raglite

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":"🔎","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: distilled Markdown is human-readable and version-controllable.

Default engine

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

Install

./scripts/install.sh

Usage

./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

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.