Viraj Sanghvi

RAGLite

知識管理v1.0.8
作者:Viraj Sanghvi更新於 2026-05-11共 10 個版本 · 最新版本釋出於 2026-02-09raglite@clawhub_virajsanghvi1/raglite
知識庫問答資訊檢索檔案問答#Docs#Rag

據 AlphaMoat 統計,截至 2026-09,RAGLite 累計下載 4,189 次、獲 4 個星標,在全部 5,078 個 AI 技能中排名第 2,395 位,在「知識管理」分類(442 個)中排名第 224 位。

累計安裝
205
累計下載
4189
星標
4
全站排名
#2,395
全站前 48% · 「知識管理」前 51%
平台累計安裝累計下載星標評論
ClawHub ↗138404931
SkillHub ↗20541894—

各平台的計數口徑不同,統計時間不一致。

這個技能是做什麼的

它明確不替代模型記憶或對話上下文,而是給智慧體一個持久存放與檢索「模型沒學過的資訊」的地方,尤其適合本地/私有知識(功課、個人筆記、醫療記錄、內部運維手冊)。方法上是「先壓縮再嵌入」:先蒸餾(減少冗餘、提示更省、檢索更穩),再本地索引進 Chroma,查詢走向量加關鍵詞的混合檢索——無需託管向量庫,蒸餾出的 Markdown 人類可讀、可版本管理。安全上把抽取的文件文字視為不可信資料:蒸餾提示詞明確要求忽略素材內的任何指令、只把來源當資料。安裝用 ./scripts/install.sh,會在 skills/raglite/.venv 建技能本地虛擬環境並安裝 PyPI 包 raglite-chromadb(CLI 仍叫 raglite);凝聚引擎預設 OpenClaw,可用 --engine 顯式指定。典型用法是 ./scripts/raglite.sh run /path/to/docs --out ./raglite_out --collection my-docs 後接 ./scripts/raglite.sh query "..."。該技能以 MIT 許可釋出,接受 issue 與 PR 貢獻。

資料釋出於 2026-10-27

可以用來做什麼

  • 把本地筆記和內部文件建成可檢索的私有知識庫
  • 對文件先蒸餾再索引,降低重複檢索成本
  • 用混合向量加關鍵詞檢索反覆查詢同一批資料

什麼時候用

  • 需要反覆檢索模型訓練資料之外的本地/私有資料時

示例提示詞

以下是根據技能文件生成的示例,不代表實際執行結果。

把這批內部文件蒸餾並索引進 RAGLite
用 RAGLite 查一下手冊裡這個流程怎麼走
給這些課程筆記建一個本地可檢索快取

月度資料趨勢

累計下載4189
2928360542822026-03 · 累計下載 30212026-05 · 累計下載 35482026-06 · 累計下載 36542026-07 · 累計下載 38602026-08 · 累計下載 39532026-09 · 累計下載 418926-0326-09
星標4
3442026-03 · 星標 32026-05 · 星標 42026-06 · 星標 32026-07 · 星標 42026-08 · 星標 42026-09 · 星標 426-0326-09

2026-09 起統計方法調整:同名技能按作者分別統計,並覆蓋全部技能。圖中虛線為調整前的月份,與之後的資料不直接比較,也不計算跨越調整的增長。

月份累計下載環比星標
2026-094189—4
2026-083953+2.4%4
2026-073860+5.6%4
2026-063654+3.0%3
2026-053548—4
2026-033021—3

SKILL.md 原文

以下是作者釋出的原始檔案,按純文字顯示,本站未做修改或稽核;其中的連結不可點選,命令請核實後再執行。抓取於 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"] }
    }
展開全部(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.

「知識管理」分類下的熱門技能

資料月份:2026-09 · 下載/星標/安裝數聚合自公開技能註冊庫(ClawHub、SkillHub),按月更新。口徑說明見資料方法。