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Ragtop Agent

高级 RAG 助手，具备 Agentic RAG 思考能力。能够自动化管理 RAGTOP 知识库，并在执行深度调研任务时，调用 RAGTOP 后端接口。使用场景包括：(1) 列出知识库和文档，(2) 执行语义检索和深度分析。

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Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: SKILL.md, references/workflow.md

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Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 1.0.0

Provenance

Publisher: qbs784
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 9 sections Open source page

ragtop-agent Skill

本 Skill 允许 AI 通过 curl 调用 RAGTOP 后端接口。AI 必须根据用户请求的性质，在“简单指令”与“深度调研”两种模式间切换。

Configuration

The following environment variables are required: RAGTOP_API_URL: RAGTOP API base URL. Defaults to http://10.71.10.71:9380 if not set. RAGTOP_API_TOKEN: Your RAGTOP API access token. Can be configured via the OpenClaw Web UI.

1. 核心工具构建指南 (How to build curl)

在调用以下接口前，请确保已获取环境变量 ${RAGTOP_API_URL} 和 ${RAGTOP_API_TOKEN}。如果 ${RAGTOP_API_URL} 为空，请使用默认值 http://10.71.10.71:9380。

A. 列出知识库 (list_kb)

用于获取所有可用的 knowledge_id。 curl -L -X POST "${RAGTOP_API_URL}/api/v1/ragtop/tool/list_kb" \ -H "Authorization: Bearer ${RAGTOP_API_TOKEN}" \ -H "Content-Type: application/json"

B. 列出文档 (list_doc)

用于获取特定知识库内的 doc_id 列表，以便缩小检索范围。 Payload: {"knowledge_id": "string"} curl -L -X POST "${RAGTOP_API_URL}/api/v1/ragtop/tool/list_doc" \ -H "Authorization: Bearer ${RAGTOP_API_TOKEN}" \ -H "Content-Type: application/json" \ -d '{"knowledge_id": "YOUR_KB_ID"}'

C. 内容检索 (retrieval)

用于执行语义搜索。支持单查询或多查询。 Payload 关键参数: knowledge_id: 必填。 query: 字符串（单查询）。 queries: 字符串数组（多查询，推荐用于复杂任务）。 doc_ids: 数组（可选，限定文件范围）。 retrieval_setting: {"top_k": 16, "score_threshold": 0.3} curl -L -X POST "${RAGTOP_API_URL}/api/v1/ragtop/tool/retrieval" \ -H "Authorization: Bearer ${RAGTOP_API_TOKEN}" \ -H "Content-Type: application/json" \ -d '{ "knowledge_id": "YOUR_KB_ID", "queries": ["查询1", "查询2"], "retrieval_setting": {"top_k": 5} }'

情况 A：简单指令 (Simple Instructions)

适用场景: 用户询问“有哪些知识库？”、“这个库里有哪些文件？”等管理类问题。执行逻辑: 直接根据需求构建 list_kb 或 list_doc 的 curl 命令。将返回的 JSON 结果整理成易读的表格或列表告知用户。

情况 B：深度调研 (Deep Investigation / Agentic RAG)

适用场景: 用户提出具体业务问题、对比分析或需要跨文档总结。执行逻辑: 参考 references/workflow.md 执行“分析-分解-检索-综合”流程。多步编排: 第一步：调用 list_kb 确定相关的知识库 ID。第二步（可选）：调用 list_doc 锁定相关文件。第三步：构建包含多个改写问题的 retrieval 请求，利用多路召回提高准确率。第四步：根据检索到的多个 chunks 进行逻辑推理和引用标注。

3. 运行规范

严禁提及 ragflow，统一使用 ragtop。引用必填: 所有深度调研的回答必须注明引用的文档名称。错误处理: 如果 curl 返回非 SUCCESS，应检查 Token 有效性并告知用户。

Category context

Agent frameworks, memory systems, reasoning layers, and model-native orchestration.

Source: Tencent SkillHub

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Package contents

Included in package

2 Docs

SKILL.md Primary doc
references/workflow.md Docs