313aeb13bf
Part 1(框架,cypher-executor webhooks-named.ts):加同步查詢 trigger。
現有 named webhook /trigger 回 {success,data,trace,duration_ms} 信封、只有 POST;
查詢面(console/MCP 打 graph neighbors/traverse)要 GET + 直接拿最終節點輸出當 response。
新端點同步 await 執行 workflow graph → 回 result.data(最終節點輸出)本身當 body(非 202):
- GET /q/:ns/:name (namespace 走 path,input 走 query string)
- GET /webhooks/named/:name/query (X-Arcrun-API-Key header,input 走 query string)
- POST /webhooks/named/:name/query (header,input 走 body)
- POST /webhooks/named/:ns/:name/query (namespace 走 path,input 走 body)
認證沿用 X-Arcrun-API-Key。誠實(mindset §7):節點失敗回 error+trace(500,非假綠);
paused 工作流無法同步回答 → 409 明講;輸出 5 MiB 硬上限(超過 413);duration 走 header 不污染 body。
Part 2(A 類 workflow.yaml):registry/examples/graph-neighbors/。
http_request 打 base custom domain kbdb.finally.click(避 CF 1042)撈 triplet records
→ code 零件記憶體 BFS(對照 kbdb-graph-plugin graph-traverse.ts)→ 同步回鄰居。
把 graph plugin 內建 GET /graph/neighbors 泛化成查詢面 workflow 的示範。
測試:cypher-executor/tests/query-trigger.test.ts(7 測,全綠)——同步回輸出(非 202)、
GET/POST × header/path 四端點、節點失敗回錯+trace、缺 key 401、不存在 404。
用內建 comp_uppercase(純記憶體)證明 Part 1 同步 trigger 機制本身可用。
待驗:graph_neighbors 需 code 零件部署 leo21c 後才能 live 端到端(另線處理);
triplet template id 上線前對一次(workflow 已參數化未寫死)。
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015d5jDbuqT5Htwv3Q88XXKk
113 lines
5.8 KiB
YAML
113 lines
5.8 KiB
YAML
name: graph_neighbors
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description: >
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同步查詢:給一個節點 → 從 KBDB triplet 記錄建鄰接表 → 記憶體 BFS 找 N 跳鄰居 → 同步回鄰居清單。
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這是「查詢面工作流」示範:用同步查詢 trigger(GET /q/:ns/graph_neighbors 或
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POST /webhooks/named/:name/query),把 workflow 最終節點輸出直接當 HTTP response 拿回,
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取代 graph plugin 內建的 GET /graph/neighbors。對照 kbdb-graph-plugin graph-traverse.ts 的記憶體 BFS。
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# ── 為什麼走 base custom domain(kbdb.finally.click)而非 workers.dev ──
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# cypher-executor 執行 http_request 節點時對 kbdb 發 fetch。若打 *.uncle6-me.workers.dev 同 zone
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# 會踩 CF 1042(same-zone self-fetch)。打 base 的對外 custom domain kbdb.finally.click 屬跨 zone,
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# 前門公網進出,避開 1042(同 credential-primitives-wasm Phase 7 的 global_fetch_strictly_public 精神)。
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# ── 觸發(同步查詢 trigger,非 202)──
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# GET https://cypher.arcrun.dev/q/{namespace}/graph_neighbors?node=Arcrun&depth=2&template=graph_triplet&namespace={namespace}
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# POST https://cypher.arcrun.dev/webhooks/named/graph_neighbors/query
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# -H "X-Arcrun-API-Key: {namespace}"
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# -d '{"node":"Arcrun","depth":2,"template":"graph_triplet","namespace":"{namespace}"}'
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# → 直接回 { success, start, depth, directed, neighbors:[...], count }(最終節點輸出本身)。
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flow:
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- "input >> ON_SUCCESS >> fetch_triplets"
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- "fetch_triplets >> ON_SUCCESS >> bfs_neighbors"
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config:
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# 1) 撈本租戶的 triplet 記錄。triplet = base 萬用表的一個 template(graph plugin 寫入),
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# slots = subject / predicate / object。base 端點:GET /records/by-template/:template?owner_id=
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# 回 { success, records:[{ record_id, values:{subject,predicate,object} }], count }。
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# ⚠️ template 名(此處 {{input.template}},預設由呼叫者帶 graph_triplet)以實際部署的
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# kbdb-graph-plugin triplet template id 為準——上線前對一次。
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fetch_triplets:
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component: http_request
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method: GET
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url: "https://kbdb.finally.click/records/by-template/{{input.template}}?owner_id={{input.namespace}}"
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headers:
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Accept: "application/json"
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# 2) ★ 記憶體 BFS(通用 code 零件,sandbox inline JS,無 LLM、無 fs/網路,stdin→stdout JSON)。
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# 對照 kbdb-graph-plugin graph-traverse.ts:23-51 的記憶體 BFS:把 triplet 當有向邊
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# subject --predicate--> object 建鄰接表,從 start 逐跳擴張到 depth 上限,收集新訪節點當鄰居。
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# directed=false(預設)時把邊當雙向(無向圖鄰居);directed=true 只走 subject→object。
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bfs_neighbors:
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component: code
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code: |
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// graph_neighbors — 記憶體 BFS 找 N 跳鄰居(純函式、決定性、零 token)。
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// input(由下方 input: 映射解析後注入):
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// records[] : triplet 記錄({ values:{subject,predicate,object} } 或扁平 {subject,predicate,object})
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// start : 起點節點名(字串)
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// depth : 最大跳數(字串或數字,來自 query string 時是字串)
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// directed : "true" 只走 subject→object;否則當無向
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const records = Array.isArray(input.records) ? input.records : [];
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const start = String(input.start == null ? '' : input.start);
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const maxDepth = Math.max(1, parseInt(String(input.depth == null ? 1 : input.depth), 10) || 1);
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const directed = String(input.directed == null ? '' : input.directed) === 'true';
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if (!start) {
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return { success: false, error: 'graph_neighbors 缺 start(node)參數' };
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}
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// 建鄰接表:subject --predicate--> object。無向時同時加反向邊。
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const adj = new Map();
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function addEdge(from, to, predicate) {
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if (!adj.has(from)) adj.set(from, []);
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adj.get(from).push({ node: to, predicate: predicate });
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}
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for (const r of records) {
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const v = (r && typeof r === 'object' && r.values && typeof r.values === 'object') ? r.values : r;
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if (!v || typeof v !== 'object') continue;
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const s = v.subject, p = v.predicate, o = v.object;
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if (!s || !o) continue;
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addEdge(s, o, p);
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if (!directed) addEdge(o, s, p);
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}
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// BFS:一層一跳,收集首次訪到的節點當鄰居(記 depth / 來源 / 關係)。
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const visited = new Set([start]);
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let frontier = [start];
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const neighbors = [];
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for (let d = 1; d <= maxDepth; d++) {
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const next = [];
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for (const cur of frontier) {
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const outs = adj.get(cur) || [];
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for (const e of outs) {
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if (visited.has(e.node)) continue;
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visited.add(e.node);
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neighbors.push({ node: e.node, predicate: e.predicate, from: cur, depth: d });
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next.push(e.node);
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}
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}
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frontier = next;
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if (frontier.length === 0) break;
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}
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return {
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success: true,
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start: start,
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depth: maxDepth,
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directed: directed,
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neighbors: neighbors,
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count: neighbors.length,
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};
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# input 映射:{{...}} 對 workflow context 展開後注入 code 沙箱的 `input` 變數。
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# {{input.X}} 的 input = 上游 input 節點輸出(=觸發 context);{{fetch_triplets.data.records}}
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# = http_request 回應 body 的 records 陣列(單一 ref pass-through 保留陣列型別)。
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input:
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records: "{{fetch_triplets.data.records}}"
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start: "{{input.node}}"
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depth: "{{input.depth}}"
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directed: "{{input.directed}}"
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limits:
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timeout_ms: 3000 # 純 CPU BFS,充裕
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max_output_bytes: 2097152 # 鄰居清單上限 2 MiB(呼叫端同步查詢輸出也有 5 MiB 硬上限)
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