raglite – OpenClaw Skill

---

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:

./scripts/install.sh

It installs from GitHub:

• git+https://github.com/VirajSanghvi1/raglite.git@main

Usage

One-command pipeline (recommended)

./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

./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.