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Arcrun/registry/examples/llm-classify/description.md
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uncle6me-web 922a57fe34 arcrun — AI workflow execution engine (clean history)
Self-hosted 開源:WASM 零件 + recipe + cypher-executor,跑在你自己的 Cloudflare。

此為重建的乾淨歷史起點(移除曾誤 commit 的 GCP SA 金鑰,舊歷史保留在
richblack/arcrun 與本地 backup 分支)。含:
- acr init --self-hosted installer(建 KV/R2 + codeload 拉預編譯 wasm + wrangler deploy + seed recipe)
- recipe push 把關(資料外流提醒 + 打通檢查)
- 19 個正當零件預編譯 wasm(claude_api/km_writer/kbdb_upsert_block 排除:違反 DECISIONS §1)
- CLI / cypher-executor / registry / 完整 SDD

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
2026-06-03 15:52:38 +08:00

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# llm-classify
## 解決什麼問題
LLM 結構化輸出最常見場景:把自由文字分到固定 category。
claude_api 用 `_recipe_output_format: json` 自動 parse + validate 必填欄位。
## 怎麼觸發
```bash
curl -X POST https://cypher.arcrun.dev/webhooks/named/llm_classify_example/trigger \
-H "X-Arcrun-API-Key: ak_xxx" \
-d '{"api_key":"ak_xxx","text":"How to deploy Cloudflare Workers in production?"}'
```
## 預期結果
- claude 回 JSON `{category, confidence, reason}`
- KBDB 寫一筆 blocktags_json 含 `category:tech`
- response 回 `{success: true, data: {id, ...}}`
## 為什麼這 pattern 重要
- `_recipe_output_format: json` + `_recipe_output_required_fields` 是 claude_api 的 magic
Claude 回 JSON 後 cypher-executor 自動:
1. 剝 ```json fence
2. parse
3. 驗 required fields 存在
4. 把每個欄位(category / confidence / reason)放到 ctx 頂層,下游 `{{category}}` 直接用
- 不用寫 parse / validate / shape 邏輯,純 prompt + schema
## 改成你自己的
- prompt 改你的分類規則(category 清單可長可短)
- 下游 save_with_tag 可換成 telegram 推播 / gmail / 等
- 若需要多步分類(先粗分後細分),鏈兩個 claude_api 節點即可
## 注意
- claude_api 走 mira daemon (Phase A),會 paused 一陣子等 callback resume
- 若 prompt 抽不出 required_fields,會 validation_failed 不寫 KBDBsafer than partial save