44 lines
82 KiB
JSON
44 lines
82 KiB
JSON
{"type":"session","version":3,"id":"019ed368-f7a6-7a9d-8a22-d3e37bafc0f3","timestamp":"2026-06-17T02:28:52.262Z","cwd":"/shumengya/project/agent/sproutclaw"}
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{"type":"model_change","id":"a79272fa","parentId":null,"timestamp":"2026-06-17T02:28:52.547Z","provider":"xiaomimimo-openai","modelId":"mimo-v2.5"}
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{"type":"thinking_level_change","id":"3865489c","parentId":"a79272fa","timestamp":"2026-06-17T02:28:52.547Z","thinkingLevel":"high"}
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{"type":"message","id":"e8a802a4","parentId":"3865489c","timestamp":"2026-06-17T02:29:54.196Z","message":{"role":"user","content":[{"type":"text","text":"查看一下/shumengya/project/agent/mengya-rag项目给我详细介绍一下"}],"timestamp":1781663394195}}
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{"type":"message","id":"b9cbca94","parentId":"e8a802a4","timestamp":"2026-06-17T02:29:56.852Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"用户想查看 `/shumengya/project/agent/mengya-rag` 项目并获取详细介绍。首先，我需要浏览该目录结构，查看主要文件。让我使用 `read` 工具来读取目录内容。由于这是一个目录，我应该先列出其中的文件。我可以使用 `bash` 命令来列出文件。","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_59020e5a7a6c460dba7c7234","name":"bash","arguments":{"command":"ls -la /shumengya/project/agent/mengya-rag"}}],"api":"openai-completions","provider":"xiaomimimo-openai","model":"mimo-v2.5","usage":{"input":1297,"output":113,"cacheRead":10240,"cacheWrite":0,"totalTokens":11650,"cost":{"input":0.0005188,"output":0.000226,"cacheRead":0.0008192,"cacheWrite":0,"total":0.0015639999999999999}},"stopReason":"toolUse","timestamp":1781663394197,"responseId":"686ac2c606ec43ee99ee213a6f19be64"}}
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{"type":"message","id":"8f9aaaff","parentId":"b9cbca94","timestamp":"2026-06-17T02:29:56.867Z","message":{"role":"toolResult","toolCallId":"call_59020e5a7a6c460dba7c7234","toolName":"bash","content":[{"type":"text","text":"总计 596\ndrwxr-xr-x 7 root root 4096 5月12日 17:25 .\ndrwxr-xr-x 5 www www 4096 6月15日 02:03 ..\ndrwxr-xr-x 4 root root 4096 5月12日 16:53 data\n-rw-r--r-- 1 root root 398 5月12日 21:41 .env\n-rw-r--r-- 1 root root 359 5月12日 21:41 .env.example\n-rw-r--r-- 1 root root 76 5月12日 17:09 .gitignore\n-rw-r--r-- 1 root root 623 5月13日 14:16 pyproject.toml\n-rw-r--r-- 1 root root 8402 5月13日 14:22 README.md\ndrwxr-xr-x 2 root root 4096 5月12日 17:13 scripts\ndrwxr-xr-x 3 root root 4096 5月12日 16:53 src\ndrwxr-xr-x 2 root root 4096 5月12日 16:53 tests\n-rw-r--r-- 1 root root 550536 5月12日 21:44 uv.lock\ndrwxr-xr-x 5 root root 4096 5月12日 17:09 .venv\n"}],"isError":false,"timestamp":1781663396867}}
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{"type":"message","id":"b23d2c98","parentId":"8f9aaaff","timestamp":"2026-06-17T02:29:59.170Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"现在我需要查看 README.md 文件来了解项目概况。让我读取它。","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_7b1838497a494a8aac9c06c0","name":"read","arguments":{"path":"/shumengya/project/agent/mengya-rag/README.md"}}],"api":"openai-completions","provider":"xiaomimimo-openai","model":"mimo-v2.5","usage":{"input":557,"output":52,"cacheRead":11520,"cacheWrite":0,"totalTokens":12129,"cost":{"input":0.00022280000000000002,"output":0.000104,"cacheRead":0.0009216000000000001,"cacheWrite":0,"total":0.0012484000000000002}},"stopReason":"toolUse","timestamp":1781663396868,"responseId":"3a783ca1c5924eaba822c04b4149a2d6"}}
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{"type":"message","id":"95348a29","parentId":"b23d2c98","timestamp":"2026-06-17T02:29:59.173Z","message":{"role":"toolResult","toolCallId":"call_7b1838497a494a8aac9c06c0","toolName":"read","content":[{"type":"text","text":"# 萌芽 RAG 知识库\n\n轻量版 Obsidian 笔记 RAG 项目，基于 LlamaIndex 和 DeepSeek API。\n检索使用 Markdown 结构感知分块 + 本地小 embedding + BM25 的混合检索，适合个人 Obsidian 笔记这种规模。\n\n## 项目结构\n\n```text\nmengya-rag/\n├── data/\n│ ├── notes/ # 从 bigmengya 同步来的 Obsidian Markdown 笔记\n│ └── index/bm25/ # 本地节点和向量索引\n│ ├── rag.sqlite3 # SQLite + sqlite-vec 本地向量库\n│ └── nodes.jsonl # 分块内容和元数据备份\n├── scripts/\n│ ├── sync_notes.sh # 同步 bigmengya 笔记\n│ ├── build_index.sh # 构建索引\n│ └── ask.sh # 命令行问答\n├── src/mengya_rag/\n│ ├── config.py # 配置读取\n│ ├── markdown_chunker.py # Markdown 结构感知分块\n│ ├── indexing.py # 建索引\n│ ├── retrieval.py # 检索、融合、盘点模式\n│ ├── qa.py # DeepSeek 生成回答\n│ └── cli.py # CLI 命令入口\n├── .env\n├── .env.example\n├── pyproject.toml\n└── README.md\n```\n\n## 技术栈\n\n- RAG 基础框架：LlamaIndex\n- 本地 embedding：`BAAI/bge-small-zh-v1.5`\n- embedding 运行：FastEmbed + ONNX\n- 大模型回答：DeepSeek API\n- 当前推荐模型：`deepseek-v4-flash`\n- 检索方式：BM25 + 向量检索\n- 融合方式：RRF 类融合 + 原始相似度过滤 + 同文件去重\n- 数据库：无\n- 向量库：SQLite + sqlite-vec\n- 服务端：无，当前是命令行工具\n\n## 初始化\n\n```bash\ncd /shumengya/project/agent/mengya-rag\ncp .env.example .env\n```\n\n编辑 `.env`，填入：\n\n```env\nDEEPSEEK_API_KEY=你的 DeepSeek API Key\n```\n\n安装依赖：\n\n```bash\nuv sync\n```\n\n## 同步笔记\n\n笔记源路径：\n\n```text\nbigmengya:/shumengya/docker/mengyanote-backend/data/mengyanote/\n```\n\n同步到本地：\n\n```bash\n./scripts/sync_notes.sh\n# 或\nuv run python scripts/sync_notes.py\n```\n\n## 构建索引\n\n```bash\n./scripts/build_index.sh\n# 或\nuv run python scripts/build_index.py\n```\n\n## 提问\n\n```bash\n./scripts/ask.sh \"萌芽笔记是什么\"\n# 或\nuv run python scripts/ask.py \"萌芽笔记是什么\"\n```\n\n也可以直接使用安装后的命令：\n\n```bash\nuv run mengya-ask \"Docker 常用命令有哪些\"\n```\n\n## Agent 调用推荐\n\n统一入口是 `mengya-rag`。给大模型 Agent 或脚本调用时，推荐固定使用 `--json`，输出是一行 JSON，便于解析。\n\n```bash\n# 查看配置、索引和笔记数量\nuv run mengya-rag status --json\n\n# 只检索，不调用大模型，返回命中文档和内容\nuv run mengya-rag search \"Docker 常用命令\" -k 5 --json\n\n# 返回可直接注入上游大模型的 context 字段\nuv run mengya-rag context \"WireGuard 怎么配置\" -k 4 --json\n\n# 按 search/context 返回的 source 读取完整笔记\nuv run mengya-rag read \"Docker/Docker常用命令总结.md\" --json\n\n# 本项目自己调用 DeepSeek 生成回答\nuv run mengya-rag ask \"Docker 常用命令有哪些\" -k 4 --json\n\n# 同步笔记并重建索引\nuv run mengya-rag reindex --json\n```\n\nAgent 优先使用这几个命令：\n\n- `status --json`：检查索引是否存在、笔记数、节点数、模型配置。\n- `search --json`：拿结构化检索结果，包含 `results[].source`、`title`、`heading_path`、`score`、`content`。\n- `context --json`：拿拼好的 `context` 字符串，适合直接放进其他大模型提示词。\n- `read --json`：按 `source` 读取完整 Markdown 原文，适合检索命中后补充上下文。\n- `ask --json`：让本项目完成 RAG + DeepSeek 回答，返回 `answer` 和 `sources`。\n- `reindex --json`：一条命令完成同步笔记和重建索引。\n\n常用参数：\n\n```bash\n--mode auto|hybrid|inventory # auto 默认；hybrid 普通语义检索；inventory 盘点/目录类检索\n-k, --top-k 数字 # 控制返回条数\n--env-file /path/.env # 指定配置文件\n--notes-dir /path/notes # 覆盖笔记目录\n--index-dir /path/index # 覆盖索引目录\n```\n\n## 查询模式\n\n项目会自动区分两类问题：\n\n- 普通问答：例如“WireGuard 命令怎么用”，走 Markdown 分块 + 向量/BM25 混合检索。\n- 盘点列表：例如“查一下我目前博客文章有哪些”“查一下安卓 Gradle 相关笔记”，走文件级目录检索，优先列出相关 Markdown 文件和内容预览。\n\n## 技术原理\n\n### Markdown 分块\n\n项目不使用固定字符数暴力切割，而是使用结构感知分块：\n\n```text\nMarkdown 文件\n ↓\n解析 frontmatter\n ↓\n识别 H1-H6 标题\n ↓\n按标题层级组织分块\n ↓\n代码块和表格整体保留\n ↓\n长块使用滑动窗口兜底\n```\n\n每个 chunk 前会附加上下文前缀：\n\n```text\n文件: AI/控制台Agent工具安装教程.md\n标题: 控制台Agent工具安装教程\n标题路径: H1 > H2 > H3\n```\n\n每个 chunk 会保存这些元数据：\n\n```text\nsource_file\nrel_path\nfile_name\nfolder_path\ntitle\nheading_path\nh1\nh2\nh3\nh4\nh5\nh6\nchunk_index\ntags\ncreated_at\n```\n\n### 索引构建\n\n执行：\n\n```bash\n./scripts/build_index.sh\n```\n\n构建流程：\n\n```text\nMarkdown 文件\n ↓\n结构感知分块\n ↓\n生成 TextNode\n ↓\n写入 nodes.jsonl\n ↓\n用 bge-small-zh-v1.5 生成 embedding\n ↓\n归一化向量\n ↓\n写入 SQLite + sqlite-vec\n```\n\n当前索引规模参考：\n\n```text\n分块数: 1802\n索引大小: 约 13M\nnodes.jsonl: 约 3.6M\nrag.sqlite3: 约 9.3M，包含 chunk 表和 sqlite-vec 向量表\n```\n\n### 普通问答检索\n\n普通问答适合“怎么做”“是什么”“原理是什么”这类问题。\n\n```text\n用户问题\n ↓\nquery 清洗\n ↓\nBM25 关键词检索\n ↓\n本地向量语义检索\n ↓\nRRF 融合\n ↓\n同文件去重\n ↓\n相关性过滤\n ↓\ntop-k chunk 注入 DeepSeek\n ↓\n生成回答和来源\n```\n\n### 盘点列表检索\n\n盘点列表适合“有哪些”“查一下”“相关笔记”“目录”“清单”这类问题。\n\n```text\n用户问题\n ↓\n识别为盘点类问题\n ↓\n构建文件级摘要节点\n ↓\n文件级 BM25 检索\n ↓\n关键词覆盖过滤\n ↓\n输出 Markdown 文件列表、路径和内容预览\n```\n\n## 重新更新知识库\n\n笔记变更后按顺序执行：\n\n```bash\n./scripts/sync_notes.sh\n./scripts/build_index.sh\n```\n\n## 设计取舍\n\n- Markdown 笔记按结构感知分块：识别 frontmatter、H1-H6、代码块、表格。\n- H1-H6 标题会写入 `h1` 到 `h6` 元数据，同时保存 `heading_path` 面包屑。\n- 代码块和表格整体保留，不跨块切割；长块再用滑动窗口兜底。\n- 每个 chunk 都带 `source_file`、`heading_path`、`chunk_index`、`tags`、`created_at`。\n- 本地 embedding 使用 `BAAI/bge-small-zh-v1.5`，通过 FastEmbed/ONNX 运行，不依赖 PyTorch 和 GPU。\n- 检索采用 BM25 + 向量召回，再用 RRF 融合和同文件去重，避免只靠关键词或只靠语义。\n- 使用 DeepSeek 只负责生成回答：减少 API 调用，只在提问时调用大模型。\n- 本地向量库使用 SQLite + sqlite-vec：无需 Qdrant、Milvus、Postgres 等额外服务。\n\n## 当前效果\n\n目前比较适合处理：\n\n- 某类笔记有哪些\n- 某个教程在哪里\n- 根据笔记总结某个主题\n- 列出博客文章\n- 列出 Agent 安装教程\n- 查 Gradle 构建相关内容\n\n示例：\n\n```bash\n./scripts/ask.sh '查一下我目前博客文章有哪些'\n./scripts/ask.sh '查一下我的安卓gradle构建相关笔记'\n./scripts/ask.sh '查一下我的Agent安装教程'\n```\n\n## 当前限制\n\n- 当前是 SQLite + sqlite-vec 单机向量库，数据量很大后可以换 Qdrant 或 Chroma。\n- 没有 reranker，复杂问题可能还会有弱相关内容混入。\n- embedding 模型是轻量中文模型，效果够用但不是最强。\n- `build_index` 每次是全量重建，还没有增量索引。\n- frontmatter 解析是轻量手写版，不是完整 YAML 解析。\n- 当前是 CLI 工具，没有 Web 页面和 API 服务。\n\n## 后续优化方向\n\n- 增量索引，只重建变更文件。\n- 加 reranker，例如 `bge-reranker-v2-m3`。\n- 给盘点类问题增加目录树模式。\n- 增加 `--debug` 输出检索过程。\n- 数据量变大后接 Qdrant。\n\n## 调参\n\n`.env` 中可调整：\n\n```env\nTOP_K=6\nEMBED_MODEL=BAAI/bge-small-zh-v1.5\nEMBED_BATCH_SIZE=32\nVECTOR_WEIGHT=0.65\nBM25_WEIGHT=0.35\n```\n"}],"isError":false,"timestamp":1781663399172}}
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{"type":"message","id":"8de87fde","parentId":"95348a29","timestamp":"2026-06-17T02:30:01.866Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"现在我已经了解了项目的概况。接下来，我应该查看一些关键源代码文件，以便更深入地了解项目的实现细节。让我先看看 `src/mengya_rag` 目录下的文件。","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_936074aeb027443b9652a8bf","name":"bash","arguments":{"command":"ls -la /shumengya/project/agent/mengya-rag/src/mengya_rag/"}}],"api":"openai-completions","provider":"xiaomimimo-openai","model":"mimo-v2.5","usage":{"input":2635,"output":89,"cacheRead":12032,"cacheWrite":0,"totalTokens":14756,"cost":{"input":0.001054,"output":0.000178,"cacheRead":0.00096256,"cacheWrite":0,"total":0.00219456}},"stopReason":"toolUse","timestamp":1781663399173,"responseId":"b038084e1f374b37b17a8aca438e1f38"}}
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{"type":"message","id":"43b6b898","parentId":"8de87fde","timestamp":"2026-06-17T02:30:01.880Z","message":{"role":"toolResult","toolCallId":"call_936074aeb027443b9652a8bf","toolName":"bash","content":[{"type":"text","text":"总计 80\ndrwxr-xr-x 3 root root 4096 5月13日 14:15 .\ndrwxr-xr-x 3 root root 4096 5月12日 16:53 ..\n-rw-r--r-- 1 root root 18172 5月13日 14:22 cli.py\n-rw-r--r-- 1 root root 2227 5月13日 14:14 config.py\n-rw-r--r-- 1 root root 2448 5月12日 21:35 embeddings.py\n-rw-r--r-- 1 root root 3848 5月12日 21:46 indexing.py\n-rw-r--r-- 1 root root 53 5月12日 17:09 __init__.py\n-rw-r--r-- 1 root root 8592 5月12日 21:07 markdown_chunker.py\ndrwxr-xr-x 2 root root 4096 5月13日 14:23 __pycache__\n-rw-r--r-- 1 root root 2854 5月13日 14:23 qa.py\n-rw-r--r-- 1 root root 11689 5月12日 21:46 retrieval.py\n-rw-r--r-- 1 root root 3530 5月12日 21:46 vector_store.py\n"}],"isError":false,"timestamp":1781663401880}}
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{"type":"message","id":"521632c0","parentId":"43b6b898","timestamp":"2026-06-17T02:30:03.224Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"让我查看一下关键的源代码文件。首先看 `config.py` 了解配置：","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_4c084e23f1d146d29f64c6a2","name":"read","arguments":{"path":"/shumengya/project/agent/mengya-rag/src/mengya_rag/config.py"}}],"api":"openai-completions","provider":"xiaomimimo-openai","model":"mimo-v2.5","usage":{"input":500,"output":59,"cacheRead":14656,"cacheWrite":0,"totalTokens":15215,"cost":{"input":0.0002,"output":0.000118,"cacheRead":0.00117248,"cacheWrite":0,"total":0.0014904800000000002}},"stopReason":"toolUse","timestamp":1781663401881,"responseId":"0564b7e8ebb34172b62075e59b6a9ba7"}}
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{"type":"message","id":"88d91fca","parentId":"521632c0","timestamp":"2026-06-17T02:30:03.226Z","message":{"role":"toolResult","toolCallId":"call_4c084e23f1d146d29f64c6a2","toolName":"read","content":[{"type":"text","text":"from __future__ import annotations\n\nimport os\nfrom dataclasses import replace\nfrom dataclasses import dataclass\nfrom pathlib import Path\n\nfrom dotenv import load_dotenv\n\n\nPROJECT_ROOT = Path(__file__).resolve().parents[2]\n\n\ndef _path_from_env(name: str, default: str) -> Path:\n value = os.getenv(name, default)\n path = Path(value)\n if not path.is_absolute():\n path = PROJECT_ROOT / path\n return path\n\n\n@dataclass(frozen=True)\nclass Settings:\n notes_remote: str\n notes_dir: Path\n index_dir: Path\n deepseek_api_key: str\n deepseek_model: str\n deepseek_temperature: float\n top_k: int\n list_top_k: int\n embed_backend: str\n embed_model: str\n embed_batch_size: int\n embed_query_prompt_name: str\n vector_weight: float\n bm25_weight: float\n\n\ndef load_settings(env_file: str | Path | None = None) -> Settings:\n if env_file is None:\n load_dotenv(PROJECT_ROOT / \".env\")\n else:\n load_dotenv(env_file)\n\n return Settings(\n notes_remote=os.getenv(\n \"NOTES_REMOTE\",\n \"bigmengya:/shumengya/docker/mengyanote-backend/data/mengyanote/\",\n ),\n notes_dir=_path_from_env(\"NOTES_DIR\", \"data/notes\"),\n index_dir=_path_from_env(\"INDEX_DIR\", \"data/index/bm25\"),\n deepseek_api_key=os.getenv(\"DEEPSEEK_API_KEY\", \"\"),\n deepseek_model=os.getenv(\"DEEPSEEK_MODEL\", \"deepseek-chat\"),\n deepseek_temperature=float(os.getenv(\"DEEPSEEK_TEMPERATURE\", \"0.2\")),\n top_k=int(os.getenv(\"TOP_K\", \"6\")),\n list_top_k=int(os.getenv(\"LIST_TOP_K\", \"20\")),\n embed_backend=os.getenv(\"EMBED_BACKEND\", \"fastembed\"),\n embed_model=os.getenv(\"EMBED_MODEL\", \"BAAI/bge-small-zh-v1.5\"),\n embed_batch_size=int(os.getenv(\"EMBED_BATCH_SIZE\", \"32\")),\n embed_query_prompt_name=os.getenv(\"EMBED_QUERY_PROMPT_NAME\", \"\"),\n vector_weight=float(os.getenv(\"VECTOR_WEIGHT\", \"0.65\")),\n bm25_weight=float(os.getenv(\"BM25_WEIGHT\", \"0.35\")),\n )\n\n\ndef override_settings(settings: Settings, **values: object) -> Settings:\n overrides = {key: value for key, value in values.items() if value is not None}\n if not overrides:\n return settings\n return replace(settings, **overrides)\n"}],"isError":false,"timestamp":1781663403226}}
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{"type":"message","id":"f23ceddc","parentId":"88d91fca","timestamp":"2026-06-17T02:30:05.097Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"现在让我看看 `markdown_chunker.py`，了解 Markdown 分块逻辑：","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_508c47bb794e427c8e77d4fc","name":"read","arguments":{"path":"/shumengya/project/agent/mengya-rag/src/mengya_rag/markdown_chunker.py"}}],"api":"openai-completions","provider":"xiaomimimo-openai","model":"mimo-v2.5","usage":{"input":691,"output":59,"cacheRead":15104,"cacheWrite":0,"totalTokens":15854,"cost":{"input":0.00027640000000000005,"output":0.000118,"cacheRead":0.00120832,"cacheWrite":0,"total":0.00160272}},"stopReason":"toolUse","timestamp":1781663403227,"responseId":"9216653c1dae41ab85472bfaf842eda6"}}
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{"type":"message","id":"ec3aa683","parentId":"f23ceddc","timestamp":"2026-06-17T02:30:05.099Z","message":{"role":"toolResult","toolCallId":"call_508c47bb794e427c8e77d4fc","toolName":"read","content":[{"type":"text","text":"from __future__ import annotations\n\nimport re\nfrom dataclasses import dataclass, field\nfrom pathlib import Path\n\nfrom llama_index.core.schema import TextNode\n\n\nTARGET_TOKENS = 512\nMAX_TOKENS = 1024\nOVERLAP_TOKENS = 96\n\n\n@dataclass\nclass MarkdownBlock:\n kind: str\n text: str\n level: int | None = None\n title: str = \"\"\n\n\n@dataclass\nclass Section:\n headings: dict[int, str]\n blocks: list[MarkdownBlock] = field(default_factory=list)\n\n\ndef extract_frontmatter(text: str) -> tuple[dict[str, object], str]:\n if not text.startswith(\"---\\n\"):\n return {}, text\n\n end = text.find(\"\\n---\", 4)\n if end == -1:\n return {}, text\n\n raw = text[4:end].strip()\n body = text[end + len(\"\\n---\") :].lstrip(\"\\n\")\n metadata: dict[str, object] = {}\n lines = raw.splitlines()\n index = 0\n\n while index < len(lines):\n line = lines[index]\n if \":\" not in line:\n index += 1\n continue\n\n key, value = line.split(\":\", 1)\n key = key.strip()\n value = value.strip()\n\n if value:\n if value.startswith(\"[\") and value.endswith(\"]\"):\n metadata[key] = [\n item.strip().strip(\"'\\\"\")\n for item in value[1:-1].split(\",\")\n if item.strip()\n ]\n else:\n metadata[key] = value.strip(\"'\\\"\")\n index += 1\n continue\n\n items: list[str] = []\n index += 1\n while index < len(lines) and lines[index].lstrip().startswith(\"- \"):\n items.append(lines[index].split(\"- \", 1)[1].strip().strip(\"'\\\"\"))\n index += 1\n metadata[key] = items\n\n return metadata, body\n\n\ndef parse_markdown_blocks(text: str) -> list[MarkdownBlock]:\n lines = text.splitlines()\n blocks: list[MarkdownBlock] = []\n paragraph: list[str] = []\n index = 0\n\n def flush_paragraph() -> None:\n nonlocal paragraph\n if paragraph:\n blocks.append(MarkdownBlock(kind=\"text\", text=\"\\n\".join(paragraph).strip()))\n paragraph = []\n\n while index < len(lines):\n line = lines[index]\n stripped = line.strip()\n\n if stripped.startswith(\"```\") or stripped.startswith(\"~~~\"):\n flush_paragraph()\n fence = stripped[:3]\n code_lines = [line]\n index += 1\n while index < len(lines):\n code_lines.append(lines[index])\n if lines[index].strip().startswith(fence):\n index += 1\n break\n index += 1\n blocks.append(MarkdownBlock(kind=\"code\", text=\"\\n\".join(code_lines)))\n continue\n\n heading = re.match(r\"^(#{1,6})\\s+(.+?)\\s*$\", line)\n if heading:\n flush_paragraph()\n level = len(heading.group(1))\n title = heading.group(2).strip()\n blocks.append(\n MarkdownBlock(kind=\"heading\", text=line.strip(), level=level, title=title)\n )\n index += 1\n continue\n\n if stripped.startswith(\"|\") and \"|\" in stripped[1:]:\n flush_paragraph()\n table_lines = [line]\n index += 1\n while index < len(lines):\n next_line = lines[index]\n if not next_line.strip().startswith(\"|\"):\n break\n table_lines.append(next_line)\n index += 1\n blocks.append(MarkdownBlock(kind=\"table\", text=\"\\n\".join(table_lines)))\n continue\n\n if not stripped:\n flush_paragraph()\n index += 1\n continue\n\n paragraph.append(line)\n index += 1\n\n flush_paragraph()\n return blocks\n\n\ndef build_sections(blocks: list[MarkdownBlock]) -> list[Section]:\n sections: list[Section] = []\n headings: dict[int, str] = {}\n current = Section(headings={})\n\n for block in blocks:\n if block.kind != \"heading\":\n current.blocks.append(block)\n continue\n\n level = block.level or 1\n for old_level in list(headings):\n if old_level >= level:\n del headings[old_level]\n headings[level] = block.title\n current_headings = dict(headings)\n\n if level <= 2 or token_count(current.blocks) >= TARGET_TOKENS:\n if current.blocks:\n sections.append(current)\n current = Section(headings=current_headings, blocks=[block])\n continue\n\n current.blocks.append(block)\n current.headings = current_headings\n\n if current.blocks:\n sections.append(current)\n return sections\n\n\ndef token_count(blocks: list[MarkdownBlock]) -> int:\n text = \"\\n\".join(block.text for block in blocks)\n return estimate_tokens(text)\n\n\ndef estimate_tokens(text: str) -> int:\n chinese = len(re.findall(r\"[\\u4e00-\\u9fff]\", text))\n latin = len(re.findall(r\"[A-Za-z0-9_#+.-]+\", text))\n other = max(len(text) - chinese, 0) // 6\n return chinese + latin + other\n\n\ndef split_section_blocks(section: Section) -> list[list[MarkdownBlock]]:\n chunks: list[list[MarkdownBlock]] = []\n current: list[MarkdownBlock] = []\n\n for block in section.blocks:\n block_tokens = estimate_tokens(block.text)\n current_tokens = token_count(current)\n\n if block.kind in {\"code\", \"table\"}:\n if current and current_tokens + block_tokens > MAX_TOKENS:\n chunks.append(current)\n current = []\n current.append(block)\n continue\n\n if current and current_tokens + block_tokens > MAX_TOKENS:\n chunks.append(current)\n current = overlap_tail(current)\n\n current.append(block)\n\n if current:\n chunks.append(current)\n return chunks\n\n\ndef overlap_tail(blocks: list[MarkdownBlock]) -> list[MarkdownBlock]:\n kept: list[MarkdownBlock] = []\n total = 0\n for block in reversed(blocks):\n if block.kind in {\"code\", \"table\"}:\n continue\n count = estimate_tokens(block.text)\n if kept and total + count > OVERLAP_TOKENS:\n break\n kept.insert(0, block)\n total += count\n return kept\n\n\ndef chunk_markdown_file(path: Path, notes_dir: Path) -> list[TextNode]:\n text = path.read_text(encoding=\"utf-8\", errors=\"ignore\")\n frontmatter, body = extract_frontmatter(text)\n blocks = parse_markdown_blocks(body)\n sections = build_sections(blocks)\n\n rel_path = path.relative_to(notes_dir)\n title = path.stem\n mtime = path.stat().st_mtime\n tags = frontmatter.get(\"tags\", [])\n if isinstance(tags, str):\n tags = [tags]\n\n nodes: list[TextNode] = []\n chunk_index = 0\n for section in sections:\n heading_path = heading_path_from_levels(section.headings)\n prefix = build_context_prefix(title, rel_path, heading_path)\n for chunk_blocks in split_section_blocks(section):\n chunk_text = \"\\n\\n\".join(block.text for block in chunk_blocks).strip()\n if not chunk_text:\n continue\n node_text = f\"{prefix}\\n\\n{chunk_text}\".strip()\n nodes.append(\n TextNode(\n text=node_text,\n metadata={\n \"source_file\": str(rel_path),\n \"rel_path\": str(rel_path),\n \"file_name\": path.name,\n \"folder_path\": str(rel_path.parent),\n \"title\": title,\n \"heading_path\": \" > \".join(heading_path),\n \"h1\": section.headings.get(1, \"\"),\n \"h2\": section.headings.get(2, \"\"),\n \"h3\": section.headings.get(3, \"\"),\n \"h4\": section.headings.get(4, \"\"),\n \"h5\": section.headings.get(5, \"\"),\n \"h6\": section.headings.get(6, \"\"),\n \"chunk_index\": chunk_index,\n \"tags\": tags,\n \"created_at\": mtime,\n },\n )\n )\n chunk_index += 1\n\n return nodes\n\n\ndef heading_path_from_levels(headings: dict[int, str]) -> list[str]:\n return [headings[level] for level in sorted(headings)]\n\n\ndef build_context_prefix(title: str, rel_path: Path, heading_path: list[str]) -> str:\n lines = [\n f\"文件: {rel_path}\",\n f\"标题: {title}\",\n ]\n if heading_path:\n lines.append(f\"标题路径: {' > '.join(heading_path)}\")\n return \"\\n\".join(lines)\n"}],"isError":false,"timestamp":1781663405099}}
|
||
{"type":"message","id":"3808c0fa","parentId":"ec3aa683","timestamp":"2026-06-17T02:30:07.380Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"现在让我看看 `retrieval.py`，了解检索逻辑：","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_db5a841284024b5e8fa174e8","name":"read","arguments":{"path":"/shumengya/project/agent/mengya-rag/src/mengya_rag/retrieval.py"}}],"api":"openai-completions","provider":"xiaomimimo-openai","model":"mimo-v2.5","usage":{"input":2018,"output":56,"cacheRead":15744,"cacheWrite":0,"totalTokens":17818,"cost":{"input":0.0008072000000000001,"output":0.000112,"cacheRead":0.00125952,"cacheWrite":0,"total":0.0021787200000000003}},"stopReason":"toolUse","timestamp":1781663405100,"responseId":"8659fde596d24520965f3558d870de17"}}
|
||
{"type":"message","id":"9e6e9b5e","parentId":"3808c0fa","timestamp":"2026-06-17T02:30:07.383Z","message":{"role":"toolResult","toolCallId":"call_db5a841284024b5e8fa174e8","toolName":"read","content":[{"type":"text","text":"from __future__ import annotations\n\nimport math\nimport re\nfrom collections import Counter\nfrom dataclasses import dataclass\n\nimport numpy as np\nfrom llama_index.core.schema import TextNode\n\nfrom .config import Settings\nfrom .embeddings import embed_query\nfrom .indexing import chinese_tokenizer\nfrom .vector_store import db_path, search_vectors\n\n\n@dataclass(frozen=True)\nclass RetrievedNode:\n node: TextNode\n score: float\n raw_score: float = 0.0\n\n\ndef clean_query(query: str) -> str:\n query = query.strip()\n for phrase in [\n \"请根据笔记简要回答\",\n \"请根据笔记回答\",\n \"根据笔记简要回答\",\n \"根据笔记回答\",\n \"简要回答\",\n \"有哪些\",\n \"是什么\",\n ]:\n query = query.replace(phrase, \" \")\n for word in [\n \"我的\",\n \"我\",\n \"内容\",\n \"请\",\n \"根据\",\n \"简要\",\n \"回答\",\n \"哪些\",\n \"什么\",\n \"一下\",\n ]:\n query = query.replace(word, \" \")\n query = re.sub(r\"[，。！？、；：,.!?;:]+\", \" \", query)\n return \" \".join(query.split())\n\n\ndef inventory_keywords(query: str) -> list[str]:\n stopwords = {\n \"查一下\",\n \"目前\",\n \"我的\",\n \"我\",\n \"相关\",\n \"笔记\",\n \"教程\",\n \"有哪些\",\n \"有什么\",\n \"列表\",\n \"清单\",\n \"目录\",\n \"安装\",\n }\n words: list[str] = []\n cleaned = clean_query(query).lower()\n for part in re.findall(r\"[a-z0-9_#+.-]+|[\\u4e00-\\u9fff]+\", cleaned):\n if part in stopwords:\n continue\n if re.fullmatch(r\"[a-z0-9_#+.-]+\", part):\n words.append(part)\n continue\n for stopword in stopwords:\n part = part.replace(stopword, \"\")\n if len(part) >= 2:\n words.append(part)\n return words\n\n\nclass LocalBM25:\n def __init__(self, nodes: list[TextNode]) -> None:\n self.nodes = nodes\n self.doc_tokens = [chinese_tokenizer(self._weighted_text(node)) for node in nodes]\n self.doc_freq: Counter[str] = Counter()\n self.term_freqs: list[Counter[str]] = []\n\n for tokens in self.doc_tokens:\n term_freq = Counter(tokens)\n self.term_freqs.append(term_freq)\n self.doc_freq.update(term_freq.keys())\n\n self.doc_count = len(nodes)\n self.doc_lengths = [len(tokens) for tokens in self.doc_tokens]\n self.avg_doc_length = (\n sum(self.doc_lengths) / self.doc_count if self.doc_count else 0.0\n )\n\n def _weighted_text(self, node: TextNode) -> str:\n title = str(node.metadata.get(\"title\", \"\"))\n rel_path = str(node.metadata.get(\"rel_path\", \"\"))\n path_text = rel_path.replace(\"/\", \" \").replace(\".md\", \"\")\n content = node.get_content()\n\n return \"\\n\".join(\n [\n \" \".join([title] * 8),\n \" \".join([path_text] * 4),\n content,\n ]\n )\n\n def search(self, query: str, top_k: int) -> list[RetrievedNode]:\n query_terms = chinese_tokenizer(query)\n if not query_terms or not self.nodes:\n return []\n\n k1 = 1.5\n b = 0.75\n scored_by_file: dict[str, RetrievedNode] = {}\n extra_scores_by_file: Counter[str] = Counter()\n\n for index, term_freq in enumerate(self.term_freqs):\n score = 0.0\n doc_length = self.doc_lengths[index] or 1\n\n for term in query_terms:\n freq = term_freq.get(term, 0)\n if freq == 0:\n continue\n\n doc_freq = self.doc_freq.get(term, 0)\n idf = math.log(1 + (self.doc_count - doc_freq + 0.5) / (doc_freq + 0.5))\n denom = freq + k1 * (\n 1 - b + b * doc_length / (self.avg_doc_length or 1)\n )\n score += idf * freq * (k1 + 1) / denom\n\n if score <= 0:\n continue\n\n node = self.nodes[index]\n rel_path = str(node.metadata.get(\"rel_path\", node.node_id))\n current = scored_by_file.get(rel_path)\n if current is None or score > current.score:\n if current is not None:\n extra_scores_by_file[rel_path] += current.score * 0.2\n scored_by_file[rel_path] = RetrievedNode(\n node=node,\n score=score,\n raw_score=score,\n )\n else:\n extra_scores_by_file[rel_path] += score * 0.2\n\n scored = [\n RetrievedNode(\n node=item.node,\n score=item.score + extra_scores_by_file[rel_path],\n raw_score=item.raw_score,\n )\n for rel_path, item in scored_by_file.items()\n ]\n scored.sort(key=lambda item: item.score, reverse=True)\n return scored[:top_k]\n\n\nclass HybridRetriever:\n def __init__(self, settings: Settings, nodes: list[TextNode]) -> None:\n self.settings = settings\n self.nodes = nodes\n self.bm25 = LocalBM25(nodes)\n\n def search(self, query: str, top_k: int) -> list[RetrievedNode]:\n query = clean_query(query)\n candidate_k = min(max(top_k * 8, 30), len(self.nodes))\n bm25_results = self.bm25.search(query, candidate_k)\n vector_results = self._vector_search(query, candidate_k)\n return self._fuse_results(bm25_results, vector_results, top_k)\n\n def _vector_search(self, query: str, top_k: int) -> list[RetrievedNode]:\n query_vector = embed_query(self.settings, query)\n rows = search_vectors(db_path(self.settings.index_dir), query_vector, top_k)\n results: list[RetrievedNode] = []\n for rowid, distance in rows:\n index = rowid - 1\n if index < 0 or index >= len(self.nodes):\n continue\n score = 1.0 / (1.0 + distance)\n results.append(\n RetrievedNode(\n node=self.nodes[index],\n score=score,\n raw_score=score,\n )\n )\n return results\n\n def _fuse_results(\n self,\n bm25_results: list[RetrievedNode],\n vector_results: list[RetrievedNode],\n top_k: int,\n ) -> list[RetrievedNode]:\n fused: dict[str, tuple[TextNode, float]] = {}\n seen_files: set[str] = set()\n\n def add(results: list[RetrievedNode], weight: float) -> None:\n if not results:\n return\n best_raw = max(item.raw_score or item.score for item in results)\n if best_raw <= 0:\n return\n\n for rank, item in enumerate(results, start=1):\n key = item.node.node_id\n raw = item.raw_score or item.score\n normalized = raw / best_raw\n if normalized < 0.18:\n continue\n score = weight * normalized / (60 + rank)\n node, current = fused.get(key, (item.node, 0.0))\n fused[key] = (node, current + score)\n\n add(bm25_results, self.settings.bm25_weight)\n add(vector_results, self.settings.vector_weight)\n\n ranked = sorted(fused.values(), key=lambda pair: pair[1], reverse=True)\n if not ranked:\n return []\n\n best_score = ranked[0][1]\n final: list[RetrievedNode] = []\n for node, score in ranked:\n if score < best_score * 0.7 and len(final) >= 1:\n continue\n rel_path = str(node.metadata.get(\"rel_path\", node.node_id))\n if rel_path in seen_files:\n continue\n seen_files.add(rel_path)\n final.append(RetrievedNode(node=node, score=score))\n if len(final) >= top_k:\n break\n\n return final\n\n\ndef is_inventory_query(query: str) -> bool:\n return any(\n word in query\n for word in [\"有哪些\", \"有什么\", \"哪些\", \"列表\", \"清单\", \"目录\", \"查一下\", \"相关笔记\"]\n )\n\n\ndef build_file_nodes(nodes: list[TextNode]) -> list[TextNode]:\n by_path: dict[str, list[TextNode]] = {}\n for node in nodes:\n rel_path = str(node.metadata.get(\"rel_path\", node.node_id))\n by_path.setdefault(rel_path, []).append(node)\n\n file_nodes: list[TextNode] = []\n for rel_path, file_chunks in by_path.items():\n first = file_chunks[0]\n title = str(first.metadata.get(\"title\", rel_path.rsplit(\"/\", 1)[-1]))\n preview = \"\\n\".join(\n chunk.get_content().strip() for chunk in file_chunks[:2] if chunk.get_content().strip()\n )\n text = f\"标题: {title}\\n路径: {rel_path}\\n内容预览:\\n{preview[:1200]}\"\n file_nodes.append(\n TextNode(\n text=text,\n metadata={\n \"rel_path\": rel_path,\n \"title\": title,\n \"is_file_summary\": True,\n },\n )\n )\n return file_nodes\n\n\nclass InventoryRetriever:\n def __init__(self, nodes: list[TextNode]) -> None:\n self.file_nodes = build_file_nodes(nodes)\n self.bm25 = LocalBM25(self.file_nodes)\n\n def search(self, query: str, top_k: int) -> list[RetrievedNode]:\n query = clean_query(query)\n direct = self._direct_directory_results(query, top_k)\n if direct:\n return direct\n\n results = self.bm25.search(query, top_k)\n results = self._filter_by_keywords(query, results)\n if not results:\n return []\n\n best = results[0].score\n kept = [item for item in results if item.score >= best * 0.4]\n return kept[:top_k]\n\n def _filter_by_keywords(\n self,\n query: str,\n results: list[RetrievedNode],\n ) -> list[RetrievedNode]:\n keywords = inventory_keywords(query)\n if not keywords:\n return results\n\n latin_keywords = [\n keyword\n for keyword in keywords\n if re.fullmatch(r\"[a-z0-9_#+.-]+\", keyword)\n ]\n\n filtered: list[RetrievedNode] = []\n for item in results:\n haystack = (\n f\"{item.node.metadata.get('title', '')} \"\n f\"{item.node.metadata.get('rel_path', '')} \"\n f\"{item.node.get_content()}\"\n ).lower()\n if latin_keywords and not all(keyword in haystack for keyword in latin_keywords):\n continue\n\n matched = sum(1 for keyword in keywords if keyword in haystack)\n if matched == 0:\n continue\n if len(keywords) >= 2 and matched / len(keywords) < 0.5:\n continue\n filtered.append(item)\n\n return filtered or results[: min(3, len(results))]\n\n def _direct_directory_results(self, query: str, top_k: int) -> list[RetrievedNode]:\n candidates = [\n node\n for node in self.file_nodes\n if str(node.metadata.get(\"rel_path\", \"\")).startswith(f\"{query}/\")\n ]\n if not candidates and query.endswith(\"文章\"):\n candidates = [\n node\n for node in self.file_nodes\n if str(node.metadata.get(\"rel_path\", \"\")).startswith(f\"{query}/\")\n ]\n if not candidates:\n return []\n\n candidates.sort(key=lambda node: str(node.metadata.get(\"rel_path\", \"\")))\n return [\n RetrievedNode(node=node, score=float(top_k - index), raw_score=float(top_k - index))\n for index, node in enumerate(candidates[:top_k])\n ]\n"}],"isError":false,"timestamp":1781663407382}}
|
||
{"type":"message","id":"d5174cd6","parentId":"9e6e9b5e","timestamp":"2026-06-17T02:30:09.623Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"现在让我看看 `cli.py`，了解命令行接口：","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_f6ab5883b1ba4de1afdb02b1","name":"read","arguments":{"path":"/shumengya/project/agent/mengya-rag/src/mengya_rag/cli.py"}}],"api":"openai-completions","provider":"xiaomimimo-openai","model":"mimo-v2.5","usage":{"input":4766,"output":53,"cacheRead":15744,"cacheWrite":0,"totalTokens":20563,"cost":{"input":0.0019064000000000002,"output":0.000106,"cacheRead":0.00125952,"cacheWrite":0,"total":0.00327192}},"stopReason":"toolUse","timestamp":1781663407384,"responseId":"3784b02ab983485a92cfa701bf195d75"}}
|
||
{"type":"message","id":"2648310a","parentId":"d5174cd6","timestamp":"2026-06-17T02:30:09.626Z","message":{"role":"toolResult","toolCallId":"call_f6ab5883b1ba4de1afdb02b1","toolName":"read","content":[{"type":"text","text":"from __future__ import annotations\n\nimport argparse\nimport json\nimport os\nimport sys\nimport time\nfrom pathlib import Path\nfrom typing import Any\n\nos.environ.setdefault(\"TRANSFORMERS_VERBOSITY\", \"error\")\n\nfrom .config import Settings, load_settings, override_settings\nfrom .indexing import build_bm25_index, load_index_nodes, markdown_files\nfrom .indexing import sync_notes as sync_notes_impl\nfrom .vector_store import db_path\n\n\ndef _positive_int(value: str) -> int:\n parsed = int(value)\n if parsed <= 0:\n raise argparse.ArgumentTypeError(\"必须是大于 0 的整数\")\n return parsed\n\n\ndef _existing_file(value: str) -> Path:\n path = Path(value)\n if not path.exists():\n raise argparse.ArgumentTypeError(f\"文件不存在: {path}\")\n if not path.is_file():\n raise argparse.ArgumentTypeError(f\"不是文件: {path}\")\n return path\n\n\ndef _add_config_args(parser: argparse.ArgumentParser) -> None:\n parser.add_argument(\n \"--env-file\",\n type=_existing_file,\n default=argparse.SUPPRESS,\n help=\"指定 .env 配置文件\",\n )\n parser.add_argument(\n \"--notes-dir\",\n type=Path,\n default=argparse.SUPPRESS,\n help=\"覆盖 NOTES_DIR\",\n )\n parser.add_argument(\n \"--index-dir\",\n type=Path,\n default=argparse.SUPPRESS,\n help=\"覆盖 INDEX_DIR\",\n )\n\n\ndef _add_output_args(parser: argparse.ArgumentParser) -> None:\n parser.add_argument(\n \"--format\",\n choices=(\"text\", \"json\"),\n default=argparse.SUPPRESS,\n help=\"输出格式，Agent 调用建议使用 json\",\n )\n parser.add_argument(\n \"--json\",\n action=\"store_true\",\n default=argparse.SUPPRESS,\n help=\"等同于 --format json\",\n )\n\n\ndef _add_retrieval_args(parser: argparse.ArgumentParser) -> None:\n parser.add_argument(\n \"--mode\",\n choices=(\"auto\", \"hybrid\", \"inventory\"),\n default=\"auto\",\n help=\"检索模式，默认 auto 自动判断\",\n )\n parser.add_argument(\"-k\", \"--top-k\", type=_positive_int, help=\"覆盖返回条数\")\n\n\ndef _settings_from_args(args: argparse.Namespace) -> Settings:\n settings = load_settings(getattr(args, \"env_file\", None))\n return override_settings(\n settings,\n notes_dir=getattr(args, \"notes_dir\", None),\n index_dir=getattr(args, \"index_dir\", None),\n )\n\n\ndef _json_default(value: object) -> str:\n if isinstance(value, Path):\n return str(value)\n raise TypeError(f\"Object of type {type(value).__name__} is not JSON serializable\")\n\n\ndef _write_json(payload: dict[str, Any]) -> None:\n print(json.dumps(payload, ensure_ascii=False, default=_json_default))\n\n\ndef _wants_json(args: argparse.Namespace) -> bool:\n return bool(getattr(args, \"json\", False) or getattr(args, \"format\", \"text\") == \"json\")\n\n\ndef _emit(args: argparse.Namespace, payload: dict[str, Any], text: str | None = None) -> None:\n if _wants_json(args):\n _write_json(payload)\n return\n if text is not None:\n print(text)\n\n\ndef _fail(args: argparse.Namespace, message: str, *, code: int = 1) -> None:\n if _wants_json(args):\n _write_json({\"ok\": False, \"error\": message})\n raise SystemExit(code)\n raise SystemExit(message)\n\n\ndef _question_from_args(args: argparse.Namespace) -> str:\n question = \" \".join(getattr(args, \"question\", [])).strip()\n if not question and not sys.stdin.isatty():\n question = sys.stdin.read().strip()\n return question\n\n\ndef _print_sources(sources: list[str]) -> None:\n if not sources:\n print(\"\\n来源: 无\")\n return\n print(\"\\n来源:\")\n for source in sources:\n print(f\"- {source}\")\n\n\ndef _preview_text(text: str, limit: int) -> str:\n text = \" \".join(text.strip().split())\n if len(text) <= limit:\n return text\n return f\"{text[:limit].rstrip()}...\"\n\n\ndef _resolve_note_path(settings: Settings, source: str) -> Path:\n notes_root = settings.notes_dir.resolve()\n note_path = (notes_root / source).resolve()\n try:\n note_path.relative_to(notes_root)\n except ValueError as exc:\n raise ValueError(f\"笔记路径不能越过 NOTES_DIR: {source}\") from exc\n if not note_path.exists():\n raise FileNotFoundError(f\"笔记不存在: {source}\")\n if not note_path.is_file():\n raise IsADirectoryError(f\"不是笔记文件: {source}\")\n return note_path\n\n\ndef _source_text(item: Any) -> str:\n metadata = item.node.metadata\n rel_path = metadata.get(\"rel_path\") or metadata.get(\"file_path\") or \"未知来源\"\n return f\"{rel_path} score={item.score:.4f}\"\n\n\ndef _node_payload(item: Any, *, include_content: bool = True) -> dict[str, Any]:\n metadata = item.node.metadata\n payload: dict[str, Any] = {\n \"source\": metadata.get(\"rel_path\") or metadata.get(\"file_path\") or \"未知来源\",\n \"title\": metadata.get(\"title\", \"\"),\n \"heading_path\": metadata.get(\"heading_path\", \"\"),\n \"chunk_index\": metadata.get(\"chunk_index\"),\n \"score\": item.score,\n \"raw_score\": item.raw_score,\n \"metadata\": dict(metadata),\n }\n if include_content:\n payload[\"content\"] = item.node.get_content().strip()\n return payload\n\n\ndef _retrieve(args: argparse.Namespace) -> list[Any]:\n from .qa import retrieve_nodes\n\n question = _question_from_args(args)\n if not question:\n _fail(args, \"请提供检索内容，例如: mengya-rag search Docker\")\n\n settings = _settings_from_args(args)\n try:\n return retrieve_nodes(\n settings,\n question,\n mode=args.mode,\n top_k=args.top_k,\n )\n except Exception as exc:\n _fail(args, f\"检索失败: {exc}\")\n\n\ndef _sync_notes(args: argparse.Namespace) -> int:\n settings = _settings_from_args(args)\n started = time.time()\n try:\n sync_notes_impl(settings)\n except Exception as exc:\n _fail(args, f\"同步失败: {exc}\")\n\n payload = {\n \"ok\": True,\n \"command\": \"sync\",\n \"notes_remote\": settings.notes_remote,\n \"notes_dir\": settings.notes_dir,\n \"elapsed_seconds\": round(time.time() - started, 3),\n }\n _emit(args, payload, f\"笔记已同步到: {settings.notes_dir}\")\n return 0\n\n\ndef _build_index(args: argparse.Namespace) -> int:\n settings = _settings_from_args(args)\n started = time.time()\n try:\n count = build_bm25_index(settings)\n except Exception as exc:\n _fail(args, f\"构建索引失败: {exc}\")\n\n payload = {\n \"ok\": True,\n \"command\": \"index\",\n \"index_dir\": settings.index_dir,\n \"db_path\": db_path(settings.index_dir),\n \"node_count\": count,\n \"elapsed_seconds\": round(time.time() - started, 3),\n }\n text = f\"索引已生成: {settings.index_dir}\\n索引分块数: {count}\"\n _emit(args, payload, text)\n return 0\n\n\ndef _reindex(args: argparse.Namespace) -> int:\n settings = _settings_from_args(args)\n started = time.time()\n try:\n sync_notes_impl(settings)\n count = build_bm25_index(settings)\n except Exception as exc:\n _fail(args, f\"更新知识库失败: {exc}\")\n\n payload = {\n \"ok\": True,\n \"command\": \"reindex\",\n \"notes_remote\": settings.notes_remote,\n \"notes_dir\": settings.notes_dir,\n \"index_dir\": settings.index_dir,\n \"db_path\": db_path(settings.index_dir),\n \"node_count\": count,\n \"elapsed_seconds\": round(time.time() - started, 3),\n }\n text = f\"知识库已更新: {settings.index_dir}\\n索引分块数: {count}\"\n _emit(args, payload, text)\n return 0\n\n\ndef _ask(args: argparse.Namespace) -> int:\n from .qa import ask_question\n\n question = _question_from_args(args)\n if not question:\n _fail(args, \"请提供问题，例如: mengya-rag ask Docker 常用命令有哪些\")\n\n settings = _settings_from_args(args)\n try:\n answer, sources = ask_question(\n settings,\n question,\n mode=args.mode,\n top_k=args.top_k,\n )\n except Exception as exc:\n _fail(args, f\"提问失败: {exc}\")\n\n payload = {\n \"ok\": True,\n \"command\": \"ask\",\n \"question\": question,\n \"answer\": answer,\n \"sources\": sources,\n \"mode\": args.mode,\n \"top_k\": args.top_k,\n }\n if _wants_json(args):\n _write_json(payload)\n else:\n print(answer)\n if not args.no_source:\n _print_sources(sources)\n return 0\n\n\ndef _search(args: argparse.Namespace) -> int:\n question = _question_from_args(args)\n if not question:\n _fail(args, \"请提供检索内容，例如: mengya-rag search Docker\")\n\n nodes = _retrieve(args)\n include_content = _wants_json(args) or args.show_content\n\n if not nodes:\n _emit(\n args,\n {\n \"ok\": True,\n \"command\": \"search\",\n \"question\": question,\n \"results\": [],\n },\n \"未检索到相关内容。\",\n )\n return 0\n\n payload = {\n \"ok\": True,\n \"command\": \"search\",\n \"question\": question,\n \"mode\": args.mode,\n \"top_k\": args.top_k,\n \"results\": [\n _node_payload(item, include_content=include_content) for item in nodes\n ],\n }\n if _wants_json(args):\n _write_json(payload)\n return 0\n\n for index, item in enumerate(nodes, start=1):\n metadata = item.node.metadata\n heading_path = metadata.get(\"heading_path\", \"\")\n print(f\"{index}. {_source_text(item)}\")\n if heading_path:\n print(f\" 标题路径: {heading_path}\")\n if args.show_content:\n preview = _preview_text(item.node.get_content(), args.preview_chars)\n print(f\" 预览: {preview}\")\n return 0\n\n\ndef _context(args: argparse.Namespace) -> int:\n question = _question_from_args(args)\n if not question:\n _fail(args, \"请提供问题，例如: mengya-rag context Docker 常用命令\")\n\n nodes = _retrieve(args)\n context_parts: list[str] = []\n sources: list[dict[str, Any]] = []\n for index, item in enumerate(nodes, start=1):\n metadata = item.node.metadata\n source = metadata.get(\"rel_path\") or metadata.get(\"file_path\") or \"未知来源\"\n content = item.node.get_content().strip()\n context_parts.append(f\"[{index}] 来源: {source}\\n{content}\")\n sources.append(_node_payload(item, include_content=False))\n\n context = \"\\n\\n\".join(context_parts)\n payload = {\n \"ok\": True,\n \"command\": \"context\",\n \"question\": question,\n \"mode\": args.mode,\n \"top_k\": args.top_k,\n \"context\": context,\n \"sources\": sources,\n }\n if _wants_json(args):\n _write_json(payload)\n else:\n print(context or \"未检索到相关内容。\")\n return 0\n\n\ndef _read(args: argparse.Namespace) -> int:\n settings = _settings_from_args(args)\n source = args.source.strip()\n if not source:\n _fail(args, \"请提供笔记相对路径，例如: mengya-rag read Docker/Docker常用命令总结.md\")\n\n try:\n path = _resolve_note_path(settings, source)\n content = path.read_text(encoding=\"utf-8\")\n except Exception as exc:\n _fail(args, f\"读取笔记失败: {exc}\")\n\n original_length = len(content)\n if args.max_chars is not None:\n content = content[: args.max_chars]\n\n payload = {\n \"ok\": True,\n \"command\": \"read\",\n \"source\": source,\n \"path\": path,\n \"content\": content,\n \"truncated\": args.max_chars is not None and original_length > len(content),\n }\n _emit(args, payload, content)\n return 0\n\n\ndef _status(args: argparse.Namespace) -> int:\n settings = _settings_from_args(args)\n db_file = db_path(settings.index_dir)\n nodes_file = settings.index_dir / \"nodes.jsonl\"\n\n note_count: int | None = None\n note_error: str | None = None\n try:\n note_count = len(markdown_files(settings.notes_dir))\n except Exception as exc:\n note_error = str(exc)\n\n node_count: int | None = None\n node_error: str | None = None\n try:\n node_count = len(load_index_nodes(settings))\n except Exception as exc:\n node_error = str(exc)\n\n payload = {\n \"ok\": True,\n \"command\": \"status\",\n \"project_root\": Path(__file__).resolve().parents[2],\n \"notes_dir\": settings.notes_dir,\n \"index_dir\": settings.index_dir,\n \"db_path\": db_file,\n \"db_exists\": db_file.exists(),\n \"nodes_file\": nodes_file,\n \"nodes_file_exists\": nodes_file.exists(),\n \"deepseek_api_key_configured\": bool(settings.deepseek_api_key),\n \"deepseek_model\": settings.deepseek_model,\n \"embed_backend\": settings.embed_backend,\n \"embed_model\": settings.embed_model,\n \"top_k\": settings.top_k,\n \"list_top_k\": settings.list_top_k,\n \"markdown_note_count\": note_count,\n \"markdown_note_error\": note_error,\n \"index_node_count\": node_count,\n \"index_node_error\": node_error,\n }\n text_lines = [\n f\"项目目录: {payload['project_root']}\",\n f\"笔记目录: {settings.notes_dir}\",\n f\"索引目录: {settings.index_dir}\",\n f\"向量库: {db_file} ({'存在' if db_file.exists() else '不存在'})\",\n f\"节点文件: {nodes_file} ({'存在' if nodes_file.exists() else '不存在'})\",\n f\"DeepSeek Key: {'已配置' if settings.deepseek_api_key else '未配置'}\",\n f\"DeepSeek 模型: {settings.deepseek_model}\",\n f\"Embedding: {settings.embed_backend} / {settings.embed_model}\",\n f\"默认 top_k: {settings.top_k}\",\n f\"默认 list_top_k: {settings.list_top_k}\",\n f\"Markdown 笔记数: {note_count if note_error is None else f'读取失败 ({note_error})'}\",\n f\"索引节点数: {node_count if node_error is None else f'读取失败 ({node_error})'}\",\n ]\n _emit(args, payload, \"\\n\".join(text_lines))\n return 0\n\n\ndef _add_question_arg(parser: argparse.ArgumentParser) -> None:\n parser.add_argument(\"question\", nargs=\"*\", help=\"问题；省略时从 stdin 读取\")\n\n\ndef build_parser(prog: str = \"mengya-rag\") -> argparse.ArgumentParser:\n config_parent = argparse.ArgumentParser(add_help=False)\n _add_config_args(config_parent)\n _add_output_args(config_parent)\n\n parser = argparse.ArgumentParser(\n prog=prog,\n description=\"萌芽 RAG 知识库命令行工具\",\n parents=[config_parent],\n )\n\n subparsers = parser.add_subparsers(dest=\"command\")\n\n sync_parser = subparsers.add_parser(\n \"sync\",\n help=\"从远端同步 Markdown 笔记\",\n parents=[config_parent],\n )\n sync_parser.set_defaults(func=_sync_notes)\n\n index_parser = subparsers.add_parser(\n \"index\",\n help=\"构建本地检索索引\",\n parents=[config_parent],\n )\n index_parser.set_defaults(func=_build_index)\n\n reindex_parser = subparsers.add_parser(\n \"reindex\",\n help=\"同步笔记并重建索引\",\n parents=[config_parent],\n )\n reindex_parser.set_defaults(func=_reindex)\n\n ask_parser = subparsers.add_parser(\n \"ask\",\n help=\"检索笔记并调用 DeepSeek 回答\",\n parents=[config_parent],\n )\n _add_retrieval_args(ask_parser)\n ask_parser.add_argument(\"--no-source\", action=\"store_true\", help=\"不显示来源\")\n _add_question_arg(ask_parser)\n ask_parser.set_defaults(func=_ask)\n\n search_parser = subparsers.add_parser(\n \"search\",\n help=\"只检索来源，不调用大模型\",\n parents=[config_parent],\n )\n _add_retrieval_args(search_parser)\n search_parser.add_argument(\n \"--show-content\",\n action=\"store_true\",\n help=\"显示命中的内容预览\",\n )\n search_parser.add_argument(\n \"--preview-chars\",\n type=_positive_int,\n default=260,\n help=\"内容预览字符数，默认 260\",\n )\n _add_question_arg(search_parser)\n search_parser.set_defaults(func=_search)\n\n context_parser = subparsers.add_parser(\n \"context\",\n help=\"返回可直接注入大模型的检索上下文\",\n parents=[config_parent],\n )\n _add_retrieval_args(context_parser)\n _add_question_arg(context_parser)\n context_parser.set_defaults(func=_context)\n\n read_parser = subparsers.add_parser(\n \"read\",\n help=\"按 search 返回的 source 读取完整笔记\",\n parents=[config_parent],\n )\n read_parser.add_argument(\"source\", help=\"相对 NOTES_DIR 的 Markdown 路径\")\n read_parser.add_argument(\n \"--max-chars\",\n type=_positive_int,\n help=\"最多输出多少字符，默认完整输出\",\n )\n read_parser.set_defaults(func=_read)\n\n status_parser = subparsers.add_parser(\n \"status\",\n help=\"查看配置和索引状态\",\n parents=[config_parent],\n )\n status_parser.set_defaults(func=_status)\n\n return parser\n\n\ndef main(argv: list[str] | None = None) -> int:\n parser = build_parser()\n args = parser.parse_args(argv)\n\n if not hasattr(args, \"func\"):\n parser.print_help()\n return 0\n\n return int(args.func(args) or 0)\n\n\ndef sync_notes() -> None:\n parser = argparse.ArgumentParser(description=\"同步萌芽 RAG 笔记\")\n _add_config_args(parser)\n _add_output_args(parser)\n args = parser.parse_args()\n _sync_notes(args)\n\n\ndef build_index() -> None:\n parser = argparse.ArgumentParser(description=\"构建萌芽 RAG 索引\")\n _add_config_args(parser)\n _add_output_args(parser)\n args = parser.parse_args()\n _build_index(args)\n\n\ndef ask() -> None:\n parser = argparse.ArgumentParser(description=\"向萌芽 RAG 知识库提问\")\n _add_config_args(parser)\n _add_output_args(parser)\n _add_retrieval_args(parser)\n parser.add_argument(\"--no-source\", action=\"store_true\", help=\"不显示来源\")\n _add_question_arg(parser)\n args = parser.parse_args()\n _ask(args)\n\n\nif __name__ == \"__main__\":\n raise SystemExit(main())\n"}],"isError":false,"timestamp":1781663409625}}
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{"type":"message","id":"891399cc","parentId":"2648310a","timestamp":"2026-06-17T02:30:12.264Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"现在让我看看 `qa.py`，了解问答逻辑：","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_427814efbd1b48419721ccc3","name":"read","arguments":{"path":"/shumengya/project/agent/mengya-rag/src/mengya_rag/qa.py"}}],"api":"openai-completions","provider":"xiaomimimo-openai","model":"mimo-v2.5","usage":{"input":4402,"output":53,"cacheRead":20480,"cacheWrite":0,"totalTokens":24935,"cost":{"input":0.0017608,"output":0.000106,"cacheRead":0.0016384,"cacheWrite":0,"total":0.0035052}},"stopReason":"toolUse","timestamp":1781663409627,"responseId":"d52f538819124270a519d71dfc6c6066"}}
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||
{"type":"message","id":"3fdf352d","parentId":"891399cc","timestamp":"2026-06-17T02:30:12.266Z","message":{"role":"toolResult","toolCallId":"call_427814efbd1b48419721ccc3","toolName":"read","content":[{"type":"text","text":"from __future__ import annotations\n\nfrom .config import Settings\nfrom .indexing import load_index_nodes\nfrom .retrieval import HybridRetriever, InventoryRetriever, RetrievedNode, is_inventory_query\n\n\nQA_PROMPT = \"\"\"你是萌芽 RAG 知识库助手。请只根据下面检索到的笔记内容回答问题。\n如果笔记内容不足以回答，请直接说明没有在笔记中找到足够依据。\n回答要准确、完整，优先使用中文。\n如果用户在问“有哪些/列表/目录/相关笔记/查一下”，请优先按来源文件列出条目，不要只总结一句话。\n\n笔记内容：\n---------------------\n{context}\n---------------------\n\n问题：{question}\n回答：\"\"\"\n\n\ndef format_source(item: RetrievedNode) -> str:\n metadata = item.node.metadata\n rel_path = metadata.get(\"rel_path\") or metadata.get(\"file_path\") or \"未知来源\"\n return f\"{rel_path} score={item.score:.4f}\"\n\n\ndef format_context(items: list[RetrievedNode]) -> str:\n parts: list[str] = []\n for index, item in enumerate(items, start=1):\n rel_path = item.node.metadata.get(\"rel_path\", \"未知来源\")\n parts.append(\n f\"[{index}] 来源: {rel_path}\\n\"\n f\"{item.node.get_content().strip()}\"\n )\n return \"\\n\\n\".join(parts)\n\n\ndef retrieve_nodes(\n settings: Settings,\n question: str,\n *,\n mode: str = \"auto\",\n top_k: int | None = None,\n) -> list[RetrievedNode]:\n if mode not in {\"auto\", \"hybrid\", \"inventory\"}:\n raise ValueError(\"mode 只能是 auto、hybrid 或 inventory\")\n\n index_nodes = load_index_nodes(settings)\n use_inventory = mode == \"inventory\" or (\n mode == \"auto\" and is_inventory_query(question)\n )\n limit = top_k or (settings.list_top_k if use_inventory else settings.top_k)\n\n if use_inventory:\n retriever = InventoryRetriever(index_nodes)\n return retriever.search(question, limit)\n\n retriever = HybridRetriever(settings, index_nodes)\n return retriever.search(question, limit)\n\n\ndef ask_question(\n settings: Settings,\n question: str,\n *,\n mode: str = \"auto\",\n top_k: int | None = None,\n) -> tuple[str, list[str]]:\n from llama_index.llms.deepseek import DeepSeek\n\n if not settings.deepseek_api_key:\n raise RuntimeError(\"缺少 DEEPSEEK_API_KEY，请先复制 .env.example 为 .env 并填写。\")\n\n nodes = retrieve_nodes(settings, question, mode=mode, top_k=top_k)\n\n llm = DeepSeek(\n model=settings.deepseek_model,\n api_key=settings.deepseek_api_key,\n temperature=settings.deepseek_temperature,\n )\n prompt = QA_PROMPT.format(context=format_context(nodes), question=question)\n response = llm.complete(prompt)\n\n sources = [format_source(node) for node in nodes]\n return str(response), sources\n\n\n_format_source = format_source\n_format_context = format_context\n"}],"isError":false,"timestamp":1781663412266}}
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{"type":"message","id":"08282fa3","parentId":"3fdf352d","timestamp":"2026-06-17T02:30:14.736Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"现在让我看看 `scripts` 目录中的脚本：","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_c16a3bfaae8d45c1a610e927","name":"bash","arguments":{"command":"ls -la /shumengya/project/agent/mengya-rag/scripts/"}}],"api":"openai-completions","provider":"xiaomimimo-openai","model":"mimo-v2.5","usage":{"input":781,"output":53,"cacheRead":24832,"cacheWrite":0,"totalTokens":25666,"cost":{"input":0.0003124,"output":0.000106,"cacheRead":0.00198656,"cacheWrite":0,"total":0.0024049600000000003}},"stopReason":"toolUse","timestamp":1781663412267,"responseId":"e650bf17e24243819e4b7be8eef8b369"}}
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||
{"type":"message","id":"cddbf8ed","parentId":"08282fa3","timestamp":"2026-06-17T02:30:14.750Z","message":{"role":"toolResult","toolCallId":"call_c16a3bfaae8d45c1a610e927","toolName":"bash","content":[{"type":"text","text":"总计 32\ndrwxr-xr-x 2 root root 4096 5月12日 17:13 .\ndrwxr-xr-x 7 root root 4096 5月12日 17:25 ..\n-rw-r--r-- 1 root root 139 5月12日 17:21 ask.py\n-rwxr-xr-x 1 root root 122 5月12日 17:21 ask.sh\n-rw-r--r-- 1 root root 155 5月12日 17:21 build_index.py\n-rwxr-xr-x 1 root root 130 5月13日 14:21 build_index.sh\n-rw-r--r-- 1 root root 153 5月12日 17:21 sync_notes.py\n-rwxr-xr-x 1 root root 129 5月13日 14:21 sync_notes.sh\n"}],"isError":false,"timestamp":1781663414750}}
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{"type":"message","id":"a1a561a8","parentId":"cddbf8ed","timestamp":"2026-06-17T02:30:16.149Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"让我看看其中一个脚本，比如 `ask.sh`：","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_13aa4449e08b44a1b4d531a3","name":"read","arguments":{"path":"/shumengya/project/agent/mengya-rag/scripts/ask.sh"}}],"api":"openai-completions","provider":"xiaomimimo-openai","model":"mimo-v2.5","usage":{"input":330,"output":48,"cacheRead":25600,"cacheWrite":0,"totalTokens":25978,"cost":{"input":0.000132,"output":0.000096,"cacheRead":0.002048,"cacheWrite":0,"total":0.002276}},"stopReason":"toolUse","timestamp":1781663414750,"responseId":"ba969df81a6b4174b169fa6339fafe69"}}
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{"type":"message","id":"03d47fa1","parentId":"a1a561a8","timestamp":"2026-06-17T02:30:16.151Z","message":{"role":"toolResult","toolCallId":"call_13aa4449e08b44a1b4d531a3","toolName":"read","content":[{"type":"text","text":"#!/usr/bin/env bash\nset -euo pipefail\n\ncd \"$(dirname \"$0\")/..\"\nexport TRANSFORMERS_VERBOSITY=error\nuv run mengya-ask \"$@\"\n"}],"isError":false,"timestamp":1781663416151}}
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{"type":"message","id":"f6a9c05f","parentId":"03d47fa1","timestamp":"2026-06-17T02:30:17.824Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"现在让我看看 `pyproject.toml` 文件，了解项目的依赖和配置：","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_0adea10a378f48a4ad16cb65","name":"read","arguments":{"path":"/shumengya/project/agent/mengya-rag/pyproject.toml"}}],"api":"openai-completions","provider":"xiaomimimo-openai","model":"mimo-v2.5","usage":{"input":111,"output":53,"cacheRead":25920,"cacheWrite":0,"totalTokens":26084,"cost":{"input":0.0000444,"output":0.000106,"cacheRead":0.0020736,"cacheWrite":0,"total":0.0022240000000000003}},"stopReason":"toolUse","timestamp":1781663416152,"responseId":"dd582251a36a46eab8212cc7040e5db5"}}
|
||
{"type":"message","id":"449aa3ca","parentId":"f6a9c05f","timestamp":"2026-06-17T02:30:17.826Z","message":{"role":"toolResult","toolCallId":"call_0adea10a378f48a4ad16cb65","toolName":"read","content":[{"type":"text","text":"[project]\nname = \"mengya-rag\"\nversion = \"0.1.0\"\ndescription = \"轻量版萌芽 Obsidian RAG 知识库\"\nrequires-python = \">=3.11\"\ndependencies = [\n \"fastembed>=0.7.0\",\n \"llama-index-core>=0.12.0\",\n \"llama-index-llms-deepseek>=0.1.0\",\n \"python-dotenv>=1.0.1\",\n \"sqlite-vec>=0.1.9\",\n]\n\n[project.scripts]\nmengya-rag = \"mengya_rag.cli:main\"\nmengya-sync-notes = \"mengya_rag.cli:sync_notes\"\nmengya-build-index = \"mengya_rag.cli:build_index\"\nmengya-ask = \"mengya_rag.cli:ask\"\n\n[build-system]\nrequires = [\"hatchling\"]\nbuild-backend = \"hatchling.build\"\n\n[tool.hatch.build.targets.wheel]\npackages = [\"src/mengya_rag\"]\n"}],"isError":false,"timestamp":1781663417826}}
|
||
{"type":"message","id":"5a7b2043","parentId":"449aa3ca","timestamp":"2026-06-17T02:30:20.204Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"现在让我看看 `.env.example` 文件：","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_0fe8270d987d4636b36db96b","name":"read","arguments":{"path":"/shumengya/project/agent/mengya-rag/.env.example"}}],"api":"openai-completions","provider":"xiaomimimo-openai","model":"mimo-v2.5","usage":{"input":340,"output":44,"cacheRead":25984,"cacheWrite":0,"totalTokens":26368,"cost":{"input":0.000136,"output":0.000088,"cacheRead":0.00207872,"cacheWrite":0,"total":0.00230272}},"stopReason":"toolUse","timestamp":1781663417827,"responseId":"353714d7b3a146b19d32080cbd833e4f"}}
|
||
{"type":"message","id":"b38f88f1","parentId":"5a7b2043","timestamp":"2026-06-17T02:30:20.205Z","message":{"role":"toolResult","toolCallId":"call_0fe8270d987d4636b36db96b","toolName":"read","content":[{"type":"text","text":"DEEPSEEK_API_KEY=\nDEEPSEEK_MODEL=deepseek-chat\nDEEPSEEK_TEMPERATURE=0.2\n\nNOTES_REMOTE=bigmengya:/shumengya/docker/mengyanote-backend/data/mengyanote/\nNOTES_DIR=data/notes\nINDEX_DIR=data/index/bm25\nTOP_K=6\nLIST_TOP_K=20\nEMBED_BACKEND=fastembed\nEMBED_MODEL=BAAI/bge-small-zh-v1.5\nEMBED_BATCH_SIZE=32\nEMBED_QUERY_PROMPT_NAME=\nVECTOR_WEIGHT=0.65\nBM25_WEIGHT=0.35\n"}],"isError":false,"timestamp":1781663420205}}
|
||
{"type":"message","id":"0e833479","parentId":"b38f88f1","timestamp":"2026-06-17T02:30:21.968Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"现在让我看看 `data` 目录的内容，了解索引结构：","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_a4b07367af434f628c82df2f","name":"bash","arguments":{"command":"ls -la /shumengya/project/agent/mengya-rag/data/"}}],"api":"openai-completions","provider":"xiaomimimo-openai","model":"mimo-v2.5","usage":{"input":212,"output":56,"cacheRead":26304,"cacheWrite":0,"totalTokens":26572,"cost":{"input":0.0000848,"output":0.000112,"cacheRead":0.0021043200000000002,"cacheWrite":0,"total":0.0023011200000000002}},"stopReason":"toolUse","timestamp":1781663420206,"responseId":"2bf0bd171371482fa7e3e6da398c77c2"}}
|
||
{"type":"message","id":"9834e90d","parentId":"0e833479","timestamp":"2026-06-17T02:30:21.982Z","message":{"role":"toolResult","toolCallId":"call_a4b07367af434f628c82df2f","toolName":"bash","content":[{"type":"text","text":"总计 16\ndrwxr-xr-x 4 root root 4096 5月12日 16:53 .\ndrwxr-xr-x 7 root root 4096 5月12日 17:25 ..\ndrwxr-xr-x 3 root root 4096 5月12日 21:47 index\ndrwxr-xr-x 36 root root 4096 5月11日 08:04 notes\n"}],"isError":false,"timestamp":1781663421982}}
|
||
{"type":"message","id":"31db252e","parentId":"9834e90d","timestamp":"2026-06-17T02:30:23.850Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"让我看看 `index` 目录：","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_5b31b7ecc07c4faf81ae89d8","name":"bash","arguments":{"command":"ls -la /shumengya/project/agent/mengya-rag/data/index/bm25/"}}],"api":"openai-completions","provider":"xiaomimimo-openai","model":"mimo-v2.5","usage":{"input":214,"output":54,"cacheRead":26496,"cacheWrite":0,"totalTokens":26764,"cost":{"input":0.00008560000000000001,"output":0.000108,"cacheRead":0.00211968,"cacheWrite":0,"total":0.00231328}},"stopReason":"toolUse","timestamp":1781663421983,"responseId":"11aed2c7f804491f86d8d53c9749594e"}}
|
||
{"type":"message","id":"df546231","parentId":"31db252e","timestamp":"2026-06-17T02:30:23.865Z","message":{"role":"toolResult","toolCallId":"call_5b31b7ecc07c4faf81ae89d8","toolName":"bash","content":[{"type":"text","text":"总计 12592\ndrwxr-xr-x 2 root root 4096 5月12日 21:49 .\ndrwxr-xr-x 3 root root 4096 5月12日 21:47 ..\n-rw-r--r-- 1 root root 3554397 5月12日 21:47 nodes.jsonl\n-rw-r--r-- 1 root root 9330688 5月12日 21:49 rag.sqlite3\n"}],"isError":false,"timestamp":1781663423865}}
|
||
{"type":"message","id":"4c1f1501","parentId":"df546231","timestamp":"2026-06-17T02:30:25.487Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"现在让我看看 `notes` 目录的内容，了解笔记的结构：","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_aba9f40093814566bd6566a5","name":"bash","arguments":{"command":"ls -la /shumengya/project/agent/mengya-rag/data/notes/ | head -20"}}],"api":"openai-completions","provider":"xiaomimimo-openai","model":"mimo-v2.5","usage":{"input":225,"output":63,"cacheRead":26688,"cacheWrite":0,"totalTokens":26976,"cost":{"input":0.00009,"output":0.000126,"cacheRead":0.00213504,"cacheWrite":0,"total":0.00235104}},"stopReason":"toolUse","timestamp":1781663423866,"responseId":"e2d3083cbfe246f8ad04151211a5ab91"}}
|
||
{"type":"message","id":"6c61da32","parentId":"4c1f1501","timestamp":"2026-06-17T02:30:25.505Z","message":{"role":"toolResult","toolCallId":"call_aba9f40093814566bd6566a5","toolName":"bash","content":[{"type":"text","text":"总计 160\ndrwxr-xr-x 36 root root 4096 5月11日 08:04 .\ndrwxr-xr-x 4 root root 4096 5月12日 16:53 ..\ndrwxr-xr-x 10 root root 4096 5月10日 11:27 编程语言\ndrwxr-xr-x 2 root root 4096 5月10日 11:27 博客文章\ndrwxr-xr-x 3 root root 4096 5月11日 08:18 技术原理解析\ndrwxr-xr-x 3 root root 4096 5月10日 11:27 计算机科普\ndrwxr-xr-x 2 root root 4096 5月10日 11:27 计算机网络\ndrwxr-xr-x 3 root root 4096 5月10日 11:27 鉴政\ndrwxr-xr-x 6 root root 4096 5月10日 11:27 解决方案\ndrwxr-xr-x 2 root root 4096 5月10日 11:27 嵌入式\ndrwxr-xr-x 2 root root 4096 5月10日 11:27 量化投资\ndrwxr-xr-x 2 root root 4096 5月10日 11:27 临时\n-rw-r--r-- 1 root root 815 5月10日 11:31 📒萌芽笔记.md\ndrwxr-xr-x 6 root root 4096 5月10日 11:27 内网穿透\n-rw-r--r-- 1 root root 2057 5月10日 19:50 前后端项目env环境配置解析.md\ndrwxr-xr-x 2 root root 4096 5月10日 11:27 生活科普\ndrwxr-xr-x 2 root root 4096 5月10日 11:27 生物学科普\ndrwxr-xr-x 7 root root 4096 5月10日 11:27 实习求职\ndrwxr-xr-x 9 root root 4096 5月10日 11:27 数据库\n"}],"isError":false,"timestamp":1781663425504}}
|
||
{"type":"message","id":"70589b2d","parentId":"6c61da32","timestamp":"2026-06-17T02:30:42.220Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"现在我已经对项目有了全面的了解。让我为用户做一个详细的介绍。我需要总结这个项目的各个方面：\n\n1. 项目概述\n2. 技术架构\n3. 核心功能\n4. 使用场景\n5. 部署和配置\n6. 优缺点分析\n7. 后续发展方向\n\n让我组织一下语言，给出一个详细的介绍。","thinkingSignature":"reasoning_content"},{"type":"text","text":"好的，我已经仔细查看了 `/shumengya/project/agent/mengya-rag` 项目。这是一个非常实用的个人知识库 RAG（检索增强生成）系统。让我为你详细介绍一下 (｡･ω･｡)\n\n## 项目概述\n\n**萌芽 RAG 知识库**是一个轻量级的 Obsidian 笔记 RAG 项目，专门针对个人 Obsidian 笔记进行优化。它能够将你的 Markdown 笔记转化为可智能检索的知识库，并通过大模型提供问答服务。\n\n## 技术架构\n\n### 核心组件\n- **RAG 框架**: LlamaIndex\n- **本地 Embedding**: `BAAI/bge-small-zh-v1.5`（通过 FastEmbed + ONNX 运行，无需 GPU）\n- **大模型回答**: DeepSeek API（推荐 `deepseek-v4-flash`）\n- **向量数据库**: SQLite + sqlite-vec（轻量级，无需额外服务）\n- **检索方式**: BM25 关键词检索 + 向量语义检索的混合模式\n\n### 项目结构\n```\nmengya-rag/\n├── data/\n│ ├── notes/ # Obsidian Markdown 笔记（从 bigmengya 同步）\n│ └── index/bm25/ # 本地索引（SQLite 向量库 + 节点备份）\n├── scripts/ # 同步、构建索引、问答脚本\n├── src/mengya_rag/ # 核心代码\n│ ├── config.py # 配置管理\n│ ├── markdown_chunker.py # Markdown 结构感知分块\n│ ├── indexing.py # 索引构建\n│ ├── retrieval.py # 检索、融合、盘点模式\n│ ├── qa.py # DeepSeek 生成回答\n│ └── cli.py # 命令行接口\n└── pyproject.toml # 项目配置\n```\n\n## 核心功能\n\n### 1. 智能 Markdown 分块\n不同于简单的字符切割，项目实现了**结构感知分块**：\n- 识别 frontmatter 元数据\n- 按 H1-H6 标题层级组织分块\n- 代码块和表格整体保留，不跨块切割\n- 长块使用滑动窗口兜底\n- 每个 chunk 附带丰富的元数据（文件路径、标题路径、标签等）\n\n### 2. 混合检索系统\n- **普通问答模式**: BM25 + 向量检索 → RRF 融合 → 同文件去重 → 相关性过滤\n- **盘点列表模式**: 文件级目录检索，适合\"有哪些\"、\"查一下\"这类问题\n- **自动模式**: 根据问题类型自动选择检索方式\n\n### 3. 命令行工具\n提供完整的 CLI 接口：\n- `mengya-rag status`: 查看配置和索引状态\n- `mengya-rag search`: 只检索，不调用大模型\n- `mengya-rag context`: 返回可注入大模型的检索上下文\n- `mengya-rag ask`: 完成 RAG + DeepSeek 回答\n- `mengya-rag read`: 按路径读取完整笔记\n- `mengya-rag reindex`: 同步笔记并重建索引\n\n## 使用场景\n\n这个项目特别适合：\n- **查找笔记**: \"某个教程在哪里\"、\"某类笔记有哪些\"\n- **知识问答**: \"WireGuard 怎么配置\"、\"Docker 常用命令有哪些\"\n- **内容总结**: \"根据笔记总结某个主题\"\n- **目录浏览**: \"列出博客文章\"、\"列出 Agent 安装教程\"\n\n## 部署和配置\n\n### 初始化\n```bash\ncd /shumengya/project/agent/mengya-rag\ncp .env.example .env\n# 编辑 .env，填入 DEEPSEEK_API_KEY\nuv sync\n```\n\n### 笔记同步\n笔记源：`bigmengya:/shumengya/docker/mengyanote-backend/data/mengyanote/`\n```bash\n./scripts/sync_notes.sh\n```\n\n### 索引构建\n```bash\n./scripts/build_index.sh\n```\n\n### 使用示例\n```bash\n# 查看状态\nuv run mengya-rag status --json\n\n# 普通问答\nuv run mengya-rag ask \"Docker 常用命令有哪些\" -k 4 --json\n\n# 盘点类查询\nuv run mengya-rag ask \"查一下我目前博客文章有哪些\" --json\n\n# 只检索不生成回答\nuv run mengya-rag search \"WireGuard\" --json\n```\n\n## 技术亮点\n\n1. **轻量级部署**: 使用 SQLite + sqlite-vec，无需 Qdrant、Milvus 等外部数据库\n2. **本地 Embedding**: 使用 FastEmbed + ONNX，无需 GPU 和 PyTorch\n3. **结构感知**: 充分利用 Markdown 结构，保留代码块和表格完整性\n4. **混合检索**: 结合关键词和语义检索，提高召回率\n5. **双模式检索**: 自动区分普通问答和盘点列表问题\n6. **Agent 友好**: 所有命令支持 `--json` 输出，便于程序调用\n\n## 当前限制\n\n- 单机 SQLite 向量库，数据量很大后需要扩展\n- 没有 reranker，复杂问题可能混入弱相关内容\n- embedding 模型是轻量中文模型，效果够用但不是最强\n- `build_index` 每次全量重建，暂无增量索引\n- 当前是 CLI 工具，没有 Web 界面\n\n## 后续优化方向\n\n- 增量索引，只重建变更文件\n- 加 reranker（如 `bge-reranker-v2-m3`）\n- 给盘点类问题增加目录树模式\n- 增加 `--debug` 输出检索过程\n- 数据量变大后接 Qdrant\n\n## 索引规模参考\n\n根据 README，当前索引规模：\n- 分块数: 1802\n- 索引大小: 约 13M\n- nodes.jsonl: 约 3.6M\n- rag.sqlite3: 约 9.3M\n\n这是一个设计精良、实用性强的个人知识库系统，特别适合处理 Obsidian 笔记这类结构化文档。它的混合检索模式和结构感知分块是主要亮点 (｀・ω・´)"}],"api":"openai-completions","provider":"xiaomimimo-openai","model":"mimo-v2.5","usage":{"input":758,"output":1559,"cacheRead":26880,"cacheWrite":0,"totalTokens":29197,"cost":{"input":0.00030320000000000005,"output":0.0031179999999999997,"cacheRead":0.0021504000000000002,"cacheWrite":0,"total":0.0055715999999999995}},"stopReason":"stop","timestamp":1781663425506,"responseId":"7ff20c7985374dc38db9e5671ff33499"}}
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||
{"name":"mengya-rag项目解析","timestamp":"2026-06-17T03:00:01Z","type":"session_info"}{"type":"model_change","id":"24b84b2d","timestamp":"2026-06-18T01:31:00.134Z","provider":"xiaomimimo-openai","modelId":"mimo-v2.5"}
|
||
{"type":"thinking_level_change","id":"e30af715","parentId":"24b84b2d","timestamp":"2026-06-18T01:31:00.134Z","thinkingLevel":"high"}
|
||
{"type":"model_change","id":"32e1898f","parentId":"e30af715","timestamp":"2026-06-18T04:02:41.611Z","provider":"xiaomimimo-openai","modelId":"mimo-v2.5"}
|
||
{"type":"thinking_level_change","id":"cb5be9f1","parentId":"32e1898f","timestamp":"2026-06-18T04:02:41.611Z","thinkingLevel":"high"}
|
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{"type":"model_change","id":"d7d84c1f","parentId":"cb5be9f1","timestamp":"2026-06-21T09:44:11.243Z","provider":"opencode-go-openai","modelId":"mimo-v2.5"}
|
||
{"type":"thinking_level_change","id":"64d0b7e5","parentId":"d7d84c1f","timestamp":"2026-06-21T09:44:11.243Z","thinkingLevel":"high"}
|
||
|
||
{"name":"萌芽笔记RAG解析","timestamp":"2026-06-21T09:44:58Z","type":"session_info"} |