mirror of
https://github.com/langbot-app/LangBot.git
synced 2026-10-31 04:45:23 +08:00
99185d0b1d
* docs: add sandbox documentation for all languages Add comprehensive documentation for the code sandbox feature: - Overview, built-in tools, scope and lifecycle - Backend configuration (Docker/Nsjail/E2B) - Security profiles (default/offline_readonly/network_basic/network_extended) - Complete configuration reference Languages: zh, en, ja * docs: add skills documentation for all languages Add comprehensive Skills system documentation covering: - Design philosophy and industry practices comparison (Claude Code, OpenAI GPTs, Cursor) - Layered architecture (management, import, execution layers) - Current activation mechanism ([ACTIVATE_SKILL:] marker) with TODO for future Tool Call optimization - Agent Skills open standard compliance with field specifications - Complete tools API reference Languages: zh, en, ja Co-Authored-By: Claude Opus 4.7 <[email protected]> * docs: update sandbox documentation to include new tools and configuration options * Refactor Skills API documentation to align with Tool Call mechanism - Updated the overview of the Skills system to emphasize the Tool Call mechanism for activating and registering skills. - Removed deprecated tools and consolidated functionality into `activate` and `register_skill`. - Revised usage examples and parameters for the new tools, ensuring clarity on their requirements and expected responses. - Enhanced descriptions of the activation process, including the injection of skill instructions and mounting of skill packages. - Improved error handling documentation for better user guidance. - Added notes on sandbox requirements for tool usage and the creation of new skills. * docs: align sandbox runtime guides * docs: align sandbox & skills guides with current Box implementation Correct five inaccuracies across the zh/en/ja sandbox and skills docs so they match the actual code: - Box launch command: use the lbp CLI subcommand (`langbot_plugin.cli.__init__ box`) instead of the removed `langbot_plugin.box --mode ws`. - Environment variables: replace the obsolete `LANGBOT_BOX_LOCAL_*` vars with the unified `SECTION__SUBSECTION__KEY` override convention (`BOX__LOCAL__*`); note that the langbot_box service reads no config.yaml/env and is configured by LangBot over INIT RPC. - Skills: drop the stale "falls back to data/skills" claims — the Box Runtime is the sole source of skills; when Box is unavailable the skill list is simply empty. - Document the new `box.enabled` master switch (default true) and what it gates. - Document that the langbot_box compose service is optional, gated behind the `box`/`all` compose profile. * docs: align sandbox docs with implementation * docs: simplify sandbox naming and diagrams * docs: shorten AI capability nav labels * docs: merge sandbox configuration pages * docs: document BOX__BACKEND as sole backend selector * docs: recommend all profile for docker deploy * docs: add docker permission fix for sandbox * chore(skills): add obra/superpowers writing-skills Install the writing-skills skill from obra/superpowers via `npx skills add`. Lands the skill files under .agents/skills/ (universal layout) with a Claude Code symlink at .claude/skills/ and the pin in skills-lock.json. Also gitignore .claude/settings.local.json since it's per-user Claude Code state (skill symlinks under .claude/skills/ remain tracked). * docs(sandbox,skills): tighten user guides for onboarding Rewrite the sandbox and skills user guides to be shorter, plainer, and focused on getting users productive instead of explaining internals. Changes per page (zh/en/ja kept in lockstep): - sandbox/readme: 170 → 73 lines. Drop the "what is a sandbox" essay, decorative mermaid, and the backend table that already lives in config.mdx. Lead with the six built-in tools and the scope picker (the parts users actually configure). - sandbox/runtime-relationships: 75 → 54 lines. Replace the runtime prose with a single comparison table; tighten the troubleshooting entry-points. - skills/readme: 320 → 114 lines. Cut the "design philosophy" / "industry comparison" / "KV cache theory" sections — those are implementation details users don't need to ship. Lead with the user-visible flow (Agent reads <available_skills> → calls activate → follows the SKILL.md). - skills/skill-format: 360 → 134 lines. One excellent example (api-tester) instead of three overlapping ones. Drop the standalone "field details" subsections that just restate the table. - skills/tools-api: 200 → 122 lines. Drop the old-vs-new historical comparison and the duplicated activation section; keep the two tool signatures, parameters, returns and errors. sandbox/config.mdx left untouched (already condensed upstream). Principles applied come from obra/superpowers writing-skills (installed in 08c35a4): lead with use, one excellent example, tables for scanning, cut internals and history. * docs(sandbox): drop redundant 'how to choose' table from runtime-relationships The 对照表 above already has a '何时使用 / When to use' column that says the same thing. Repeating it as a second 怎么选 / How to Choose table was pure noise. * docs(skills): collapse to one page, link out for SKILL.md spec Delete skill-format.mdx and tools-api.mdx — Skills now has a single page. The format details and tools reference were already covered in shorter form inside readme.mdx; the separate pages were repetition. In readme.mdx: - Inline a compact 'SKILL.md format' section (one example + a three- field table) and point at agentskills.io for the canonical spec. - Drop the 'next steps' section and the tools-api link, since both destinations no longer exist. docs.json: remove the two pages from each locale's Skills nav. Add redirects from the old paths to readme so existing links don't 404. * docs(nav): flatten Skills — single page, no group wrapper Group with one child is just visual noise in the sidebar. Replace { group: Skills, pages: [...readme] } with the readme path directly in the parent AI Configuration group, in all three locales. * docs(mcp,skills): lead with LangBot Space as the preferred install path LangBot Space (https://space.langbot.app) now hosts a curated list of MCP servers and Skills. Surface that as the first install option in both user guides. - usage/mcp/readme: rework the intro Info block to recommend LangBot Space first (third-party channels demoted to "alternative source"). Add a "Install from LangBot Space (Preferred)" section above the existing manual flow; rename that flow to "Add MCP Server Manually". - usage/skills/readme: add a "Install from LangBot Space (Preferred)" section above "Creating a Skill"; rename the existing section to "Creating a Custom Skill" so the contrast is clear. zh/en/ja kept in sync. * docs(skills): reorganize the readme around the user journey The old structure interleaved "what it is" → "how it works internally" → "prerequisites" → "format spec" → install options. Reorder around how users actually approach the page: Intro → compare with plugins/MCP → install → enable → write → debug Specific moves: - Drop the standalone "Workflow" flow diagram; fold the one important point (activation pulls SKILL.md via Tool Result) into the intro. - Merge the standalone "Prerequisites" section into a short Note on the Install heading — that's where the constraint actually applies. - Bring "Install from LangBot Space" and "Create your own" together under one "Install" H2 with H3 subsections; they were sibling H2s before, which buried the relationship. - Move "Writing a SKILL.md" after install + enable, since most users start with marketplace skills. - Demote the "Tools the Agent sees" table to an H3 inside the writing section — it's reference, not a top-level concern. 114 → 100 lines, zh/en/ja in lockstep. * docs(mcp,models): reorder MCP page + move tool-use capability to Models Two related changes, same as the Skills cleanup pattern: usage/mcp/readme — reorganize around the user journey - Drop the standalone "Enable function calling on the model" section; that belongs in the Models doc (it's not MCP-specific — sandbox tools and Skills depend on the same model capability). - Drop the redundant "Effect" section — its one fact (built-in Agent + tool-use model) already lives in the Note under "Three Transport Modes". - Group install paths under one "Installing an MCP Server" H2 with Space (preferred) and manual flows as H3 siblings, mirroring the Skills doc layout. - Move the stdio MCP technical block under a clearly-labeled "Advanced: stdio MCP" H2 so casual readers skim past it. - Trim screenshot duplication and collapse three steps in the manual flow into one paragraph + one screenshot per step. - 140 → 107 lines, zh/en/ja kept in lockstep. usage/models/readme — rename and expand "function calling" → "tool use" - The checkbox is renamed to "Tool Use" (zh: 工具使用能力 / ja: ツール 使用); function calling kept in parentheses for recognition. - Expand the bullet into a Note that explicitly lists where the capability matters: MCP servers, sandbox built-ins (exec/read/write/ edit/glob/grep), Skills. Without it, even bound extensions stay silent. - Note that the underlying model must actually support function calling for the checkbox to take effect. * docs(sandbox): point sandbox/config readers to System Environment Settings The page lists box.* YAML blocks without ever telling the reader where those settings actually go. Add a one-line Note at the top linking to deploy/settings (System Environment Settings), where the data/config.yaml location and load mechanism are documented. * docs(sandbox/config): align the page with the real config structure The previous version listed local / docker / nsjail / e2b as four parallel "backends" in one flat table. That's wrong: looking at the config template and the SDK's _select_backend, the real model is: - box.backend accepts 'local' | 'docker' | 'nsjail' | 'e2b' - 'local' is shorthand for "auto-pick from Docker / Nsjail" — not a fourth backend - 'local', 'docker', 'nsjail' all *share* the box.local.* config section; the section is misleadingly named ("local" here means "host-local backends", not "for backend: 'local'") - 'e2b' uses box.e2b.* instead - 'local' is the only value that auto-falls-back; everything else is a hard selection Rewrite the page around that structure: - Group backends as "local containers" vs "cloud" with the four values inside, and call out the local-vs-fourth-backend distinction. - Split the old single "common settings" table into two clearly-scoped tables: box.local.* (for local backends) and box.e2b.* (for cloud). - Move box.runtime.endpoint into the Docker Compose section, where it actually matters. - Add an explicit note that backend is a hard selection — only 'local' fans out. zh/en/ja kept in lockstep. * docs(deploy): note Box control-plane transport differs by deploy method In Docker deployments the Box control plane runs as the langbot_box container (WebSocket); manual / uvx deployments use a stdio subprocess. Also clarify the LangBot image bundles the docker client so langbot_box can create sandbox containers via the mounted socket without extra setup. zh / en / ja. --------- Co-authored-by: Claude Opus 4.7 <[email protected]> Co-authored-by: Junyan Qin <[email protected]>
--- title: "模型 (Models)" --- ## LangBot Models LangBot Models 是 LangBot 官方提供的模型服务。当您使用 LangBot Space 账户初始化本地实例时，可用模型将被自动添加到您的实例中，无需进行任何配置。您将会获得一定的免费额度用于快速上手。 <img src="/images/zh/deploy/models/langbot_models.png" alt="langbot_models" /> 具体可用模型，请查看[LangBot Space](https://space.langbot.app/models)。 ## 自定义模型 您也可以添加其他来源的模型。LangBot 采用 **供应商（Provider）+ 模型（Model）** 的两层架构： - **供应商**：定义 API 地址（Base URL）和 API 密钥。同一供应商下的所有模型共享这些配置。 - **模型**：关联到一个供应商，指定具体的模型名称和能力。 ``` 供应商 (OpenAI) ├── Base URL: https://api.openai.com/v1 ├── API Keys: [sk-xxx] ├── LLM 模型: gpt-4o ├── LLM 模型: gpt-4o-mini ├── 向量模型: text-embedding-3-small └── 重排序模型: rerank-v3.5 ``` <Tip> 同一供应商下可以添加多个模型，它们会自动继承供应商的 API 地址和密钥，无需重复填写。 </Tip> ### 对话模型 (LLM Model) 对话模型将被流水线用于处理消息，您配置的第一个模型将被设置为默认流水线的模型。  可以添加多个模型，然后在流水线中选择具体使用什么模型。 <img width="400px" src="/images/zh/deploy/models/model_config.png" alt="model_config" /> 填入 `模型名称`、选择 `模型供应商`（或新建供应商并填入 `请求 URL` 和 `API Key`），然后提交即可。 模型能力方面，请根据具体模型特性来选择： - **视觉能力**：启用后才能识别图片 - **工具使用能力**（function calling / tool use）：启用后 LLM 才能在对话中调用工具 <Note> **只要想使用来自 MCP 服务器、沙箱内置工具（`exec`/`read`/`write`/`edit`/`glob`/`grep`）、Skills 的能力，都必须为模型启用工具使用能力。** 否则即使流水线绑定了这些扩展，Agent 也不会调用任何工具。 模型本身需要支持 function calling 才能勾选。不确定时请查阅模型供应商的文档。 </Note> ### 嵌入模型 (Embedding Model) 嵌入模型将被用于计算消息的向量，若您需要使用知识库，请配置此模型。 <img width="400px" src="/images/zh/deploy/models/embedding_model.png" alt="embedding_model" /> 填入 `模型名称`、选择 `模型供应商`，然后提交即可。之后请在知识库中配置使用此模型。 ### 使用 seekdb 内置嵌入模型（零配置） 系统已集成 seekdb 提供的官方嵌入模型，无需填写任何参数。 - 在”嵌入模型”页面选择「seekdb-内置」； - 点击「保存」即可立即使用； - 后续在知识库中选择该模型即可生效。 ### 使用 Chroma 内置嵌入模型（零配置） 系统已集成 Chroma 提供的内置嵌入模型（all-MiniLM-L6-v2），无需填写任何参数。 - 在”嵌入模型”页面选择「chroma-内置」； - 点击「保存」即可立即使用； - 后续在知识库中选择该模型即可生效。 ### 重排序模型 (Rerank Model) 重排序模型用于对 RAG 检索结果进行重新评分排序，显著提升知识库回答的精准度。当知识库返回多个候选文档时，重排序模型会使用 cross-encoder 对每个文档与查询的相关性进行精确打分，筛选出最相关的内容。 填入 `模型名称`、选择 `模型供应商`，然后提交即可。之后请在流水线的”AI 能力”配置中选择此模型。 <Tip> 重排序模型是可选的。不配置时，知识库检索结果将按向量相似度排序直接使用。配置后，检索结果会经过重排序模型重新打分，只保留最相关的 Top-K 条结果。 </Tip>