ragtop-planner
Verifiedragtop planner — AI and machine learning tool. Supports OpenClaw, list, doc/retrieval.
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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:
- `RULES_SUMMARY`:从名称为“方案”的知识库召回规则并总结执行清单。
- `CASE_SUMMARY`:从名称为“案例”的知识库召回并总结成功模式。
- `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。
- `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
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