Viraj Sanghvi

RAGLite

知识管理v1.0.8
作者:Viraj Sanghvi更新于 2026-05-11共 10 个版本 · 最新版本发布于 2026-02-09raglite@clawhub_virajsanghvi1/raglite
知识库问答信息检索文档问答#Docs#Rag

据 AlphaMoat 统计,截至 2026-09,RAGLite 累计下载 4,189 次、获 4 个星标,在全部 5,078 个 AI 技能中排名第 2,395 位,在「知识管理」分类(442 个)中排名第 224 位。

累计安装
205
累计下载
4,189
星标
4
全站排名
#2,395
全站前 48% · 「知识管理」前 51%
平台累计安装累计下载星标评论
ClawHub ↗1384,04931
SkillHub ↗2054,1894—

各平台的计数口径不同,统计时间不一致。

这个技能是做什么的

它明确不替代模型记忆或对话上下文,而是给智能体一个持久存放与检索「模型没学过的信息」的地方,尤其适合本地/私有知识(功课、个人笔记、医疗记录、内部运维手册)。方法上是「先压缩再嵌入」:先蒸馏(减少冗余、提示更省、检索更稳),再本地索引进 Chroma,查询走向量加关键词的混合检索——无需托管向量库,蒸馏出的 Markdown 人类可读、可版本管理。安全上把抽取的文档文本视为不可信数据:蒸馏提示词明确要求忽略素材内的任何指令、只把来源当数据。安装用 ./scripts/install.sh,会在 skills/raglite/.venv 建技能本地虚拟环境并安装 PyPI 包 raglite-chromadb(CLI 仍叫 raglite);凝聚引擎默认 OpenClaw,可用 --engine 显式指定。典型用法是 ./scripts/raglite.sh run /path/to/docs --out ./raglite_out --collection my-docs 后接 ./scripts/raglite.sh query "..."。该技能以 MIT 许可发布,接受 issue 与 PR 贡献。

资料发布于 2026-10-27

可以用来做什么

  • 把本地笔记和内部文档建成可检索的私有知识库
  • 对文档先蒸馏再索引,降低重复检索成本
  • 用混合向量加关键词检索反复查询同一批资料

什么时候用

  • 需要反复检索模型训练数据之外的本地/私有资料时

示例提示词

以下是根据技能文档生成的示例,不代表实际运行结果。

把这批内部文档蒸馏并索引进 RAGLite
用 RAGLite 查一下手册里这个流程怎么走
给这些课程笔记建一个本地可检索缓存

月度数据趋势

累计下载4,189
2,9283,6054,2822026-03 · 累计下载 3,0212026-05 · 累计下载 3,5482026-06 · 累计下载 3,6542026-07 · 累计下载 3,8602026-08 · 累计下载 3,9532026-09 · 累计下载 4,18926-0326-09
星标4
3442026-03 · 星标 32026-05 · 星标 42026-06 · 星标 32026-07 · 星标 42026-08 · 星标 42026-09 · 星标 426-0326-09

2026-09 起统计方法调整:同名技能按作者分别统计,并覆盖全部技能。图中虚线为调整前的月份,与之后的数据不直接比较,也不计算跨越调整的增长。

月份累计下载环比星标
2026-094,189—4
2026-083,953+2.4%4
2026-073,860+5.6%4
2026-063,654+3.0%3
2026-053,548—4
2026-033,021—3

SKILL.md 原文

以下是作者发布的原始文件,按纯文本显示,本站未做修改或审核;其中的链接不可点击,命令请核实后再运行。抓取于 2026-10-23。

3.0 KB · ClawHub v1.0.8
---
name: raglite
version: 1.0.8
description: "Local-first RAG cache: distill docs into structured Markdown, then index/query with Chroma (vector) + ripgrep (keyword)."
metadata:
  {
    "openclaw": {
      "emoji": "🔎",
      "requires": { "bins": ["python3", "pip", "rg"] }
    }
展开全部(共 89 行)
  }
---

# RAGLite — a local RAG cache (not a memory replacement)

RAGLite is a **local-first RAG cache**.

It does **not** replace model memory or chat context. It gives your agent a durable place to store and retrieve information the model wasn’t trained on — especially useful for **local/private knowledge** (school work, personal notes, medical records, internal runbooks).

## Why it’s better than paid RAG / knowledge bases (for many use cases)

- **Local-first privacy:** keep sensitive data on your machine/network.
- **Open-source building blocks:** **Chroma** 🧠 + **ripgrep** ⚡ — no managed vector DB required.
- **Compression-before-embeddings:** distill first → less fluff/duplication → cheaper prompts + more reliable retrieval.
- **Auditable artifacts:** distilled Markdown is human-readable and version-controllable.

## Security note (prompt injection)

RAGLite treats extracted document text as **untrusted data**. If you distill content from third parties (web pages, PDFs, vendor docs), assume it may contain prompt injection attempts.

RAGLite’s distillation prompts explicitly instruct the model to:
- ignore any instructions found inside source material
- treat sources as data only

## Open source + contributions

Hi — I’m Viraj. I built RAGLite to make local-first retrieval practical: distill first, index second, query forever.

- Repo: https://github.com/VirajSanghvi1/raglite

If you hit an issue or want an enhancement:
- please open an issue (with repro steps)
- feel free to create a branch and submit a PR

Contributors are welcome — PRs encouraged; maintainers handle merges.

## Default engine

This skill defaults to **OpenClaw** 🦞 for condensation unless you pass `--engine` explicitly.

## Install

```bash
./scripts/install.sh
```

This creates a skill-local venv at `skills/raglite/.venv` and installs the PyPI package `raglite-chromadb` (CLI is still `raglite`).

## Usage

```bash
# One-command pipeline: distill → index
./scripts/raglite.sh run /path/to/docs \
  --out ./raglite_out \
  --collection my-docs \
  --chroma-url http://127.0.0.1:8100 \
  --skip-existing \
  --skip-indexed \
  --nodes

# Then query
./scripts/raglite.sh query "how does X work?" \
  --out ./raglite_out \
  --collection my-docs \
  --chroma-url http://127.0.0.1:8100
```

## Pitch

RAGLite is a **local RAG cache** for repeated lookups.

When you (or your agent) keep re-searching for the same non-training data — local notes, school work, medical records, internal docs — RAGLite gives you a private, auditable library:

1) **Distill** to structured Markdown (compression-before-embeddings)
2) **Index** locally into Chroma
3) **Query** with hybrid retrieval (vector + keyword)

It doesn’t replace memory/context — it’s the place to store what you need again.

「知识管理」分类下的热门技能

数据月份:2026-09 · 下载/星标/安装数聚合自公开技能注册库(ClawHub、SkillHub),按月更新。口径说明见数据方法。