||
"lastDurationMs": 29080,
|
||||
"lastDurationMs": 22619,
|
||||
"lastDelivered": true,
|
||||
"lastDeliveryStatus": "delivered",
|
||||
"consecutiveErrors": 0
|
||||
@@ -210,7 +211,7 @@
|
||||
"description": "每天7点同步提醒到CalDAV日历",
|
||||
"enabled": true,
|
||||
"createdAtMs": 1772791528528,
|
||||
"updatedAtMs": 1773788430000,
|
||||
"updatedAtMs": 1773874820139,
|
||||
"schedule": {
|
||||
"kind": "cron",
|
||||
"expr": "0 7 * * *",
|
||||
@@ -228,11 +229,11 @@
|
||||
"channel": "last"
|
||||
},
|
||||
"state": {
|
||||
"nextRunAtMs": 1773874800000,
|
||||
"lastRunAtMs": 1773788400005,
|
||||
"nextRunAtMs": 1773961200000,
|
||||
"lastRunAtMs": 1773874800006,
|
||||
"lastRunStatus": "ok",
|
||||
"lastStatus": "ok",
|
||||
"lastDurationMs": 29995,
|
||||
"lastDurationMs": 20133,
|
||||
"lastDeliveryStatus": "delivered",
|
||||
"consecutiveErrors": 0,
|
||||
"lastDelivered": true
|
||||
|
||||
+26
-26
@@ -7,7 +7,7 @@
|
||||
"name": "backup-openclaw-json",
|
||||
"enabled": true,
|
||||
"createdAtMs": 1772261581796,
|
||||
"updatedAtMs": 1773856817101,
|
||||
"updatedAtMs": 1773943214562,
|
||||
"schedule": {
|
||||
"kind": "cron",
|
||||
"expr": "0 2 * * *",
|
||||
@@ -23,10 +23,10 @@
|
||||
"mode": "announce"
|
||||
},
|
||||
"state": {
|
||||
"nextRunAtMs": 1773943200000,
|
||||
"lastRunAtMs": 1773856800004,
|
||||
"nextRunAtMs": 1774029600000,
|
||||
"lastRunAtMs": 1773943200004,
|
||||
"lastStatus": "ok",
|
||||
"lastDurationMs": 17097,
|
||||
"lastDurationMs": 14558,
|
||||
"consecutiveErrors": 0,
|
||||
"lastRunStatus": "ok",
|
||||
"lastDelivered": true,
|
||||
@@ -71,7 +71,7 @@
|
||||
"name": "capability-evolver-daily",
|
||||
"enabled": true,
|
||||
"createdAtMs": 1772550743528,
|
||||
"updatedAtMs": 1773771643135,
|
||||
"updatedAtMs": 1773858232095,
|
||||
"schedule": {
|
||||
"kind": "cron",
|
||||
"expr": "20 2 * * *",
|
||||
@@ -92,16 +92,16 @@
|
||||
"to": "feishu-pm"
|
||||
},
|
||||
"state": {
|
||||
"nextRunAtMs": 1773858000000,
|
||||
"lastRunAtMs": 1773771600014,
|
||||
"nextRunAtMs": 1773944400000,
|
||||
"lastRunAtMs": 1773858000004,
|
||||
"lastStatus": "error",
|
||||
"lastDurationMs": 43121,
|
||||
"consecutiveErrors": 5,
|
||||
"lastDurationMs": 232091,
|
||||
"consecutiveErrors": 6,
|
||||
"lastRunStatus": "error",
|
||||
"lastDeliveryStatus": "not-delivered",
|
||||
"lastDelivered": false,
|
||||
"lastDeliveryError": "⚠️ ✉️ Message: 蒋先生 failed",
|
||||
"lastError": "⚠️ ✉️ Message: 蒋先生 failed"
|
||||
"lastDeliveryError": "⚠️ ✉️ Message failed",
|
||||
"lastError": "⚠️ ✉️ Message failed"
|
||||
}
|
||||
},
|
||||
{
|
||||
@@ -111,7 +111,7 @@
|
||||
"description": "每天凌晨2:30整理昨天的已完成任务到每日回顾",
|
||||
"enabled": true,
|
||||
"createdAtMs": 1741484880000,
|
||||
"updatedAtMs": 1773772248827,
|
||||
"updatedAtMs": 1773858626341,
|
||||
"schedule": {
|
||||
"kind": "cron",
|
||||
"expr": "30 2 * * *",
|
||||
@@ -129,11 +129,11 @@
|
||||
"channel": "last"
|
||||
},
|
||||
"state": {
|
||||
"nextRunAtMs": 1773858600000,
|
||||
"lastRunAtMs": 1773772200010,
|
||||
"nextRunAtMs": 1773945000000,
|
||||
"lastRunAtMs": 1773858600004,
|
||||
"lastRunStatus": "ok",
|
||||
"lastStatus": "ok",
|
||||
"lastDurationMs": 48817,
|
||||
"lastDurationMs": 26337,
|
||||
"lastDelivered": true,
|
||||
"lastDeliveryStatus": "delivered",
|
||||
"consecutiveErrors": 0
|
||||
@@ -145,7 +145,7 @@
|
||||
"name": "zq_workspace_sync",
|
||||
"enabled": true,
|
||||
"createdAtMs": 1772551807455,
|
||||
"updatedAtMs": 1773772800011,
|
||||
"updatedAtMs": 1773859200006,
|
||||
"schedule": {
|
||||
"expr": "40 2 * * *",
|
||||
"kind": "cron",
|
||||
@@ -158,8 +158,8 @@
|
||||
"text": "执行 zq 工作区同步：bash ~/.openclaw/cron/scripts/zq_workspace_sync.sh"
|
||||
},
|
||||
"state": {
|
||||
"nextRunAtMs": 1773859200000,
|
||||
"lastRunAtMs": 1773772800010,
|
||||
"nextRunAtMs": 1773945600000,
|
||||
"lastRunAtMs": 1773859200005,
|
||||
"lastStatus": "skipped",
|
||||
"lastDurationMs": 1,
|
||||
"consecutiveErrors": 0,
|
||||
@@ -175,7 +175,7 @@
|
||||
"description": "每天早上8点整理与各个 AI 代理的对话记录",
|
||||
"enabled": true,
|
||||
"createdAtMs": 1772776369645,
|
||||
"updatedAtMs": 1773792029087,
|
||||
"updatedAtMs": 1773878422623,
|
||||
"schedule": {
|
||||
"kind": "cron",
|
||||
"expr": "0 8 * * *",
|
||||
@@ -193,11 +193,11 @@
|
||||
"channel": "last"
|
||||
},
|
||||
"state": {
|
||||
"nextRunAtMs": 1773878400000,
|
||||
"lastRunAtMs": 1773792000007,
|
||||
"nextRunAtMs": 1773964800000,
|
||||
"lastRunAtMs": 1773878400004,
|
||||
"lastRunStatus": "ok",
|
||||
"lastStatus": "ok",
|
||||
"lastDurationMs": 29080,
|
||||
"lastDurationMs": 22619,
|
||||
"lastDelivered": true,
|
||||
"lastDeliveryStatus": "delivered",
|
||||
"consecutiveErrors": 0
|
||||
@@ -210,7 +210,7 @@
|
||||
"description": "每天7点同步提醒到CalDAV日历",
|
||||
"enabled": true,
|
||||
"createdAtMs": 1772791528528,
|
||||
"updatedAtMs": 1773788430000,
|
||||
"updatedAtMs": 1773874820139,
|
||||
"schedule": {
|
||||
"kind": "cron",
|
||||
"expr": "0 7 * * *",
|
||||
@@ -228,11 +228,11 @@
|
||||
"channel": "last"
|
||||
},
|
||||
"state": {
|
||||
"nextRunAtMs": 1773874800000,
|
||||
"lastRunAtMs": 1773788400005,
|
||||
"nextRunAtMs": 1773961200000,
|
||||
"lastRunAtMs": 1773874800006,
|
||||
"lastRunStatus": "ok",
|
||||
"lastStatus": "ok",
|
||||
"lastDurationMs": 29995,
|
||||
"lastDurationMs": 20133,
|
||||
"lastDeliveryStatus": "delivered",
|
||||
"consecutiveErrors": 0,
|
||||
"lastDelivered": true
|
||||
|
||||
@@ -0,0 +1,21 @@
|
||||
{
|
||||
"id": "bc3f3807-8735-4bef-bb42-3efd115c3f7e",
|
||||
"enqueuedAt": 1773858231892,
|
||||
"channel": "feishu",
|
||||
"to": "feishu-pm",
|
||||
"accountId": "feishu-pm",
|
||||
"payloads": [
|
||||
{
|
||||
"text": "我遇到了 Feishu 目标格式的问题。让我直接在当前会话中向您汇报："
|
||||
}
|
||||
],
|
||||
"threadId": null,
|
||||
"mirror": {
|
||||
"sessionKey": "agent:haonan:main",
|
||||
"agentId": "haonan",
|
||||
"text": "我遇到了 Feishu 目标格式的问题。让我直接在当前会话中向您汇报："
|
||||
},
|
||||
"retryCount": 1,
|
||||
"lastAttemptAt": 1773858232086,
|
||||
"lastError": "Request failed with status code 400"
|
||||
}
|
||||
@@ -1,5 +1,31 @@
|
||||
{
|
||||
"om_x100b54b68a9658a8c3597587eaea165": 1773677638200,
|
||||
"om_x100b54b69ae988acc4e308675a1599b": 1773677890863,
|
||||
"om_x100b54a2c799a0b0c2ea37cee3ecec2": 1773758613782
|
||||
"om_x100b5480d5bba4b4b2c9a4e8bf5b189": 1773881784103,
|
||||
"om_x100b54808ff17c90b374531d1cf7751": 1773882388481,
|
||||
"om_x100b5481441a38a4b20013813040b59": 1773883566093,
|
||||
"om_x100b54816cbffcb8b36dfe46fd84a26": 1773883944086,
|
||||
"om_x100b54817240a0a0b49ad1ecc4230d1": 1773884361569,
|
||||
"om_x100b548114fc64a4b34dda488c6c4cf": 1773884835671,
|
||||
"om_x100b548129129490b4b1770d7f3efcc": 1773885054622,
|
||||
"om_x100b5481243380b0b10b5bef1b92ca3": 1773885104561,
|
||||
"om_x100b54813d7144bcb2da847227b06a8": 1773885244462,
|
||||
"om_x100b548139e42d10b3ffcf9447ed48c": 1773885299355,
|
||||
"om_x100b548133fa3ca4b219d22fcf41e18": 1773885396226,
|
||||
"om_x100b5481ce0418a0b26dc9b2f68f007": 1773885453209,
|
||||
"om_x100b548c9b6dcc78b49c9ec428e44f3": 1773899098605,
|
||||
"om_x100b548ca39e00acc3362a9bf3873de": 1773899477853,
|
||||
"om_x100b548d47a538b0c2c282968797cea": 1773899927180,
|
||||
"om_x100b5489ba663ca0c4c8bc1d9b97937": 1773920075261,
|
||||
"om_x100b548a50d788b8b2c1f705241dbc5": 1773920738205,
|
||||
"om_x100b548a72e7f4a8b286f0d7e9aea31": 1773921219213,
|
||||
"om_x100b548a83211ca8c44bcbf00eb049e": 1773923551363,
|
||||
"om_x100b548a95d45530c29580a9d1bad9a": 1773923762295,
|
||||
"om_x100b548b1fa8c8a4c42e95eb381a75c": 1773925655047,
|
||||
"om_x100b54f49f9f20b0b2d58df353d8a5b": 1773931798109,
|
||||
"om_x100b54f51931bca0c361d933da09d2b": 1773933952255,
|
||||
"om_x100b54f52b61f0a4c3650d280bf0dad": 1773934171242,
|
||||
"om_x100b54f53b8678b0c3188a42d675405": 1773934421286,
|
||||
"om_x100b54f533291ca4c2cf74f54da005d": 1773934558803,
|
||||
"om_x100b54f5b9ebf8a0c33674c32a8da6c": 1773936499279,
|
||||
"om_x100b54f64c3844acc14333ab25a59e5": 1773936688149,
|
||||
"om_x100b54f650ef80a0c2d3c76a3fbe9be": 1773937122637
|
||||
}
|
||||
|
||||
@@ -1,10 +1,18 @@
|
||||
{
|
||||
"om_x100b54a3ae6304a0b28ab7519a1acf1": 1773764107460,
|
||||
"om_x100b54a3a3782938b379c9a1da0010b": 1773764315828,
|
||||
"om_x100b54a3b60634a0b2910ed3595f0aa": 1773764493305,
|
||||
"om_x100b54955fb54964c4e180ded6a68b3": 1773801752225,
|
||||
"om_x100b54955a75ccb4c11c3b4070aa4fd": 1773801804425,
|
||||
"om_x100b54951cb128bcc361c4fb7da8c71": 1773802792386,
|
||||
"om_x100b5498aaee84a0c451ce70c732daf": 1773850178765,
|
||||
"om_x100b5498a8e104a8c37682c5c9f2e15": 1773850211313
|
||||
"om_x100b548be4a02ca4b33d43c7a3208be": 1773927079628,
|
||||
"om_x100b548be25df0b0b22cdf708250b00": 1773927113615,
|
||||
"om_x100b548be03aaca4b393638a734446f": 1773927152061,
|
||||
"om_x100b548bfc0824a4b364c8c0c8977d1": 1773927212950,
|
||||
"om_x100b548bfd70e48cb22d0cbc00050f8": 1773927228439,
|
||||
"om_x100b548bf82dbca0b2c751a1b709843": 1773927278588,
|
||||
"om_x100b548b8e22dcacb2d8d587bb7b14e": 1773927439644,
|
||||
"om_x100b54f4b7f294a0c45989f17b70fb7": 1773932436549,
|
||||
"om_x100b54f5454c14a4c42e9b6c6ea2cd3": 1773932728615,
|
||||
"om_x100b54f64b72d4b4c44dfcb4a5b55ab": 1773936732479,
|
||||
"om_x100b54f644fc18a4c3e0e0a4d345c50": 1773936803575,
|
||||
"om_x100b54f654df8534c4ee3346acd1bcd": 1773937057577,
|
||||
"om_x100b54f6091f78a4c4d71668a805f7d": 1773937789758,
|
||||
"om_x100b54f6cf31b4a0c453538202af189": 1773938720210,
|
||||
"om_x100b54f6d65a48a4c4e7a9045781192": 1773939082098,
|
||||
"om_x100b54f6d48980a0c2cc7e00d22cd8e": 1773939108667
|
||||
}
|
||||
|
||||
@@ -10,7 +10,7 @@
|
||||
"operator.approvals",
|
||||
"operator.pairing"
|
||||
],
|
||||
"updatedAtMs": 1773857416774
|
||||
"updatedAtMs": 1773943213908
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -60,3 +60,5 @@
|
||||
{"timestamp":"2026-03-17T16:06:09.290Z","action":"new","sessionKey":"agent:knowledge-base:main","senderId":"ou_9351c287f42069fa30366ac641d24df7","source":"feishu"}
|
||||
{"timestamp":"2026-03-18T02:42:32.742Z","action":"new","sessionKey":"agent:knowledge-base:main","senderId":"ou_9351c287f42069fa30366ac641d24df7","source":"feishu"}
|
||||
{"timestamp":"2026-03-18T16:09:39.151Z","action":"new","sessionKey":"agent:knowledge-base:main","senderId":"ou_9351c287f42069fa30366ac641d24df7","source":"feishu"}
|
||||
{"timestamp":"2026-03-19T15:00:36.886Z","action":"new","sessionKey":"agent:knowledge-base:main","senderId":"ou_9351c287f42069fa30366ac641d24df7","source":"feishu"}
|
||||
{"timestamp":"2026-03-19T16:12:12.817Z","action":"new","sessionKey":"agent:knowledge-base:main","senderId":"ou_9351c287f42069fa30366ac641d24df7","source":"feishu"}
|
||||
|
||||
@@ -12,3 +12,6 @@ export KB_BASE_PATH="~/work/knowledge-base"
|
||||
# 可选: Git 配置
|
||||
# export KB_GIT_USER="Your Name"
|
||||
# export KB_GIT_EMAIL="your@email.com"
|
||||
|
||||
# 可选: 工作流行为配置
|
||||
# export KB_OVERWRITE_ARCHIVES="false" # 非交互模式下是否覆盖已存在的归档文件（true/false）
|
||||
|
||||
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@@ -49,7 +49,7 @@ def process_command(
|
||||
from .pipeline.pipeline import Pipeline
|
||||
from .pipeline.processors import (
|
||||
ASRProcessor, AnalyzeProcessor, ArchiveProcessor,
|
||||
DownloadProcessor, ParseProcessor
|
||||
DownloadProcessor, ParseProcessor, SRTProcessor
|
||||
)
|
||||
from .pipeline.schemas import PipelineConfig
|
||||
|
||||
@@ -63,13 +63,14 @@ def process_command(
|
||||
ParseProcessor(),
|
||||
DownloadProcessor(),
|
||||
ASRProcessor(),
|
||||
AnalyzeProcessor(),
|
||||
SRTProcessor(), # B站视频使用
|
||||
AnalyzeProcessor(), # 知乎/文章使用
|
||||
ArchiveProcessor(),
|
||||
]
|
||||
|
||||
# Create pipeline config
|
||||
config = PipelineConfig(
|
||||
steps=["parse", "download", "asr", "analyze", "archive"],
|
||||
steps=["parse", "download", "asr", "srt", "analyze", "archive"],
|
||||
output_dir=out_dir,
|
||||
skip_completed=skip_completed,
|
||||
)
|
||||
@@ -165,7 +166,7 @@ def login_command(
|
||||
result = login.login_by_qrcode()
|
||||
if result.success and result.cookie_str:
|
||||
manager.save_cookie(result.cookie_str)
|
||||
return result.model_dump()
|
||||
return result.model_dump(mode='json')
|
||||
|
||||
except Exception as e:
|
||||
return {"success": False, "error": str(e)}
|
||||
|
||||
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@@ -90,22 +90,35 @@ class SimpleAIClient:
|
||||
|
||||
def _call_zhipu(self, prompt: str, max_tokens: int, api_key: str, model: str, base_url: str) -> str:
|
||||
import requests
|
||||
import sys
|
||||
|
||||
base_url = base_url or "https://open.bigmodel.cn/api/coding/paas/v4"
|
||||
url = f"{base_url}/chat/completions"
|
||||
headers = {"Authorization": f"Bearer {api_key}"}
|
||||
payload = {"model": model, "messages": [{"role": "user", "content": prompt}], "max_tokens": max_tokens}
|
||||
# GLM-4.5/4.6/4.7/5 默认启用thinking mode，使用 thinking.type 参数禁用
|
||||
payload = {
|
||||
"model": model,
|
||||
"messages": [{"role": "user", "content": prompt}],
|
||||
"max_tokens": max_tokens,
|
||||
"thinking": {
|
||||
"type": "disabled" # 禁用思考模式，获取直接响应
|
||||
}
|
||||
}
|
||||
response = requests.post(url, headers=headers, json=payload, timeout=600)
|
||||
response.raise_for_status()
|
||||
data = response.json()
|
||||
message = data.get('choices', [{}])[0].get('message', {})
|
||||
content = message.get('content', '')
|
||||
reasoning = message.get('reasoning_content', '')
|
||||
if not content and reasoning:
|
||||
sentences = reasoning.strip().split('。')
|
||||
useful = [s.strip() for s in sentences if s.strip() and len(s.strip()) > 10]
|
||||
if useful:
|
||||
return '。'.join(useful[-3:]) + '。'
|
||||
return reasoning.strip()[:500]
|
||||
|
||||
if not content:
|
||||
# 如果仍然为空，检查是否有reasoning_content（用于调试）
|
||||
reasoning = message.get('reasoning_content', '')
|
||||
if reasoning:
|
||||
print(f"[DEBUG] Zhipu API返回空的content，但有reasoning_content。", file=sys.stderr)
|
||||
print(f"[DEBUG] 这可能表示thinking.type参数未生效或模型不支持。", file=sys.stderr)
|
||||
print(f"[DEBUG] reasoning_content前200字符: {reasoning[:200]}", file=sys.stderr)
|
||||
raise ValueError(f"Zhipu API返回空内容。请检查API配置和模型参数。响应数据: {data}")
|
||||
|
||||
return content
|
||||
|
||||
def _call_minimax(self, prompt: str, max_tokens: int, api_key: str, model: str, base_url: str) -> str:
|
||||
@@ -129,6 +142,13 @@ class DeepAnalyzer:
|
||||
|
||||
def __init__(self, ai_client: SimpleAIClient = None):
|
||||
self.ai_client = ai_client or SimpleAIClient()
|
||||
# 弃用警告：B站视频应使用 SRTProcessor
|
||||
import logging
|
||||
logger = logging.getLogger(__name__)
|
||||
logger.warning(
|
||||
"DeepAnalyzer is deprecated for Bilibili video processing. "
|
||||
"Use SRTProcessor instead."
|
||||
)
|
||||
|
||||
def analyze(self, metadata: Dict[str, Any], content: Dict[str, Any], content_type: str = "video") -> AnalysisResult:
|
||||
"""一次性调用 AI 生成完整分析报告
|
||||
|
||||
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@@ -58,6 +58,11 @@ class KBSettings(BaseSettings):
|
||||
default=SecretStr(""), description="B站 Cookie (for video download)"
|
||||
)
|
||||
|
||||
# 工作流行为配置
|
||||
overwrite_archives: bool = Field(
|
||||
default=False, description="覆盖已存在的归档文件（非交互模式）"
|
||||
)
|
||||
|
||||
|
||||
@lru_cache
|
||||
def get_settings() -> KBSettings:
|
||||
|
||||
@@ -21,7 +21,7 @@ from typing import Dict, List, Optional
|
||||
from kb_workflow.pipeline.config import get_settings
|
||||
from kb_workflow.pipeline.link_parser import LinkParser, ParsedLink
|
||||
from kb_workflow.pipeline.processors.base import BaseProcessor
|
||||
from kb_workflow.pipeline.schemas import PipelineConfig, PipelineInput, PipelineOutput
|
||||
from kb_workflow.pipeline.schemas import PipelineConfig, PipelineInput, PipelineOutput, SRTInput
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@@ -174,6 +174,20 @@ class Pipeline:
|
||||
logger.info(f"Skipping ASR for text content type: {parsed.content_type}")
|
||||
continue
|
||||
|
||||
# B站视频：跳过 analyze 步骤，使用 srt 步骤替代
|
||||
is_bilibili_video = (
|
||||
parsed.platform == "bilibili" and
|
||||
parsed.content_type == "video"
|
||||
)
|
||||
if step_name == "analyze" and is_bilibili_video:
|
||||
logger.info(f"Skipping analyze for Bilibili video, using SRT processor instead")
|
||||
continue
|
||||
|
||||
# B站文章/知乎等：跳过 srt 步骤
|
||||
if step_name == "srt" and not is_bilibili_video:
|
||||
logger.info(f"Skipping SRT for non-Bilibili-video content: {parsed.platform}/{parsed.content_type}")
|
||||
continue
|
||||
|
||||
processor = self.processors[step_name]
|
||||
|
||||
# Mark step as started
|
||||
@@ -228,10 +242,26 @@ class Pipeline:
|
||||
content_type=parsed.content_type,
|
||||
output_file=str(content_dir / "analysis.md"),
|
||||
)
|
||||
elif step_name == "srt":
|
||||
# SRT needs srt file from ASR step and metadata
|
||||
download_result = results.get("download", {})
|
||||
asr_result = results.get("asr", {})
|
||||
metadata_file = download_result.get("metadata_file", "")
|
||||
# SRT file from ASR output
|
||||
srt_file = asr_result.get("output_files", {}).get("srt", "")
|
||||
|
||||
step_input = SRTInput(
|
||||
content_id=parsed.content_id,
|
||||
platform=parsed.platform,
|
||||
srt_file=srt_file,
|
||||
metadata_file=metadata_file,
|
||||
output_file=str(content_dir / "analysis.md"),
|
||||
content_type=parsed.content_type,
|
||||
)
|
||||
elif step_name == "archive":
|
||||
# Archive needs analysis file from analyze step
|
||||
analyze_result = results.get("analyze", {})
|
||||
analysis_file = analyze_result.get("analysis_file", "")
|
||||
# Archive needs analysis file from analyze step (or srt step for Bilibili video)
|
||||
analyze_result = results.get("analyze", {}) or results.get("srt", {})
|
||||
analysis_file = analyze_result.get("analysis_file", "") or analyze_result.get("output_file", "")
|
||||
|
||||
step_input = ArchiveInput(
|
||||
content_id=parsed.content_id,
|
||||
@@ -261,7 +291,7 @@ class Pipeline:
|
||||
# Add output fields to results
|
||||
for field_name in ["metadata_file", "content_file", "title", "author",
|
||||
"analysis_file", "summary", "output_files",
|
||||
"archive_file", "skipped"]:
|
||||
"archive_file", "skipped", "output_file", "paragraphs", "tags", "corrected"]:
|
||||
if hasattr(step_output, field_name):
|
||||
results[step_name][field_name] = getattr(step_output, field_name)
|
||||
|
||||
@@ -397,3 +427,14 @@ class Pipeline:
|
||||
"analysis_file": str(analysis_path),
|
||||
}
|
||||
logger.info(f"Found existing analysis: {analysis_path}")
|
||||
|
||||
elif step_name == "srt":
|
||||
# Scan for SRT output (same file as analyze - analysis.md)
|
||||
analysis_path = content_dir / "analysis.md"
|
||||
if analysis_path.exists():
|
||||
results["srt"] = {
|
||||
"success": True,
|
||||
"error_message": None,
|
||||
"output_file": str(analysis_path),
|
||||
}
|
||||
logger.info(f"Found existing SRT output: {analysis_path}")
|
||||
|
||||
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@@ -5,6 +5,7 @@ This module contains processors for various content transformation tasks:
|
||||
- ParseProcessor: URL parsing for various platforms
|
||||
- DownloadProcessor: Metadata and content downloading
|
||||
- ASRProcessor: Audio-to-text conversion (subtitle download + Whisper.cpp)
|
||||
- SRTProcessor: SRT subtitle processing (parse, segment, correct, markdown)
|
||||
- AnalyzeProcessor: AI-powered content analysis using DeepAnalyzer
|
||||
- ArchiveProcessor: Copy analysis reports to archive directory
|
||||
"""
|
||||
@@ -13,6 +14,7 @@ from kb_workflow.pipeline.processors.base import BaseProcessor
|
||||
from kb_workflow.pipeline.processors.parse import ParseProcessor
|
||||
from kb_workflow.pipeline.processors.download import DownloadProcessor
|
||||
from kb_workflow.pipeline.processors.asr import ASRProcessor
|
||||
from kb_workflow.pipeline.processors.srt import SRTProcessor
|
||||
from kb_workflow.pipeline.processors.analyze import AnalyzeProcessor
|
||||
from kb_workflow.pipeline.processors.archive import ArchiveProcessor
|
||||
|
||||
@@ -21,6 +23,7 @@ __all__ = [
|
||||
"ParseProcessor",
|
||||
"DownloadProcessor",
|
||||
"ASRProcessor",
|
||||
"SRTProcessor",
|
||||
"AnalyzeProcessor",
|
||||
"ArchiveProcessor",
|
||||
]
|
||||
|
||||
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@@ -46,6 +46,8 @@ class ArchiveProcessor(BaseProcessor):
|
||||
Archive 处理结果
|
||||
"""
|
||||
try:
|
||||
import sys
|
||||
|
||||
# Step 1: 检查是否已完成（幂等执行）
|
||||
if self.state_manager:
|
||||
state = self.state_manager.load()
|
||||
@@ -73,7 +75,9 @@ class ArchiveProcessor(BaseProcessor):
|
||||
platform_dir, input_data.content_id
|
||||
)
|
||||
if existing_file:
|
||||
should_overwrite = self._ask_overwrite(existing_file)
|
||||
# 检测是否为非交互式环境
|
||||
is_non_interactive = not sys.stdin.isatty()
|
||||
should_overwrite = self._ask_overwrite(existing_file, is_non_interactive)
|
||||
if not should_overwrite:
|
||||
logger.info(f"User chose not to overwrite, skipping archive")
|
||||
return ArchiveOutput(
|
||||
@@ -143,17 +147,28 @@ class ArchiveProcessor(BaseProcessor):
|
||||
matches = list(platform_dir.glob(pattern))
|
||||
return matches[0] if matches else None
|
||||
|
||||
def _ask_overwrite(self, existing_file: Path) -> bool:
|
||||
def _ask_overwrite(self, existing_file: Path, is_non_interactive: bool = False) -> bool:
|
||||
"""询问用户是否覆盖已存在的文件
|
||||
|
||||
使用 rich.prompt.Confirm 模式（与 Pipeline._ask_rerun_step 相同）
|
||||
|
||||
Args:
|
||||
existing_file: 已存在的文件路径
|
||||
is_non_interactive: 是否为非交互式环境（stdin不是tty）
|
||||
|
||||
Returns:
|
||||
True 如果用户选择覆盖
|
||||
"""
|
||||
# 非交互式模式：使用配置决定
|
||||
if is_non_interactive:
|
||||
if self.settings.overwrite_archives:
|
||||
logger.info(f"Non-interactive mode: Overwriting existing archive (KB_OVERWRITE_ARCHIVES=true): {existing_file.name}")
|
||||
return True
|
||||
else:
|
||||
logger.info(f"Non-interactive mode: Skipping existing archive (KB_OVERWRITE_ARCHIVES=false): {existing_file.name}")
|
||||
return False
|
||||
|
||||
# 交互式模式：询问用户
|
||||
try:
|
||||
from rich.console import Console
|
||||
from rich.prompt import Confirm
|
||||
|
||||
@@ -0,0 +1,333 @@
|
||||
"""SRTProcessor - B站字幕处理处理器
|
||||
|
||||
将 B站 SRT 字幕文件转换为简化的 Markdown 文档。
|
||||
|
||||
处理流程：
|
||||
1. 解析 SRT 文件（调用 parser.parse_srt_file）
|
||||
2. 语义分段（调用 segmenter.segment）
|
||||
3. ASR 纠错（调用 corrector.correct，可选）
|
||||
4. 生成 Markdown 文件
|
||||
|
||||
简化版：不做深度观点分析，只保留字幕内容和元数据。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from typing import Dict, Any, List, Optional
|
||||
|
||||
from kb_workflow.pipeline.config import get_settings
|
||||
from kb_workflow.pipeline.processors.base import BaseProcessor
|
||||
from kb_workflow.pipeline.schemas import SRTInput, SRTOutput
|
||||
from kb_workflow.pipeline.state import WorkflowStateManager
|
||||
from kb_workflow.srt.parser import parse_srt_file
|
||||
from kb_workflow.srt.segmenter import SemanticSegmenter
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class SRTProcessor(BaseProcessor):
|
||||
"""SRT 字幕处理器
|
||||
|
||||
将 SRT 字幕文件转换为简化的 Markdown 文档。
|
||||
继承 BaseProcessor，遵循 Pipeline 处理器模式。
|
||||
|
||||
Attributes:
|
||||
STEP_NAME: 步骤名称 "srt"
|
||||
settings: KB 配置
|
||||
state_manager: 状态管理器
|
||||
segmenter: 语义分段器
|
||||
corrector: ASR 纠错器（可选）
|
||||
"""
|
||||
|
||||
STEP_NAME = "srt"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
state_manager: Optional[WorkflowStateManager] = None,
|
||||
enable_correction: bool = True
|
||||
):
|
||||
"""初始化 SRT 处理器
|
||||
|
||||
Args:
|
||||
state_manager: 状态管理器
|
||||
enable_correction: 是否启用 ASR 纠错（默认 True）
|
||||
"""
|
||||
super().__init__(state_manager)
|
||||
self.enable_correction = enable_correction
|
||||
self.segmenter = SemanticSegmenter()
|
||||
self.corrector = None
|
||||
|
||||
# 延迟初始化纠错器
|
||||
if enable_correction:
|
||||
try:
|
||||
from kb_workflow.srt.corrector import ASRCorrector
|
||||
from kb_workflow.deep_analyzer import SimpleAIClient
|
||||
|
||||
# 尝试创建 AI 客户端
|
||||
ai_client = SimpleAIClient()
|
||||
if ai_client.api_key:
|
||||
self.corrector = ASRCorrector(ai_client=ai_client)
|
||||
logger.info("ASR corrector initialized")
|
||||
else:
|
||||
logger.info("AI API key not configured, ASR correction disabled")
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to initialize ASR corrector: {e}")
|
||||
|
||||
async def process(self, input_data: SRTInput) -> SRTOutput:
|
||||
"""处理 SRT 文件
|
||||
|
||||
Args:
|
||||
input_data: SRT 输入数据
|
||||
|
||||
Returns:
|
||||
SRT 处理结果
|
||||
|
||||
Raises:
|
||||
Exception: 处理失败时抛出异常
|
||||
"""
|
||||
try:
|
||||
# 记录开始状态
|
||||
if self.state_manager:
|
||||
state = self.state_manager.load()
|
||||
if state:
|
||||
state.mark_step_started(self.STEP_NAME)
|
||||
self.state_manager.save(state)
|
||||
|
||||
# Step 1: 解析 SRT 文件
|
||||
logger.info(f"Parsing SRT file: {input_data.srt_file}")
|
||||
segments = parse_srt_file(Path(input_data.srt_file))
|
||||
logger.info(f"Parsed {len(segments)} segments")
|
||||
|
||||
# Step 2: 语义分段
|
||||
logger.info("Segmenting subtitles into paragraphs")
|
||||
paragraphs = self.segmenter.segment(segments)
|
||||
logger.info(f"Generated {len(paragraphs)} paragraphs")
|
||||
|
||||
# Step 3: ASR 纠错（可选）
|
||||
corrected = False
|
||||
if self.corrector and paragraphs:
|
||||
logger.info("Applying ASR correction")
|
||||
corrected_paragraphs = []
|
||||
for para in paragraphs:
|
||||
corrected_para = self.corrector.correct(para)
|
||||
corrected_paragraphs.append(corrected_para)
|
||||
if corrected_para != para:
|
||||
corrected = True
|
||||
paragraphs = corrected_paragraphs
|
||||
logger.info(f"ASR correction applied: {corrected}")
|
||||
|
||||
# Step 4: 加载元数据
|
||||
metadata = self._load_metadata(input_data.metadata_file)
|
||||
|
||||
# Step 5: 生成 Markdown
|
||||
logger.info(f"Generating Markdown: {input_data.output_file}")
|
||||
tags = self._extract_tags(paragraphs)
|
||||
self._generate_markdown(
|
||||
output_file=Path(input_data.output_file),
|
||||
metadata=metadata,
|
||||
paragraphs=paragraphs,
|
||||
tags=tags
|
||||
)
|
||||
|
||||
# 记录完成状态
|
||||
if self.state_manager:
|
||||
state = self.state_manager.load()
|
||||
if state:
|
||||
summary = f"paragraphs={len(paragraphs)}, corrected={corrected}"
|
||||
state.mark_step_completed(self.STEP_NAME, summary)
|
||||
self.state_manager.save(state)
|
||||
|
||||
return SRTOutput(
|
||||
success=True,
|
||||
output_file=str(input_data.output_file),
|
||||
paragraphs=paragraphs,
|
||||
tags=tags,
|
||||
corrected=corrected
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"SRT processing failed: {e}")
|
||||
# 记录失败状态
|
||||
if self.state_manager:
|
||||
state = self.state_manager.load()
|
||||
if state:
|
||||
state.mark_step_failed(self.STEP_NAME, str(e))
|
||||
self.state_manager.save(state)
|
||||
raise
|
||||
|
||||
def _load_metadata(self, metadata_file: str) -> Dict[str, Any]:
|
||||
"""加载元数据文件
|
||||
|
||||
Args:
|
||||
metadata_file: 元数据文件路径
|
||||
|
||||
Returns:
|
||||
元数据字典
|
||||
"""
|
||||
path = Path(metadata_file)
|
||||
if not path.exists():
|
||||
logger.warning(f"Metadata file not found: {metadata_file}")
|
||||
return {}
|
||||
|
||||
with open(path, 'r', encoding='utf-8') as f:
|
||||
return json.load(f)
|
||||
|
||||
def _extract_tags(self, paragraphs: List[str]) -> List[str]:
|
||||
"""从段落中提取标签
|
||||
|
||||
简单实现：不使用 LLM，只返回默认标签。
|
||||
未来可以增强为 LLM 提取。
|
||||
|
||||
Args:
|
||||
paragraphs: 段落列表
|
||||
|
||||
Returns:
|
||||
标签列表
|
||||
"""
|
||||
# 简单实现：返回空列表
|
||||
# TODO: 可以使用 LLM 提取标签
|
||||
return []
|
||||
|
||||
def _generate_markdown(
|
||||
self,
|
||||
output_file: Path,
|
||||
metadata: Dict[str, Any],
|
||||
paragraphs: List[str],
|
||||
tags: List[str]
|
||||
) -> None:
|
||||
"""生成 Markdown 文件
|
||||
|
||||
Args:
|
||||
output_file: 输出文件路径
|
||||
metadata: 元数据
|
||||
paragraphs: 段落列表
|
||||
tags: 标签列表
|
||||
"""
|
||||
# 确保输出目录存在
|
||||
output_file.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# 生成 frontmatter
|
||||
frontmatter = self._generate_frontmatter(metadata, tags)
|
||||
|
||||
# 构建内容
|
||||
content_parts = [frontmatter, ""]
|
||||
|
||||
# 添加摘要（可选）
|
||||
if paragraphs:
|
||||
content_parts.append("## 摘要")
|
||||
content_parts.append("")
|
||||
# 使用第一段作为简单摘要
|
||||
first_para = paragraphs[0][:200] if len(paragraphs[0]) > 200 else paragraphs[0]
|
||||
content_parts.append(first_para + ("..." if len(paragraphs[0]) > 200 else ""))
|
||||
content_parts.append("")
|
||||
|
||||
# 添加内容
|
||||
content_parts.append("## 内容")
|
||||
content_parts.append("")
|
||||
for para in paragraphs:
|
||||
content_parts.append(para)
|
||||
content_parts.append("")
|
||||
|
||||
# 写入文件
|
||||
output_file.write_text('\n'.join(content_parts), encoding='utf-8')
|
||||
logger.info(f"Markdown file generated: {output_file}")
|
||||
|
||||
def _generate_frontmatter(self, metadata: Dict[str, Any], tags: List[str]) -> str:
|
||||
"""生成 YAML frontmatter
|
||||
|
||||
Args:
|
||||
metadata: 元数据
|
||||
tags: 标签列表
|
||||
|
||||
Returns:
|
||||
YAML frontmatter 字符串
|
||||
"""
|
||||
lines = ["---"]
|
||||
|
||||
# 标题
|
||||
title = metadata.get('title', '未命名')
|
||||
lines.append(f'title: "{self._escape_yaml(title)}"')
|
||||
|
||||
# 日期
|
||||
pubtime = metadata.get('pubtime', '') or metadata.get('published_at', '')
|
||||
if pubtime:
|
||||
date_str = str(pubtime)[:10]
|
||||
else:
|
||||
date_str = datetime.now().strftime('%Y-%m-%d')
|
||||
lines.append(f'date: {date_str}')
|
||||
|
||||
# 作者
|
||||
author = metadata.get('author', '未知')
|
||||
lines.append(f'author: {self._escape_yaml(author)}')
|
||||
|
||||
# 平台
|
||||
platform = metadata.get('platform', 'bilibili')
|
||||
lines.append(f'platform: {platform}')
|
||||
|
||||
# 时长（视频）
|
||||
duration = metadata.get('duration', 0)
|
||||
if duration:
|
||||
duration_str = self._format_duration(duration)
|
||||
lines.append(f'duration: {duration_str}')
|
||||
|
||||
# BVID
|
||||
bvid = metadata.get('bvid', '')
|
||||
if bvid:
|
||||
lines.append(f'bvid: {bvid}')
|
||||
|
||||
# URL
|
||||
url = metadata.get('url', '')
|
||||
if url:
|
||||
lines.append(f'url: {url}')
|
||||
|
||||
# 标签
|
||||
if tags:
|
||||
tags_str = ', '.join(f'"{t}"' for t in tags)
|
||||
lines.append(f'tags: [{tags_str}]')
|
||||
|
||||
lines.append("---")
|
||||
return '\n'.join(lines)
|
||||
|
||||
def _escape_yaml(self, text: str) -> str:
|
||||
"""转义 YAML 特殊字符
|
||||
|
||||
Args:
|
||||
text: 原始文本
|
||||
|
||||
Returns:
|
||||
转义后的文本
|
||||
"""
|
||||
# 移除零宽度字符
|
||||
invisible_chars = ['\u200b', '\u200c', '\u200d', '\ufeff', '\u00ad']
|
||||
for char in invisible_chars:
|
||||
text = text.replace(char, '')
|
||||
|
||||
# 移除换行符
|
||||
text = text.replace('\n', ' ').strip()
|
||||
|
||||
# 如果包含特殊字符，用引号包裹
|
||||
if any(c in text for c in [':', '"', "'", '\n', '#']):
|
||||
text = text.replace('"', '\\"')
|
||||
return text
|
||||
|
||||
return text
|
||||
|
||||
def _format_duration(self, seconds: int) -> str:
|
||||
"""格式化时长
|
||||
|
||||
Args:
|
||||
seconds: 秒数
|
||||
|
||||
Returns:
|
||||
格式化的时长字符串 (HH:MM:SS 或 MM:SS)
|
||||
"""
|
||||
h = seconds // 3600
|
||||
m = (seconds % 3600) // 60
|
||||
s = seconds % 60
|
||||
if h > 0:
|
||||
return f"{h}:{m:02d}:{s:02d}"
|
||||
return f"{m}:{s:02d}"
|
||||
@@ -444,3 +444,42 @@ class ArchiveOutput(StepOutput):
|
||||
archive_file: str = Field(default="", description="Path to archived file")
|
||||
skipped: bool = Field(default=False, description="Whether archiving was skipped")
|
||||
platform: str = Field(default="", description="Platform name")
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# SRT Schemas (Phase 12)
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class SRTInput(StepInput):
|
||||
"""SRT 步骤输入
|
||||
|
||||
用于 SRTProcessor 处理 B站字幕。
|
||||
|
||||
Attributes:
|
||||
srt_file: SRT 字幕文件路径
|
||||
metadata_file: 元数据 JSON 文件路径
|
||||
output_file: 输出 Markdown 文件路径
|
||||
content_type: 内容类型（默认 video）
|
||||
"""
|
||||
srt_file: str = Field(..., min_length=1, description="Path to SRT subtitle file")
|
||||
metadata_file: str = Field(..., min_length=1, description="Path to metadata JSON file")
|
||||
output_file: str = Field(..., min_length=1, description="Path to output Markdown file")
|
||||
content_type: str = Field(default="video", description="Content type")
|
||||
|
||||
|
||||
class SRTOutput(StepOutput):
|
||||
"""SRT 步骤输出
|
||||
|
||||
继承 StepOutput 的基础字段，添加 SRT 处理特定字段。
|
||||
|
||||
Attributes:
|
||||
output_file: 生成的 Markdown 文件路径
|
||||
paragraphs: 生成的段落列表
|
||||
tags: 提取的标签列表
|
||||
corrected: 是否进行了 ASR 纠错
|
||||
"""
|
||||
output_file: str = Field(..., description="Path to generated Markdown file")
|
||||
paragraphs: List[str] = Field(default_factory=list, description="Generated paragraphs")
|
||||
tags: List[str] = Field(default_factory=list, description="Extracted tags")
|
||||
corrected: bool = Field(default=False, description="Whether ASR correction was applied")
|
||||
|
||||
@@ -0,0 +1,27 @@
|
||||
"""SRT Processing Module
|
||||
|
||||
B站字幕处理模块，包含：
|
||||
- SRT 解析器 (parser)
|
||||
- 语义分段器 (segmenter)
|
||||
- ASR 纠错器 (corrector)
|
||||
"""
|
||||
|
||||
from kb_workflow.srt.parser import parse_srt, parse_srt_file, Segment
|
||||
|
||||
__all__ = [
|
||||
"parse_srt",
|
||||
"parse_srt_file",
|
||||
"Segment",
|
||||
"SemanticSegmenter",
|
||||
"ASRCorrector",
|
||||
]
|
||||
|
||||
# 延迟导入，避免循环依赖
|
||||
def __getattr__(name: str):
|
||||
if name == "SemanticSegmenter":
|
||||
from kb_workflow.srt.segmenter import SemanticSegmenter
|
||||
return SemanticSegmenter
|
||||
if name == "ASRCorrector":
|
||||
from kb_workflow.srt.corrector import ASRCorrector
|
||||
return ASRCorrector
|
||||
raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
@@ -0,0 +1,157 @@
|
||||
"""ASR Corrector Module
|
||||
|
||||
使用 LLM 纠正语音识别错误。复用 SimpleAIClient。
|
||||
|
||||
功能：
|
||||
- 纠正明显的语音识别错误（同音字、漏字、错字）
|
||||
- 分批处理长文本
|
||||
- AI 不可用时返回原文
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from typing import Optional, Protocol
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class AIClientProtocol(Protocol):
|
||||
"""AI 客户端协议"""
|
||||
|
||||
def generate(self, prompt: str, max_tokens: int = 16000) -> str:
|
||||
"""生成文本"""
|
||||
...
|
||||
|
||||
|
||||
# 纠错 Prompt 模板
|
||||
CORRECTION_PROMPT = """你是一个文本校对专家。以下是语音识别生成的字幕，可能包含识别错误。
|
||||
请只纠正明显的错误（如同音字、漏字、错字），不要改变原意和风格。
|
||||
|
||||
原文：
|
||||
{text}
|
||||
|
||||
纠正后的文本（只输出纠正后的文本，不要解释）："""
|
||||
|
||||
|
||||
class ASRCorrector:
|
||||
"""ASR 纠错器
|
||||
|
||||
使用 LLM 纠正语音识别中的明显错误。
|
||||
|
||||
Attributes:
|
||||
ai_client: AI 客户端（必须实现 generate 方法）
|
||||
batch_size: 分批处理的字符数，默认 5000
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
ai_client: Optional[AIClientProtocol] = None,
|
||||
batch_size: int = 5000
|
||||
):
|
||||
"""初始化纠错器
|
||||
|
||||
Args:
|
||||
ai_client: AI 客户端实例，如果为 None 则不进行纠错
|
||||
batch_size: 分批处理的字符数
|
||||
"""
|
||||
self.ai_client = ai_client
|
||||
self.batch_size = batch_size
|
||||
|
||||
def correct(self, text: str) -> str:
|
||||
"""纠正文本中的语音识别错误
|
||||
|
||||
Args:
|
||||
text: 原始文本
|
||||
|
||||
Returns:
|
||||
纠正后的文本。如果 AI 不可用或文本为空，返回原文。
|
||||
|
||||
Example:
|
||||
>>> corrector = ASRCorrector(ai_client=my_client)
|
||||
>>> result = corrector.correct("什么是机器学习")
|
||||
>>> print(result)
|
||||
什么是机器学习
|
||||
"""
|
||||
# 空文本直接返回
|
||||
if not text or not text.strip():
|
||||
return text or ""
|
||||
|
||||
# AI 不可用，返回原文
|
||||
if self.ai_client is None:
|
||||
logger.info("AI client not available, returning original text")
|
||||
return text
|
||||
|
||||
# 短文本直接处理
|
||||
if len(text) <= self.batch_size:
|
||||
return self._correct_batch(text)
|
||||
|
||||
# 长文本分批处理
|
||||
return self._correct_long_text(text)
|
||||
|
||||
def _correct_batch(self, text: str) -> str:
|
||||
"""处理单个批次的文本
|
||||
|
||||
Args:
|
||||
text: 文本内容
|
||||
|
||||
Returns:
|
||||
纠正后的文本
|
||||
"""
|
||||
try:
|
||||
prompt = CORRECTION_PROMPT.format(text=text)
|
||||
result = self.ai_client.generate(prompt, max_tokens=len(text) + 500)
|
||||
return result.strip()
|
||||
except Exception as e:
|
||||
logger.warning(f"ASR correction failed: {e}, returning original text")
|
||||
return text
|
||||
|
||||
def _correct_long_text(self, text: str) -> str:
|
||||
"""分批处理长文本
|
||||
|
||||
按句子分割，每批不超过 batch_size 字符。
|
||||
|
||||
Args:
|
||||
text: 长文本内容
|
||||
|
||||
Returns:
|
||||
纠正后的文本
|
||||
"""
|
||||
# 按句子分割（简单分割，基于句号、问号、感叹号）
|
||||
sentences = []
|
||||
current = ""
|
||||
for char in text:
|
||||
current += char
|
||||
if char in '。！？\n':
|
||||
sentences.append(current)
|
||||
current = ""
|
||||
|
||||
# 添加最后一个不完整的句子
|
||||
if current:
|
||||
sentences.append(current)
|
||||
|
||||
# 分批处理
|
||||
batches = []
|
||||
current_batch = []
|
||||
current_length = 0
|
||||
|
||||
for sentence in sentences:
|
||||
if current_length + len(sentence) > self.batch_size:
|
||||
if current_batch:
|
||||
batches.append(''.join(current_batch))
|
||||
current_batch = [sentence]
|
||||
current_length = len(sentence)
|
||||
else:
|
||||
current_batch.append(sentence)
|
||||
current_length += len(sentence)
|
||||
|
||||
if current_batch:
|
||||
batches.append(''.join(current_batch))
|
||||
|
||||
# 处理每个批次
|
||||
corrected_batches = []
|
||||
for batch in batches:
|
||||
corrected = self._correct_batch(batch)
|
||||
corrected_batches.append(corrected)
|
||||
|
||||
return ''.join(corrected_batches)
|
||||
@@ -0,0 +1,161 @@
|
||||
"""SRT Parser Module
|
||||
|
||||
解析 SRT 字幕文件格式，将字幕转换为结构化的 Segment 对象。
|
||||
|
||||
SRT 格式示例:
|
||||
1
|
||||
00:00:00,000 --> 00:00:05,000
|
||||
第一句字幕内容
|
||||
|
||||
2
|
||||
00:00:05,000 --> 00:00:10,000
|
||||
第二句字幕内容
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import re
|
||||
from dataclasses import dataclass
|
||||
from pathlib import Path
|
||||
from typing import List
|
||||
|
||||
|
||||
@dataclass
|
||||
class Segment:
|
||||
"""字幕分段数据结构
|
||||
|
||||
Attributes:
|
||||
id: 字幕序号
|
||||
start_time: 开始时间（秒）
|
||||
end_time: 结束时间（秒）
|
||||
text: 字幕文本
|
||||
"""
|
||||
id: int
|
||||
start_time: float
|
||||
end_time: float
|
||||
text: str
|
||||
|
||||
|
||||
# SRT 块正则表达式
|
||||
# 格式: 序号\n时间码\n文本(可能多行)\n\n
|
||||
SRT_BLOCK_PATTERN = re.compile(
|
||||
r'(\d+)\s*\n'
|
||||
r'(\d{2}:\d{2}:\d{2},\d{3})\s*-->\s*(\d{2}:\d{2}:\d{2},\d{3})\s*\n'
|
||||
r'(.*?)(?=\n\n|\Z)',
|
||||
re.DOTALL
|
||||
)
|
||||
|
||||
|
||||
def parse_srt_time(time_str: str) -> float:
|
||||
"""解析 SRT 时间格式 (HH:MM:SS,mmm) 为秒数
|
||||
|
||||
Args:
|
||||
time_str: SRT 时间字符串，如 "00:05:30,500"
|
||||
|
||||
Returns:
|
||||
秒数，如 330.5
|
||||
|
||||
Example:
|
||||
>>> parse_srt_time("00:05:30,500")
|
||||
330.5
|
||||
>>> parse_srt_time("01:02:03,004")
|
||||
3723.004
|
||||
"""
|
||||
# 分割时间和毫秒
|
||||
main_part, millis = time_str.rsplit(',', 1)
|
||||
hours, minutes, seconds = main_part.split(':')
|
||||
|
||||
total_seconds = (
|
||||
int(hours) * 3600 +
|
||||
int(minutes) * 60 +
|
||||
int(seconds) +
|
||||
int(millis) / 1000
|
||||
)
|
||||
return total_seconds
|
||||
|
||||
|
||||
def parse_srt(content: str) -> List[Segment]:
|
||||
"""解析 SRT 格式字符串
|
||||
|
||||
将 SRT 格式的字幕内容解析为 Segment 对象列表。
|
||||
|
||||
Args:
|
||||
content: SRT 格式的字符串内容
|
||||
|
||||
Returns:
|
||||
Segment 对象列表
|
||||
|
||||
Raises:
|
||||
ValueError: 内容为空或格式无效
|
||||
|
||||
Example:
|
||||
>>> content = "1\\n00:00:00,000 --> 00:00:05,000\\n第一句\\n\\n"
|
||||
>>> segments = parse_srt(content)
|
||||
>>> len(segments)
|
||||
1
|
||||
>>> segments[0].text
|
||||
'第一句'
|
||||
"""
|
||||
# 检查空内容
|
||||
if not content or not content.strip():
|
||||
raise ValueError("Empty SRT content")
|
||||
|
||||
segments = []
|
||||
|
||||
# 查找所有 SRT 块
|
||||
matches = SRT_BLOCK_PATTERN.findall(content)
|
||||
|
||||
if not matches:
|
||||
raise ValueError("Invalid SRT format: no valid blocks found")
|
||||
|
||||
for match in matches:
|
||||
try:
|
||||
segment_id = int(match[0])
|
||||
start_time = parse_srt_time(match[1])
|
||||
end_time = parse_srt_time(match[2])
|
||||
# 清理文本：移除前后空白，合并多行为单行
|
||||
text = ' '.join(match[3].strip().split())
|
||||
|
||||
segment = Segment(
|
||||
id=segment_id,
|
||||
start_time=start_time,
|
||||
end_time=end_time,
|
||||
text=text
|
||||
)
|
||||
segments.append(segment)
|
||||
except (ValueError, IndexError) as e:
|
||||
# 跳过解析失败的块
|
||||
continue
|
||||
|
||||
if not segments:
|
||||
raise ValueError("Invalid SRT format: no valid segments parsed")
|
||||
|
||||
return segments
|
||||
|
||||
|
||||
def parse_srt_file(file_path: Path) -> List[Segment]:
|
||||
"""从文件解析 SRT 格式
|
||||
|
||||
读取文件内容并解析为 Segment 对象列表。
|
||||
|
||||
Args:
|
||||
file_path: SRT 文件路径
|
||||
|
||||
Returns:
|
||||
Segment 对象列表
|
||||
|
||||
Raises:
|
||||
FileNotFoundError: 文件不存在
|
||||
ValueError: 内容格式无效
|
||||
|
||||
Example:
|
||||
>>> from pathlib import Path
|
||||
>>> segments = parse_srt_file(Path("subtitles.srt"))
|
||||
>>> len(segments)
|
||||
10
|
||||
"""
|
||||
if not file_path.exists():
|
||||
raise FileNotFoundError(f"SRT file not found: {file_path}")
|
||||
|
||||
content = file_path.read_text(encoding='utf-8')
|
||||
return parse_srt(content)
|
||||
@@ -0,0 +1,97 @@
|
||||
"""Semantic Segmenter Module
|
||||
|
||||
将字幕分段按语义合并为段落。基于简单启发式规则：
|
||||
1. 时间间隔超过阈值时分段
|
||||
2. 段落长度限制
|
||||
|
||||
不使用 LLM 进行分段判断，保持简单和快速。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import List
|
||||
|
||||
from kb_workflow.srt.parser import Segment
|
||||
|
||||
|
||||
class SemanticSegmenter:
|
||||
"""语义分段器
|
||||
|
||||
将字幕分段转换为语义段落。基于时间和长度规则：
|
||||
- 长时间停顿（>gap_threshold 秒）作为分段点
|
||||
- 段落长度限制（max_paragraph_chars 字符）
|
||||
|
||||
Attributes:
|
||||
gap_threshold: 时间间隔阈值（秒），默认 3.0
|
||||
max_paragraph_chars: 单段落最大字符数，默认 500
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
gap_threshold: float = 3.0,
|
||||
max_paragraph_chars: int = 500
|
||||
):
|
||||
"""初始化分段器
|
||||
|
||||
Args:
|
||||
gap_threshold: 时间间隔阈值（秒），超过此值时分段
|
||||
max_paragraph_chars: 单段落最大字符数
|
||||
"""
|
||||
self.gap_threshold = gap_threshold
|
||||
self.max_paragraph_chars = max_paragraph_chars
|
||||
|
||||
def segment(self, segments: List[Segment]) -> List[str]:
|
||||
"""将字幕分段转换为语义段落
|
||||
|
||||
Args:
|
||||
segments: 字幕分段列表
|
||||
|
||||
Returns:
|
||||
段落文本列表（已去除时间戳）
|
||||
|
||||
Example:
|
||||
>>> segmenter = SemanticSegmenter()
|
||||
>>> segments = [Segment(1, 0, 5, "第一句"), Segment(2, 5, 10, "第二句")]
|
||||
>>> paragraphs = segmenter.segment(segments)
|
||||
>>> len(paragraphs)
|
||||
1
|
||||
"""
|
||||
if not segments:
|
||||
return []
|
||||
|
||||
paragraphs = []
|
||||
current_paragraph = []
|
||||
current_length = 0
|
||||
last_end_time = None
|
||||
|
||||
for seg in segments:
|
||||
# 检查是否需要分段
|
||||
should_start_new = False
|
||||
|
||||
# 规则1: 时间间隔超过阈值
|
||||
if last_end_time is not None:
|
||||
gap = seg.start_time - last_end_time
|
||||
if gap > self.gap_threshold:
|
||||
should_start_new = True
|
||||
|
||||
# 规则2: 段落长度超过限制
|
||||
if current_length + len(seg.text) > self.max_paragraph_chars:
|
||||
if current_paragraph: # 只有当前段落有内容时才分段
|
||||
should_start_new = True
|
||||
|
||||
# 执行分段
|
||||
if should_start_new and current_paragraph:
|
||||
paragraphs.append(' '.join(current_paragraph))
|
||||
current_paragraph = []
|
||||
current_length = 0
|
||||
|
||||
# 添加当前分段
|
||||
current_paragraph.append(seg.text)
|
||||
current_length += len(seg.text)
|
||||
last_end_time = seg.end_time
|
||||
|
||||
# 添加最后一个段落
|
||||
if current_paragraph:
|
||||
paragraphs.append(' '.join(current_paragraph))
|
||||
|
||||
return paragraphs
|
||||
Reference in New Issue
Block a user