Skill: virajsanghvi1/raglite-library (version 1.0.0)

--- name: raglite version: 1.0.0 description: "Local-first RAG cache: distill docs into structured Markdown, then index/query with Chroma + hybrid search (vector + keyword)." metadata: { "openclaw": { "emoji": "🔎", "os": ["darwin", "linux"], "requires": { "bins": ["python3", "pip"] } } } --- # 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:** the distilled Markdown is human-readable and version-controllable. If you later outgrow local, you can swap in a hosted DB — but you often don’t need to. ## What it does ### 1) Condense ✍️ Turns docs into structured Markdown outputs (low fluff, more “what matters”). ### 2) Index 🧠 Embeds the distilled outputs into a **Chroma** collection (one DB, many collections). ### 3) Query 🔎 Hybrid retrieval: - vector similarity via Chroma - keyword matches via ripgrep (`rg`) ## Default engine This skill defaults to **OpenClaw** 🦞 for condensation unless you pass `--engine` explicitly. ## Prereqs - **Python 3.11+** - For indexing/query: - Chroma server reachable (default `http://127.0.0.1:8100`) - For hybrid keyword search: - `rg` installed (`brew install ripgrep`) - For OpenClaw engine: - OpenClaw Gateway `/v1/responses` reachable - `OPENCLAW_GATEWAY_TOKEN` set if your gateway requires auth ## Install (skill runtime) This skill installs RAGLite into a skill-local venv: ```bash ./scripts/install.sh ``` It installs from GitHub: - `git+https://github.com/VirajSanghvi1/raglite.git@main` ## Usage ### One-command pipeline (recommended) ```bash ./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 ``` ### Query ```bash ./scripts/raglite.sh query ./raglite_out \ --collection my-docs \ --top-k 5 \ --keyword-top-k 5 \ "rollback procedure" ``` ## Outputs (what gets written) In `--out` you’ll see: - `*.tool-summary.md` - `*.execution-notes.md` - optional: `*.outline.md` - optional: `*/nodes/*.md` plus per-doc `*.index.md` and a root `index.md` - metadata in `.raglite/` (cache, run stats, errors) ## Troubleshooting - **Chroma not reachable** → check `--chroma-url`, and that Chroma is running. - **No keyword results** → install ripgrep (`rg --version`). - **OpenClaw engine errors** → ensure gateway is up and token env var is set. ## Pitch (for ClawHub listing) 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.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (HIGH)