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>
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# llm-classify
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## 解決什麼問題
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LLM 結構化輸出最常見場景:把自由文字分到固定 category。
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claude_api 用 `_recipe_output_format: json` 自動 parse + validate 必填欄位。
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## 怎麼觸發
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```bash
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curl -X POST https://cypher.arcrun.dev/webhooks/named/llm_classify_example/trigger \
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-H "X-Arcrun-API-Key: ak_xxx" \
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-d '{"api_key":"ak_xxx","text":"How to deploy Cloudflare Workers in production?"}'
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```
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## 預期結果
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- claude 回 JSON `{category, confidence, reason}`
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- KBDB 寫一筆 block,tags_json 含 `category:tech`
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- response 回 `{success: true, data: {id, ...}}`
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## 為什麼這 pattern 重要
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- `_recipe_output_format: json` + `_recipe_output_required_fields` 是 claude_api 的 magic:
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Claude 回 JSON 後 cypher-executor 自動:
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1. 剝 ```json fence
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2. parse
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3. 驗 required fields 存在
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4. 把每個欄位(category / confidence / reason)放到 ctx 頂層,下游 `{{category}}` 直接用
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- 不用寫 parse / validate / shape 邏輯,純 prompt + schema
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## 改成你自己的
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- prompt 改你的分類規則(category 清單可長可短)
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- 下游 save_with_tag 可換成 telegram 推播 / gmail / 等
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- 若需要多步分類(先粗分後細分),鏈兩個 claude_api 節點即可
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## 注意
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- claude_api 走 mira daemon (Phase A),會 paused 一陣子等 callback resume
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- 若 prompt 抽不出 required_fields,會 validation_failed 不寫 KBDB(safer than partial save)
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["llm", "claude", "classify", "structured-output", "kbdb", "common-pattern"]
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name: llm_classify_example
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description: webhook 收文字 → claude 分類 → 寫 KBDB 加 tag
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flow:
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- "input >> ON_SUCCESS >> classify"
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- "classify >> ON_SUCCESS >> save_with_tag"
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config:
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classify:
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component: claude_api
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timeout_ms: 30000
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_recipe_output_format: json
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_recipe_output_required_fields:
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- category
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- confidence
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prompt: |
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分類以下文字到下列其中一個 category:
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- tech / business / personal / other
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只回 JSON:
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{"category": "tech", "confidence": 0.85, "reason": "..."}
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文字:{{input.text}}
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save_with_tag:
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component: kbdb_create_block
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api_key: "{{api_key}}"
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type: "note"
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source: "llm-classified"
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user_id: "ai_classifier"
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content: "{{input.text}}"
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tags_json: '["llm-classified", "category:{{category}}"]'
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