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ragtop-planner

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ragtop planner — AI and machine learning tool. Supports OpenClaw, list, doc/retrieval.

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About This Skill

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# ragtop-planner Skill

该 Skill 将达人推广方案制定流程改造为外部可执行编排,外部服务无需改后端即可调用。

Configuration

必须配置以下环境变量:

  • `RAGTOP_API_TOKEN`:API Token(必填)
  • `RAGTOP_API_URL`:API Base URL(可选,默认 `http://10.71.10.71:9380`)

可用工具(tool_cli)

统一前缀:`${RAGTOP_API_URL}/api/v1/ragtop/tool`

1) list_kb

  • 方法:`POST`
  • 路径:`/list_kb`
  • 认证:`Authorization: Bearer ${RAGTOP_API_TOKEN}`
  • 返回(关键字段):`data.kbs[]`、`data.total`

```bash curl -L -X POST "${RAGTOP_API_URL}/api/v1/ragtop/tool/list_kb" \ -H "Authorization: Bearer ${RAGTOP_API_TOKEN}" \ -H "Content-Type: application/json" ```

2) list_doc

  • 方法:`POST`
  • 路径:`/list_doc`
  • 必填:`knowledge_id`
  • 返回(关键字段):`data.docs[]`、`data.total`

```bash 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"}' ```

3) retrieval

  • 方法:`POST`
  • 路径:`/retrieval`
  • 必填:`knowledge_id` + (`query` 或 `queries`)
  • 可选:`doc_ids`、`retrieval_setting.top_k`、`retrieval_setting.score_threshold`
  • 返回:`records[]`(注意该接口直接返回 `records`,不是 `data.records`)

```bash 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A","查询B"], "retrieval_setting":{"top_k":16,"score_threshold":0.3} }' ```

FH Workflow(外部执行)

请按顺序执行以下四步:

  1. `RULES_SUMMARY`:从名称为“方案”的知识库召回规则并总结执行清单。
  2. `CASE_SUMMARY`:从名称为“案例”的知识库召回并总结成功模式。
  3. `KOL_SELECTOR`:从名称为“价格”的知识库召回候选达人并生成 HTML 筛选表。
  4. `PLAN_GENERATION`:融合规则、案例、达人表和用户需求生成最终方案。

详细步骤见:

  • `references/workflow.md`
  • `references/prompts.md`
  • `references/error_handling.md`

执行规则

  • 必须先 `list_kb`,并匹配三个知识库名称:`方案`、`案例`、`价格`。
  • 优先使用 `queries` 多路召回;仅在简单请求时用单 `query`。
  • 如用户指定文件范围,先调用 `list_doc`,再把 `doc_ids` 传给 `retrieval`。
  • 最终回答必须做预算合规检查(总价 <= 用户预算)。
  • 所有关键结论必须可追溯到召回来源(文档名或记录来源)。
  • 输出中统一使用 `ragtop` 命名。

推荐默认参数

  • 规则召回:`top_k=24`,`score_threshold=0.2`
  • 案例召回:`top_k=8`,`score_threshold=0.2`
  • 价格召回:`top_k=100`,`score_threshold=0.1`

失败与降级

  • 鉴权失败:提示用户检查 Token 是否有效或是否过期。
  • 知识库缺失:明确指出缺少 `方案/案例/价格` 中的哪个库。
  • 召回为空:建议用户细化关键词、指定文档或降低阈值后重试。
  • 预算冲突:要求剔除低优先级达人,直至满足预算。

Use Cases

  • Build retrieval-augmented generation (RAG) systems for knowledge-grounded AI
  • Index and search document collections for relevant context retrieval
  • Construct vector databases and embedding pipelines for semantic search
  • Configure chunking, embedding, and retrieval strategies for RAG applications
  • Integrate RAG capabilities into existing AI agent workflows

Pros & Cons

Pros

  • +Clean CLI interface integrates well with automation pipelines and AI agents
  • +Leverages AI models for intelligent automation beyond simple rule-based tools
  • +Configurable parameters allow tuning for different quality and cost tradeoffs

Cons

  • -Depends on external AI model APIs which may incur usage costs
  • -Output quality varies based on input specificity and model capabilities

FAQ

What does ragtop-planner do?
ragtop planner — AI and machine learning tool. Supports OpenClaw, list, doc/retrieval.
What platforms support ragtop-planner?
ragtop-planner is available on Claude Code, OpenClaw.
What are the use cases for ragtop-planner?
Build retrieval-augmented generation (RAG) systems for knowledge-grounded AI. Index and search document collections for relevant context retrieval. Construct vector databases and embedding pipelines for semantic search.

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