AI Chat Assistant

An Obsidian plugin: chat with multiple LLM providers, search your notes with a local knowledge base (RAG), run agents that can read and edit notes, keep long-term memory, and roleplay with character cards.

Since v2.0.0 this plugin is free and open source (MIT). The paid activation system has been removed from the code and all old license keys are void. Users who paid before can open an issue to request a refund.

Features

• Multi-provider chat — OpenAI, Claude, Gemini, DashScope (Qwen), DeepSeek, or any OpenAI-compatible endpoint; local models through Ollama; streaming output; per-purpose model binding (chat / agent / embedding).
• Knowledge base (RAG) — hybrid search (vector similarity + keywords + recency) fused with RRF, query rewriting and reranking, graph-aware retrieval that follows your note links, vectors stored in IndexedDB with a Web Worker so the UI stays responsive.
• Agents — tool calling (read and write notes, create folders, full-text search, web search…), multi-step task execution with progress summaries, and permission prompts before a tool changes anything.
• Memory and roleplay — long-term memory, character cards (SillyTavern-compatible), multi-character group chat, writing styles.
• MCP tools — connect standard MCP servers over HTTP / SSE.
• Inline AI — rewrite or continue a selection from the editor.

Install (manual)

1. Download main.js, manifest.json and styles.css from the latest release.
2. Put them in <your vault>/.obsidian/plugins/ai-chat-assistant/.
3. Restart Obsidian → Settings → Community plugins → enable AI Chat Assistant.

Then open Settings → AI Chat Assistant and add a provider (API key, or a local Ollama URL with the key left empty). Requires Obsidian 1.7.2 or newer.

Privacy

• No telemetry — the plugin contains no analytics or data collection code.
• Chat content and notes are only sent to the model provider you configure. With a local model and the local vector store, the plugin can run fully offline.

Disclosure: network, files and processes

So that nothing surprises you (or the community review):

• Network use is limited to the endpoints you configure: LLM providers, MCP servers, web search, and the optional remote skill/service APIs.
• Files outside the vault: the agent tools use the Obsidian vault API for notes, but some features (skill installers, importing/exporting packages, the built-in HTTP API server) use Node's fs and can read or write paths outside your vault. Nothing is touched unless you trigger those features.
• Shell execution: certain skill features can run the scripts shipped inside a skill you installed, through child_process. Tools are gated behind permission prompts, but installing an untrusted skill is still equivalent to running its code.
• Clipboard: used for the copy/paste actions in chat.
• Dynamic code in skills: installed skills may be executed as scripts. Only install skills you trust.

Known limitations (honestly)

• DeepSeek prompt caching is not properly optimised — the prompt prefix is not stable, so the cache benefit is much lower than expected.
• Skills and MCP are early work — MCP supports HTTP/SSE only (no stdio) and has not been exercised against many real-world servers.
• Large files, real debt — main.ts and src/ui/views/view.ts are several thousand lines each; the split is unfinished.
• No automated tests — changes are verified by hand.
• The built-in HTTP API server has no authentication (off by default, meant for localhost only — do not expose it to the internet).
• Mobile is supported but slow — indexing and retrieval on a large vault are not great.

License

MIT — see LICENSE. Source code is public; releases carry the built artifacts.

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AI Chat Assistant（中文）

一个 Obsidian 插件：多模型对话 + 知识库检索（RAG）+ 能读写你自己笔记的智能体 + 长期记忆与角色扮演。

从 v2.0.0 起：完全免费、源码公开（MIT）、不再有激活码。 授权模块已从代码里删除，所有旧激活码作废——也不再需要。曾付费购买过的用户可开 issue 联系退款。

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为什么开源

这个插件的功能正在被各种本地/云端 agent 取代，作者自己也不再用它赚钱了，所以决定不再维护付费授权那一套，直接把源码放开：能用你就拿去用，能改你就自己改。也欢迎有人接手把它做得更好。

功能

🤖 多模型对话

• 支持 OpenAI / Claude / Gemini / 通义千问（DashScope）/ DeepSeek 以及任何 OpenAI 兼容端点
• 流式输出、自定义 Base URL、按用途绑定不同模型（聊天 / 智能体 / 向量）
• 本地模型：可接 Ollama

📚 知识库（RAG）

• 混合检索：向量相似度 + 关键词 + 时间衰减，再用 RRF 融合
• 查询改写与结果重排
• 图谱感知：顺着笔记的双链把上下文一并取回
• IndexedDB 存向量、Web Worker 做检索，不阻塞界面

🛠️ 智能体

• 工具调用：读写笔记、建文件夹、全文搜索、网络搜索等
• 默认工具零接线自动注册（registerDefaultTools）
• 多步任务执行 + 阶段小结

🧠 记忆与角色扮演

• 长期记忆：自动抽取用户事实与事件小结
• 角色卡（SillyTavern PNG/JSON 导入）、人格、世界状态、关系图谱

🔀 多模型协作

• 助手 → 专家 → 项目经理 → CEO 四级升级
• 按任务复杂度自动路由

⚠️ 现状与已知不足（说实话版）

作者自己也觉得这个插件"很多地方没优化好"，公开源码的同时把这些一并说清楚，免得别人踩坑后骂人：

• DeepSeek 缓存的命中率没有真正优化好：prompt 前缀不稳定，缓存命中收益远低于预期。
• Skills 与 MCP 支持是雏形：MCP 只实现了 HTTP/SSE 传输，没有 stdio，也没有在真实客户端上充分验证；Skills 更多是"能跑通"，不是"能靠"。
• 大文件技术债：main.ts 2500+ 行、src/ui/views/view.ts 3200+ 行、settings-tab.ts 2000+ 行，拆分工作没做完。
• 没有自动化测试：只有几个手动跑的脚本，改代码靠人肉验证。
• 内置 HTTP API server 没有鉴权：默认关闭，只适合本机自用，别暴露到公网。
• 移动端能用但慢：大仓库的索引与检索在手机上体验一般。

安装

插件未上架 Obsidian 社区插件市场，手动装：

1. 到 Releases 下载 main.js、manifest.json、styles.css
2. 在你的 vault 里建目录 <vault>/.obsidian/plugins/ai-chat-assistant/
3. 把 3 个文件放进去
4. 重开 Obsidian → 设置 → 第三方插件 → 启用

GitHub 在国内可能需要自备网络条件。

配置（三步）

1. 设置 → AI Chat Assistant → 模型 → 添加连接（名称 / Base URL / API Key）→ 测试连接 → 拉取模型
2. 给"聊天 / 智能体 / 向量"分别绑定模型（用知识库必须配一个可用的向量模型）
3. 要 RAG 就在命令面板执行"更新知识库索引（增量）"

开发

npm install
npm run dev      # esbuild watch
npm run build    # tsc 类型检查 + esbuild 生产构建 → main.js
npm run verify   # 只做类型检查

目录

main.ts               插件入口（注册视图/命令/设置页，装配各服务）
src/core/             设置、类型、插件上下文
src/services/         llm / rag / vector / context / storage / code
src/features/         agent · memory · skills · collaboration · world · roleplay
src/ui/               视图、设置页、各种弹窗、内联补全
src/mcp/              MCP 客户端与工具注册
styles.css            插件样式

隐私

• 不采集遥测，没有埋点上报
• 对话只发给你自己配置的模型服务；用 Ollama 可以完全离线
• 笔记内容只在本地建索引（IndexedDB）

历史版本

见 CHANGELOG.md。

License

MIT —— 见 LICENSE。

作者：3zh · GitHub