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pilot-knowledge-base-rag-setuppilot 知识 base RAG 设置

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

2,889

周安装

118

GitHub Stars

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下载量

935
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

2

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:pilot-knowledge-base-rag-setup(pilot 知识 base RAG 设置)
来源仓库:https://github.com/teoslayer/pilot-knowledge-base-rag-setup
安装命令:
openclaw skills install pilot-knowledge-base-rag-setup
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 pilot-knowledge-base-rag-setup

简介

部署由4个代理构成的RAG管道,支持文档摄取、向量检索与答案生成全流程。

  • 适用于企业知识问答、客户支持自动化等需引用权威资料的场景。
  • 自动解析PDF/Word等格式文件并构建语义索引供后续查询使用。
  • 必须明确标注答案来源,禁止虚构未命中知识库的信息内容。
  • 建议定期增量更新向量库并监控召回质量指标变化趋势。

SKILL.md

name
pilot-knowledge-base-rag-setup
description
>
tags
license
AGPL-3.0
metadata
author
vulture-labs
version
1.0
openclaw
requires
bins
homepage
https://pilotprotocol.network
allowed-tools

Knowledge Base (RAG) Setup

Deploy 4 agents: ingest, embed, index, and query.

Roles

RoleHostnameSkillsPurpose
ingest<prefix>-rag-ingestpilot-s3-bridge, pilot-share, pilot-chunk-transfer, pilot-cronPulls and chunks documents
embedder<prefix>-rag-embedderpilot-task-parallel, pilot-share, pilot-metrics, pilot-task-chainGenerates vector embeddings
indexer<prefix>-rag-indexerpilot-database-bridge, pilot-share, pilot-task-chain, pilot-healthStores embeddings in vector DB
query<prefix>-rag-querypilot-api-gateway, pilot-health, pilot-load-balancer, pilot-metricsServes search queries

Setup Procedure

Step 1: Ask the user which role and prefix.

Step 2: Install skills:

# ingest:
clawhub install pilot-s3-bridge pilot-share pilot-chunk-transfer pilot-cron
# embedder:
clawhub install pilot-task-parallel pilot-share pilot-metrics pilot-task-chain
# indexer:
clawhub install pilot-database-bridge pilot-share pilot-task-chain pilot-health
# query:
clawhub install pilot-api-gateway pilot-health pilot-load-balancer pilot-metrics

Step 3: Set hostname and write manifest to ~/.pilot/setups/knowledge-base-rag.json.

Step 4: Handshake along the pipeline: ingest↔embedder, embedder↔indexer, indexer↔query.

Manifest Templates Per Role

ingest

{
  "setup": "knowledge-base-rag", "role": "ingest", "role_name": "Document Ingestion",
  "hostname": "<prefix>-rag-ingest",
  "skills": {
    "pilot-s3-bridge": "Pull documents from S3 buckets.",
    "pilot-share": "Send document files to embedder.",
    "pilot-chunk-transfer": "Split large documents into chunks.",
    "pilot-cron": "Schedule periodic ingestion sweeps."
  },
  "data_flows": [{ "direction": "send", "peer": "<prefix>-rag-embedder", "port": 1001, "topic": "doc-ingested", "description": "Document chunks" }],
  "handshakes_needed": ["<prefix>-rag-embedder"]
}

embedder

{
  "setup": "knowledge-base-rag", "role": "embedder", "role_name": "Embedding Generator",
  "hostname": "<prefix>-rag-embedder",
  "skills": {
    "pilot-task-parallel": "Generate embeddings in parallel for throughput.",
    "pilot-share": "Receive docs from ingest, send embeddings to indexer.",
    "pilot-metrics": "Track embedding throughput and latency.",
    "pilot-task-chain": "Chain chunking and embedding steps."
  },
  "data_flows": [
    { "direction": "receive", "peer": "<prefix>-rag-ingest", "port": 1001, "topic": "doc-ingested", "description": "Document chunks" },
    { "direction": "send", "peer": "<prefix>-rag-indexer", "port": 1001, "topic": "embeddings-ready", "description": "Vector embeddings" }
  ],
  "handshakes_needed": ["<prefix>-rag-ingest", "<prefix>-rag-indexer"]
}

indexer

{
  "setup": "knowledge-base-rag", "role": "indexer", "role_name": "Vector Indexer",
  "hostname": "<prefix>-rag-indexer",
  "skills": {
    "pilot-database-bridge": "Write embeddings to vector database.",
    "pilot-share": "Receive embeddings from embedder.",
    "pilot-task-chain": "Chain indexing operations.",
    "pilot-health": "Monitor index health and query latency."
  },
  "data_flows": [
    { "direction": "receive", "peer": "<prefix>-rag-embedder", "port": 1001, "topic": "embeddings-ready", "description": "Vector embeddings" },
    { "direction": "receive", "peer": "<prefix>-rag-query", "port": 1001, "topic": "search-query", "description": "Search queries" },
    { "direction": "send", "peer": "<prefix>-rag-query", "port": 1001, "topic": "search-results", "description": "Ranked results" }
  ],
  "handshakes_needed": ["<prefix>-rag-embedder", "<prefix>-rag-query"]
}

query

{
  "setup": "knowledge-base-rag", "role": "query", "role_name": "Query Server",
  "hostname": "<prefix>-rag-query",
  "skills": {
    "pilot-api-gateway": "Accept search queries from external clients.",
    "pilot-health": "Monitor query endpoint health.",
    "pilot-load-balancer": "Distribute queries across indexer replicas.",
    "pilot-metrics": "Track QPS, latency, result quality."
  },
  "data_flows": [
    { "direction": "send", "peer": "<prefix>-rag-indexer", "port": 1001, "topic": "search-query", "description": "Search queries" },
    { "direction": "receive", "peer": "<prefix>-rag-indexer", "port": 1001, "topic": "search-results", "description": "Ranked results" }
  ],
  "handshakes_needed": ["<prefix>-rag-indexer"]
}

Data Flows

  • ingest → embedder : document chunks (port 1001)
  • embedder → indexer : vector embeddings (port 1001)
  • query ↔ indexer : search queries and results (port 1001)

Workflow Example

# On ingest:
pilotctl --json send-file <prefix>-rag-embedder ./docs/guide.pdf
pilotctl --json publish <prefix>-rag-embedder doc-ingested '{"doc_id":"doc-42","chunks":24}'
# On embedder:
pilotctl --json publish <prefix>-rag-indexer embeddings-ready '{"doc_id":"doc-42","vectors":24,"dims":1536}'
# On query:
pilotctl --json task submit <prefix>-rag-indexer --task '{"query":"How does auth work?","top_k":5}'

Dependencies

Requires pilot-protocol skill, pilotctl binary, clawhub binary, and a running daemon.

适合场景

01

研究助手

02

事实核查

03

知识库问答

04

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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安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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

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