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。
| 平台 | 累計安裝 | 累計下載 | 星標 | 評論 |
|---|---|---|---|---|
| ClawHub ↗ | 138 | 4049 | 3 | 1 |
| SkillHub ↗ | 205 | 4189 | 4 | — |
各平台的計數口徑不同,統計時間不一致。
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
以下是根據技能文件生成的示例,不代表實際執行結果。
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 |
|---|---|---|---|
| 2026-09 | 4189 | — | 4 |
| 2026-08 | 3953 | +2.4% | 4 |
| 2026-07 | 3860 | +5.6% | 4 |
| 2026-06 | 3654 | +3.0% | 3 |
| 2026-05 | 3548 | — | 4 |
| 2026-03 | 3021 | — | 3 |
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。
---
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"] }
}} --- # 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.
把長文字、文件與網頁內容壓縮成要點和關鍵詞的摘要工具。
透過 IMA OpenAPI 管理筆記與知識庫內容。
規定如何為網頁請求選擇合適工具的必讀指引。
呼叫百度 AI 搜尋介面做網頁檢索的技能。
用 uvx 直接呼叫 markitdown,把各類檔案轉成 Markdown。
面向 OpenClaw 的預設網頁搜尋,即時檢索多個引擎。
用 DuckDuckGo 介面做網頁、新聞、圖片與影片檢索的技能。
檢索並發現可用 OpenClaw 技能的技能。
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。