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RAGLite - Local Expandable Library AI Library

作者 Viraj Sanghvi · GitHub ↗ · v1.0.0
darwinlinux ⚠ suspicious
1632
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在 OpenClaw 中安装
/install raglite-library
功能描述
Local-first RAG cache: distill docs into structured Markdown, then index/query with Chroma + hybrid search (vector + keyword).
安全使用建议
This skill looks like a legitimate local RAG tool, but take precautions before installing/using it: - Be aware the wrapper will default to engine 'openclaw' unless you pass --engine explicitly; that can cause documents to be sent to an OpenClaw gateway. Always pass --engine <local|ollama|etc.> if you want to avoid outbound network usage. - The install script pip-installs from github:@main. Review the upstream repository (or request a pinned release/tag) before running install.sh and prefer installing in an isolated environment (container or VM). - If you have an OPENCLAW_GATEWAY_TOKEN in your environment, the installed library may use it even though the skill did not declare it. Remove or unset tokens you don't want used, or explicitly set a safe engine. - Ensure your Chroma server is local (chroma-url default is http://127.0.0.1:8100) and that ripgrep is installed if you need keyword search. - If you need higher assurance: clone and inspect the raglite repo code (or ask the author for a signed/pinned release) and run installs in a sandbox before trusting it with sensitive documents.
功能分析
Package: raglite (xpi) Version: 1.0.6 Description: Local-first RAG cache: distill docs into structured Markdown, then index/query with Chroma + hybrid search (vector + keyword). The package installs its core functionality, the 'raglite' Python package, directly from the 'main' branch of an external GitHub repository (git+https://github.com/VirajSanghvi1/raglite.git@main) into a local virtual environment. This method of installation introduces a significant supply chain risk because the code executed is not version-pinned and can change at any time without explicit updates to the xpi package. A compromise of the external GitHub repository or malicious changes by its maintainer would directly affect users of this extension. The core logic of the 'raglite' Python package itself is not included in the provided source code for analysis, preventing a full security assessment of its functionality. The shell scripts otherwise perform standard virtual environment setup and command execution.
能力评估
Purpose & Capability
Name/description match the scripts and SKILL.md: python3/pip are reasonable prerequisites and the scripts install and run a raglite CLI that condenses, indexes, and queries docs. The SKILL.md asks for ripgrep and a local Chroma endpoint as optional prerequisites which aligns with the hybrid search claim.
Instruction Scope
The runtime wrapper (scripts/raglite.sh) injects '--engine openclaw' when the user doesn't specify an engine, which forces the library to use the OpenClaw engine by default. SKILL.md states OpenClaw Gateway /v1/responses must be reachable and that OPENCLAW_GATEWAY_TOKEN may be required — but the skill does not declare that env var. This means documents you process could be sent to an external gateway or cause outbound network activity without an explicit opt-in from the user.
Install Mechanism
Installation uses pip to install directly from GitHub (git+https://github.com/VirajSanghvi1/raglite.git@main). Installing from a GitHub main branch runs code from an evolving source (moderate risk). It is better to pin a release/tag or audit the upstream repository before installing.
Credentials
The skill declares no required env vars, but SKILL.md references OPENCLAW_GATEWAY_TOKEN and requires a reachable OpenClaw gateway when the default engine is used. The skill may read that token from the environment if present (not declared), which is disproportionate to a purely local RAG cache and could leak data if the gateway is remote/untrusted.
Persistence & Privilege
always is false and the skill installs into a skill-local virtualenv; it does not request system-wide changes or modify other skills' configs. No elevated persistence is requested.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install raglite-library
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /raglite-library 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
- Initial release of RAGLite. - Local-first RAG cache: distills documents into structured Markdown for privacy and auditability. - Uses Chroma (vector) and ripgrep (keyword) for hybrid search and retrieval. - OpenClaw is the default condensation engine. - All features documented, with install and usage instructions included.
元数据
Slug raglite-library
版本 1.0.0
许可证
累计安装 2
当前安装数 0
历史版本数 1
常见问题

RAGLite - Local Expandable Library AI Library 是什么?

Local-first RAG cache: distill docs into structured Markdown, then index/query with Chroma + hybrid search (vector + keyword). 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 1632 次。

如何安装 RAGLite - Local Expandable Library AI Library?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install raglite-library」即可一键安装,无需额外配置。

RAGLite - Local Expandable Library AI Library 是免费的吗?

是的,RAGLite - Local Expandable Library AI Library 完全免费(开源免费),可自由下载、安装和使用。

RAGLite - Local Expandable Library AI Library 支持哪些平台?

RAGLite - Local Expandable Library AI Library 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(darwin, linux)。

谁开发了 RAGLite - Local Expandable Library AI Library?

由 Viraj Sanghvi(@virajsanghvi1)开发并维护,当前版本 v1.0.0。

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