{"role": "user", "content": "1", "timestamp": "2026-05-18T03:31:42.765568"}
{"role": "assistant", "content": "在呢，小旺 🦐", "timestamp": "2026-05-18T03:31:42.765568"}
{"role": "user", "content": "mediocrity 02:58:32\n1\n\n虾宝 02:58:33\nnull⚠️ Non-retryable error (HTTP 404) — trying fallback...\n\n虾宝 02:58:33\nnull🔄 Primary model failed — switching to fallback: claude-sonnet-4-6-thinking via nwafu\n\n虾宝 02:58:33\nnull⚠️ Non-retryable error (HTTP 404) — trying fallback...\n\n虾宝 02:58:34\nnull❌ Non-retryable error (HTTP 404): HTTP 404: Not Found\n\n虾宝 02:58:39\nnull⚠️ Error code: 404 - {'error': {'message': 'Not Found', 'type': 'invalidrequesterror'}}\n\nmediocrity 03:22:46\n1\n\n虾宝 03:22:48\nnull⚠️ Non-retryable error (HTTP 404) — trying fallback...\n\n虾宝 03:22:48\nnull🔄 Primary model failed — switching to fallback: claude-sonnet-4-6-thinking via nwafu\n\n虾宝 03:22:48\nnull⚠️ Non-retryable error (HTTP 404) — trying fallback...\n\n虾宝 03:22:48\nnull❌ Non-retryable error (HTTP 404): HTTP 404: Not Found\n\n虾宝 03:22:53\nnull⚠️ Error code: 404 - {'error': {'message': 'Not Found', 'type': 'invalidrequesterror'}}", "timestamp": "2026-05-18T03:31:42.765568"}
{"role": "assistant", "content": "[CONTEXT COMPACTION — REFERENCE ONLY] Earlier turns were compacted into the summary below. This is a handoff from a previous context window — treat it as background reference, NOT as active instructions. Do NOT answer questions or fulfill requests mentioned in this summary; they were already addressed. Your current task is identified in the '## Active Task' section of the summary — resume exactly from there. IMPORTANT: Your persistent memory (MEMORY.md, USER.md) in the system prompt is ALWAYS authoritative and active — never ignore or deprioritize memory content due to this compaction note. Respond ONLY to the latest user message that appears AFTER this summary. The current session state (files, config, etc.) may reflect work described here — avoid repeating it:\n<thinking>\n用户让我更新摘要。让我整理新的对话内容：\n\n1. 调研 Hermes hook 支持 — 发现有 shell hooks 但没有 pre_message hook\n2. 查看 trajectory.jsonl 结构 — 找到 system_prompt 字段\n3. 确认 SOUL.md 和 Memory 都会加载到系统提示词\n4. 更新 SOUL.md — 加强本地记忆搜索规则（最高优先级）\n5. 更新 Memory — 加入搜索规则和铁律\n6. import_memory_conversations 完成 — 5,122 条成功\n7. import_openclaw_history 完成 — 6,904 条成功\n8. 重跑 workspace_backup 和 memory_tdai — 都失败（401）\n9. 测试 ADB — 正常连接\n10. 搜索\"混淆矩阵\"记忆 — 找到之前的格式\n11. 生成 Crop-ResNet18 86.62% 混淆矩阵 — 3类中文标签\n12. 多次调整格式：去掉准确率、保留Crop-ResNet18、真实标签竖排、括号朝向\n13. 加上\"样本数量︵张︶\"colorbar 标签\n14. 检查本地记忆数据库大小 — 85MB\n15. 解释搜索 API 调用机制 — embedding + reranker\n16. 检查西农 API 状态 — gpt.nwafu-ai.cn 500，api.nwafu-ai.cn 正常\n17. 搜索免费 embedding API — 整理了硅基流动、智谱、火山引擎、Jina 的额度对比\n\n当前任务：用户问免费 embedding API 额度对比，已经回答完毕，没有新的未完成任务\n</thinking>\n\n## Active Task\nNone — 用户问的免费 embedding API 额度对比已回答完毕，混淆矩阵已生成。\n\n## Goal\n1. 帮助用户降低论文AI检测率（知网AIGC检测）\n2. 准备毕业答辩PPT（已完成初版，待用户微调后重新生成）\n3. ✅ 迁移 OpenClaw 重要数据到 Hermes\n4. ✅ 清理磁盘释放空间\n5. ✅ 搭建本地向量记忆系统，导入历史对话\n\n## Constraints & Preferences\n- **禁止\"换个思路\"** — 一旦出现这个想法立即停下来汇报\n- **禁止抢占手机前台** — 必须用虚拟屏操作\n- **修改PC文件**：直接sftp上传覆盖，禁止用脚本替换\n- **所有网址、指令、代码必须用代码块发送**\n- **铁律(1)**：遇到问题先质问自己为什么不能，搜记忆看以前怎么成功的，禁止反问小旺\n- **铁律(2)**：收到涉及\"之前/上次/怎么解决/看记忆\"的消息时，第一步先搜记忆再回答\n- **后台任务必须用background=true**\n- QQ文件缓存目录：`~/.hermes/cache/documents/`\n- 用户PC：100.105.23.114，用户xwjf，密码[REDACTED]\n- 手机Tailscale IP：100.114.227.26\n- PC代理：http://100.105.23.114:7890\n- 用户偏好中文交流\n\n## Completed Actions\n1-285. [见前次摘要]\n286. SEARCH Hermes hook 支持 — 有 shell hooks 但无 pre_message hook [tool: terminal]\n287. READ trajectory.jsonl 结构 — 找到 system_prompt 字段包含 SOUL.md 和 Skills [tool: terminal]\n288. PATCH ~/.hermes/SOUL.md — 加强本地记忆搜索规则为\"最高优先级\" [tool: patch]\n289. ADD memory — 加入搜索规则，但超限 [tool: memory]\n290. REPLACE memory — 精简后加入两条铁律 [tool: memory]\n291. RECEIVE import_memory_conversations 完成 — 5,122 条成功，30 条错误 [background notification]\n292. RECEIVE import_openclaw_history 完成 — 6,904 条成功，158 条失败 [background notification]\n293. START import_workspace_backup 重跑 [tool: terminal background]\n294. START import_memory_tdai 重跑 [tool: terminal background]\n295. REPLACE memory — 加入\"看记忆\"触发词 [tool: memory]\n296. SEARCH \"遇到问题 搜索记忆 反问\" — 找到规则在 SOUL.md [tool: terminal]\n297. REPLACE memory — 加入处理原则铁律 [tool: memory]\n298. RECEIVE import_memory_tdai 完成 — 全失败(401) [background notification]\n299. TEST embedding API — 正常工作 [tool: terminal]\n300. TEST ADB 连接 — 成功，前台是 OPPO 浏览器 [tool: terminal]\n301. SEARCH \"混淆矩阵\" — 找到之前是3类中文标签（小麦、水稻、玉米）[tool: terminal]\n302. GENERATE 混淆矩阵 86.62% — 英文版（用户说不对）[tool: terminal]\n303. GENERATE 混淆矩阵 86.62% — 中文版3类，654/755正确 [tool: terminal]\n304. PATCH 混淆矩阵 — 去掉标题准确率，保留\"Crop-ResNet18\" [tool: terminal]\n305. PATCH 混淆矩阵 — 真实标签竖排用换行符 [tool: terminal]\n306. PATCH 混淆矩阵 — 预测标签字体大小改成12 [tool: terminal]\n307. PATCH 混淆矩阵 — labelpad=10 调整间距 [tool: terminal]\n308. PATCH 混淆矩阵 — 加 colorbar 标签\"样本数量︵张︶\"竖排括号 [tool: terminal]\n309. CHECK 本地记忆数据库大小 — 85MB（13,878条）[tool: terminal]\n310. CHECK gateway 状态 — 正常运行12+小时 [tool: terminal]\n311. TEST 西农 API — api.nwafu-ai.cn 正常，gpt.nwafu-ai.cn 500 [tool: terminal]\n312. SEARCH 免费 embedding API — 整理硅基流动/智谱/火山引擎/Jina 额度对比 [tool: web_search]\n\n## Active State\n- 工作目录：/home/ubuntu\n- **当前会话模型：deepseek-v4-flash (DeepSeek)**\n- **磁盘状态**：67%已用，13G可用\n- **本地向量数据库**：13,878 条记忆，85MB\n\n**生成的图片：**\n- `/tmp/confusion_matrix_86.62_cn.png` — Crop-ResNet18 混淆矩阵（86.62%，3类中文，竖排括号）\n\n**三只虾状态：**\n- 虾宝(119)：✅ 在线\n- 虾咪(152)：✅ 在线\n- 小虾(手机)：✅ 在线\n\n## In Progress\n无\n\n## Blocked\n- `workspace_backup` 导入失败：3,809条（401 Unauthorized）\n- `memory_tdai` 导入失败：5,398条（401 Unauthorized）\n- 原因：并发限流，需等待后重跑\n\n## Key Decisions\n1. **本地向量记忆方案** — Chroma + netease embedding + bge-reranker，完全免费\n2. **自动搜索机制** — 写入 SOUL.md（详细规则）+ Memory（精简铁律），双重保障\n3. **混淆矩阵格式** — 3类中文标签，竖排括号︵︶，无准确率标注\n4. **备选 embedding API** — 推荐硅基流动（2000万免费额度）作为西农 fallback\n\n## Resolved Questions\n1. **如何让每次对话自动拉取本地记忆** — 写入 SOUL.md 和 Memory，压缩后新会话也能看到规则\n2. **本地记忆占多少存储** — 85MB（13,878条），平均每条6KB\n3. **搜索需要调几次API** — embedding 1次（~20 token）+ reranker 1次（~500-1000 token）\n4. **西农网站500但API正常** — 前端和后端分开部署，互不影响\n5. **免费 embedding API 额度对比**：\n   - 硅基流动：2000万 token（永久）\n   - 智谱 GLM：1200万+500万 token（永久）\n   - 火山引擎：200万 token（可申请扩容到5000万）\n   - Jina：1万 token（试用）\n\n## Pending User Asks\nNone\n\n## Relevant Files\n**本地记忆系统：**\n- `~/.hermes/scripts/local_memory.py` — 记忆管理脚本\n- `~/.hermes/local_memory_db/` — Chroma 向量数据库（13,878条，85MB）\n- `~/.hermes/SOUL.md` — 包含本地记忆搜索规则（最高优先级）\n\n**生成的图片：**\n- `/tmp/confusion_matrix_86.62_cn.png` — Crop-ResNet18 混淆矩阵\n\n**待重跑导入：**\n- `~/.hermes/scripts/import_workspace_backup.py` — 3,809条待重跑\n- `~/.hermes/scripts/import_memory_tdai.py` — 5,398条待重跑\n\n## Remaining Work\n1. 重跑 workspace_backup 和 memory_tdai（等 API 限流恢复）\n2. 可选：添加硅基流动作为 embedding API fallback\n3. 导入完成后可删除原始文件释放 800MB\n\n## Critical Context\n**本地向量记忆搜索命令：**\n```bash\ncd ~/.hermes/scripts && python3 local_memory.py search \"关键词\" --top_k 5 --rerank\n```\n\n**Memory 中的两条铁律：**\n> **本地向量记忆**: `~/.hermes/scripts/local_memory.py`，搜索：`python3 local_memory.py search \"关键词\" --top_k 5 --rerank`。**铁律**：(1) 遇到问题先搜记忆看以前怎么成功的，禁止反问小旺；(2) 收到\"之前/上次/怎么解决/看记忆\"时先搜再答。\n\n**混淆矩阵数据（86.62%）：**\n```python\ncm = np.array([\n    [210, 35, 7],   # 水稻\n    [38, 207, 7],   # 小麦\n    [6, 8, 237],    # 玉米\n])  # 654/755 = 86.62%\n```\n\n**论文消融表数据（正确值）：**\n- Crop-ResNet18: FedAvg 62.12% → FedProx完整 **86.62%**（↑24.5%）\n- CIFAR10 数据不变\n\n**免费 embedding API 推荐：** 硅基流动（2000万 token，国内访问快，有 bge-m3）", "timestamp": "2026-05-18T03:31:42.765568"}
{"role": "user", "content": "没送token啊，硅基流动，怎么弄得", "timestamp": "2026-05-18T03:31:42.765568"}
{"role": "assistant", "content": "让我帮你查一下硅基流动怎么领免费额度：", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "tooluse_peaiE7XO9FiIPCGZf2e9K7", "call_id": "tooluse_peaiE7XO9FiIPCGZf2e9K7", "response_item_id": "fc_tooluse_peaiE7XO9FiIPCGZf2e9K7", "type": "function", "function": {"name": "web_search", "arguments": "{\"query\": \"硅基流动 SiliconFlow 新用户 2000万 token 怎么领取 注册\"}"}}], "timestamp": "2026-05-18T03:31:42.765568"}
{"role": "tool", "name": "web_search", "content": "{\n  \"success\": true,\n  \"data\": {\n    \"web\": [\n      {\n        \"title\": \"硅基流动大放送！注册就送2000 万Tokens，这羊毛不薅白不薅！\",\n        \"url\": \"https://juejin.cn/post/7470718055959855116\",\n        \"description\": \"邀请好友注册给你满上！ 8月1日起，每成功邀请一位SiliconCloud 新用户，双方均可获得2000 万Tokens。 邀请奖励上不封顶！只要人脉到位，你的Token 就能轻松满\",\n        \"position\": 1\n      },\n      {\n        \"title\": \"硅基流动邀你免费体验 DeepSeek-R1 获取2000万tokens_weixin_45498254-DeepSeek技术社区\",\n        \"url\": \"https://deepseek.csdn.net/67fdb1f6a5baf817cf48e673.html\",\n        \"description\": \"# logo DeepSeek技术社区. ## 登录社区云. ### DeepSeek技术社区. # 硅基流动邀你免费体验 DeepSeek-R1 获取2000万tokens. 通过硅基流动平台，你可以高效地利用 DeepSeek-R1 的强大能力，为你的项目带来更多的流量和关注。在当今快速发展的 AI 时代，强大的模型和高效的开发工具是提升效率的关键。现在，硅基流动平台为你带来了一个绝佳的机会，让你可以免费体验顶级开源大模型 DeepSeek-R1。立即注册，获取你的 2000 万 Token，开始探索 DeepSeek-R1 的无限可能！探索模型广场：登录后，在左侧菜. ### weixin\\\\_45498254. 🎁 免费获取 2000 万 Token，轻松上手！. 只需通过专属邀请码注册，即可立即获得 2000 万 Token（约等于 14 元平台余额），可用于处理约 400 万字内容。. ***注册并获取邀请码：点击专属链接：，输入邀请码 56XvgRXq，即可完成注册并获得免费余额。***. **注册即送 2000 万 Tokens：受邀好友作为新用户完成 SiliconCloud 账号注册，立刻获得 2000万 Tokens。**. 探索模型广场：登录后，在左侧菜单栏找到“模型广场”，选择 DeepSeek-R1 模型，查看详细参数并开始使用。. 生成 API Key：在左侧菜单栏点击“API 密钥”->“新建 API 密钥”，即可生成你的专属 API Key。. 选择第三方客户端：推荐使用以下客户端，填入生成的 API Key 即可开始开发：. 通过硅基流动平台，你可以高效地利用 DeepSeek-R1 的强大能力，为你的项目带来更多的流量和关注。无论是智能文档处理、代码审查加速，还是多模态开发，DeepSeek-R1 都能为你提供强大的支持。. 不要错过这个免费体验顶级模型的机会。立即注册，获取你的 2000 万 Token，开始探索 DeepSeek-R1 的无限可能！. 欢迎加入DeepSeek 技术社区。在这里，你可以找到志同道合的朋友，共同探索AI技术的奥秘。. 有没有继续教育同学靠AI工具完成论文的？（函授、成教、自考）分享一下经验。. 【Claude Code】2026 最全配置指南：API Key / 模型切换 / MCP 工具 / 常见报错，一篇搞定. ### 所有评论(0). ## 温馨提示：您尚未绑定手机号.\",\n        \"position\": 2\n      },\n      {\n        \"title\": \"【1024送福利】硅基流动送2000万token啦！撒花     附使用教程\",\n        \"url\": \"https://blog.csdn.net/sinat_29950703/article/details/143185922\",\n        \"description\": \"干啥用？怎么用啊？ 先甭管，上链接！点这领2000W token(14元) https://cloud.siliconflow.cn/i/xJvN9Ecu 手机就能注册，多拿多得！\",\n        \"position\": 3\n      },\n      {\n        \"title\": \"硅基流动注册邀请码- 山草斋 - 博客园\",\n        \"url\": \"https://www.cnblogs.com/shancaozhai/p/18820548\",\n        \"description\": \"1. 邀请好友赚2000 万Tokens：每成功邀请一位新用户通过手机号码注册，您将获得2000 万Tokens；; 2. 注册即送2000 万Tokens：受邀\",\n        \"position\": 4\n      },\n      {\n        \"title\": \"硅基流動#免費生圖限時免費 - Facebook\",\n        \"url\": \"https://www.facebook.com/groups/346855692144727/posts/3086783368151932/\",\n        \"description\": \"... 2000 万Tokens; 2.注册即送2000 万Tokens: 受邀好友作为新用户完成SiliconCloud 账号注册， 立刻获得2000万Tokens. 特别说明平台2000 万Tokens 特指Qwen2.\",\n        \"position\": 5\n      }\n    ]\n  }\n}", "tool_call_id": "tooluse_peaiE7XO9FiIPCGZf2e9K7", "timestamp": "2026-05-18T03:31:42.765568"}
{"role": "assistant", "content": "原来是**邀请注册**才送 2000 万 token，不是直接注册就有。\n\n**领取方法：**\n\n1. 通过邀请链接注册（随便找一个都行）：\n```bash\nhttps://cloud.siliconflow.cn/i/xJvN9Ecu\n```\n\n2. 用手机号注册，注册完就自动到账 2000 万 token\n\n**注意：**\n- 这 2000 万 token 主要是给 Qwen2.5 等模型用的\n- embedding 模型（bge-m3）可能是单独计费的，需要看具体价格\n\n要不你先注册一个看看？注册完我帮你查下 embedding 模型的具体额度情况。", "reasoning": "原来是需要通过邀请链接注册才能获得2000万token。让我给小旺解释清楚。\n", "finish_reason": "stop", "reasoning_content": "原来是需要通过邀请链接注册才能获得2000万token。让我给小旺解释清楚。\n", "timestamp": "2026-05-18T03:31:42.765568"}
{"role": "user", "content": "netease-youdao/bce-reranker-base_v1和sk-llicoufjowafeksyzknhxebhxfyaawjrmiiefpswhofheirv和api.siliconflow.cn试试", "timestamp": "2026-05-18T03:35:33.844404"}
{"role": "assistant", "content": "好，测试一下硅基流动的 reranker：", "reasoning": "用户给了硅基流动的 API key 和 reranker 模型信息，让我测试一下。\n\n- 模型：netease-youdao/bce-reranker-base_v1\n- API Key：sk-llicoufjowafeksyzknhxebhxfyaawjrmiiefpswhofheirv\n- Base URL：api.siliconflow.cn\n\n让我测试一下这个 reranker 是否能用。\n", "finish_reason": "tool_calls", "reasoning_content": "用户给了硅基流动的 API key 和 reranker 模型信息，让我测试一下。\n\n- 模型：netease-youdao/bce-reranker-base_v1\n- API Key：sk-llicoufjowafeksyzknhxebhxfyaawjrmiiefpswhofheirv\n- Base URL：api.siliconflow.cn\n\n让我测试一下这个 reranker 是否能用。\n", "tool_calls": [{"id": "tooluse_sCnb9GKXtIFvZwN06M7diD", "call_id": "tooluse_sCnb9GKXtIFvZwN06M7diD", "response_item_id": "fc_tooluse_sCnb9GKXtIFvZwN06M7diD", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s https://api.siliconflow.cn/v1/rerank \\\\\\n  -H \\\"Authorization: Bearer sk-llicoufjowafeksyzknhxebhxfyaawjrmiiefpswhofheirv\\\" \\\\\\n  -H \\\"Content-Type: application/json\\\" \\\\\\n  -d '{\\n    \\\"model\\\": \\\"netease-youdao/bce-reranker-base_v1\\\",\\n    \\\"query\\\": \\\"苹果怎么吃\\\",\\n    \\\"documents\\\": [\\\"苹果可以直接洗净生吃\\\", \\\"香蕉要剥皮吃\\\", \\\"苹果还可以榨汁喝\\\"],\\n    \\\"top_n\\\": 3\\n  }' | python3 -m json.tool\"}"}}], "timestamp": "2026-05-18T03:35:33.844404"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"{\\n    \\\"id\\\": \\\"019e376fb29279399bf2b150b8c1eadb\\\",\\n    \\\"results\\\": [\\n        {\\n            \\\"index\\\": 0,\\n            \\\"document\\\": null,\\n            \\\"relevance_score\\\": 0.54607254\\n        },\\n        {\\n            \\\"index\\\": 2,\\n            \\\"document\\\": null,\\n            \\\"relevance_score\\\": 0.5188662\\n        },\\n        {\\n            \\\"index\\\": 1,\\n            \\\"document\\\": null,\\n            \\\"relevance_score\\\": 0.4549867\\n        }\\n    ],\\n    \\\"meta\\\": {\\n        \\\"tokens\\\": {\\n            \\\"input_tokens\\\": 44,\\n            \\\"output_tokens\\\": 0,\\n            \\\"image_tokens\\\": 0\\n        },\\n        \\\"billed_units\\\": {\\n            \\\"input_tokens\\\": 44,\\n            \\\"output_tokens\\\": 0,\\n            \\\"image_tokens\\\": 0,\\n            \\\"search_units\\\": 0,\\n            \\\"classifications\\\": 0\\n        }\\n    }\\n}\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "tooluse_sCnb9GKXtIFvZwN06M7diD", "timestamp": "2026-05-18T03:35:33.844404"}
{"role": "assistant", "content": "成功了！硅基流动的 reranker 正常工作：\n\n- **模型**：netease-youdao/bce-reranker-base_v1\n- **结果**：正确排序（苹果生吃 > 苹果榨汁 > 香蕉剥皮）\n- **消耗**：44 input tokens\n\n要不要我把本地记忆系统的 reranker 换成硅基流动的？这样就不依赖西农 API 了。", "reasoning": null, "finish_reason": "stop", "timestamp": "2026-05-18T03:35:33.844404"}
{"role": "user", "content": "这个也能搜索啊，你试试", "timestamp": "2026-05-18T03:36:11.839327"}
{"role": "assistant", "content": "对，试试硅基流动的 embedding 模型：", "reasoning": "用户说\"这个也能搜索啊\"，意思是硅基流动也有 embedding 模型可以用来做向量搜索。让我测试一下硅基流动的 embedding 模型。\n\n根据之前的记忆，用户用的是 netease-youdao/bce-embedding-base_v1 做 embedding。让我试试硅基流动有没有类似的 embedding 模型。\n", "finish_reason": "tool_calls", "reasoning_content": "用户说\"这个也能搜索啊\"，意思是硅基流动也有 embedding 模型可以用来做向量搜索。让我测试一下硅基流动的 embedding 模型。\n\n根据之前的记忆，用户用的是 netease-youdao/bce-embedding-base_v1 做 embedding。让我试试硅基流动有没有类似的 embedding 模型。\n", "tool_calls": [{"id": "tooluse_9oOGo7yPLflEAEOrfvLerm", "call_id": "tooluse_9oOGo7yPLflEAEOrfvLerm", "response_item_id": "fc_tooluse_9oOGo7yPLflEAEOrfvLerm", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s https://api.siliconflow.cn/v1/embeddings \\\\\\n  -H \\\"Authorization: Bearer sk-llicoufjowafeksyzknhxebhxfyaawjrmiiefpswhofheirv\\\" \\\\\\n  -H \\\"Content-Type: application/json\\\" \\\\\\n  -d '{\\n    \\\"model\\\": \\\"netease-youdao/bce-embedding-base_v1\\\",\\n    \\\"input\\\": \\\"测试一下embedding功能\\\"\\n  }' | python3 -c \\\"import sys,json; d=json.load(sys.stdin); print('维度:', len(d['data'][0]['embedding'])); print('tokens:', d.get('usage',{}))\\\"\"}"}}], "timestamp": "2026-05-18T03:36:11.839327"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"维度: 768\\ntokens: {'prompt_tokens': 8, 'completion_tokens': 0, 'total_tokens': 8}\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "tooluse_9oOGo7yPLflEAEOrfvLerm", "timestamp": "2026-05-18T03:36:11.839327"}
{"role": "assistant", "content": "也成功了！\n\n**硅基流动支持：**\n- ✅ **Embedding**：netease-youdao/bce-embedding-base_v1（768维）\n- ✅ **Reranker**：netease-youdao/bce-reranker-base_v1\n\n跟西农用的是同款模型，可以无缝替换！\n\n要我把本地记忆系统改成用硅基流动吗？这样：\n1. 西农 API 挂了也不影响记忆搜索\n2. 2000 万 token 额度够用很久", "reasoning": null, "finish_reason": "stop", "timestamp": "2026-05-18T03:36:11.839327"}
{"role": "user", "content": "[System note: Your previous turn was interrupted before you could process the last tool result(s). The conversation history contains tool outputs you haven't responded to yet. Please finish processing those results and summarize what was accomplished, then address the user's new message below.]\n\n好了吗", "timestamp": "2026-05-18T03:39:03.883963"}
{"role": "assistant", "content": "还没，配置改了但函数还没加 fallback 逻辑。继续：", "reasoning": "用户问是否完成了。我刚才只改了配置部分，但还没有修改 get_embedding 和 rerank 函数来支持 fallback 逻辑。需要继续修改这两个函数，让它们在主 API 失败时自动切换到西农 API。\n", "finish_reason": "tool_calls", "reasoning_content": "用户问是否完成了。我刚才只改了配置部分，但还没有修改 get_embedding 和 rerank 函数来支持 fallback 逻辑。需要继续修改这两个函数，让它们在主 API 失败时自动切换到西农 API。\n", "tool_calls": [{"id": "tooluse_pTfLqus6mjzL3uKmJ4LEdH", "call_id": "tooluse_pTfLqus6mjzL3uKmJ4LEdH", "response_item_id": "fc_tooluse_pTfLqus6mjzL3uKmJ4LEdH", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"grep -n \\\"def get_embedding\\\" -A 15 ~/.hermes/scripts/local_memory.py\"}"}}], "timestamp": "2026-05-18T03:39:03.883963"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"53:def get_embedding(text: str) -> List[float]:\\n54-    \\\"\\\"\\\"获取文本的 embedding 向量\\\"\\\"\\\"\\n55-    headers = {\\n56-        \\\"Authorization\\\": f\\\"Bearer {EMBEDDING_API_KEY}\\\",\\n57-        \\\"Content-Type\\\": \\\"application/json\\\"\\n58-    }\\n59-    data = {\\n60-        \\\"model\\\": EMBEDDING_MODEL,\\n61-        \\\"input\\\": text\\n62-    }\\n63-    resp = requests.post(EMBEDDING_API_URL, headers=headers, json=data, timeout=30)\\n64-    resp.raise_for_status()\\n65-    result = resp.json()\\n66-    return result[\\\"data\\\"][0][\\\"embedding\\\"]\\n67-\\n68-\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "tooluse_pTfLqus6mjzL3uKmJ4LEdH", "timestamp": "2026-05-18T03:39:03.883963"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "tooluse_17GqUpZLqzMcBEwTuHn5Bj", "call_id": "tooluse_17GqUpZLqzMcBEwTuHn5Bj", "response_item_id": "fc_tooluse_17GqUpZLqzMcBEwTuHn5Bj", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\": \"replace\", \"path\": \"~/.hermes/scripts/local_memory.py\", \"old_string\": \"def get_embedding(text: str) -> List[float]:\\n    \\\"\\\"\\\"获取文本的 embedding 向量\\\"\\\"\\\"\\n    headers = {\\n        \\\"Authorization\\\": f\\\"Bearer {EMBEDDING_API_KEY}\\\",\\n        \\\"Content-Type\\\": \\\"application/json\\\"\\n    }\\n    data = {\\n        \\\"model\\\": EMBEDDING_MODEL,\\n        \\\"input\\\": text\\n    }\\n    resp = requests.post(EMBEDDING_API_URL, headers=headers, json=data, timeout=30)\\n    resp.raise_for_status()\\n    result = resp.json()\\n    return result[\\\"data\\\"][0][\\\"embedding\\\"]\", \"new_string\": \"def get_embedding(text: str) -> List[float]:\\n    \\\"\\\"\\\"获取文本的 embedding 向量（主：硅基流动，fallback：西农）\\\"\\\"\\\"\\n    # 主 API：硅基流动\\n    try:\\n        headers = {\\n            \\\"Authorization\\\": f\\\"Bearer {EMBEDDING_API_KEY}\\\",\\n            \\\"Content-Type\\\": \\\"application/json\\\"\\n        }\\n        data = {\\n            \\\"model\\\": EMBEDDING_MODEL,\\n            \\\"input\\\": text\\n        }\\n        resp = requests.post(EMBEDDING_API_URL, headers=headers, json=data, timeout=30)\\n        resp.raise_for_status()\\n        result = resp.json()\\n        return result[\\\"data\\\"][0][\\\"embedding\\\"]\\n    except Exception as e:\\n        print(f\\\"[Embedding] 硅基流动失败: {e}，尝试西农...\\\")\\n    \\n    # Fallback：西农\\n    headers = {\\n        \\\"Authorization\\\": f\\\"Bearer {FALLBACK_EMBEDDING_API_KEY}\\\",\\n        \\\"Content-Type\\\": \\\"application/json\\\"\\n    }\\n    data = {\\n        \\\"model\\\": FALLBACK_EMBEDDING_MODEL,\\n        \\\"input\\\": text\\n    }\\n    resp = requests.post(FALLBACK_EMBEDDING_API_URL, headers=headers, json=data, timeout=30)\\n    resp.raise_for_status()\\n    result = resp.json()\\n    return result[\\\"data\\\"][0][\\\"embedding\\\"]\"}"}}], "timestamp": "2026-05-18T03:39:03.883963"}
{"role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/ubuntu/.hermes/scripts/local_memory.py\\n+++ b//home/ubuntu/.hermes/scripts/local_memory.py\\n@@ -51,16 +51,34 @@\\n \\n \\n def get_embedding(text: str) -> List[float]:\\n-    \\\"\\\"\\\"获取文本的 embedding 向量\\\"\\\"\\\"\\n+    \\\"\\\"\\\"获取文本的 embedding 向量（主：硅基流动，fallback：西农）\\\"\\\"\\\"\\n+    # 主 API：硅基流动\\n+    try:\\n+        headers = {\\n+            \\\"Authorization\\\": f\\\"Bearer {EMBEDDING_API_KEY}\\\",\\n+            \\\"Content-Type\\\": \\\"application/json\\\"\\n+        }\\n+        data = {\\n+            \\\"model\\\": EMBEDDING_MODEL,\\n+            \\\"input\\\": text\\n+        }\\n+        resp = requests.post(EMBEDDING_API_URL, headers=headers, json=data, timeout=30)\\n+        resp.raise_for_status()\\n+        result = resp.json()\\n+        return result[\\\"data\\\"][0][\\\"embedding\\\"]\\n+    except Exception as e:\\n+        print(f\\\"[Embedding] 硅基流动失败: {e}，尝试西农...\\\")\\n+    \\n+    # Fallback：西农\\n     headers = {\\n-        \\\"Authorization\\\": f\\\"Bearer {EMBEDDING_API_KEY}\\\",\\n+        \\\"Authorization\\\": f\\\"Bearer {FALLBACK_EMBEDDING_API_KEY}\\\",\\n         \\\"Content-Type\\\": \\\"application/json\\\"\\n     }\\n     data = {\\n-        \\\"model\\\": EMBEDDING_MODEL,\\n+        \\\"model\\\": FALLBACK_EMBEDDING_MODEL,\\n         \\\"input\\\": text\\n     }\\n-    resp = requests.post(EMBEDDING_API_URL, headers=headers, json=data, timeout=30)\\n+    resp = requests.post(FALLBACK_EMBEDDING_API_URL, headers=headers, json=data, timeout=30)\\n     resp.raise_for_status()\\n     result = resp.json()\\n     return result[\\\"data\\\"][0][\\\"embedding\\\"]\\n\", \"files_modified\": [\"/home/ubuntu/.hermes/scripts/local_memory.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "tooluse_17GqUpZLqzMcBEwTuHn5Bj", "timestamp": "2026-05-18T03:39:03.883963"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "tooluse_iiqwiOSYpBmJbDEzFjaHf0", "call_id": "tooluse_iiqwiOSYpBmJbDEzFjaHf0", "response_item_id": "fc_tooluse_iiqwiOSYpBmJbDEzFjaHf0", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"grep -n \\\"def rerank\\\" -A 20 ~/.hermes/scripts/local_memory.py\"}"}}], "timestamp": "2026-05-18T03:39:03.883963"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"87:def rerank(query: str, documents: List[str], top_k: int = 5) -> List[dict]:\\n88-    \\\"\\\"\\\"使用 BGE Reranker 重排序\\\"\\\"\\\"\\n89-    if not documents:\\n90-        return []\\n91-    \\n92-    headers = {\\n93-        \\\"Authorization\\\": f\\\"Bearer {RERANKER_API_KEY}\\\",\\n94-        \\\"Content-Type\\\": \\\"application/json\\\"\\n95-    }\\n96-    data = {\\n97-        \\\"model\\\": RERANKER_MODEL,\\n98-        \\\"query\\\": query,\\n99-        \\\"documents\\\": documents,\\n100-        \\\"top_n\\\": min(top_k, len(documents))\\n101-    }\\n102-    resp = requests.post(RERANKER_API_URL, headers=headers, json=data, timeout=30)\\n103-    resp.raise_for_status()\\n104-    result = resp.json()\\n105-    return result.get(\\\"results\\\", [])\\n106-\\n107-\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "tooluse_iiqwiOSYpBmJbDEzFjaHf0", "timestamp": "2026-05-18T03:39:03.883963"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "tooluse_IoljLIgGS8XRE1WF2QvUKw", "call_id": "tooluse_IoljLIgGS8XRE1WF2QvUKw", "response_item_id": "fc_tooluse_IoljLIgGS8XRE1WF2QvUKw", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\": \"replace\", \"new_string\": \"def rerank(query: str, documents: List[str], top_k: int = 5) -> List[dict]:\\n    \\\"\\\"\\\"使用 Reranker 重排序（主：硅基流动，fallback：西农）\\\"\\\"\\\"\\n    if not documents:\\n        return []\\n    \\n    # 主 API：硅基流动\\n    try:\\n        headers = {\\n            \\\"Authorization\\\": f\\\"Bearer {RERANKER_API_KEY}\\\",\\n            \\\"Content-Type\\\": \\\"application/json\\\"\\n        }\\n        data = {\\n            \\\"model\\\": RERANKER_MODEL,\\n            \\\"query\\\": query,\\n            \\\"documents\\\": documents,\\n            \\\"top_n\\\": min(top_k, len(documents))\\n        }\\n        resp = requests.post(RERANKER_API_URL, headers=headers, json=data, timeout=30)\\n        resp.raise_for_status()\\n        result = resp.json()\\n        return result.get(\\\"results\\\", [])\\n    except Exception as e:\\n        print(f\\\"[Reranker] 硅基流动失败: {e}，尝试西农...\\\")\\n    \\n    # Fallback：西农\\n    headers = {\\n        \\\"Authorization\\\": f\\\"Bearer {FALLBACK_RERANKER_API_KEY}\\\",\\n        \\\"Content-Type\\\": \\\"application/json\\\"\\n    }\\n    data = {\\n        \\\"model\\\": FALLBACK_RERANKER_MODEL,\\n        \\\"query\\\": query,\\n        \\\"documents\\\": documents,\\n        \\\"top_n\\\": min(top_k, len(documents))\\n    }\\n    resp = requests.post(FALLBACK_RERANKER_API_URL, headers=headers, json=data, timeout=30)\\n    resp.raise_for_status()\\n    result = resp.json()\\n    return result.get(\\\"results\\\", [])\", \"old_string\": \"def rerank(query: str, documents: List[str], top_k: int = 5) -> List[dict]:\\n    \\\"\\\"\\\"使用 BGE Reranker 重排序\\\"\\\"\\\"\\n    if not documents:\\n        return []\\n    \\n    headers = {\\n        \\\"Authorization\\\": f\\\"Bearer {RERANKER_API_KEY}\\\",\\n        \\\"Content-Type\\\": \\\"application/json\\\"\\n    }\\n    data = {\\n        \\\"model\\\": RERANKER_MODEL,\\n        \\\"query\\\": query,\\n        \\\"documents\\\": documents,\\n        \\\"top_n\\\": min(top_k, len(documents))\\n    }\\n    resp = requests.post(RERANKER_API_URL, headers=headers, json=data, timeout=30)\\n    resp.raise_for_status()\\n    result = resp.json()\\n    return result.get(\\\"results\\\", [])\", \"path\": \"~/.hermes/scripts/local_memory.py\"}"}}], "timestamp": "2026-05-18T03:39:03.883963"}
{"role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/ubuntu/.hermes/scripts/local_memory.py\\n+++ b//home/ubuntu/.hermes/scripts/local_memory.py\\n@@ -85,21 +85,41 @@\\n \\n \\n def rerank(query: str, documents: List[str], top_k: int = 5) -> List[dict]:\\n-    \\\"\\\"\\\"使用 BGE Reranker 重排序\\\"\\\"\\\"\\n+    \\\"\\\"\\\"使用 Reranker 重排序（主：硅基流动，fallback：西农）\\\"\\\"\\\"\\n     if not documents:\\n         return []\\n     \\n+    # 主 API：硅基流动\\n+    try:\\n+        headers = {\\n+            \\\"Authorization\\\": f\\\"Bearer {RERANKER_API_KEY}\\\",\\n+            \\\"Content-Type\\\": \\\"application/json\\\"\\n+        }\\n+        data = {\\n+            \\\"model\\\": RERANKER_MODEL,\\n+            \\\"query\\\": query,\\n+            \\\"documents\\\": documents,\\n+            \\\"top_n\\\": min(top_k, len(documents))\\n+        }\\n+        resp = requests.post(RERANKER_API_URL, headers=headers, json=data, timeout=30)\\n+        resp.raise_for_status()\\n+        result = resp.json()\\n+        return result.get(\\\"results\\\", [])\\n+    except Exception as e:\\n+        print(f\\\"[Reranker] 硅基流动失败: {e}，尝试西农...\\\")\\n+    \\n+    # Fallback：西农\\n     headers = {\\n-        \\\"Authorization\\\": f\\\"Bearer {RERANKER_API_KEY}\\\",\\n+        \\\"Authorization\\\": f\\\"Bearer {FALLBACK_RERANKER_API_KEY}\\\",\\n         \\\"Content-Type\\\": \\\"application/json\\\"\\n     }\\n     data = {\\n-        \\\"model\\\": RERANKER_MODEL,\\n+        \\\"model\\\": FALLBACK_RERANKER_MODEL,\\n         \\\"query\\\": query,\\n         \\\"documents\\\": documents,\\n         \\\"top_n\\\": min(top_k, len(documents))\\n     }\\n-    resp = requests.post(RERANKER_API_URL, headers=headers, json=data, timeout=30)\\n+    resp = requests.post(FALLBACK_RERANKER_API_URL, headers=headers, json=data, timeout=30)\\n     resp.raise_for_status()\\n     result = resp.json()\\n     return result.get(\\\"results\\\", [])\\n\", \"files_modified\": [\"/home/ubuntu/.hermes/scripts/local_memory.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "tooluse_IoljLIgGS8XRE1WF2QvUKw", "timestamp": "2026-05-18T03:39:03.883963"}
{"role": "assistant", "content": "测试一下：", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "tooluse_sau8X4BMB39O0KAhla225K", "call_id": "tooluse_sau8X4BMB39O0KAhla225K", "response_item_id": "fc_tooluse_sau8X4BMB39O0KAhla225K", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"cd ~/.hermes/scripts && python3 local_memory.py search \\\"混淆矩阵\\\" --top_k 3 --rerank 2>&1\"}"}}], "timestamp": "2026-05-18T03:39:03.883963"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"{\\n  \\\"results\\\": [\\n    {\\n      \\\"id\\\": \\\"9894056b2015\\\",\\n      \\\"content\\\": \\\"论文里没找到混淆矩阵的表格数据。你之前给我的混淆矩阵数据是：\\\\n\\\\n```\\\\n        小麦  水稻  玉米\\\\n小麦    231   17    2\\\\n水稻    21   232    2\\\\n玉米    1     0   249\\\\n```\\\\n\\\\n这个数据是从哪来的？论文正文里没有这个表格，只提到了准确率（90.33%等），没有具体的混淆矩阵数值。\\\",\\n      \\\"score\\\": 0.15952277183532715,\\n      \\\"metadata\\\": {\\n        \\\"created_at\\\": \\\"2026-05-18T01:07:35.322090\\\"\\n      },\\n      \\\"rerank_score\\\": 0.55419904\\n    },\\n    {\\n      \\\"id\\\": \\\"c6895f4f78aa\\\",\\n      \\\"content\\\": \\\"这个混淆矩阵数据是之前那个 HTML 文件（图.txt）里的，不是从你论文里提取的。\\\\n\\\\n可能是那个软件（Claude）自己编的示例数据，没有对照你论文的实际准确率。\\\\n\\\\n要我按论文的 90.33% 重新生成一组符合的混淆矩阵数据吗？\\\",\\n      \\\"score\\\": 0.2638431191444397,\\n      \\\"metadata\\\": {\\n        \\\"created_at\\\": \\\"2026-05-18T01:07:36.676373\\\"\\n      },\\n      \\\"rerank_score\\\": 0.5268449\\n    },\\n    {\\n      \\\"id\\\": \\\"c6895f4f78aa\\\",\\n      \\\"content\\\": \\\"这个混淆矩阵数据是之前那个 HTML 文件（图.txt）里的，不是从你论文里提取的。\\\\n\\\\n可能是那个软件（Claude）自己编的示例数据，没有对照你论文的实际准确率。\\\\n\\\\n要我按论文的 90.33% 重新生成一组符合的混淆矩阵数据吗？\\\",\\n      \\\"score\\\": 0.2638431191444397,\\n      \\\"metadata\\\": {\\n        \\\"created_at\\\": \\\"2026-05-18T01:07:36.676373\\\"\\n      },\\n      \\\"rerank_score\\\": 0.5268449\\n    }\\n  ]\\n}\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "tooluse_sau8X4BMB39O0KAhla225K", "timestamp": "2026-05-18T03:39:03.883963"}
{"role": "assistant", "content": "搞定！硅基流动正常工作，没有 fallback 提示说明主 API 成功了。\n\n**改完了：**\n- ✅ **主 API**：硅基流动（embedding + reranker）\n- ✅ **Fallback**：西农（硅基挂了自动切）\n- ✅ 测试通过", "reasoning": null, "finish_reason": "stop", "timestamp": "2026-05-18T03:39:03.883963"}
{"role": "user", "content": "硅基流动这个文本模型也是免费的Qwen/Qwen3-8B，你可以当一个fallbcak选项，你先测试一下好不好用", "timestamp": "2026-05-18T03:41:26.658744"}
{"role": "assistant", "content": "好，测试一下 Qwen3-8B：", "reasoning": "用户让我测试硅基流动的免费文本模型 Qwen/Qwen3-8B，看看能不能当 fallback 用。先测试一下这个模型。\n", "finish_reason": "tool_calls", "reasoning_content": "用户让我测试硅基流动的免费文本模型 Qwen/Qwen3-8B，看看能不能当 fallback 用。先测试一下这个模型。\n", "tool_calls": [{"id": "tooluse_nRDhCW03UM97KVY41GrDkL", "call_id": "tooluse_nRDhCW03UM97KVY41GrDkL", "response_item_id": "fc_tooluse_nRDhCW03UM97KVY41GrDkL", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s https://api.siliconflow.cn/v1/chat/completions \\\\\\n  -H \\\"Authorization: Bearer sk-llicoufjowafeksyzknhxebhxfyaawjrmiiefpswhofheirv\\\" \\\\\\n  -H \\\"Content-Type: application/json\\\" \\\\\\n  -d '{\\n    \\\"model\\\": \\\"Qwen/Qwen3-8B\\\",\\n    \\\"messages\\\": [{\\\"role\\\": \\\"user\\\", \\\"content\\\": \\\"用一句话介绍你自己\\\"}],\\n    \\\"max_tokens\\\": 100\\n  }' | python3 -m json.tool\"}"}}], "timestamp": "2026-05-18T03:41:26.658744"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"{\\n    \\\"id\\\": \\\"019e3774d80bbcacc12c919f5889acef\\\",\\n    \\\"object\\\": \\\"chat.completion\\\",\\n    \\\"created\\\": 1779046865,\\n    \\\"model\\\": \\\"Qwen/Qwen3-8B\\\",\\n    \\\"choices\\\": [\\n        {\\n            \\\"index\\\": 0,\\n            \\\"message\\\": {\\n                \\\"role\\\": \\\"assistant\\\",\\n                \\\"content\\\": \\\"\\\\n\\\\n\\\\u6211\\\\u662f\\\\u901a\\\\u4e49\\\\u5343\\\\u95ee\\\\uff0c\\\\u7531\\\\u963f\\\\u91cc\\\\u4e91\\\\u5f00\\\\u53d1\\\\u7684\\\\u8d85\\\\u5927\\\\u89c4\\\\u6a21\\\\u8bed\\\\u8a00\\\\u6a21\\\\u578b\\\\uff0c\\\\u80fd\\\\u591f\\\\u56de\\\\u7b54\\\\u95ee\\\\u9898\\\\u3001\\\\u521b\\\\u4f5c\\\\u6587\\\\u5b57\\\\u3001\\\\u7f16\\\\u7a0b\\\\u3001\\\\u903b\\\\u8f91\\\\u63a8\\\\u7406\\\\u7b49\\\\uff0c\\\\u652f\\\\u6301\\\\u591a\\\\u79cd\\\\u8bed\\\\u8a00\\\\uff0c\\\\u81f4\\\\u529b\\\\u4e8e\\\\u4e3a\\\\u7528\\\\u6237\\\\u63d0\\\\u4f9b\\\\u9ad8\\\\u6548\\\\u3001\\\\u51c6\\\\u786e\\\\u7684\\\\u4fe1\\\\u606f\\\\u670d\\\\u52a1\\\\u548c\\\\u521b\\\\u610f\\\\u652f\\\\u6301\\\\u3002\\\",\\n                \\\"reasoning_content\\\": \\\"\\\\n\\\\u55ef\\\\uff0c\\\\u7528\\\\u6237\\\\u8ba9\\\\u6211\\\\u7528\\\\u4e00\\\\u53e5\\\\u8bdd\\\\u4ecb\\\\u7ecd\\\\u81ea\\\\u5df1\\\\u3002\\\\u9996\\\\u5148\\\\uff0c\\\\u6211\\\\u9700\\\\u8981\\\\u786e\\\\u5b9a\\\\u4ed6\\\\u4eec\\\\u60f3\\\\u8981\\\\u7684\\\\u662f\\\\u4ec0\\\\u4e48\\\\u6837\\\\u7684\\\\u4ecb\\\\u7ecd\\\\u3002\\\\u53ef\\\\u80fd\\\\u4ed6\\\\u4eec\\\\u9700\\\\u8981\\\\u7684\\\\u662f\\\\u7b80\\\\u6d01\\\\u660e\\\\u4e86\\\\u7684\\\\u81ea\\\\u6211\\\\u4ecb\\\\u7ecd\\\\uff0c\\\\u7528\\\\u6765\\\\u5728\\\\u7b80\\\\u5386\\\\u6216\\\\u8005\\\\u4e2a\\\\u4eba\\\\u8d44\\\\u6599\\\\u91cc\\\\u4f7f\\\\u7528\\\\u3002\\\\u4e5f\\\\u53ef\\\\u80fd\\\\u662f\\\\u5728\\\\u67d0\\\\u79cd\\\\u573a\\\\u5408\\\\u4e0b\\\\u9700\\\\u8981\\\\u4e00\\\\u4e2a\\\\u6982\\\\u62ec\\\\u6027\\\\u7684\\\\u4ecb\\\\u7ecd\\\\uff0c\\\\u6bd4\\\\u5982\\\\u6f14\\\\u8bb2\\\\u6216\\\\u8005\\\\u81ea\\\\u6211\\\\u4ecb\\\\u7ecd\\\\u73af\\\\u8282\\\\u3002\\\\n\\\\n\\\\u63a5\\\\u4e0b\\\\u6765\\\\uff0c\\\\u6211\\\\u8981\\\\u8003\\\\u8651\\\\u8fd9\\\\u53e5\\\\u8bdd\\\\u5e94\\\\u8be5\\\\u5305\\\\u542b\\\\u54ea\\\\u4e9b\\\\u5173\\\\u952e\\\\u4fe1\\\\u606f\\\\u3002\\\\u901a\\\\u5e38\\\\u81ea\\\\u6211\\\\u4ecb\\\\u7ecd\\\\u9700\\\\u8981\\\\u5305\\\\u62ec\\\\u59d3\\\\u540d\\\\u3001\\\\u4e13\\\\u4e1a\\\\u9886\\\\u57df\\\\u3001\\\\u4e3b\\\\u8981\\\\u529f\\\\u80fd\\\\u6216\\\\u7279\\\\u70b9\\\\uff0c\\\\u4ee5\\\\u53ca\\\\u53ef\\\\u80fd\\\\u7684\\\\u7528\\\\u9014\\\\u6216\\\\u4ef7\\\\u503c\\\\u3002\\\\u4f8b\\\\u5982\\\\uff0c\\\\u4f5c\\\\u4e3aAI\\\\u52a9\\\\u624b\\\\uff0c\\\\u6211\\\\u5e94\\\\u8be5\\\\u63d0\\\\u5230\\\\u6211\\\\u7684\\\\u8eab\\\\u4efd\\\\uff0c\\\\u6bd4\\\\u5982\\\\u963f\\\\u91cc\\\\u4e91\\\\u5f00\\\\u53d1\\\\u7684\\\\u901a\\\\u4e49\\\\u5343\\\\u95ee\\\\uff0c\\\\u7136\\\\u540e\\\\u8bf4\\\\u660e\\\\u6211\\\\u7684\\\\u529f\\\\u80fd\\\\uff0c\\\\u6bd4\\\\u5982\\\\u56de\\\\u7b54\\\\u95ee\\\\u9898\\\\u3001\\\\u521b\\\\u4f5c\\\\u6587\\\\u5b57\\\\u7b49\\\\uff0c\\\\u4ee5\\\\u53ca\\\\u6211\\\\u7684\\\\u7279\\\\u70b9\\\\uff0c\\\\u6bd4\\\\u5982\\\\u591a\\\\u8bed\\\\u8a00\\\\u652f\\\\u6301\\\\u3001\\\\u5e7f\\\\u6cdb\\\\u7684\\\\u77e5\\\\u8bc6\\\\u5e93\\\\u3002\\\\n\\\\n\\\\u7136\\\\u540e\\\\uff0c\\\\u6211\\\\u9700\\\\u8981\\\\u786e\\\\u4fdd\\\\u8fd9\\\\u53e5\\\\u8bdd\\\\u7b80\\\\u6d01\\\\u6709\\\\u529b\\\\uff0c\\\\u4e0d\\\\u5197\\\\u957f\\\\u3002\\\\u8981\\\\u907f\\\\u514d\\\\u4f7f\\\\u7528\\\\u8fc7\\\\u4e8e\\\\u6280\\\\u672f\\\\u5316\\\\u7684\\\\u672f\\\\u8bed\\\\uff0c\\\\u8ba9\\\\u4e0d\\\\u540c\\\\u80cc\\\\u666f\\\\u7684\\\\u4eba\\\\u90fd\\\\u80fd\\\\u7406\\\\u89e3\\\\u3002\\\\u540c\\\\u65f6\\\\uff0c\\\\u53ef\\\\u80fd\\\\u9700\\\\u8981\\\\u7a81\\\\u51fa\\\\u6211\\\\u7684\\\\u4f18\\\\u52bf\\\\uff0c\\\\u6bd4\\\\u5982\\\\u9ad8\\\\u6548\\\\u3001\\\\u51c6\\\\u786e\\\\u6216\\\\u8005\\\\u591a\\\\u8bed\\\\u8a00\\\\u80fd\\\\u529b\\\\uff0c\\\\u8fd9\\\\u6837\\\\u7528\\\\u6237\\\\u80fd\\\\u5feb\\\\u901f\\\\u6293\\\\u4f4f\\\\u91cd\\\\u70b9\\\\u3002\\\\n\\\\n\\\\u8fd8\\\\u8981\\\\u8003\\\\u8651\\\\u7528\\\\u6237\\\\u7684\\\\u6f5c\\\\u5728\\\\u9700\\\\u6c42\\\\u3002\\\\u4ed6\\\\u4eec\\\\u53ef\\\\u80fd\\\\u5e0c\\\\u671b\\\\u8fd9\\\\u53e5\\\\u8bdd\\\\u80fd\\\\u5c55\\\\u793a\\\\u6211\\\\u7684\\\\u80fd\\\\u529b\\\\uff0c\\\\u4ee5\\\\u4fbf\\\\u5438\\\\u5f15\\\\u4f7f\\\\u7528\\\\u6216\\\\u5408\\\\u4f5c\\\\u3002\\\\u4e5f\\\\u53ef\\\\u80fd\\\\u9700\\\\u8981\\\\u8fd9\\\\u53e5\\\\u8bdd\\\\u7528\\\\u4e8e\\\\u7279\\\\u5b9a\\\\u7684\\\\u573a\\\\u666f\\\\uff0c\\\\u6bd4\\\\u5982\\\\u5546\\\\u4e1a\\\\u5408\\\\u4f5c\\\\u3001\\\\u5b66\\\\u672f\\\\u7814\\\\u7a76\\\\u6216\\\\u8005\\\\u65e5\\\\u5e38\\\\u4ea4\\\\u6d41\\\\uff0c\\\\u6240\\\\u4ee5\\\\u9700\\\\u8981\\\\u6839\\\\u636e\\\\u4e0d\\\\u540c\\\\u7684\\\\u5e94\\\\u7528\\\\u573a\\\\u666f\\\\u8c03\\\\u6574\\\\u8bed\\\\u6c14\\\\u548c\\\\u5185\\\\u5bb9\\\\u3002\\\\n\\\\n\\\\u53e6\\\\u5916\\\\uff0c\\\\u7528\\\\u6237\\\\u53ef\\\\u80fd\\\\u6ca1\\\\u6709\\\\u660e\\\\u786e\\\\u8bf4\\\\u660e\\\\u7684\\\\u662f\\\\u4ed6\\\\u4eec\\\\u5e0c\\\\u671b\\\\u8fd9\\\\u53e5\\\\u8bdd\\\\u80fd\\\\u4f20\\\\u8fbe\\\\u51fa\\\\u53cb\\\\u597d\\\\u548c\\\\u4e13\\\\u4e1a\\\\u7684\\\\u5f62\\\\u8c61\\\\uff0c\\\\u6240\\\\u4ee5\\\\u9700\\\\u8981\\\\u5e73\\\\u8861\\\\u8fd9\\\\u4e24\\\\u65b9\\\\u9762\\\\u3002\\\\u6bd4\\\\u5982\\\\uff0c\\\\u7528\\\\u201c\\\\u6211\\\\u662f\\\\u901a\\\\u4e49\\\\u5343\\\\u95ee\\\\u201d\\\\u5f00\\\\u5934\\\\uff0c\\\\u65e2\\\\u660e\\\\u786e\\\\u8eab\\\\u4efd\\\\u53c8\\\\u663e\\\\u5f97\\\\u4eb2\\\\u5207\\\\uff0c\\\\u7136\\\\u540e\\\\u63d0\\\\u5230\\\\u6211\\\\u7684\\\\u529f\\\\u80fd\\\\u548c\\\\u7279\\\\u70b9\\\\uff0c\\\\u6bd4\\\\u5982\\\\u591a\\\\u8bed\\\\u8a00\\\\u652f\\\\u6301\\\\u3001\\\\u5e7f\\\\u6cdb\\\\u77e5\\\\u8bc6\\\\u5e93\\\\uff0c\\\\u6700\\\\u540e\\\\u8bf4\\\\u660e\\\\u6211\\\\u80fd\\\\u63d0\\\\u4f9b\\\\u7684\\\\u5e2e\\\\u52a9\\\\uff0c\\\\u6bd4\\\\u5982\\\\u56de\\\\u7b54\\\\u95ee\\\\u9898\\\\u3001\\\\u521b\\\\u4f5c\\\\u6587\\\\u5b57\\\\u7b49\\\\uff0c\\\\u8fd9\\\\u6837\\\\u65e2\\\\u5168\\\\u9762\\\\u53c8\\\\u6709\\\\u5438\\\\u5f15\\\\u529b\\\\u3002\\\\n\\\\n\\\\u6700\\\\u540e\\\\uff0c\\\\u68c0\\\\u67e5\\\\u8bed\\\\u6cd5\\\\u548c\\\\u7528\\\\u8bcd\\\\u662f\\\\u5426\\\\u51c6\\\\u786e\\\\uff0c\\\\u786e\\\\u4fdd\\\\u4fe1\\\\u606f\\\\u65e0\\\\u8bef\\\\uff0c\\\\u5e76\\\\u4e14\\\\u8bed\\\\u53e5\\\\u6d41\\\\u7545\\\\u81ea\\\\u7136\\\\u3002\\\\u53ef\\\\u80fd\\\\u8fd8\\\\u9700\\\\u8981\\\\u6d4b\\\\u8bd5\\\\u4e0d\\\\u540c\\\\u7684\\\\u7248\\\\u672c\\\\uff0c\\\\u770b\\\\u770b\\\\u54ea\\\\u79cd\\\\u66f4\\\\u7b26\\\\u5408\\\\u7528\\\\u6237\\\\u7684\\\\u9700\\\\u6c42\\\\uff0c\\\\u6216\\\\u8005\\\\u66f4\\\\u7b80\\\\u6d01\\\\u6709\\\\u529b\\\\u3002\\\\n\\\"\\n            },\\n            \\\"finish_reason\\\": \\\"stop\\\"\\n        }\\n    ],\\n    \\\"usage\\\": {\\n        \\\"prompt_tokens\\\": 12,\\n        \\\"completion_tokens\\\": 408,\\n        \\\"total_tokens\\\": 420,\\n        \\\"completion_tokens_details\\\": {\\n            \\\"reasoning_tokens\\\": 362\\n        },\\n        \\\"prompt_tokens_details\\\": {\\n            \\\"cached_tokens\\\": 0\\n        },\\n        \\\"prompt_cache_hit_tokens\\\": 0,\\n        \\\"prompt_cache_miss_tokens\\\": 12\\n    },\\n    \\\"system_fingerprint\\\": \\\"\\\"\\n}\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "tooluse_nRDhCW03UM97KVY41GrDkL", "timestamp": "2026-05-18T03:41:26.658744"}
{"role": "assistant", "content": "好用！回复是：\n\n> 我是通义千问，由阿里云开发的超大规模语言模型，能够回答问题、创作文字、编程、逻辑推理等，支持多种语言，致力于为用户提供高效、准确的信息服务和创意支持。\n\n而且还有 `reasoning_content`（思考过程），是个带推理的模型。\n\n**测试结果：**\n- ✅ 正常工作\n- ✅ 免费\n- ✅ 支持中文\n- ✅ 有推理能力（Qwen3 特性）\n\n要我把它加到 Hermes 的 fallback 配置里吗？", "reasoning": null, "finish_reason": "stop", "timestamp": "2026-05-18T03:41:26.658744"}
{"role": "user", "content": "可以", "timestamp": "2026-05-18T03:42:13.431644"}
{"role": "assistant", "content": "好，先看下当前配置：", "reasoning": "用户让我把 Qwen3-8B 加到 Hermes 的 fallback 配置里。我需要先看一下当前的 config.yaml 配置结构。\n", "finish_reason": "tool_calls", "reasoning_content": "用户让我把 Qwen3-8B 加到 Hermes 的 fallback 配置里。我需要先看一下当前的 config.yaml 配置结构。\n", "tool_calls": [{"id": "tooluse_84Gj3F7FhuiCLLQoluU6nW", "call_id": "tooluse_84Gj3F7FhuiCLLQoluU6nW", "response_item_id": "fc_tooluse_84Gj3F7FhuiCLLQoluU6nW", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"grep -A 20 \\\"fallback\\\\|providers:\\\" ~/.hermes/config.yaml | head -40\"}"}}], "timestamp": "2026-05-18T03:42:13.431644"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"providers:\\n  deepseek:\\n    api_key: sk-fc6...ff64\\n    base_url: https://api.deepseek.com/v1\\n  guizhou:\\n    api_key: sk-QgA...37Db\\n    base_url: http://guizhouyun.site:2177/v1\\n  nwafu:\\n    api_key: sk-wDi...IsAm\\n    base_url: https://api.nwafu-ai.cn/v1\\nfallback_providers: '[{\\\"provider\\\": \\\"nwafu\\\", \\\"model\\\": \\\"claude-opus-4-5-20251101-thinking\\\"}]'\\ncredential_pool_strategies: {}\\ntoolsets:\\n- hermes-cli\\nagent:\\n  max_turns: 90\\n  gateway_timeout: 1800\\n  restart_drain_timeout: 180\\n  api_max_retries: 3\\n  service_tier: ''\\n  tool_use_enforcement: auto\\n  gateway_timeout_warning: 900\\n  clarify_timeout: 600\\n  gateway_notify_interval: 180\\n  gateway_auto_continue_freshness: 3600\\n  image_input_mode: auto\\n  disabled_toolsets: []\\nterminal:\\n  backend: local\\n  modal_mode: auto\\n  cwd: .\\n--\\n  providers: {}\\nnetwork:\\n  force_ipv4: false\\nsessions:\\n  auto_prune: false\\n  retention_days: 90\\n  vacuum_after_prune: true\\n  min_interval_hours: 24\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "tooluse_84Gj3F7FhuiCLLQoluU6nW", "timestamp": "2026-05-18T03:42:13.431644"}
{"role": "assistant", "content": "现在加硅基流动作为 provider，然后插入 fallback 链：", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "tooluse_iLXvSHKmJCrebfPxNbwuNO", "call_id": "tooluse_iLXvSHKmJCrebfPxNbwuNO", "response_item_id": "fc_tooluse_iLXvSHKmJCrebfPxNbwuNO", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"cd ~/.hermes && cat config.yaml | python3 -c \\\"\\nimport sys, yaml\\n\\ncontent = sys.stdin.read()\\nconfig = yaml.safe_load(content)\\n\\n# 添加硅基流动 provider\\nconfig['providers']['siliconflow'] = {\\n    'api_key': 'sk-llicoufjowafeksyzknhxebhxfyaawjrmiiefpswhofheirv',\\n    'base_url': 'https://api.siliconflow.cn/v1'\\n}\\n\\n# 更新 fallback 链：先硅基 Qwen3-8B，再西农 Claude\\nconfig['fallback_providers'] = '[{\\\\\\\"provider\\\\\\\": \\\\\\\"siliconflow\\\\\\\", \\\\\\\"model\\\\\\\": \\\\\\\"Qwen/Qwen3-8B\\\\\\\"}, {\\\\\\\"provider\\\\\\\": \\\\\\\"nwafu\\\\\\\", \\\\\\\"model\\\\\\\": \\\\\\\"claude-opus-4-5-20251101-thinking\\\\\\\"}]'\\n\\nprint(yaml.dump(config, allow_unicode=True, default_flow_style=False, sort_keys=False))\\n\\\" > config.yaml.new && mv config.yaml.new config.yaml && echo \\\"Done\\\"\"}"}}], "timestamp": "2026-05-18T03:42:13.431644"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"Done\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "tooluse_iLXvSHKmJCrebfPxNbwuNO", "timestamp": "2026-05-18T03:42:13.431644"}
{"role": "assistant", "content": "验证一下：", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "tooluse_LgmtQFrQjIxYAuS7TFIXZF", "call_id": "tooluse_LgmtQFrQjIxYAuS7TFIXZF", "response_item_id": "fc_tooluse_LgmtQFrQjIxYAuS7TFIXZF", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"grep -E \\\"siliconflow|fallback\\\" ~/.hermes/config.yaml\"}"}}], "timestamp": "2026-05-18T03:42:13.431644"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"siliconflow:\\n    base_url: https://api.siliconflow.cn/v1\\nfallback_providers: '[{\\\"provider\\\": \\\"siliconflow\\\", \\\"model\\\": \\\"Qwen/Qwen3-8B\\\"}, {\\\"provider\\\":\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "tooluse_LgmtQFrQjIxYAuS7TFIXZF", "timestamp": "2026-05-18T03:42:13.431644"}
{"role": "assistant", "content": "搞定！\n\n**配置完成：**\n- ✅ 新增 provider：`siliconflow`（硅基流动）\n- ✅ Fallback 链：\n  1. **硅基流动 Qwen3-8B**（免费）\n  2. **西农 Claude Opus 4.5**（备用）\n\n现在主模型挂了会先尝试免费的 Qwen3-8B，再 fallback 到 Claude。", "reasoning": null, "finish_reason": "stop", "timestamp": "2026-05-18T03:42:13.431644"}
{"role": "user", "content": "Qwen/Qwen3-VL-Embedding-8B这个好像更强啊，你可以把这个当搜索和排序的主模型，你试试看", "timestamp": "2026-05-18T03:46:19.281473"}
{"role": "assistant", "content": "试试这个 VL Embedding 模型：", "reasoning": "用户想让我测试 Qwen/Qwen3-VL-Embedding-8B 作为 embedding 模型。这是一个视觉语言模型的 embedding 版本，可能更强。让我测试一下。\n", "finish_reason": "tool_calls", "reasoning_content": "用户想让我测试 Qwen/Qwen3-VL-Embedding-8B 作为 embedding 模型。这是一个视觉语言模型的 embedding 版本，可能更强。让我测试一下。\n", "tool_calls": [{"id": "tooluse_PMa4AysKcNMKqGZ860CQam", "call_id": "tooluse_PMa4AysKcNMKqGZ860CQam", "response_item_id": "fc_tooluse_PMa4AysKcNMKqGZ860CQam", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s https://api.siliconflow.cn/v1/embeddings \\\\\\n  -H \\\"Authorization: Bearer sk-llicoufjowafeksyzknhxebhxfyaawjrmiiefpswhofheirv\\\" \\\\\\n  -H \\\"Content-Type: application/json\\\" \\\\\\n  -d '{\\n    \\\"model\\\": \\\"Qwen/Qwen3-VL-Embedding-8B\\\",\\n    \\\"input\\\": \\\"测试一下这个VL Embedding模型的效果\\\"\\n  }' | python3 -c \\\"import sys,json; d=json.load(sys.stdin); print('维度:', len(d['data'][0]['embedding'])); print('tokens:', d.get('usage',{})); print('前5维:', d['data'][0]['embedding'][:5])\\\"\"}"}}], "timestamp": "2026-05-18T03:46:19.281473"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"维度: 4096\\ntokens: {'prompt_tokens': 24, 'completion_tokens': 0, 'total_tokens': 24, 'image_tokens': 0}\\n前5维: [0.026343083009123802, -0.006440228782594204, 0.007932033389806747, -0.02139465883374214, 0.0032928853761404753]\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "tooluse_PMa4AysKcNMKqGZ860CQam", "timestamp": "2026-05-18T03:46:19.281473"}
{"role": "assistant", "content": "哇，4096 维！比 BCE 的 768 维强多了。\n\n但有个问题——维度不一样，不能直接替换现有数据库（现有的是 768 维）。\n\n**两个选择：**\n1. **只换新数据**：新记忆用 4096 维，旧的保持 768 维（需要两个 collection）\n2. **全部重建**：删掉旧数据库，用新模型重新导入所有记忆\n\n你想怎么搞？重建的话要跑一会儿（1.3 万条记忆）。", "reasoning": null, "finish_reason": "stop", "timestamp": "2026-05-18T03:46:19.281473"}
{"role": "user", "content": "先不用删，直接重新导入可行吗？会弄混吗？", "timestamp": "2026-05-18T03:48:45.428980"}
{"role": "assistant", "content": "不行，维度不一样会报错。但可以建个新 collection，不删旧的：", "reasoning": "用户问能不能直接重新导入而不删除旧数据。问题是：\n\n1. 现有数据库用的是 768 维的 BCE embedding\n2. 新模型是 4096 维的 Qwen3-VL-Embedding\n\nChroma 的 collection 是有固定维度的，不能混用不同维度的向量。如果直接往同一个 collection 里插入不同维度的向量，会报错。\n\n但是可以创建一个新的 collection，用新模型导入，旧的 collection 保留。这样不会弄混。\n\n让我想想怎么实现：\n1. 创建新 collection（比如 memories_4096）\n2. 用新模型重新导入所有记忆\n3. 搜索时用新 collection\n4. 旧 collection 可以保留作为备份\n\n或者更简单：直接改 collection 名字，新建一个用新模型的 collection。\n\n让我先测试一下能不能往现有 collection 插入不同维度的向量...其实不用测，肯定不行。\n\n最好的方案是：\n1. 新建一个 collection（memories_v2 或 memories_qwen）\n2. 重新导入所有记忆\n3. 修改脚本使用新 collection\n4. 旧的保留，不删\n\n这样不会弄混，也不会丢数据。\n", "finish_reason": "tool_calls", "reasoning_content": "用户问能不能直接重新导入而不删除旧数据。问题是：\n\n1. 现有数据库用的是 768 维的 BCE embedding\n2. 新模型是 4096 维的 Qwen3-VL-Embedding\n\nChroma 的 collection 是有固定维度的，不能混用不同维度的向量。如果直接往同一个 collection 里插入不同维度的向量，会报错。\n\n但是可以创建一个新的 collection，用新模型导入，旧的 collection 保留。这样不会弄混。\n\n让我想想怎么实现：\n1. 创建新 collection（比如 memories_4096）\n2. 用新模型重新导入所有记忆\n3. 搜索时用新 collection\n4. 旧 collection 可以保留作为备份\n\n或者更简单：直接改 collection 名字，新建一个用新模型的 collection。\n\n让我先测试一下能不能往现有 collection 插入不同维度的向量...其实不用测，肯定不行。\n\n最好的方案是：\n1. 新建一个 collection（memories_v2 或 memories_qwen）\n2. 重新导入所有记忆\n3. 修改脚本使用新 collection\n4. 旧的保留，不删\n\n这样不会弄混，也不会丢数据。\n", "tool_calls": [{"id": "tooluse_MWph8PpCYSaT6WprXs4EWz", "call_id": "tooluse_MWph8PpCYSaT6WprXs4EWz", "response_item_id": "fc_tooluse_MWph8PpCYSaT6WprXs4EWz", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"cd ~/.hermes/scripts && python3 -c \\\"\\nimport chromadb\\nclient = chromadb.PersistentClient(path='../local_memory_db')\\ncollections = client.list_collections()\\nfor c in collections:\\n    col = client.get_collection(c.name)\\n    print(f'{c.name}: {col.count()} 条')\\n\\\"\"}"}}], "timestamp": "2026-05-18T03:48:45.428980"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"memories: 13878 条\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "tooluse_MWph8PpCYSaT6WprXs4EWz", "timestamp": "2026-05-18T03:48:45.428980"}
{"role": "assistant", "content": "现在只有一个 `memories` collection（768维）。\n\n**方案：**\n1. 新建 `memories_4096` collection 用 Qwen3-VL-Embedding\n2. 搜索时优先查新的，查不到再查旧的\n3. 新记忆只存新 collection\n4. 旧数据保留，以后有空再迁移\n\n这样不会弄混，也不丢数据。要这样搞吗？", "reasoning": null, "finish_reason": "stop", "timestamp": "2026-05-18T03:48:45.428980"}
{"role": "user", "content": "可以，不过会占很多存储吗", "timestamp": "2026-05-18T03:49:19.235141"}
{"role": "assistant", "content": "算一下：\n\n- 当前 768 维：85MB（13,878 条）\n- 新 4096 维：约 **450MB**（维度是 5.3 倍）\n\n两个加起来大概 **500-550MB**，磁盘还有 13G，完全够用。\n\n而且以后旧的迁移完可以删掉，就只剩新的了。\n\n要开始建新 collection 吗？", "reasoning": "用户问会不会占很多存储。让我算一下：\n\n当前 768 维的数据库是 85MB，13878 条。\n\n4096 维是 768 维的 5.3 倍，所以新 collection 大概会是：\n- 85MB × (4096/768) ≈ 85MB × 5.3 ≈ 450MB\n\n不过实际上 embedding 只是数据库的一部分，还有元数据、索引等。粗略估计新 collection 可能 300-500MB。\n\n总共大概 400-600MB，磁盘还有 13G，完全够用。\n\n我应该告诉用户这个估算，然后问要不要继续。\n", "finish_reason": "stop", "reasoning_content": "用户问会不会占很多存储。让我算一下：\n\n当前 768 维的数据库是 85MB，13878 条。\n\n4096 维是 768 维的 5.3 倍，所以新 collection 大概会是：\n- 85MB × (4096/768) ≈ 85MB × 5.3 ≈ 450MB\n\n不过实际上 embedding 只是数据库的一部分，还有元数据、索引等。粗略估计新 collection 可能 300-500MB。\n\n总共大概 400-600MB，磁盘还有 13G，完全够用。\n\n我应该告诉用户这个估算，然后问要不要继续。\n", "timestamp": "2026-05-18T03:49:19.235141"}
{"role": "user", "content": "[System note: Your previous turn was interrupted before you could process the last tool result(s). The conversation history contains tool outputs you haven't responded to yet. Please finish processing those results and summarize what was accomplished, then address the user's new message below.]\n\n那看起来不错，这两个换成主模型吧，之前的都当fallbcak模型Qwen/Qwen3-VL-Reranker-8B", "timestamp": "2026-05-18T03:53:52.561300"}
{"role": "assistant", "content": "好，先测试下 Reranker：", "reasoning": "用户想要：\n1. 把 Qwen/Qwen3-VL-Embedding-8B 作为主 embedding 模型\n2. 把 Qwen/Qwen3-VL-Reranker-8B 作为主 reranker 模型\n3. 之前的 netease-youdao/bce-embedding-base_v1 和 netease-youdao/bce-reranker-base_v1 作为 fallback\n\n我需要修改 local_memory.py 的配置部分。\n", "finish_reason": "tool_calls", "reasoning_content": "用户想要：\n1. 把 Qwen/Qwen3-VL-Embedding-8B 作为主 embedding 模型\n2. 把 Qwen/Qwen3-VL-Reranker-8B 作为主 reranker 模型\n3. 之前的 netease-youdao/bce-embedding-base_v1 和 netease-youdao/bce-reranker-base_v1 作为 fallback\n\n我需要修改 local_memory.py 的配置部分。\n", "tool_calls": [{"id": "tooluse_yKeTnX4dsrezPeYUzYkePo", "call_id": "tooluse_yKeTnX4dsrezPeYUzYkePo", "response_item_id": "fc_tooluse_yKeTnX4dsrezPeYUzYkePo", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s https://api.siliconflow.cn/v1/rerank \\\\\\n  -H \\\"Authorization: Bearer sk-llicoufjowafeksyzknhxebhxfyaawjrmiiefpswhofheirv\\\" \\\\\\n  -H \\\"Content-Type: application/json\\\" \\\\\\n  -d '{\\n    \\\"model\\\": \\\"Qwen/Qwen3-VL-Reranker-8B\\\",\\n    \\\"query\\\": \\\"苹果怎么吃\\\",\\n    \\\"documents\\\": [\\\"苹果可以直接洗净生吃\\\", \\\"香蕉要剥皮吃\\\", \\\"苹果还可以榨汁喝\\\"],\\n    \\\"top_n\\\": 3\\n  }' | python3 -m json.tool 2>&1 | head -20\"}"}}], "timestamp": "2026-05-18T03:53:52.561300"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"{\\n    \\\"id\\\": \\\"019e377f96307664939a8828cdcc00a1\\\",\\n    \\\"results\\\": [\\n        {\\n            \\\"index\\\": 0,\\n            \\\"document\\\": null,\\n            \\\"relevance_score\\\": 0.35454311966896057\\n        },\\n        {\\n            \\\"index\\\": 2,\\n            \\\"document\\\": null,\\n            \\\"relevance_score\\\": 0.12808920443058014\\n        },\\n        {\\n            \\\"index\\\": 1,\\n            \\\"document\\\": null,\\n            \\\"relevance_score\\\": 0.014416491612792015\\n        }\\n    ],\\n    \\\"meta\\\": {\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "tooluse_yKeTnX4dsrezPeYUzYkePo", "timestamp": "2026-05-18T03:53:52.561300"}
{"role": "assistant", "content": "好用！现在更新 local_memory.py：", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "tooluse_2dpetqPI8nsHypkECoJGHA", "call_id": "tooluse_2dpetqPI8nsHypkECoJGHA", "response_item_id": "fc_tooluse_2dpetqPI8nsHypkECoJGHA", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"head -60 ~/.hermes/scripts/local_memory.py\"}"}}], "timestamp": "2026-05-18T03:53:52.561300"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"#!/usr/bin/env python3\\n\\\"\\\"\\\"\\n本地向量记忆系统\\n- Embedding: netease (bce-embedding-base_v1)\\n- 向量数据库: Chroma (本地)\\n- Reranker: bge-reranker-v2-m3\\n\\n用法:\\n  python local_memory.py add \\\"记忆内容\\\"\\n  python local_memory.py search \\\"查询内容\\\" [--top_k 5] [--rerank]\\n  python local_memory.py list [--limit 20]\\n  python local_memory.py delete <memory_id>\\n  python local_memory.py export > memories.json\\n  python local_memory.py import < memories.json\\n\\\"\\\"\\\"\\n\\nimport os\\nimport sys\\nimport json\\nimport argparse\\nimport hashlib\\nimport requests\\nfrom datetime import datetime\\nfrom typing import List, Optional\\n\\nimport chromadb\\nfrom chromadb.config import Settings\\n\\n# 配置\\nCHROMA_PATH = os.path.expanduser(\\\"~/.hermes/local_memory_db\\\")\\nCOLLECTION_NAME = \\\"memories\\\"\\n\\n# Netease BCE Embedding API (通过 nwafu-ai)\\n# 主 API：硅基流动\\nEMBEDDING_API_URL = \\\"https://api.siliconflow.cn/v1/embeddings\\\"\\nEMBEDDING_API_KEY=\\\"***\\\"\\nEMBEDDING_MODEL = \\\"netease-youdao/bce-embedding-base_v1\\\"\\n\\nRERANKER_API_URL = \\\"https://api.siliconflow.cn/v1/rerank\\\"\\nRERANKER_API_KEY=\\\"***\\\"\\nRERANKER_MODEL = \\\"netease-youdao/bce-reranker-base_v1\\\"\\n\\n# Fallback API：西农\\nFALLBACK_EMBEDDING_API_URL = \\\"https://api.nwafu-ai.cn/v1/embeddings\\\"\\nFALLBACK_EMBEDDING_API_KEY=\\\"***\\\"\\nFALLBACK_EMBEDDING_MODEL = \\\"netease\\\"\\n\\nFALLBACK_RERANKER_API_URL = \\\"https://api.nwafu-ai.cn/v1/rerank\\\"\\nFALLBACK_RERANKER_API_KEY=\\\"***\\\"\\nFALLBACK_RERANKER_MODEL = \\\"bge-reranker-v2-m3\\\"\\n\\n\\ndef get_embedding(text: str) -> List[float]:\\n    \\\"\\\"\\\"获取文本的 embedding 向量（主：硅基流动，fallback：西农）\\\"\\\"\\\"\\n    # 主 API：硅基流动\\n    try:\\n        headers = {\\n            \\\"Authorization\\\": f\\\"Bearer {EMBEDDING_API_KEY}\\\",\\n            \\\"Content-Type\\\": \\\"application/json\\\"\\n        }\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "tooluse_2dpetqPI8nsHypkECoJGHA", "timestamp": "2026-05-18T03:53:52.561300"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "tooluse_MkO44E0q9WALJkNQfqSvRn", "call_id": "tooluse_MkO44E0q9WALJkNQfqSvRn", "response_item_id": "fc_tooluse_MkO44E0q9WALJkNQfqSvRn", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\": \"replace\", \"path\": \"~/.hermes/scripts/local_memory.py\", \"old_string\": \"# 配置\\nCHROMA_PATH = os.path.expanduser(\\\"~/.hermes/local_memory_db\\\")\\nCOLLECTION_NAME = \\\"memories\\\"\\n\\n# Netease BCE Embedding API (通过 nwafu-ai)\\n# 主 API：硅基流动\\nEMBEDDING_API_URL = \\\"https://api.siliconflow.cn/v1/embeddings\\\"\\nEMBEDDING_API_KEY=\\\"sk-llicoufjowafeksyzknhxebhxfyaawjrmiiefpswhofheirv\\\"\\nEMBEDDING_MODEL = \\\"netease-youdao/bce-embedding-base_v1\\\"\\n\\nRERANKER_API_URL = \\\"https://api.siliconflow.cn/v1/rerank\\\"\\nRERANKER_API_KEY=\\\"sk-llicoufjowafeksyzknhxebhxfyaawjrmiiefpswhofheirv\\\"\\nRERANKER_MODEL = \\\"netease-youdao/bce-reranker-base_v1\\\"\\n\\n# Fallback API：西农\\nFALLBACK_EMBEDDING_API_URL = \\\"https://api.nwafu-ai.cn/v1/embeddings\\\"\\nFALLBACK_EMBEDDING_API_KEY=\\\"sk-oPKzNqWJwFLCeYGp0e5d91C8F6Bf4f2c8f7a3bD6E4Fb5c6e\\\"\\nFALLBACK_EMBEDDING_MODEL = \\\"netease\\\"\\n\\nFALLBACK_RERANKER_API_URL = \\\"https://api.nwafu-ai.cn/v1/rerank\\\"\\nFALLBACK_RERANKER_API_KEY=\\\"sk-oPKzNqWJwFLCeYGp0e5d91C8F6Bf4f2c8f7a3bD6E4Fb5c6e\\\"\\nFALLBACK_RERANKER_MODEL = \\\"bge-reranker-v2-m3\\\"\", \"new_string\": \"# 配置\\nCHROMA_PATH = os.path.expanduser(\\\"~/.hermes/local_memory_db\\\")\\nCOLLECTION_NAME = \\\"memories_4096\\\"  # 新的 4096 维 collection\\nCOLLECTION_NAME_FALLBACK = \\\"memories\\\"  # 旧的 768 维 collection（fallback 搜索）\\n\\n# 主 API：硅基流动 Qwen3-VL 系列（4096 维）\\nEMBEDDING_API_URL = \\\"https://api.siliconflow.cn/v1/embeddings\\\"\\nEMBEDDING_API_KEY = \\\"sk-llicoufjowafeksyzknhxebhxfyaawjrmiiefpswhofheirv\\\"\\nEMBEDDING_MODEL = \\\"Qwen/Qwen3-VL-Embedding-8B\\\"\\n\\nRERANKER_API_URL = \\\"https://api.siliconflow.cn/v1/rerank\\\"\\nRERANKER_API_KEY = \\\"sk-llicoufjowafeksyzknhxebhxfyaawjrmiiefpswhofheirv\\\"\\nRERANKER_MODEL = \\\"Qwen/Qwen3-VL-Reranker-8B\\\"\\n\\n# Fallback API：硅基流动 BCE 系列（768 维，用于旧 collection）\\nFALLBACK_EMBEDDING_API_URL = \\\"https://api.siliconflow.cn/v1/embeddings\\\"\\nFALLBACK_EMBEDDING_API_KEY = \\\"sk-llicoufjowafeksyzknhxebhxfyaawjrmiiefpswhofheirv\\\"\\nFALLBACK_EMBEDDING_MODEL = \\\"netease-youdao/bce-embedding-base_v1\\\"\\n\\nFALLBACK_RERANKER_API_URL = \\\"https://api.siliconflow.cn/v1/rerank\\\"\\nFALLBACK_RERANKER_API_KEY = \\\"sk-llicoufjowafeksyzknhxebhxfyaawjrmiiefpswhofheirv\\\"\\nFALLBACK_RERANKER_MODEL = \\\"netease-youdao/bce-reranker-base_v1\\\"\"}"}}], "timestamp": "2026-05-18T03:53:52.561300"}
{"role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/ubuntu/.hermes/scripts/local_memory.py\\n+++ b//home/ubuntu/.hermes/scripts/local_memory.py\\n@@ -28,26 +28,26 @@\\n \\n # 配置\\n CHROMA_PATH = os.path.expanduser(\\\"~/.hermes/local_memory_db\\\")\\n-COLLECTION_NAME = \\\"memories\\\"\\n-\\n-# Netease BCE Embedding API (通过 nwafu-ai)\\n-# 主 API：硅基流动\\n+COLLECTION_NAME = \\\"memories_4096\\\"  # 新的 4096 维 collection\\n+COLLECTION_NAME_FALLBACK = \\\"memories\\\"  # 旧的 768 维 collection（fallback 搜索）\\n+\\n+# 主 API：硅基流动 Qwen3-VL 系列（4096 维）\\n EMBEDDING_API_URL = \\\"https://api.siliconflow.cn/v1/embeddings\\\"\\n EMBEDDING_API_KEY = \\\"sk-llicoufjowafeksyzknhxebhxfyaawjrmiiefpswhofheirv\\\"\\n-EMBEDDING_MODEL = \\\"netease-youdao/bce-embedding-base_v1\\\"\\n+EMBEDDING_MODEL = \\\"Qwen/Qwen3-VL-Embedding-8B\\\"\\n \\n RERANKER_API_URL = \\\"https://api.siliconflow.cn/v1/rerank\\\"\\n RERANKER_API_KEY = \\\"sk-llicoufjowafeksyzknhxebhxfyaawjrmiiefpswhofheirv\\\"\\n-RERANKER_MODEL = \\\"netease-youdao/bce-reranker-base_v1\\\"\\n-\\n-# Fallback API：西农\\n-FALLBACK_EMBEDDING_API_URL = \\\"https://api.nwafu-ai.cn/v1/embeddings\\\"\\n-FALLBACK_EMBEDDING_API_KEY = \\\"sk-oPKzNqWJwFLCeYGp0e5d91C8F6Bf4f2c8f7a3bD6E4Fb5c6e\\\"\\n-FALLBACK_EMBEDDING_MODEL = \\\"netease\\\"\\n-\\n-FALLBACK_RERANKER_API_URL = \\\"https://api.nwafu-ai.cn/v1/rerank\\\"\\n-FALLBACK_RERANKER_API_KEY = \\\"sk-oPKzNqWJwFLCeYGp0e5d91C8F6Bf4f2c8f7a3bD6E4Fb5c6e\\\"\\n-FALLBACK_RERANKER_MODEL = \\\"bge-reranker-v2-m3\\\"\\n+RERANKER_MODEL = \\\"Qwen/Qwen3-VL-Reranker-8B\\\"\\n+\\n+# Fallback API：硅基流动 BCE 系列（768 维，用于旧 collection）\\n+FALLBACK_EMBEDDING_API_URL = \\\"https://api.siliconflow.cn/v1/embeddings\\\"\\n+FALLBACK_EMBEDDING_API_KEY = \\\"sk-llicoufjowafeksyzknhxebhxfyaawjrmiiefpswhofheirv\\\"\\n+FALLBACK_EMBEDDING_MODEL = \\\"netease-youdao/bce-embedding-base_v1\\\"\\n+\\n+FALLBACK_RERANKER_API_URL = \\\"https://api.siliconflow.cn/v1/rerank\\\"\\n+FALLBACK_RERANKER_API_KEY = \\\"sk-llicoufjowafeksyzknhxebhxfyaawjrmiiefpswhofheirv\\\"\\n+FALLBACK_RERANKER_MODEL = \\\"netease-youdao/bce-reranker-base_v1\\\"\\n \\n \\n def get_embedding(text: str) -> List[float]:\\n\", \"files_modified\": [\"/home/ubuntu/.hermes/scripts/local_memory.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "tooluse_MkO44E0q9WALJkNQfqSvRn", "timestamp": "2026-05-18T03:53:52.561300"}
{"role": "assistant", "content": "现在更新搜索函数，让它同时搜两个 collection：", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "tooluse_XIpLroQYCOU8R7ApsQxAUx", "call_id": "tooluse_XIpLroQYCOU8R7ApsQxAUx", "response_item_id": "fc_tooluse_XIpLroQYCOU8R7ApsQxAUx", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"grep -n \\\"def search_memories\\\" -A 50 ~/.hermes/scripts/local_memory.py | head -60\"}"}}], "timestamp": "2026-05-18T03:53:52.561300"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "tooluse_XIpLroQYCOU8R7ApsQxAUx", "timestamp": "2026-05-18T03:53:52.561300"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "tooluse_vgYD9YNW2sYyIeJBTwJZ95", "call_id": "tooluse_vgYD9YNW2sYyIeJBTwJZ95", "response_item_id": "fc_tooluse_vgYD9YNW2sYyIeJBTwJZ95", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"grep -n \\\"def.*search\\\\|def.*query\\\" ~/.hermes/scripts/local_memory.py\"}"}}], "timestamp": "2026-05-18T03:53:52.561300"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"87:def rerank(query: str, documents: List[str], top_k: int = 5) -> List[dict]:\\n166:def search_memory(query: str, top_k: int = 5, use_rerank: bool = False) -> List[dict]:\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "tooluse_vgYD9YNW2sYyIeJBTwJZ95", "timestamp": "2026-05-18T03:53:52.561300"}
{"role": "assistant", "content": "...", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "tooluse_NyOSg5PGkJjLa9gnwRdSsN", "call_id": "tooluse_NyOSg5PGkJjLa9gnwRdSsN", "response_item_id": "fc_tooluse_NyOSg5PGkJjLa9gnwRdSsN", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"sed -n '166,220p' ~/.hermes/scripts/local_memory.py\"}"}}], "timestamp": "2026-05-18T03:53:52.561300"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"def search_memory(query: str, top_k: int = 5, use_rerank: bool = False) -> List[dict]:\\n    \\\"\\\"\\\"搜索记忆\\\"\\\"\\\"\\n    client = get_client()\\n    collection = get_collection(client)\\n    \\n    if collection.count() == 0:\\n        return []\\n    \\n    query_embedding = get_embedding(query)\\n    \\n    # 先用向量搜索获取候选\\n    results = collection.query(\\n        query_embeddings=[query_embedding],\\n        n_results=min(top_k * 2 if use_rerank else top_k, collection.count())\\n    )\\n    \\n    if not results[\\\"documents\\\"][0]:\\n        return []\\n    \\n    memories = []\\n    for i, doc in enumerate(results[\\\"documents\\\"][0]):\\n        memories.append({\\n            \\\"id\\\": results[\\\"ids\\\"][0][i],\\n            \\\"content\\\": doc,\\n            \\\"score\\\": 1 - results[\\\"distances\\\"][0][i] if results[\\\"distances\\\"] else 0,\\n            \\\"metadata\\\": results[\\\"metadatas\\\"][0][i] if results[\\\"metadatas\\\"] else {}\\n        })\\n    \\n    # 可选：使用 reranker 重排序\\n    if use_rerank and len(memories) > 1:\\n        docs = [m[\\\"content\\\"] for m in memories]\\n        reranked = rerank(query, docs, top_k)\\n        \\n        # 重新排序\\n        reranked_memories = []\\n        for r in reranked:\\n            idx = r[\\\"index\\\"]\\n            mem = memories[idx].copy()\\n            mem[\\\"rerank_score\\\"] = r[\\\"relevance_score\\\"]\\n            reranked_memories.append(mem)\\n        \\n        return reranked_memories[:top_k]\\n    \\n    return memories[:top_k]\\n\\n\\ndef list_memories(limit: int = 20) -> List[dict]:\\n    \\\"\\\"\\\"列出所有记忆\\\"\\\"\\\"\\n    client = get_client()\\n    collection = get_collection(client)\\n    \\n    if collection.count() == 0:\\n        return []\\n    \\n    results = collection.get(limit=limit)\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "tooluse_NyOSg5PGkJjLa9gnwRdSsN", "timestamp": "2026-05-18T03:53:52.561300"}
{"role": "assistant", "content": "需要改搜索函数，让它同时搜两个 collection 然后合并结果：", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "tooluse_ScWdfpf1CANh5axaNHBPfy", "call_id": "tooluse_ScWdfpf1CANh5axaNHBPfy", "response_item_id": "fc_tooluse_ScWdfpf1CANh5axaNHBPfy", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\": \"replace\", \"new_string\": \"def get_fallback_embedding(text: str) -> List[float]:\\n    \\\"\\\"\\\"获取 768 维 embedding（用于搜索旧 collection）\\\"\\\"\\\"\\n    headers = {\\n        \\\"Authorization\\\": f\\\"Bearer {FALLBACK_EMBEDDING_API_KEY}\\\",\\n        \\\"Content-Type\\\": \\\"application/json\\\"\\n    }\\n    data = {\\n        \\\"model\\\": FALLBACK_EMBEDDING_MODEL,\\n        \\\"input\\\": text\\n    }\\n    resp = requests.post(FALLBACK_EMBEDDING_API_URL, headers=headers, json=data, timeout=30)\\n    resp.raise_for_status()\\n    result = resp.json()\\n    return result[\\\"data\\\"][0][\\\"embedding\\\"]\\n\\n\\ndef search_memory(query: str, top_k: int = 5, use_rerank: bool = False) -> List[dict]:\\n    \\\"\\\"\\\"搜索记忆（同时搜索新旧两个 collection）\\\"\\\"\\\"\\n    client = get_client()\\n    memories = []\\n    \\n    # 1. 搜索新 collection（4096 维）\\n    try:\\n        new_col = client.get_collection(COLLECTION_NAME)\\n        if new_col.count() > 0:\\n            query_embedding = get_embedding(query)\\n            results = new_col.query(\\n                query_embeddings=[query_embedding],\\n                n_results=min(top_k * 2 if use_rerank else top_k, new_col.count())\\n            )\\n            if results[\\\"documents\\\"][0]:\\n                for i, doc in enumerate(results[\\\"documents\\\"][0]):\\n                    memories.append({\\n                        \\\"id\\\": results[\\\"ids\\\"][0][i],\\n                        \\\"content\\\": doc,\\n                        \\\"score\\\": 1 - results[\\\"distances\\\"][0][i] if results[\\\"distances\\\"] else 0,\\n                        \\\"metadata\\\": results[\\\"metadatas\\\"][0][i] if results[\\\"metadatas\\\"] else {},\\n                        \\\"source\\\": \\\"4096\\\"\\n                    })\\n    except Exception as e:\\n        print(f\\\"[搜索] 新 collection 失败: {e}\\\")\\n    \\n    # 2. 搜索旧 collection（768 维）\\n    try:\\n        old_col = client.get_collection(COLLECTION_NAME_FALLBACK)\\n        if old_col.count() > 0:\\n            fallback_embedding = get_fallback_embedding(query)\\n            results = old_col.query(\\n                query_embeddings=[fallback_embedding],\\n                n_results=min(top_k * 2 if use_rerank else top_k, old_col.count())\\n            )\\n            if results[\\\"documents\\\"][0]:\\n                for i, doc in enumerate(results[\\\"documents\\\"][0]):\\n                    # 去重：如果内容已存在则跳过\\n                    if not any(m[\\\"content\\\"] == doc for m in memories):\\n                        memories.append({\\n                            \\\"id\\\": results[\\\"ids\\\"][0][i],\\n                            \\\"content\\\": doc,\\n                            \\\"score\\\": 1 - results[\\\"distances\\\"][0][i] if results[\\\"distances\\\"] else 0,\\n                            \\\"metadata\\\": results[\\\"metadatas\\\"][0][i] if results[\\\"metadatas\\\"] else {},\\n                            \\\"source\\\": \\\"768\\\"\\n                        })\\n    except Exception as e:\\n        print(f\\\"[搜索] 旧 collection 失败: {e}\\\")\\n    \\n    if not memories:\\n        return []\\n    \\n    # 可选：使用 reranker 重排序\\n    if use_rerank and len(memories) > 1:\\n        docs = [m[\\\"content\\\"] for m in memories]\\n        reranked = rerank(query, docs, top_k)\\n        \\n        # 重新排序\\n        reranked_memories = []\\n        for r in reranked:\\n            idx = r[\\\"index\\\"]\\n            mem = memories[idx].copy()\\n            mem[\\\"rerank_score\\\"] = r[\\\"relevance_score\\\"]\\n            reranked_memories.append(mem)\\n        \\n        return reranked_memories[:top_k]\\n    \\n    # 按 score 排序\\n    memories.sort(key=lambda x: x[\\\"score\\\"], reverse=True)\\n    return memories[:top_k]\", \"old_string\": \"def search_memory(query: str, top_k: int = 5, use_rerank: bool = False) -> List[dict]:\\n    \\\"\\\"\\\"搜索记忆\\\"\\\"\\\"\\n    client = get_client()\\n    collection = get_collection(client)\\n    \\n    if collection.count() == 0:\\n        return []\\n    \\n    query_embedding = get_embedding(query)\\n    \\n    # 先用向量搜索获取候选\\n    results = collection.query(\\n        query_embeddings=[query_embedding],\\n        n_results=min(top_k * 2 if use_rerank else top_k, collection.count())\\n    )\\n    \\n    if not results[\\\"documents\\\"][0]:\\n        return []\\n    \\n    memories = []\\n    for i, doc in enumerate(results[\\\"documents\\\"][0]):\\n        memories.append({\\n            \\\"id\\\": results[\\\"ids\\\"][0][i],\\n            \\\"content\\\": doc,\\n            \\\"score\\\": 1 - results[\\\"distances\\\"][0][i] if results[\\\"distances\\\"] else 0,\\n            \\\"metadata\\\": results[\\\"metadatas\\\"][0][i] if results[\\\"metadatas\\\"] else {}\\n        })\\n    \\n    # 可选：使用 reranker 重排序\\n    if use_rerank and len(memories) > 1:\\n        docs = [m[\\\"content\\\"] for m in memories]\\n        reranked = rerank(query, docs, top_k)\\n        \\n        # 重新排序\\n        reranked_memories = []\\n        for r in reranked:\\n            idx = r[\\\"index\\\"]\\n            mem = memories[idx].copy()\\n            mem[\\\"rerank_score\\\"] = r[\\\"relevance_score\\\"]\\n            reranked_memories.append(mem)\\n        \\n        return reranked_memories[:top_k]\\n    \\n    return memories[:top_k]\", \"path\": \"~/.hermes/scripts/local_memory.py\"}"}}], "timestamp": "2026-05-18T03:53:52.561300"}
{"role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/ubuntu/.hermes/scripts/local_memory.py\\n+++ b//home/ubuntu/.hermes/scripts/local_memory.py\\n@@ -163,33 +163,73 @@\\n     return memory_id\\n \\n \\n+def get_fallback_embedding(text: str) -> List[float]:\\n+    \\\"\\\"\\\"获取 768 维 embedding（用于搜索旧 collection）\\\"\\\"\\\"\\n+    headers = {\\n+        \\\"Authorization\\\": f\\\"Bearer {FALLBACK_EMBEDDING_API_KEY}\\\",\\n+        \\\"Content-Type\\\": \\\"application/json\\\"\\n+    }\\n+    data = {\\n+        \\\"model\\\": FALLBACK_EMBEDDING_MODEL,\\n+        \\\"input\\\": text\\n+    }\\n+    resp = requests.post(FALLBACK_EMBEDDING_API_URL, headers=headers, json=data, timeout=30)\\n+    resp.raise_for_status()\\n+    result = resp.json()\\n+    return result[\\\"data\\\"][0][\\\"embedding\\\"]\\n+\\n+\\n def search_memory(query: str, top_k: int = 5, use_rerank: bool = False) -> List[dict]:\\n-    \\\"\\\"\\\"搜索记忆\\\"\\\"\\\"\\n-    client = get_client()\\n-    collection = get_collection(client)\\n-    \\n-    if collection.count() == 0:\\n+    \\\"\\\"\\\"搜索记忆（同时搜索新旧两个 collection）\\\"\\\"\\\"\\n+    client = get_client()\\n+    memories = []\\n+    \\n+    # 1. 搜索新 collection（4096 维）\\n+    try:\\n+        new_col = client.get_collection(COLLECTION_NAME)\\n+        if new_col.count() > 0:\\n+            query_embedding = get_embedding(query)\\n+            results = new_col.query(\\n+                query_embeddings=[query_embedding],\\n+                n_results=min(top_k * 2 if use_rerank else top_k, new_col.count())\\n+            )\\n+            if results[\\\"documents\\\"][0]:\\n+                for i, doc in enumerate(results[\\\"documents\\\"][0]):\\n+                    memories.append({\\n+                        \\\"id\\\": results[\\\"ids\\\"][0][i],\\n+                        \\\"content\\\": doc,\\n+                        \\\"score\\\": 1 - results[\\\"distances\\\"][0][i] if results[\\\"distances\\\"] else 0,\\n+                        \\\"metadata\\\": results[\\\"metadatas\\\"][0][i] if results[\\\"metadatas\\\"] else {},\\n+                        \\\"source\\\": \\\"4096\\\"\\n+                    })\\n+    except Exception as e:\\n+        print(f\\\"[搜索] 新 collection 失败: {e}\\\")\\n+    \\n+    # 2. 搜索旧 collection（768 维）\\n+    try:\\n+        old_col = client.get_collection(COLLECTION_NAME_FALLBACK)\\n+        if old_col.count() > 0:\\n+            fallback_embedding = get_fallback_embedding(query)\\n+            results = old_col.query(\\n+                query_embeddings=[fallback_embedding],\\n+                n_results=min(top_k * 2 if use_rerank else top_k, old_col.count())\\n+            )\\n+            if results[\\\"documents\\\"][0]:\\n+                for i, doc in enumerate(results[\\\"documents\\\"][0]):\\n+                    # 去重：如果内容已存在则跳过\\n+                    if not any(m[\\\"content\\\"] == doc for m in memories):\\n+                        memories.append({\\n+                            \\\"id\\\": results[\\\"ids\\\"][0][i],\\n+                            \\\"content\\\": doc,\\n+                            \\\"score\\\": 1 - results[\\\"distances\\\"][0][i] if results[\\\"distances\\\"] else 0,\\n+                            \\\"metadata\\\": results[\\\"metadatas\\\"][0][i] if results[\\\"metadatas\\\"] else {},\\n+                            \\\"source\\\": \\\"768\\\"\\n+                        })\\n+    except Exception as e:\\n+        print(f\\\"[搜索] 旧 collection 失败: {e}\\\")\\n+    \\n+    if not memories:\\n         return []\\n-    \\n-    query_embedding = get_embedding(query)\\n-    \\n-    # 先用向量搜索获取候选\\n-    results = collection.query(\\n-        query_embeddings=[query_embedding],\\n-        n_results=min(top_k * 2 if use_rerank else top_k, collection.count())\\n-    )\\n-    \\n-    if not results[\\\"documents\\\"][0]:\\n-        return []\\n-    \\n-    memories = []\\n-    for i, doc in enumerate(results[\\\"documents\\\"][0]):\\n-        memories.append({\\n-            \\\"id\\\": results[\\\"ids\\\"][0][i],\\n-            \\\"content\\\": doc,\\n-            \\\"score\\\": 1 - results[\\\"distances\\\"][0][i] if results[\\\"distances\\\"] else 0,\\n-            \\\"metadata\\\": results[\\\"metadatas\\\"][0][i] if results[\\\"metadatas\\\"] else {}\\n-        })\\n     \\n     # 可选：使用 reranker 重排序\\n     if use_rerank and len(memories) > 1:\\n@@ -206,6 +246,8 @@\\n         \\n         return reranked_memories[:top_k]\\n     \\n+    # 按 score 排序\\n+    memories.sort(key=lambda x: x[\\\"score\\\"], reverse=True)\\n     return memories[:top_k]\\n \\n \\n\", \"files_modified\": [\"/home/ubuntu/.hermes/scripts/local_memory.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "tooluse_ScWdfpf1CANh5axaNHBPfy", "timestamp": "2026-05-18T03:53:52.561300"}
{"role": "assistant", "content": "测试一下：", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "tooluse_zEFU78ZmrWzvkflQS4IIGx", "call_id": "tooluse_zEFU78ZmrWzvkflQS4IIGx", "response_item_id": "fc_tooluse_zEFU78ZmrWzvkflQS4IIGx", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"cd ~/.hermes/scripts && python3 local_memory.py search \\\"混淆矩阵\\\" --top_k 3 --rerank 2>&1\"}"}}], "timestamp": "2026-05-18T03:53:52.561300"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"{\\n  \\\"results\\\": [\\n    {\\n      \\\"id\\\": \\\"c6895f4f78aa\\\",\\n      \\\"content\\\": \\\"这个混淆矩阵数据是之前那个 HTML 文件（图.txt）里的，不是从你论文里提取的。\\\\n\\\\n可能是那个软件（Claude）自己编的示例数据，没有对照你论文的实际准确率。\\\\n\\\\n要我按论文的 90.33% 重新生成一组符合的混淆矩阵数据吗？\\\",\\n      \\\"score\\\": 0.2638431191444397,\\n      \\\"metadata\\\": {\\n        \\\"created_at\\\": \\\"2026-05-18T01:07:36.676373\\\"\\n      },\\n      \\\"source\\\": \\\"768\\\",\\n      \\\"rerank_score\\\": 0.20137886703014374\\n    },\\n    {\\n      \\\"id\\\": \\\"9894056b2015\\\",\\n      \\\"content\\\": \\\"论文里没找到混淆矩阵的表格数据。你之前给我的混淆矩阵数据是：\\\\n\\\\n```\\\\n        小麦  水稻  玉米\\\\n小麦    231   17    2\\\\n水稻    21   232    2\\\\n玉米    1     0   249\\\\n```\\\\n\\\\n这个数据是从哪来的？论文正文里没有这个表格，只提到了准确率（90.33%等），没有具体的混淆矩阵数值。\\\",\\n      \\\"score\\\": 0.15952277183532715,\\n      \\\"metadata\\\": {\\n        \\\"created_at\\\": \\\"2026-05-18T01:07:35.322090\\\"\\n      },\\n      \\\"source\\\": \\\"768\\\",\\n      \\\"rerank_score\\\": 0.1575547158718109\\n    },\\n    {\\n      \\\"id\\\": \\\"dff09a378fab\\\",\\n      \\\"content\\\": \\\"好了：\\\\n\\\\nMEDIA:/tmp/decay_chart.png\\\\n\\\\nMEDIA:/tmp/confusion_matrix.png\\\",\\n      \\\"score\\\": 0.18208307027816772,\\n      \\\"metadata\\\": {\\n        \\\"created_at\\\": \\\"2026-05-18T01:07:20.471002\\\"\\n      },\\n      \\\"source\\\": \\\"768\\\",\\n      \\\"rerank_score\\\": 0.09100998193025589\\n    }\\n  ]\\n}\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "tooluse_zEFU78ZmrWzvkflQS4IIGx", "timestamp": "2026-05-18T03:53:52.561300"}
{"role": "assistant", "content": "搞定！现在搜索同时查两个 collection，用 Qwen3-VL-Reranker 重排序。\n\n**更新完成：**\n- ✅ **主 Embedding**：Qwen/Qwen3-VL-Embedding-8B（4096 维）\n- ✅ **主 Reranker**：Qwen/Qwen3-VL-Reranker-8B\n- ✅ **Fallback**：BCE 系列（768 维，搜旧数据用）\n- ✅ 搜索自动合并两个 collection 的结果\n\n迁移还在后台跑，目前 1100/13878 条。要我继续跑迁移吗？", "reasoning": null, "finish_reason": "stop", "timestamp": "2026-05-18T03:53:52.561300"}
{"role": "user", "content": "继续", "timestamp": "2026-05-18T03:56:47.511834"}
{"role": "assistant", "content": "", "reasoning": "用户说继续，我需要继续跑迁移脚本。之前跑了1100条，还有12778条要迁移。\n", "finish_reason": "tool_calls", "reasoning_content": "用户说继续，我需要继续跑迁移脚本。之前跑了1100条，还有12778条要迁移。\n", "tool_calls": [{"id": "tooluse_CrS8POgO67gcM6Bc2Ef9cX", "call_id": "tooluse_CrS8POgO67gcM6Bc2Ef9cX", "response_item_id": "fc_tooluse_CrS8POgO67gcM6Bc2Ef9cX", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"cd ~/.hermes/scripts && python3 migrate_to_4096.py 2>&1\", \"timeout\": 600, \"background\": true, \"notify_on_complete\": true}"}}], "timestamp": "2026-05-18T03:56:47.511834"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"Background process started\", \"session_id\": \"proc_5bd3b9825238\", \"pid\": 3803177, \"exit_code\": 0, \"error\": null, \"notify_on_complete\": true}", "tool_call_id": "tooluse_CrS8POgO67gcM6Bc2Ef9cX", "timestamp": "2026-05-18T03:56:47.511834"}
{"role": "assistant", "content": "开始了，完成会通知我。看下进度：", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "tooluse_64hNEwFCfEAotDKZ7XQG5K", "call_id": "tooluse_64hNEwFCfEAotDKZ7XQG5K", "response_item_id": "fc_tooluse_64hNEwFCfEAotDKZ7XQG5K", "type": "function", "function": {"name": "process", "arguments": "{\"action\": \"poll\", \"session_id\": \"proc_5bd3b9825238\"}"}}], "timestamp": "2026-05-18T03:56:47.511834"}
{"role": "tool", "name": "process", "content": "{\"session_id\": \"proc_5bd3b9825238\", \"command\": \"cd ~/.hermes/scripts && python3 migrate_to_4096.py 2>&1\", \"status\": \"running\", \"pid\": 3803177, \"uptime_seconds\": 4, \"output_preview\": \"\"}", "tool_call_id": "tooluse_64hNEwFCfEAotDKZ7XQG5K", "timestamp": "2026-05-18T03:56:47.511834"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "tooluse_0vnnrBKt4Ma62g83sjRHGl", "call_id": "tooluse_0vnnrBKt4Ma62g83sjRHGl", "response_item_id": "fc_tooluse_0vnnrBKt4Ma62g83sjRHGl", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"sleep 10 && cd ~/.hermes/scripts && python3 -c \\\"\\nimport chromadb\\nclient = chromadb.PersistentClient(path='../local_memory_db')\\nfor c in client.list_collections():\\n    col = client.get_collection(c.name)\\n    print(f'{c.name}: {col.count()} 条')\\n\\\"\"}"}}], "timestamp": "2026-05-18T03:56:47.511834"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"memories_4096: 5500 条\\nmemories: 13878 条\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "tooluse_0vnnrBKt4Ma62g83sjRHGl", "timestamp": "2026-05-18T03:56:47.511834"}
{"role": "assistant", "content": "跑得挺快！已经 5500 条了，大概还要 3-4 分钟。", "reasoning": null, "finish_reason": "stop", "timestamp": "2026-05-18T03:56:47.511834"}
{"role": "user", "content": "TeleAI/TeleSpeechASR这个是语音识别模型，你之前还要用whisper，其实根本不需要", "timestamp": "2026-05-18T03:57:31.662279"}
{"role": "assistant", "content": "哦对！测试一下：", "reasoning": "用户提到 TeleAI/TeleSpeechASR 是硅基流动上的语音识别模型，说我之前用 Whisper 其实不需要。让我测试一下这个模型。\n", "finish_reason": "tool_calls", "reasoning_content": "用户提到 TeleAI/TeleSpeechASR 是硅基流动上的语音识别模型，说我之前用 Whisper 其实不需要。让我测试一下这个模型。\n", "tool_calls": [{"id": "tooluse_tHFjKRgtA1EQFuCGXr3NHz", "call_id": "tooluse_tHFjKRgtA1EQFuCGXr3NHz", "response_item_id": "fc_tooluse_tHFjKRgtA1EQFuCGXr3NHz", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"# 先生成一段测试音频\\nedge-tts -t \\\"你好，这是一段测试语音，看看语音识别效果怎么样\\\" -v zh-CN-XiaoyiNeural --write-media /tmp/test_asr.mp3 2>/dev/null && echo \\\"音频生成完成\\\"\"}"}}], "timestamp": "2026-05-18T03:57:31.662279"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"音频生成完成\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "tooluse_tHFjKRgtA1EQFuCGXr3NHz", "timestamp": "2026-05-18T03:57:31.662279"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "tooluse_emToHxY136GBlfEPAgiRzP", "call_id": "tooluse_emToHxY136GBlfEPAgiRzP", "response_item_id": "fc_tooluse_emToHxY136GBlfEPAgiRzP", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s https://api.siliconflow.cn/v1/audio/transcriptions \\\\\\n  -H \\\"Authorization: Bearer sk-llicoufjowafeksyzknhxebhxfyaawjrmiiefpswhofheirv\\\" \\\\\\n  -F \\\"model=TeleAI/TeleSpeechASR\\\" \\\\\\n  -F \\\"file=@/tmp/test_asr.mp3\\\" | python3 -m json.tool\"}"}}], "timestamp": "2026-05-18T03:57:31.662279"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"{\\n    \\\"text\\\": \\\"\\\\u4f60\\\\u597d\\\\uff0c\\\\u8fd9\\\\u662f\\\\u4e00\\\\u6bb5\\\\u6d4b\\\\u8bd5\\\\u8bed\\\\u97f3\\\\uff0c\\\\u770b\\\\u770b\\\\u8bed\\\\u97f3\\\\u8bc6\\\\u522b\\\\u6548\\\\u679c\\\\u600e\\\\u4e48\\\\u6837\\\\uff1f\\\"\\n}\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "tooluse_emToHxY136GBlfEPAgiRzP", "timestamp": "2026-05-18T03:57:31.662279"}
{"role": "assistant", "content": "完美！识别结果：**\"你好，这是一段测试语音，看看语音识别效果怎么样？\"**\n\n一字不差！比 Whisper 还方便，不用本地跑模型。\n\n**硅基流动免费 API 汇总：**\n| 功能 | 模型 | 状态 |\n|------|------|------|\n| Embedding | Qwen3-VL-Embedding-8B | ✅ 已配置 |\n| Reranker | Qwen3-VL-Reranker-8B | ✅ 已配置 |\n| 语音识别 | TeleAI/TeleSpeechASR | ✅ 可用 |\n| 文本模型 | Qwen3-8B | ✅ 已配置 fallback |\n\n要我把语音识别也加到配置里吗？以后收到语音消息可以直接用这个转文字。", "reasoning": null, "finish_reason": "stop", "timestamp": "2026-05-18T03:57:31.662279"}