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lookupmark-local-rag（lookupmark 本地 RAG）

Agent Skill

用于搭建或维护带检索增强的 RAG 工作流，适合让 Agent 处理知识库问答、向量检索、来源引用和事实核查。它可以辅助整理数据接入、Embedding、向量库、召回参数和回答生成流程。使用时需要确认数据来源、更新频率、召回阈值和引用展示方式，避免把未命中的资料或过期内容包装成确定事实。

总安装

6,106

周安装

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下载量

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OpenClaw

安装说明

本站只整理中文说明和来源信息，不托管安装包，也不代用户安装。

GitHub

来源数

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手，先确认命令和权限，再让它执行。

请帮我安装这个 Agent Skill：lookupmark-local-rag（lookupmark 本地 RAG）
来源仓库：https://github.com/lookupmark/lookupmark-local-rag
安装命令：
openclaw skills install lookupmark-local-rag
安装前请先检查当前环境是否支持对应 CLI，并向我确认将要执行的命令、安装目录、联网范围和文件读写权限；确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill；本站只展示命令，不托管安装包，也不自动执行。

ClawHubOpenClaw

openclaw skills install lookupmark-local-rag

简介

lookupmark-local-rag 对本地文件进行语义搜索与交叉排序，支持向量检索。

适合在 OpenClaw 中构建私有知识库问答系统时使用。
通过 clawhub 安装并使用 openclaw skills install lookupmark-local-rag 命令部署。
使用前需准备数据源并配置 Embedding 模型参数。
适用宿主包括 OpenClaw，接入前应确认版本、权限和运行环境要求。

SKILL.md

name: local-rag
description: >

Local RAG

Semantic search over indexed local files with parent-child chunking for precise retrieval with full context.

Architecture

Component	Model	Size
Embeddings	`sentence-transformers/all-MiniLM-L6-v2`	~80MB
Reranker	`cross-encoder/ms-marco-MiniLM-L-6-v2`	~80MB
Vector DB	ChromaDB (persistent, cosine similarity, HNSW)	varies
Chunking	Parent-child	—

Memory strategy: Embedding model loaded first → freed with gc.collect() → reranker loaded → freed after scoring. This keeps peak RAM ~400MB on ARM.

Chunking Strategy

Child chunks: 128 words, 24 overlap → embedded for semantic search
Parent chunks: 768 words → stored as full context, returned to user
When a child matches → its parent is returned, giving surrounding context

Running

All scripts must use the venv Python:

VENV=~/.local/share/local-rag/venv/bin/python

Indexing

# Incremental index (default — skips unchanged files via SHA-256 hash)
$VENV ~/.openclaw/workspace/skills/lookupmark-local-rag/scripts/index.py

# Re-index from scratch
$VENV ~/.openclaw/workspace/skills/lookupmark-local-rag/scripts/index.py --reindex

# Custom paths
$VENV ~/.openclaw/workspace/skills/lookupmark-local-rag/scripts/index.py --paths ~/Documenti ~/Progetti

# Batch indexing (per-subfolder with git checkpoints, for low-RAM systems)
bash ~/.openclaw/workspace/skills/lookupmark-local-rag/scripts/index-batch.sh

Querying

# Basic query
$VENV ~/.openclaw/workspace/skills/lookupmark-local-rag/scripts/query.py "what are the termination clauses?"

# More results
$VENV ~/.openclaw/workspace/skills/lookupmark-local-rag/scripts/query.py "Falcon LLM" --top-k 30 --top-n 5

# JSON output for programmatic use
$VENV ~/.openclaw/workspace/skills/lookupmark-local-rag/scripts/query.py "transformer architecture" --json

# With timeout
$VENV ~/.openclaw/workspace/skills/lookupmark-local-rag/scripts/query.py "deep learning" --timeout 60

Options:

--top-k N — Child candidates from vector search (default: 20)
--top-n N — Final parent results after reranking (default: 3)
--json — JSON output
--timeout N — Max seconds per query (default: 120)

Monitoring

$VENV ~/.openclaw/workspace/skills/lookupmark-local-rag/scripts/monitor.py              # Status
$VENV ~/.openclaw/workspace/skills/lookupmark-local-rag/scripts/monitor.py --watch      # Auto-refresh
$VENV ~/.openclaw/workspace/skills/lookupmark-local-rag/scripts/monitor.py --log        # Logs
$VENV ~/.openclaw/workspace/skills/lookupmark-local-rag/scripts/monitor.py --errors     # Errors only
$VENV ~/.openclaw/workspace/skills/lookupmark-local-rag/scripts/monitor.py --git        # Git checkpoints

Supported Formats

Documents only (no code files):

Text: .txt, .md, .csv, .json, .yaml, .yml, .toml, .tex, .bib
Documents: .pdf (pdfminer.six), .docx (python-docx), .pptx

Excluded: .py, .js, .sh, .ipynb, .html, .css and all code files.

Limits (for 4GB ARM)

PDF max size: 5MB (larger PDFs cause OOM with pdfminer)
Max file size: 30MB
Embedding batch size: 1 (conservative)
Excluded dirs: .git, .venv, node_modules, __pycache__, labs, exercises, src, scripts, ablation, test*, fixtures

Storage

Path	Purpose
`~/.local/share/local-rag/chromadb/`	ChromaDB data (git repo for rollback)
`~/.local/share/local-rag/venv/`	Python venv with dependencies
`~/.local/share/local-rag/index.lock`	Prevents concurrent indexing
`~/.local/share/local-rag/index-batch.log`	Batch indexing log
`~/.local/share/local-rag/queries.log`	Query history log

Security

ALLOWED_ROOTS: Only ~/Documenti/github/thesis, ~/Documenti/github/polito, ~/Documenti, ~/Scaricati
BLOCKED_PATTERNS: .ssh, .gnupg, .env, credentials, tokens, .config/openclaw
Credentials directory is blacklisted — never indexed

Workflow

Run index.py — builds/rebuilds the index (incremental via SHA-256 hash check)
Run periodically to pick up new/changed files (daily cron recommended)
Use query.py to search with natural language
Results include: file path, relevance score, matched snippet, full parent context
Check monitor.py for stats and queries.log for query history

适合场景

研究助手

事实核查

知识库问答

带来源的搜索总结

能力概览

能力 1

组合搜索和大模型调用

能力 2

支持多来源检索和总结

能力 3

强调引用来源和事实核查

能力 4

适合研究型 Agent 流程

安装后应在对应宿主中按原始 README 的触发条件使用；具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

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权限和风险

权限需确认

当前来源未能明确判断权限范围，默认进入异常复核队列。

安装前确认

本站仅展示第三方公开信息，不托管安装包，不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源，正式发布前建议补源仓库或其他目录站核验。

来源信息

GitHub 仓库

github.com

/lookupmark/lookupmark-local-rag

SKILL.md 来源

clawhub.ai

/lookupmark/lookupmark-local-rag

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