merge main：把 t176（拔雲端 LLM 下發）與 workers_ai_chat 種子併進 CIS 分支
出貨前發現兩條分支各有一半： main → t176 拔掉雲端下發 extractor／刪 admin/extractor／workers_ai_chat 種子 fix/cis-round3-portal → t181 新萃取端點 /portal/daemon/extract、CIS 視覺 **要合起來才是完整的出貨內容**（實測：合併前 bundle 仍含 admin/extractor ×2）。 原始碼三檔全自動合併；只有 portal-admin.test.ts 衝突—— HEAD 側是**過時的 t131/t122 測試**（測 main 已刪的端點，留著必紅）， main 側是刪除。解法：接受刪除、保留我這側的 t181 守衛。 測試 31 passed，唯一 failed 是基準線既有的 GET /portal 靜態資源案。 Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
This commit is contained in:
uncle6me-web
@@ -3,24 +3,33 @@ import { ExecutionError, WorkflowPaused } from '../types';
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import { GraphExecutor } from '../graph-executor';
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import { graphSchema } from '../lib/schemas';
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import { createComponentLoader } from '../lib/component-loader';
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import { writeEvaluation, updateComponentStats } from './execution-evaluator';
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import { recordComponentStats } from './execution-evaluator';
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import { parseTriplets } from './triplet-parser';
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import { searchNodes } from './search-nodes';
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import { searchNodes, type SearchMode, type SearchTarget } from './search-nodes';
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import { buildExecutionGraph } from './graph-builder';
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export async function handleCypherSearch(
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triplets: unknown[],
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env: Bindings,
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mode: SearchMode = 'discover',
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target?: SearchTarget,
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): Promise<{ nodes: Record<string, unknown>; cypher: unknown; missing: string[] }> {
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const parsed = parseTriplets(triplets);
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if (!parsed) {
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throw new Error('無法解析任何節點');
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}
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const { nodeResults } = searchNodes(parsed);
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// 2026-07-30：查 registry 判真實存在（workflow-discovery）。
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// `missing` 以前寫死 []，等於告訴 AI「什麼都有」——那是「腹語術」的入口。
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//
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// t158（07-31 迴歸修復，leo：「這裡只是複製一些工作流的 data 過去，沒有要在這裡驗證」）：
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// 誠實化只屬於 **discover**（AI 問「有沒有」）；**compile**（部署/推送的複製路徑）
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// 純編圖零查詢——那本來就是既有設計（workflows.json＝打包期預編的搬運），
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// 5cadc60 起誠實化漏進複製路徑＝迴歸（冷實例 8 節點 25.7s、安裝器 timeout 炸）。
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const { nodeResults, missingNodes } = await searchNodes(parsed, undefined, env, mode, target);
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const graph = buildExecutionGraph(parsed, nodeResults, 'cypher-search-result', 'Cypher Search Result');
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return { nodes: nodeResults, cypher: { nodes: graph.nodes, edges: graph.edges }, missing: [] };
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return { nodes: nodeResults, cypher: { nodes: graph.nodes, edges: graph.edges }, missing: missingNodes };
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}
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export async function handleCypherExecute(
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@@ -50,7 +59,9 @@ export async function handleCypherExecute(
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throw new Error('無法解析任何節點');
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}
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const { nodeResults } = searchNodes(parsed, config);
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// t158：執行路徑＝compile（零 discovery round-trip）——存在性由 component-loader
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// 在載入該節點時決定（原本的權威），查詢層不重複驗。
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const { nodeResults } = await searchNodes(parsed, config, env, 'compile');
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const graph = buildExecutionGraph(parsed, nodeResults, graphId, graphName, config);
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const parseResult = graphSchema.safeParse(graph);
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@@ -66,18 +77,8 @@ export async function handleCypherExecute(
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const result = await executor.execute(parseResult.data as ExecutionGraph, context ?? {}, env.EXEC_CONTEXT);
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const duration_ms = Date.now() - start;
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// 非同步記錄統計（Phase 7 補充 analytics，目前為 no-op）
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const componentId = graph.nodes.find(n => n.componentId)?.componentId ?? graphId;
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const runId = `${graphId}-${Date.now()}`;
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waitUntil(writeEvaluation(env, {
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run_id: runId,
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workflow_id: graphId,
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component_id: componentId,
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verdict: 'success',
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duration_ms,
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evaluated_at: Date.now(),
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}));
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waitUntil(updateComponentStats(env, componentId, 'success', duration_ms));
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// 非同步回寫每顆零件的執行統計（design.md「執行統計設計」；fire-and-forget 不阻擋回應）
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waitUntil(recordComponentStats(env, graph.nodes, result.trace));
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return { success: true, data: result.data, trace: result.trace, duration_ms, graph };
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} catch (err) {
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@@ -99,19 +100,10 @@ export async function handleCypherExecute(
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}
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const errMsg = err instanceof Error ? err.message : String(err);
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const componentId = graph.nodes.find(n => n.componentId)?.componentId ?? graphId;
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const runId = `${graphId}-${Date.now()}`;
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waitUntil(writeEvaluation(env, {
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run_id: runId,
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workflow_id: graphId,
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component_id: componentId,
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verdict: 'failed',
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duration_ms,
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error_message: errMsg.slice(0, 200),
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evaluated_at: Date.now(),
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}));
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waitUntil(updateComponentStats(env, componentId, 'failed', duration_ms));
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// 失敗路徑同樣回寫每顆零件統計：ExecutionError 帶完整 trace（失敗節點有 error、
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// 之前成功的節點照記成功）；非 ExecutionError 無 trace 可歸因 → 不記（誠實：不瞎猜）。
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if (err instanceof ExecutionError) {
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waitUntil(recordComponentStats(env, graph.nodes, err.trace));
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const traceFormatted = err.trace.map(s => ({
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node: s.nodeId,
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status: s.error ? 'failed' : 'success',
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@@ -1,36 +1,96 @@
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/**
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* Execution Analytics — 零件執行後的統計記錄
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* Execution Analytics — 零件執行後的統計回寫
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*
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* Phase 1 MVP：stub（不寫入任何外部服務）
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* Phase 7 補充：fire-and-forget POST 至 registry.arcrun.dev/analytics/record
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* SDD: system-dev/docs/3-specs/arcrun-core-mvp/design.md「執行統計設計」
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* 執行完成處（cypher-handlers / webhook-handlers 收尾）對本次用到的**每顆零件**
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* fire-and-forget POST registry `/analytics/record`——統計失敗不影響執行、不增加同步延遲
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* （呼叫端一律用 waitUntil 包，仿 recordRecipeStats / recordTelemetry 既有慣例）。
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*
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* 每顆零件的成敗判定來源＝執行 trace（per-node）：
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* - trace step 有 `error` → 失敗（runner throw）
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* - output 是物件且 `success === false` → 失敗（makeHttpRunner 對非 2xx 不 throw，回這種）
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* - 其餘 → 成功
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* FOREACH 重複執行同一節點 → trace 有幾筆就記幾次（每次真實執行都算一次樣本）。
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*/
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import type { Bindings } from '../types';
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import type { GraphNode, TraceStep } from '../types';
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import { wasmWorkerUrl } from '../lib/component-loader';
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export interface EvaluationRecord {
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run_id: string;
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workflow_id: string;
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/** 本模組需要的環境子集（傳整份 Bindings 也相容，仿 SearchNodesEnv 慣例）。 */
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export type AnalyticsEnv = {
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WORKER_SUBDOMAIN?: string;
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/** registry 位置覆蓋（可選；本地 wrangler dev / self-hosted 用）。未設 → wasmWorkerUrl('registry', WORKER_SUBDOMAIN)。 */
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REGISTRY_BASE_URL?: string;
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};
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export interface ComponentVerdict {
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component_id: string;
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verdict: 'success' | 'failed' | 'timeout';
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success: boolean;
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duration_ms: number;
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error_message?: string;
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evaluated_at: number;
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}
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/** 記錄執行結果（MVP：no-op，Phase 7 補充 analytics）*/
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export async function writeEvaluation(
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_env: Bindings,
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_record: EvaluationRecord,
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): Promise<void> {
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// Phase 7: POST to registry.arcrun.dev/analytics/record
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/** 從執行 trace 導出每顆零件的成敗（只算 type=Component 且有 componentId 的節點）。 */
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export function componentVerdictsFromTrace(
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nodes: GraphNode[],
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trace: TraceStep[],
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): ComponentVerdict[] {
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const componentByNodeId = new Map<string, string>();
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for (const n of nodes) {
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if (n.type === 'Component' && n.componentId) componentByNodeId.set(n.id, n.componentId);
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}
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const verdicts: ComponentVerdict[] = [];
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for (const step of trace) {
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const componentId = componentByNodeId.get(step.nodeId);
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if (!componentId) continue;
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const out = step.output;
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const outputSaysFailed =
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typeof out === 'object' && out !== null && !Array.isArray(out) &&
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(out as Record<string, unknown>).success === false;
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verdicts.push({
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component_id: componentId,
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success: !step.error && !outputSaysFailed,
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duration_ms: Math.max(0, Number(step.duration_ms) || 0),
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});
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}
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return verdicts;
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}
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/** 更新零件統計（MVP：no-op，Phase 7 補充）*/
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export async function updateComponentStats(
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_env: Bindings,
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_componentId: string,
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_verdict: 'success' | 'failed' | 'timeout',
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_durationMs: number,
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/**
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* 對本次執行用到的每顆零件回寫統計到 registry（design.md「Analytics Record」）。
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* 永不 throw；呼叫端用 waitUntil 包，不阻擋主流程。
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*/
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export async function recordComponentStats(
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env: AnalyticsEnv,
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nodes: GraphNode[],
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trace: TraceStep[],
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): Promise<void> {
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// Phase 7: update ANALYTICS_KV via registry worker
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try {
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const base = (
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env.REGISTRY_BASE_URL ??
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(env.WORKER_SUBDOMAIN ? wasmWorkerUrl('registry', env.WORKER_SUBDOMAIN) : undefined)
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)?.replace(/\/$/, '');
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if (!base) return;
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const verdicts = componentVerdictsFromTrace(nodes, trace);
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if (verdicts.length === 0) return;
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await Promise.all(
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verdicts.map(v =>
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fetch(`${base}/analytics/record`, {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({
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canonical_id: v.component_id,
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success: v.success,
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duration_ms: v.duration_ms,
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}),
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}).catch(() => undefined), // 統計失敗不影響執行
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),
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);
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} catch {
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// fire-and-forget：不拋錯，不影響主流程
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}
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}
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@@ -43,12 +43,28 @@ export function buildExecutionGraph(
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iterator = foreachMatch[1];
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label = '對每個'; // 改回標準 label 走 SEMANTIC_EDGE_MAP
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}
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const edge: { from: string; to: string; type: ReturnType<typeof toEdgeType>; iterator?: string } = {
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// 「ON_BRANCH(標籤)」抽 branch：意圖語法表達具名分支（SDD workflow-discovery 3.11）
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// 例：'my_switch >> ON_BRANCH(branch_active) >> 處理啟用' → type=ON_BRANCH, branch='branch_active'
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// 沒有這段的話，帶括號的 label 會落到 toEdgeType 的預設值 PIPE ⇒ 分支靜默失效
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// （即「教了語法但引擎不收」——比沒做更糟，故與 skill 文件同批補上）
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let branch: string | undefined;
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const branchMatch = label.match(/^(?:ON_BRANCH|分支)\s*[（(]\s*([\w-]+)\s*[）)]$/i);
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if (branchMatch) {
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branch = branchMatch[1];
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label = 'ON_BRANCH';
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}
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const edge: {
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from: string; to: string; type: ReturnType<typeof toEdgeType>;
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iterator?: string; branch?: string;
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} = {
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from: e.from.toLowerCase().replace(/\s+/g, '-'),
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to: e.to.toLowerCase().replace(/\s+/g, '-'),
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type: toEdgeType(label),
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};
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if (iterator) edge.iterator = iterator;
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if (branch) edge.branch = branch;
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return edge;
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});
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@@ -1,40 +1,721 @@
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import type { ParsedTriplets, NodeRole } from './triplet-parser';
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import { resolveNodeRole } from './triplet-parser';
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import { resolveNodeRole, isVirtualIoName } from './triplet-parser';
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import { wasmWorkerUrl } from '../lib/component-loader';
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import { resolveRecipe } from '../routes/recipes';
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import type { RecipeDefinition } from '../routes/recipes';
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import { branchHintFor } from '../lib/branch-hints';
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import type { BranchHint } from '../lib/branch-hints';
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/**
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* `not_found` 而非 `missing`：欄位契約以頂層機械考
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* `system-dev/docs/3-specs/arcrun-usable/verify.sh` 為準（01 組 grep `not_found`）。
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*/
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/** `unchecked`＝compile 模式的誠實標記：沒查、不知道有沒有（≠found 的假信號）。 */
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/** `resolved`＝意圖節點被媒合替換成真實零件/recipe（步驟 4；≠字面 exact 的 found）。 */
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export type NodeStatus = 'found' | 'not_found' | 'unknown' | 'unchecked' | 'resolved';
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|
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/**
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* 意圖節點 → 真實零件/recipe 的替換結果（CP 步驟 4，workflow-discovery 3.x 搜尋端延伸）。
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* 目的（CP 原文）：AI 只要填 payload——系統把「傳到 telegram」翻成
|
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* `http_request`＋recipe `telegram_send`，並明說缺什麼。
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*/
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export type NodeSubstitution = {
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/** 原始意圖節點名（替換前）。 */
|
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from: string;
|
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/**
|
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* 執行底層零件：component 替換＝該零件本身；
|
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* recipe 替換＝`http_request`（recipe 是 http_request＋參數模板的具名封裝）。
|
||||
*/
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componentId: string;
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/** recipe 替換時的 canonical_id——workflow config 寫 `component: <此值>` 即可直接用。 */
|
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recipe?: string;
|
||||
/** 為什麼這樣換（簡單可解釋規則的命中說明，不接 LLM）。 */
|
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reason: string;
|
||||
};
|
||||
|
||||
/**
|
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* 指定搜尋對象（leo 07-31：「難道我不能指定要搜尋工作流或節點或 recipe 嗎？」）。
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* 不給＝現行混搜兩庫＋意圖替換；`component`＝只查零件 registry；`recipe`＝只查 recipe 庫。
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* `workflow` 不進本函式——workflow 搜尋是名字搜尋（route 層走既有 /workflows/search 機制）。
|
||||
*/
|
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export type SearchTarget = 'component' | 'recipe';
|
||||
|
||||
export type NodeInfo = {
|
||||
status: NodeStatus;
|
||||
componentId?: string;
|
||||
type: NodeRole;
|
||||
/** found 時標來源庫：零件 registry（component）或 recipe 庫（recipe）。 */
|
||||
source?: 'component' | 'recipe';
|
||||
/** 零件契約（found 時附上，讓 AI 知道怎麼填 payload）。 */
|
||||
input_schema?: unknown;
|
||||
/** 成功率（found 時附上，讓「被測過幾次」看得見）。 */
|
||||
success_rate?: number;
|
||||
stability?: string;
|
||||
/** recipe found 時附上（AI 看得懂這個 recipe 在打哪個 API）。 */
|
||||
description?: string;
|
||||
endpoint?: string;
|
||||
/**
|
||||
* recipe 的 payload／回應用法自我說明（3.12，同 branch_hint 的動機）：
|
||||
* 逐顆查 recipe 時光看 endpoint 不知道「payload 怎麼填、回應怎麼取值」⇒ 會退回寫 code。
|
||||
*/
|
||||
payload_hint?: {
|
||||
/** 這個 recipe 期望的 body 形狀（body_template 的欄位骨架，值是 {{var}} 佔位） */
|
||||
body_template?: unknown;
|
||||
/** 回應正規化規則存在時，說明取值路徑等 */
|
||||
response_map?: unknown;
|
||||
/** 一行說明：怎麼用這個 recipe */
|
||||
usage: string;
|
||||
};
|
||||
/**
|
||||
* not_found 時的分型指路（task 3.7）：兩庫（零件 registry＋recipe 庫）都查過才點名，
|
||||
* 並告訴 AI 該走哪條補件路＋去哪裡看做法。欄位名 `suggestion`（單數字串）＝verify.sh 03 組契約。
|
||||
*/
|
||||
suggestion?: string;
|
||||
/** not_found 時的相近零件候選（自然語言節點名 → 既有零件的媒合）。 */
|
||||
similar_components?: string[];
|
||||
/** not_found 時的相近 recipe 候選。 */
|
||||
similar_recipes?: string[];
|
||||
/** resolved 時的替換明細（步驟 4：意圖節點 → 真實零件/recipe）。 */
|
||||
substitution?: NodeSubstitution;
|
||||
/**
|
||||
* 分支用法自我說明（3.11）：只有「本身會分岔」的零件才有
|
||||
* （if_control／switch／try_catch）。
|
||||
* 存在的理由＝走 n8n 式「逐顆查、自己組圖」的 AI，光看 input_schema 不知道
|
||||
* 「判斷完之後兩條路怎麼接」⇒ 會回頭寫 code。判準：只看這一顆的回應就知道怎麼接下一步。
|
||||
*/
|
||||
branch_hint?: BranchHint;
|
||||
};
|
||||
|
||||
export type SearchResult = {
|
||||
nodeResults: Record<string, { status: 'found' | 'missing'; componentId?: string; type: NodeRole }>;
|
||||
nodeResults: Record<string, NodeInfo>;
|
||||
missingNodes: string[];
|
||||
};
|
||||
|
||||
/**
|
||||
* 對所有節點進行解析，確認每個節點對應的零件 ID。
|
||||
*
|
||||
* 注意：此步驟只做靜態解析，不做遠端查找。
|
||||
* 零件是否真的存在由 component-loader 在執行時決定（Service Binding / KV / URL）。
|
||||
*
|
||||
* 優先序：
|
||||
* 1. Input/Output 角色：自動標記，componentId = 小寫節點名稱
|
||||
* 2. config[nodeName].component 已指定：使用 config 提供的 componentId
|
||||
* 3. 其他：componentId = 節點名稱（交給 component-loader 在執行時解析）
|
||||
* t158（leo 07-31 定調「部署≠發現」）：
|
||||
* 「這裡只是複製一些工作流的 data 過去，沒有要在這裡驗證，難怪這麼慢。
|
||||
* 就算是我自己寫了錯的工作流，也可以跑跑看，如果錯誤就修改，
|
||||
* 沒有說有錯誤還要一個個驗證這回事。」
|
||||
* - `compile`＝純編圖：**零外部查詢**（不打 registry、不掃 recipe、不算相似度、不擋 missing）。
|
||||
* 部署/推送/執行路徑用——寫錯的 workflow 照樣部署，錯在執行時現形。
|
||||
* - `discover`＝誠實查詢（預設，`/cypher/search` 的既有契約）：AI 問「有沒有」時用，
|
||||
* not_found＋分型指路＋相似候選全保留。
|
||||
*/
|
||||
export function searchNodes(
|
||||
export type SearchMode = 'discover' | 'compile';
|
||||
|
||||
/** searchNodes 需要的環境子集（cypher-handlers 傳整份 Bindings 進來也相容）。 */
|
||||
export type SearchNodesEnv = {
|
||||
WORKER_SUBDOMAIN?: string;
|
||||
/**
|
||||
* registry 位置覆蓋（可選，非機密）。未設 → 用 wasmWorkerUrl('registry', WORKER_SUBDOMAIN)
|
||||
* 現算（比照 KBDB_GRAPH_URL 慣例）。本地 wrangler dev / self-hosted 把 registry 掛別處時用。
|
||||
*/
|
||||
REGISTRY_BASE_URL?: string;
|
||||
/** recipe 庫（本 worker 自己的 KV；task 3.6 兩庫都查的第二庫）。 */
|
||||
RECIPES?: KVNamespace;
|
||||
};
|
||||
|
||||
/**
|
||||
* 對所有節點進行解析，確認每個節點對應的零件／recipe 是否**真的存在**。
|
||||
*
|
||||
* ⚠️ 2026-07-30 改為會查 registry（workflow-discovery task 3.x）；
|
||||
* 2026-07-31 再加 recipe 庫查詢＋缺件分型指路（task 3.6／3.7）。
|
||||
*
|
||||
* 改之前的行為（病灶）：無條件回 `status: 'found'`、`missingNodes` 永遠是空陣列——
|
||||
* 實測「完全不存在的東西xyz」也回 found。
|
||||
*
|
||||
* 為什麼這是嚴重問題（leo 2026-07-30 定性「腹語術」）：
|
||||
* AI 寫意圖 → 查詢回「都 found」（假信號）→ 實際零件不存在
|
||||
* → 部署/執行才發現 → 最快的修法是改寫成 `code` 節點自己寫 JS
|
||||
* → 於是正式 workflow 只用 2 個零件、8 個 code 節點含 if×61
|
||||
* ⇒ 「零件被測過 1000 次所以 AI 只要填 payload」的價值完全落空。
|
||||
*
|
||||
* 設計基調（leo 2026-07-31 二次定調）：**回覆的重點是「缺哪些」不是「有哪些」**——
|
||||
* 有的照常編圖不必報告；缺的要兩庫（零件 registry＋recipe 庫）都搜過後點名＋給正確指示：
|
||||
* 缺外部 API → 自己寫 recipe（skill `write_recipe`）；
|
||||
* 缺計算原語 → 投稿零件 PR（skill `add_new_wasm_component`）。
|
||||
*
|
||||
* 誠實限制：查不到 registry（未部署／網路失敗）時回 `'unknown'` 而不是 `'not_found'`——
|
||||
* 不能因為查詢失敗就宣告零件不存在（那會讓 AI 誤判而重寫 code，正是要避免的事）。
|
||||
*/
|
||||
export async function searchNodes(
|
||||
parsed: ParsedTriplets,
|
||||
config?: Record<string, Record<string, unknown>>,
|
||||
): SearchResult {
|
||||
const nodeResults: Record<string, { status: 'found' | 'missing'; componentId?: string; type: NodeRole }> = {};
|
||||
env?: SearchNodesEnv,
|
||||
mode: SearchMode = 'discover',
|
||||
target?: SearchTarget,
|
||||
): Promise<SearchResult> {
|
||||
const nodeResults: Record<string, NodeInfo> = {};
|
||||
const missingNodes: string[] = [];
|
||||
|
||||
// ── compile：純編圖，零外部查詢（t158，部署≠發現）─────────────────────────
|
||||
if (mode === 'compile') {
|
||||
for (const nodeName of parsed.nodeNames) {
|
||||
const role = resolveNodeRole(nodeName, parsed);
|
||||
if ((role === 'Input' || role === 'Output') && isVirtualIoName(nodeName)) {
|
||||
nodeResults[nodeName] = { status: 'found', componentId: nodeName.toLowerCase(), type: role };
|
||||
continue;
|
||||
}
|
||||
const configComponent = config?.[nodeName]?.component as string | undefined;
|
||||
// unchecked＝誠實「沒查」；存在性由 component-loader 在執行時決定
|
||||
nodeResults[nodeName] = {
|
||||
status: configComponent ? 'found' : 'unchecked',
|
||||
componentId: configComponent ?? nodeName,
|
||||
type: role,
|
||||
};
|
||||
}
|
||||
return { nodeResults, missingNodes };
|
||||
}
|
||||
|
||||
const sub = env?.WORKER_SUBDOMAIN;
|
||||
const registryBase = env?.REGISTRY_BASE_URL ?? (sub ? wasmWorkerUrl('registry', sub) : undefined);
|
||||
|
||||
// target 限庫（leo 07-31）：component＝只查零件 registry；recipe＝只查 recipe 庫。
|
||||
// 不給＝混搜兩庫（既有行為）。
|
||||
const wantComponents = target !== 'recipe';
|
||||
const wantRecipes = target !== 'component';
|
||||
|
||||
// ── discover 批次化（t158）：兩庫各抓**一次**，之後全在記憶體內比對。────────
|
||||
// 病史（07-31 stage 實測）：舊版對每個 missing 節點各打「1 次逐顆查＋最多 9 次
|
||||
// 相似搜尋＋一輪 recipe KV 掃描」⇒ 冷實例 8 節點 /cypher/search 25.7s，
|
||||
// 安裝器 15s timeout 必炸。批次化後每 request 固定 1 次 catalog＋1 次 recipe 清單。
|
||||
// 步驟 4 的意圖替換也在**同一份清單**上做——不加任何新 round-trip。
|
||||
const catalog = !wantComponents
|
||||
? { status: 'ok' as const, entries: [] } // target=recipe：registry 不參與，不因此回 unknown
|
||||
: registryBase ? await fetchCatalog(registryBase) : { status: 'unreachable' as const, entries: [] };
|
||||
const recipes = wantRecipes && env?.RECIPES ? await listAllRecipes(env.RECIPES) : [];
|
||||
const byId = new Map<string, CatalogFullRecord>();
|
||||
for (const e of catalog.entries) {
|
||||
const prev = byId.get(e.canonical_id);
|
||||
if (!prev || (e.score ?? 0) > (prev.score ?? 0)) byId.set(e.canonical_id, e);
|
||||
for (const a of e.aliases ?? []) if (!byId.has(a)) byId.set(a, e);
|
||||
}
|
||||
|
||||
for (const nodeName of parsed.nodeNames) {
|
||||
const role = resolveNodeRole(nodeName, parsed);
|
||||
|
||||
if (role === 'Input' || role === 'Output') {
|
||||
// 只有**字面上的虛擬 IO 名**（input/trigger/…/output/done）才免查——
|
||||
// 位置上是頭節點但名字是真零件（`aes_encrypt >> … >> code` 的頭，role 也是 Input）
|
||||
// 仍要照常查兩庫，否則缺件被角色掩蓋、又回到「假 found」。
|
||||
if ((role === 'Input' || role === 'Output') && isVirtualIoName(nodeName)) {
|
||||
nodeResults[nodeName] = { status: 'found', componentId: nodeName.toLowerCase(), type: role };
|
||||
continue;
|
||||
}
|
||||
|
||||
const configComponent = config?.[nodeName]?.component as string | undefined;
|
||||
const componentId = configComponent ?? nodeName;
|
||||
nodeResults[nodeName] = { status: 'found', componentId, type: role };
|
||||
|
||||
// config 明確給了 component（多半是安裝器代入的 worker URL 或既有 workflow）
|
||||
// → 不判 not_found。這條路徑的存在性由 component-loader 在執行時決定（原行為）。
|
||||
if (configComponent) {
|
||||
nodeResults[nodeName] = { status: 'found', componentId, type: role };
|
||||
continue;
|
||||
}
|
||||
|
||||
// registry 完全查不通（未部署／網路失敗）⇒ 誠實回 unknown。
|
||||
// **不能誤判 not_found**——那會讓 AI 以為零件不存在而重寫 code，正是要避免的事。
|
||||
// 舊 registry 沒有 /catalog 端點（no_endpoint）→ 退回逐顆查（相容路徑）。
|
||||
if (catalog.status === 'unreachable') {
|
||||
nodeResults[nodeName] = { status: 'unknown', componentId, type: role };
|
||||
continue;
|
||||
}
|
||||
if (catalog.status === 'no_endpoint') {
|
||||
const legacy = await legacyPerNodeLookup(registryBase!, componentId, nodeName, role, env, recipes);
|
||||
nodeResults[nodeName] = legacy.info;
|
||||
if (legacy.missing) missingNodes.push(nodeName);
|
||||
continue;
|
||||
}
|
||||
|
||||
// ── 第一庫：零件 catalog（記憶體）────────────────────────────────────────
|
||||
const hit = byId.get(componentId);
|
||||
if (hit) {
|
||||
nodeResults[nodeName] = {
|
||||
status: 'found',
|
||||
componentId,
|
||||
type: role,
|
||||
source: 'component',
|
||||
input_schema: hit.input_schema,
|
||||
success_rate: typeof hit.success_rate === 'number' ? hit.success_rate : undefined,
|
||||
stability: typeof hit.stability === 'string' ? hit.stability : undefined,
|
||||
branch_hint: branchHintFor(componentId),
|
||||
};
|
||||
continue;
|
||||
}
|
||||
|
||||
// ── 第二庫：recipe 清單（記憶體；canonical_id 精確比對）──────────────────
|
||||
const recipe = recipes.find(r => r.canonical_id === componentId);
|
||||
if (recipe) {
|
||||
nodeResults[nodeName] = {
|
||||
status: 'found',
|
||||
componentId: recipe.canonical_id,
|
||||
type: role,
|
||||
source: 'recipe',
|
||||
description: recipe.description,
|
||||
endpoint: recipe.endpoint,
|
||||
payload_hint: buildPayloadHint(recipe),
|
||||
};
|
||||
continue;
|
||||
}
|
||||
|
||||
// ── 步驟 4：意圖節點 → 真實零件/recipe 替換（同一份清單、全記憶體）────────
|
||||
// 字面 exact 兩庫都落空的自然語言節點（例「傳到 telegram」「判斷有沒有新資料」），
|
||||
// 先試保守的替換規則；換得到＝resolved（回應直接可組 workflow），換不到才 not_found。
|
||||
const substituted = trySubstitution(nodeName, catalog.entries, recipes);
|
||||
if (substituted) {
|
||||
nodeResults[nodeName] = { ...substituted, type: role };
|
||||
continue;
|
||||
}
|
||||
|
||||
// ── 兩庫都沒有 ⇒ not_found ＋ 分型指路（task 3.7）＋ 相近候選（全記憶體）──
|
||||
const similarComponents = similarFromCatalog(catalog.entries, nodeName);
|
||||
const similarRecipes = similarFromRecipes(recipes, nodeName);
|
||||
|
||||
nodeResults[nodeName] = {
|
||||
status: 'not_found',
|
||||
componentId,
|
||||
type: role,
|
||||
suggestion: buildSuggestion(componentId),
|
||||
...(similarComponents.length > 0 ? { similar_components: similarComponents } : {}),
|
||||
...(similarRecipes.length > 0 ? { similar_recipes: similarRecipes } : {}),
|
||||
};
|
||||
missingNodes.push(nodeName);
|
||||
}
|
||||
|
||||
return { nodeResults, missingNodes: [] };
|
||||
return { nodeResults, missingNodes };
|
||||
}
|
||||
|
||||
// ── t158 批次化 helpers ────────────────────────────────────────────────────────
|
||||
|
||||
type CatalogFullRecord = {
|
||||
canonical_id: string;
|
||||
display_name?: string;
|
||||
description?: string;
|
||||
aliases?: string[];
|
||||
tags?: string[];
|
||||
score?: number;
|
||||
input_schema?: unknown;
|
||||
success_rate?: number;
|
||||
stability?: string;
|
||||
};
|
||||
|
||||
type CatalogFetch = { status: 'ok' | 'no_endpoint' | 'unreachable'; entries: CatalogFullRecord[] };
|
||||
|
||||
/** 一次抓 registry 全目錄。404＝舊版 registry 沒這端點 → 呼叫端退回逐顆查。 */
|
||||
async function fetchCatalog(registryBase: string): Promise<CatalogFetch> {
|
||||
try {
|
||||
const res = await fetch(`${registryBase}/components/catalog`, { signal: AbortSignal.timeout(10000) });
|
||||
if (res.status === 404) return { status: 'no_endpoint', entries: [] };
|
||||
if (!res.ok) return { status: 'unreachable', entries: [] };
|
||||
const body = (await res.json()) as { data?: { components?: CatalogFullRecord[] } };
|
||||
return { status: 'ok', entries: body.data?.components ?? [] };
|
||||
} catch {
|
||||
return { status: 'unreachable', entries: [] };
|
||||
}
|
||||
}
|
||||
|
||||
/** 一次抓 recipe 全清單（本部署 recipe 數量小；exact 與相似度共用同一份）。
|
||||
* export 給 target=recipe 的名字搜尋（actions/target-search.ts）共用同一份讀法。 */
|
||||
export async function listAllRecipes(kv: KVNamespace): Promise<RecipeDefinition[]> {
|
||||
try {
|
||||
const list = await kv.list({ prefix: 'recipe:' });
|
||||
return (await Promise.all(
|
||||
list.keys.map(k => kv.get(k.name, 'json') as Promise<RecipeDefinition | null>),
|
||||
)).filter(Boolean) as RecipeDefinition[];
|
||||
} catch {
|
||||
return [];
|
||||
}
|
||||
}
|
||||
|
||||
/** 相似零件（記憶體版）：全名 substring 優先，否則斷詞計數 top3——判準與舊 HTTP 版一致。 */
|
||||
function similarFromCatalog(entries: CatalogFullRecord[], nodeName: string): string[] {
|
||||
const searchableOf = (e: CatalogFullRecord) =>
|
||||
[e.canonical_id, e.display_name ?? '', e.description ?? '', ...(e.aliases ?? []), ...(e.tags ?? [])]
|
||||
.join(' ').toLowerCase();
|
||||
const full = nodeName.toLowerCase();
|
||||
const direct = entries.filter(e => searchableOf(e).includes(full)).map(e => e.canonical_id);
|
||||
if (direct.length > 0) return [...new Set(direct)].slice(0, 3);
|
||||
|
||||
const tokens = extractTokens(nodeName);
|
||||
if (tokens.length === 0) return [];
|
||||
const count = new Map<string, number>();
|
||||
for (const e of entries) {
|
||||
const hay = searchableOf(e);
|
||||
const hits = tokens.filter(t => hay.includes(t)).length;
|
||||
if (hits > 0) count.set(e.canonical_id, Math.max(count.get(e.canonical_id) ?? 0, hits));
|
||||
}
|
||||
return [...count.entries()].sort((a, b) => b[1] - a[1]).slice(0, 3).map(([id]) => id);
|
||||
}
|
||||
|
||||
/** 相似 recipe（記憶體版；判準沿用 searchSimilarRecipes）。 */
|
||||
function similarFromRecipes(recipes: RecipeDefinition[], nodeName: string): string[] {
|
||||
const tokens = [nodeName.toLowerCase(), ...extractTokens(nodeName)];
|
||||
const seen = new Set<string>();
|
||||
const matched: string[] = [];
|
||||
for (const r of recipes) {
|
||||
if (seen.has(r.canonical_id)) continue;
|
||||
const hay = `${r.canonical_id} ${r.display_name ?? ''} ${r.description ?? ''}`.toLowerCase();
|
||||
if (tokens.some(t => hay.includes(t))) {
|
||||
seen.add(r.canonical_id);
|
||||
matched.push(r.canonical_id);
|
||||
}
|
||||
}
|
||||
return matched.slice(0, 3);
|
||||
}
|
||||
|
||||
/** 舊 registry（無 /catalog 端點）的相容路徑：維持逐顆查語義。 */
|
||||
async function legacyPerNodeLookup(
|
||||
registryBase: string,
|
||||
componentId: string,
|
||||
nodeName: string,
|
||||
role: NodeRole,
|
||||
env: SearchNodesEnv | undefined,
|
||||
recipes: RecipeDefinition[],
|
||||
): Promise<{ info: NodeInfo; missing: boolean }> {
|
||||
const q = await fetchComponent(registryBase, componentId);
|
||||
if (!q.ok) return { info: { status: 'unknown', componentId, type: role }, missing: false };
|
||||
if (q.entry) {
|
||||
return {
|
||||
info: {
|
||||
status: 'found', componentId, type: role, source: 'component',
|
||||
input_schema: q.entry.input_schema, success_rate: q.entry.success_rate, stability: q.entry.stability,
|
||||
branch_hint: branchHintFor(componentId),
|
||||
},
|
||||
missing: false,
|
||||
};
|
||||
}
|
||||
const recipe = recipes.find(r => r.canonical_id === componentId)
|
||||
?? (env?.RECIPES ? await resolveRecipe(componentId, env.RECIPES) : null);
|
||||
if (recipe) {
|
||||
return {
|
||||
info: {
|
||||
status: 'found', componentId: recipe.canonical_id, type: role, source: 'recipe',
|
||||
description: recipe.description, endpoint: recipe.endpoint,
|
||||
payload_hint: buildPayloadHint(recipe),
|
||||
},
|
||||
missing: false,
|
||||
};
|
||||
}
|
||||
const similarComponents = await searchSimilarComponents(registryBase, nodeName);
|
||||
const similarRecipes = similarFromRecipes(recipes, nodeName);
|
||||
return {
|
||||
info: {
|
||||
status: 'not_found', componentId, type: role, suggestion: buildSuggestion(componentId),
|
||||
...(similarComponents.length > 0 ? { similar_components: similarComponents } : {}),
|
||||
...(similarRecipes.length > 0 ? { similar_recipes: similarRecipes } : {}),
|
||||
},
|
||||
missing: true,
|
||||
};
|
||||
}
|
||||
|
||||
// ── 步驟 4：意圖節點 → 真實零件/recipe 替換 ────────────────────────────────────
|
||||
//
|
||||
// 目的（CP arcrun-usable 步驟 4）：AI 只要填 payload——系統把「傳到 telegram」翻成
|
||||
// `http_request`＋recipe `telegram_send`。媒合在「一次抓好的兩庫清單」記憶體內做，
|
||||
// 零新增 round-trip；規則沿用 task 3.7 的服務詞判型＋既有斷詞媒合（extractTokens），
|
||||
// 刻意簡單可解釋、不接 LLM。
|
||||
//
|
||||
// 兩條規則（保守——換錯比不換更糟，寧可 not_found＋候選讓 AI 自己選）：
|
||||
// A) 服務詞規則（recipe 路）：節點名含 SERVICE_HINTS 服務詞 → 名字裡**全部**服務詞
|
||||
// 都命中同一個 recipe、且該 recipe **唯一**才替換。
|
||||
// 例「傳到 telegram」：服務詞 [telegram] → 唯一命中 telegram_send ⇒ 換。
|
||||
// 反例「google_slides_create」：服務詞 [google, slides] → google_sheets_* 只中
|
||||
// google 不中 slides ⇒ 不換（照 3.7 指去寫 recipe）。
|
||||
// 有服務詞的節點**不落入規則 B**——外部服務就該是 recipe，不硬配零件
|
||||
// （否則「google_slides」會被 display_name 含 Google 的零件誤吃）。
|
||||
// B) 強欄位規則（零件路）：斷詞後只算**強欄位**（canonical_id／display_name／aliases）
|
||||
// 命中為主：分數＝強命中×10＋弱命中（description／tags）×1，
|
||||
// 需「至少一個強命中」且「分數唯一最高」才替換。
|
||||
// 例「判斷有沒有新資料」：2-gram「判斷」命中 if_control display_name「條件判斷」
|
||||
// （強 10 分），try_catch 只在 description 中「判斷」（弱 1 分）⇒ 唯一最高 ⇒ 換。
|
||||
// 反例「aes_encrypt」：無任何強命中 ⇒ 不換（照 3.7 指去投零件 PR）。
|
||||
|
||||
type SubstitutionHit = Pick<
|
||||
NodeInfo,
|
||||
'status' | 'componentId' | 'source' | 'substitution' |
|
||||
'input_schema' | 'success_rate' | 'stability' | 'description' | 'endpoint' | 'branch_hint'
|
||||
>;
|
||||
|
||||
function trySubstitution(
|
||||
nodeName: string,
|
||||
catalogEntries: CatalogFullRecord[],
|
||||
recipes: RecipeDefinition[],
|
||||
): SubstitutionHit | null {
|
||||
const lower = nodeName.toLowerCase();
|
||||
const serviceHits = SERVICE_HINTS.filter(w => lower.includes(w));
|
||||
|
||||
// 規則 A：服務詞 → recipe（全部服務詞命中＋唯一）
|
||||
if (serviceHits.length > 0) {
|
||||
const matched = new Map<string, RecipeDefinition>();
|
||||
for (const r of recipes) {
|
||||
const hay = `${r.canonical_id} ${r.display_name ?? ''} ${r.description ?? ''}`.toLowerCase();
|
||||
if (serviceHits.every(h => hay.includes(h))) matched.set(r.canonical_id, r);
|
||||
}
|
||||
if (matched.size !== 1) return null; // 0＝真缺件走 not_found；≥2＝歧義，候選留給 similar_recipes
|
||||
const recipe = [...matched.values()][0];
|
||||
return {
|
||||
status: 'resolved',
|
||||
componentId: recipe.canonical_id,
|
||||
source: 'recipe',
|
||||
description: recipe.description,
|
||||
endpoint: recipe.endpoint,
|
||||
substitution: {
|
||||
from: nodeName,
|
||||
componentId: 'http_request', // recipe＝http_request＋參數模板的具名封裝
|
||||
recipe: recipe.canonical_id,
|
||||
reason:
|
||||
`服務詞「${serviceHits.join('、')}」唯一命中 recipe「${recipe.canonical_id}」；` +
|
||||
`workflow config 寫 component: ${recipe.canonical_id}（底層零件＝http_request），只需填 payload`,
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
// 規則 B：強欄位斷詞媒合 → 零件（至少一強命中＋分數唯一最高）
|
||||
const tokens = extractTokens(nodeName);
|
||||
if (tokens.length === 0) return null;
|
||||
|
||||
type Scored = { entry: CatalogFullRecord; score: number; strongHits: string[] };
|
||||
const byCanonical = new Map<string, Scored>();
|
||||
for (const e of catalogEntries) {
|
||||
const strongHay = [e.canonical_id, e.display_name ?? '', ...(e.aliases ?? [])].join(' ').toLowerCase();
|
||||
const weakHay = [e.description ?? '', ...(e.tags ?? [])].join(' ').toLowerCase();
|
||||
const strongHits = tokens.filter(t => strongHay.includes(t));
|
||||
const weakCount = tokens.filter(t => weakHay.includes(t)).length;
|
||||
const score = strongHits.length * 10 + weakCount;
|
||||
if (score === 0) continue;
|
||||
const prev = byCanonical.get(e.canonical_id);
|
||||
if (!prev || score > prev.score) byCanonical.set(e.canonical_id, { entry: e, score, strongHits });
|
||||
}
|
||||
const ranked = [...byCanonical.values()].sort((a, b) => b.score - a.score);
|
||||
const top = ranked[0];
|
||||
if (!top || top.strongHits.length === 0) return null; // 沒有強命中＝證據不足
|
||||
if (ranked[1] && ranked[1].score >= top.score) return null; // 同分歧義＝不硬猜
|
||||
|
||||
return {
|
||||
status: 'resolved',
|
||||
componentId: top.entry.canonical_id,
|
||||
source: 'component',
|
||||
input_schema: top.entry.input_schema,
|
||||
success_rate: typeof top.entry.success_rate === 'number' ? top.entry.success_rate : undefined,
|
||||
stability: typeof top.entry.stability === 'string' ? top.entry.stability : undefined,
|
||||
// 替換成分岔零件時（例「判斷有沒有新資料」→ if_control）一併附分支用法，
|
||||
// 否則 AI 換到零件卻不知道怎麼接兩條路，仍會退回寫 code。
|
||||
branch_hint: branchHintFor(top.entry.canonical_id),
|
||||
substitution: {
|
||||
from: nodeName,
|
||||
componentId: top.entry.canonical_id,
|
||||
reason:
|
||||
`斷詞「${top.strongHits.join('、')}」命中零件「${top.entry.canonical_id}」` +
|
||||
`（${top.entry.display_name ?? ''}）強欄位且分數唯一最高；只需照 input_schema 填 payload`,
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
// ── 缺件分型（task 3.7）────────────────────────────────────────────────────────
|
||||
//
|
||||
// 分型判準（刻意用簡單可解釋的規則，不接 LLM——查詢端點要快、要可預測）：
|
||||
// 1) 名字含**外部服務詞**（google／telegram／slack…）→「外部 API 樣貌」
|
||||
// → recipe 路：recipe 是 http_request＋參數模板的具名封裝，用戶自己就能寫，不用改平台。
|
||||
// 2) 否則名字含**計算原語詞**（encrypt／hash／encode…）→「計算原語樣貌」
|
||||
// → 零件路：純計算得進 WASM 沙箱跑，要走 GitHub PR 投稿（人 merge＝人類閘門，mindset §4）。
|
||||
// 3) 都不含 → 判不出型，誠實說判不出，兩條路都給（不硬猜——猜錯會把人指去錯的路）。
|
||||
// 判斷順序：服務詞優先於計算詞——「google_sheets_parse」雖含 parse，本質仍是打外部 API。
|
||||
|
||||
const SERVICE_HINTS = [
|
||||
'google', 'gmail', 'sheets', 'slides', 'gdocs', 'drive', 'calendar', 'youtube',
|
||||
'slack', 'telegram', 'discord', 'line', 'whatsapp', 'twilio',
|
||||
'notion', 'airtable', 'trello', 'jira', 'asana', 'linear',
|
||||
'github', 'gitea', 'gitlab', 'bitbucket',
|
||||
'stripe', 'paypal', 'shopify', 'hubspot', 'salesforce',
|
||||
'openai', 'anthropic', 'claude', 'gemini', 'groq',
|
||||
'twitter', 'facebook', 'instagram', 'linkedin', 'dropbox', 'zoom',
|
||||
'sendgrid', 'mailgun', 'kbdb',
|
||||
];
|
||||
|
||||
const COMPUTE_HINTS = [
|
||||
'encrypt', 'decrypt', 'cipher', 'aes', 'rsa', 'sha', 'md5', 'hmac', 'hash',
|
||||
'sign', 'verify', 'encode', 'decode', 'base64', 'hex',
|
||||
'compress', 'decompress', 'zip', 'gzip',
|
||||
'uuid', 'random', 'regex', 'math', 'calc',
|
||||
'sort', 'dedup', 'diff', 'template', 'render', 'convert', 'transform',
|
||||
'parse', 'format', 'csv', 'xml',
|
||||
];
|
||||
|
||||
function buildSuggestion(componentId: string): string {
|
||||
const lower = componentId.toLowerCase();
|
||||
const serviceHit = SERVICE_HINTS.find(w => lower.includes(w));
|
||||
const computeHit = COMPUTE_HINTS.find(w => lower.includes(w));
|
||||
|
||||
if (serviceHit) {
|
||||
return (
|
||||
`兩庫都查過，零件 registry 與 recipe 庫皆無「${componentId}」。` +
|
||||
`名字含服務詞「${serviceHit}」＝外部 API 樣貌 → 沒有此 recipe，可自己寫：` +
|
||||
`寫法看 skill「write_recipe」（arcrun_get_skill('write_recipe')），` +
|
||||
`寫好用 acr recipe push 或 POST /recipes 裝上即可用，不用改平台。`
|
||||
);
|
||||
}
|
||||
if (computeHit) {
|
||||
return (
|
||||
`兩庫都查過，零件 registry 與 recipe 庫皆無「${componentId}」。` +
|
||||
`名字含計算詞「${computeHit}」＝計算原語樣貌 → 沒有此零件，可投稿 PR 新增 WASM component：` +
|
||||
`做法看 skill「add_new_wasm_component」（arcrun_get_skill('add_new_wasm_component')）。`
|
||||
);
|
||||
}
|
||||
return (
|
||||
`兩庫都查過，零件 registry 與 recipe 庫皆無「${componentId}」，且名字判不出型。` +
|
||||
`缺外部 API → 自己寫 recipe（skill「write_recipe」）；` +
|
||||
`缺計算能力 → 投稿零件 PR（skill「add_new_wasm_component」，component 進 WASM 沙箱）。`
|
||||
);
|
||||
}
|
||||
|
||||
/**
|
||||
* recipe 的 payload／回應用法自我說明（3.12）。
|
||||
* 動機同 branch_hint：逐顆查 recipe（n8n 式）時，光看 endpoint 不知道 payload 怎麼填、
|
||||
* 回應怎麼取值 ⇒ AI 會退回把整包寫進 workflow code。
|
||||
*/
|
||||
export function buildPayloadHint(recipe: RecipeDefinition): NodeInfo['payload_hint'] {
|
||||
const parts: string[] = [];
|
||||
|
||||
if (recipe.body_template) {
|
||||
parts.push('payload 已收在 recipe 的 body_template 裡，你只要把 {{變數}} 對應的值放進節點 context');
|
||||
} else if (recipe.body) {
|
||||
parts.push('payload 形狀見 body 欄位（{{變數}} 由節點 context 填）');
|
||||
} else {
|
||||
parts.push('未定義 body_template：節點 context 會整包當 body 送出（_ 開頭的內部欄位會被剔除）');
|
||||
}
|
||||
|
||||
if (recipe.response_map) {
|
||||
parts.push('回應已正規化：執行結果除了原始 data，另附 text（取值路徑等規則寫在 recipe 裡，換源不必改 workflow）');
|
||||
} else {
|
||||
parts.push('未定義 response_map：回應原樣放在 data，取值要自己指路徑');
|
||||
}
|
||||
|
||||
if (recipe.auth === 'binding') {
|
||||
parts.push(`認證＝binding（免金鑰，用平台內建 ${recipe.binding_name ?? 'AI'}）`);
|
||||
} else if (recipe.auth_service) {
|
||||
parts.push(`認證走 auth recipe「${recipe.auth_service}」（金鑰由系統在執行前注入，你不必也不該填）`);
|
||||
}
|
||||
|
||||
return {
|
||||
body_template: recipe.body_template,
|
||||
response_map: recipe.response_map,
|
||||
usage: parts.join('；') + '。',
|
||||
};
|
||||
}
|
||||
|
||||
// ── registry 查詢 ─────────────────────────────────────────────────────────────
|
||||
|
||||
type CatalogEntry = {
|
||||
input_schema?: unknown;
|
||||
success_rate?: number;
|
||||
stability?: string;
|
||||
};
|
||||
|
||||
/**
|
||||
* 查單一零件是否存在於 registry。
|
||||
*
|
||||
* ⚠️ 為什麼逐個查而非抓整份目錄：registry **沒有列表端點**
|
||||
* （實測 `GET /components` → 404，只有 `GET /components/<id>`）。
|
||||
* 這是 CP2-B 記載的缺口（「修 /components 404」）——補了列表端點後可改為抓一次。
|
||||
* 現階段逐個查：節點數通常 <10，且有 5s timeout，可接受。
|
||||
*
|
||||
* 回傳 `ok:false` 代表「查不到 registry」，由呼叫端區分：
|
||||
* 整體查不通 → `unknown`；查得通但這顆沒有 → 繼續查 recipe 庫。
|
||||
*/
|
||||
async function fetchComponent(
|
||||
registryBase: string,
|
||||
id: string,
|
||||
): Promise<{ ok: boolean; entry?: CatalogEntry }> {
|
||||
try {
|
||||
const res = await fetch(`${registryBase}/components/${encodeURIComponent(id)}`, {
|
||||
signal: AbortSignal.timeout(5000),
|
||||
});
|
||||
if (res.status === 404) return { ok: true }; // registry 活著，但沒這顆
|
||||
if (!res.ok) return { ok: false };
|
||||
const body = (await res.json()) as { success?: boolean; data?: Record<string, unknown> };
|
||||
if (body.success === false) return { ok: true }; // 同上：回「零件不存在」
|
||||
const d = body.data ?? (body as unknown as Record<string, unknown>);
|
||||
return {
|
||||
ok: true,
|
||||
entry: {
|
||||
input_schema: d.input_schema,
|
||||
success_rate: typeof d.success_rate === 'number' ? d.success_rate : undefined,
|
||||
stability: typeof d.stability === 'string' ? d.stability : undefined,
|
||||
},
|
||||
};
|
||||
} catch {
|
||||
return { ok: false };
|
||||
}
|
||||
}
|
||||
|
||||
// ── 相近候選（自然語言節點名 → 既有零件/recipe 的媒合）──────────────────────────
|
||||
//
|
||||
// 節點名常是自然語言（例「判斷有沒有新資料」）。leo：「AI 不用知道零件存在」——
|
||||
// 所以 not_found 時要主動給相近候選，讓 AI 看回覆就知道「其實有 if_control 可用」。
|
||||
// 做法：先拿全名打 registry `/components/search`；沒中再斷詞重試——
|
||||
// ASCII 取 3 字以上的詞、中日韓取 2-gram（registry search 是子字串比對，整句中文必落空，
|
||||
// 2-gram 才撈得到「判斷」→ if_control（display_name「條件判斷」）這種命中）。
|
||||
|
||||
function extractTokens(name: string): string[] {
|
||||
const tokens: string[] = [];
|
||||
const ascii = name.toLowerCase().match(/[a-z0-9]{3,}/g) ?? [];
|
||||
tokens.push(...ascii);
|
||||
const cjkRuns = name.match(/[一-鿿]+/g) ?? [];
|
||||
for (const run of cjkRuns) {
|
||||
for (let i = 0; i + 2 <= run.length; i++) tokens.push(run.slice(i, i + 2));
|
||||
}
|
||||
return [...new Set(tokens)].slice(0, 8); // 上限 8 個 token，避免對 registry 掃太多輪
|
||||
}
|
||||
|
||||
async function searchRegistryIds(registryBase: string, q: string): Promise<string[]> {
|
||||
try {
|
||||
const res = await fetch(`${registryBase}/components/search?q=${encodeURIComponent(q)}`, {
|
||||
signal: AbortSignal.timeout(5000),
|
||||
});
|
||||
if (!res.ok) return [];
|
||||
const body = (await res.json()) as { data?: { results?: Array<{ canonical_id?: string }> } };
|
||||
return (body.data?.results ?? []).map(r => r.canonical_id).filter((s): s is string => !!s);
|
||||
} catch {
|
||||
return [];
|
||||
}
|
||||
}
|
||||
|
||||
async function searchSimilarComponents(registryBase: string, nodeName: string): Promise<string[]> {
|
||||
// 1) 全名直接搜
|
||||
const direct = await searchRegistryIds(registryBase, nodeName);
|
||||
if (direct.length > 0) return direct.slice(0, 3);
|
||||
|
||||
// 2) 斷詞搜，依命中次數排序
|
||||
const tokens = extractTokens(nodeName);
|
||||
if (tokens.length === 0) return [];
|
||||
const hits = await Promise.all(tokens.map(t => searchRegistryIds(registryBase, t)));
|
||||
const count = new Map<string, number>();
|
||||
for (const ids of hits) {
|
||||
for (const id of ids) count.set(id, (count.get(id) ?? 0) + 1);
|
||||
}
|
||||
return [...count.entries()].sort((a, b) => b[1] - a[1]).slice(0, 3).map(([id]) => id);
|
||||
}
|
||||
|
||||
/** recipe 庫的相近候選：KV 全列（本部署 recipe 數量小）後子字串比對。 */
|
||||
async function searchSimilarRecipes(kv: KVNamespace, nodeName: string): Promise<string[]> {
|
||||
try {
|
||||
const list = await kv.list({ prefix: 'recipe:' });
|
||||
const all = (await Promise.all(
|
||||
list.keys.map(k => kv.get(k.name, 'json') as Promise<RecipeDefinition | null>),
|
||||
)).filter(Boolean) as RecipeDefinition[];
|
||||
|
||||
const tokens = [nodeName.toLowerCase(), ...extractTokens(nodeName)];
|
||||
const seen = new Set<string>();
|
||||
const matched: string[] = [];
|
||||
for (const r of all) {
|
||||
if (seen.has(r.canonical_id)) continue;
|
||||
const hay = `${r.canonical_id} ${r.display_name ?? ''} ${r.description ?? ''}`.toLowerCase();
|
||||
if (tokens.some(t => hay.includes(t))) {
|
||||
seen.add(r.canonical_id);
|
||||
matched.push(r.canonical_id);
|
||||
}
|
||||
}
|
||||
return matched.slice(0, 3);
|
||||
} catch {
|
||||
return [];
|
||||
}
|
||||
}
|
||||
|
||||
@@ -0,0 +1,113 @@
|
||||
/**
|
||||
* target-search — POST /cypher/search 的「指定搜尋對象」名字搜尋（t159）
|
||||
*
|
||||
* leo 07-31：「search 節點名稱和 search 工作流名稱是同一個？一個死了另一個不能動？
|
||||
* 難道我不能指定要搜尋工作流或節點或 recipe 嗎？」
|
||||
*
|
||||
* ⇒ discover 入口加 `target`（component／recipe／workflow）＋`query`：
|
||||
* - target=component → 轉發 registry GET /components/search（MCP arcrun_search_components 同一條路）
|
||||
* - target=recipe → 掃私庫 RECIPES KV（與 discover 混搜的第二庫**同一份讀法** listAllRecipes）；
|
||||
* 公庫（多作者市場）另有 /public-recipes＝MCP arcrun_recipe_search，回應註明
|
||||
* - target=workflow → lib/workflow-search.ts（GET /workflows/search＝MCP arcrun_search_workflows 同一條路）
|
||||
*
|
||||
* 「外部 API 只有一條一致的路」：三個 target 各自對應**既有**搜尋機制，本檔只做轉接，
|
||||
* 不新造第二套搜尋。flag 安全：主動 pull，無輪詢。
|
||||
*/
|
||||
|
||||
import { wasmWorkerUrl } from '../lib/component-loader';
|
||||
import { fetchTenantWorkflowSearch } from '../lib/workflow-search';
|
||||
import { listAllRecipes, buildPayloadHint, type SearchNodesEnv } from './search-nodes';
|
||||
import { branchHintFor } from '../lib/branch-hints';
|
||||
|
||||
export type TargetQueryEnv = SearchNodesEnv & {
|
||||
KBDB_BASE_URL?: string;
|
||||
KBDB_INTERNAL_TOKEN?: string;
|
||||
};
|
||||
|
||||
export type TargetQueryResult =
|
||||
| { ok: true; body: Record<string, unknown> }
|
||||
| { ok: false; status: 400 | 401 | 502; error: string };
|
||||
|
||||
export async function searchByTarget(
|
||||
target: 'component' | 'recipe' | 'workflow',
|
||||
query: string,
|
||||
env: TargetQueryEnv,
|
||||
apiKey?: string,
|
||||
): Promise<TargetQueryResult> {
|
||||
if (target === 'component') {
|
||||
const sub = env.WORKER_SUBDOMAIN;
|
||||
const registryBase = env.REGISTRY_BASE_URL ?? (sub ? wasmWorkerUrl('registry', sub) : undefined);
|
||||
if (!registryBase) return { ok: false, status: 502, error: 'registry 位置未設定（WORKER_SUBDOMAIN／REGISTRY_BASE_URL 皆缺）' };
|
||||
try {
|
||||
const res = await fetch(
|
||||
`${registryBase}/components/search?q=${encodeURIComponent(query)}`,
|
||||
{ signal: AbortSignal.timeout(10000) },
|
||||
);
|
||||
if (!res.ok) return { ok: false, status: 502, error: `registry 搜尋失敗（HTTP ${res.status}）` };
|
||||
const body = (await res.json()) as { data?: { results?: unknown[]; count?: number } };
|
||||
// 3.11：逐顆查零件（n8n 式「自己一顆一顆填」）時，會分岔的零件要自我說明分支用法。
|
||||
// leo 08-01：「它可以一一查詢自己手工填寫每個零件，就像在 n8n 那樣」——
|
||||
// 這條路徑若只回 input_schema，AI 拿到 if_control／switch 仍不知道兩條路怎麼接 ⇒ 回頭寫 code。
|
||||
const results = (body.data?.results ?? []).map(r => {
|
||||
if (!r || typeof r !== 'object') return r;
|
||||
const rec = r as Record<string, unknown>;
|
||||
const hint = branchHintFor(typeof rec.canonical_id === 'string' ? rec.canonical_id : undefined);
|
||||
return hint ? { ...rec, branch_hint: hint } : rec;
|
||||
});
|
||||
return {
|
||||
ok: true,
|
||||
body: {
|
||||
target,
|
||||
query,
|
||||
results,
|
||||
count: body.data?.count ?? 0,
|
||||
},
|
||||
};
|
||||
} catch (e) {
|
||||
return { ok: false, status: 502, error: `registry 查不通：${e instanceof Error ? e.message : String(e)}` };
|
||||
}
|
||||
}
|
||||
|
||||
if (target === 'recipe') {
|
||||
if (!env.RECIPES) return { ok: false, status: 502, error: 'RECIPES KV 未綁定' };
|
||||
const all = await listAllRecipes(env.RECIPES);
|
||||
const q = query.toLowerCase();
|
||||
// 與 discover 混搜同一份庫（私庫＝workflow 實際引用得到的）；子字串比對、canonical 去重
|
||||
const seen = new Set<string>();
|
||||
const results: Array<{
|
||||
canonical_id: string; display_name?: string; description?: string; endpoint: string;
|
||||
payload_hint?: unknown;
|
||||
}> = [];
|
||||
for (const r of all) {
|
||||
if (seen.has(r.canonical_id)) continue;
|
||||
const hay = `${r.canonical_id} ${r.display_name ?? ''} ${r.description ?? ''}`.toLowerCase();
|
||||
if (!hay.includes(q)) continue;
|
||||
seen.add(r.canonical_id);
|
||||
results.push({
|
||||
canonical_id: r.canonical_id,
|
||||
display_name: r.display_name,
|
||||
description: r.description,
|
||||
endpoint: r.endpoint,
|
||||
// 3.12：逐顆查 recipe 時也要說得出「payload 怎麼填、回應怎麼取值」
|
||||
payload_hint: buildPayloadHint(r),
|
||||
});
|
||||
}
|
||||
return {
|
||||
ok: true,
|
||||
body: {
|
||||
target,
|
||||
query,
|
||||
results,
|
||||
count: results.length,
|
||||
note: '搜的是本部署私庫（workflow 可直接 component: <canonical_id> 引用）。公庫（多作者市場）走 MCP arcrun_recipe_search／GET /public-recipes。',
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
// target === 'workflow'：租戶隔離，必帶 API key（同 GET /workflows/search 的既有契約）
|
||||
if (!apiKey) return { ok: false, status: 401, error: 'target=workflow 需要 X-Arcrun-API-Key header（workflow 搜尋限本租戶）' };
|
||||
const res = await fetchTenantWorkflowSearch(env, apiKey, query);
|
||||
if (!res.ok) return { ok: false, status: 502, error: `workflow 搜尋失敗（KBDB HTTP ${res.status}）` };
|
||||
const body = (await res.json()) as Record<string, unknown>;
|
||||
return { ok: true, body: { target, query, ...body } };
|
||||
}
|
||||
@@ -105,6 +105,17 @@ export function parseTriplets(rawTriplets: unknown[]): ParsedTriplets | null {
|
||||
const INPUT_NAMES = new Set(['input', 'trigger', 'webhook', 'start']);
|
||||
const OUTPUT_NAMES = new Set(['output', 'result', 'end', 'done']);
|
||||
|
||||
/**
|
||||
* 是否為「虛擬 IO 節點名」（input/output 這類非零件的佔位節點）。
|
||||
* searchNodes 用它決定存在性查詢的短路：**只有字面上是虛擬 IO 名**才免查——
|
||||
* 位置上是頭節點但名字是真零件（例 `aes_encrypt >> ON_SUCCESS >> code` 的頭）
|
||||
* 仍要查兩庫，否則缺件被角色掩蓋、又回到「假 found」（task 3.7 實測踩到）。
|
||||
*/
|
||||
export function isVirtualIoName(name: string): boolean {
|
||||
const lower = name.toLowerCase();
|
||||
return INPUT_NAMES.has(lower) || OUTPUT_NAMES.has(lower);
|
||||
}
|
||||
|
||||
/** 根據節點在圖中的位置決定其 type
|
||||
*
|
||||
* 規則：
|
||||
|
||||
@@ -14,7 +14,7 @@ export async function resolveWebhookGraph(
|
||||
const parsed = parseTriplets(body.triplets as unknown[]);
|
||||
if (!parsed) return { resolvedGraph: {}, error: '無法解析 triplets' };
|
||||
|
||||
const { nodeResults } = searchNodes(parsed);
|
||||
const { nodeResults } = await searchNodes(parsed);
|
||||
|
||||
const graphId = `webhook-${Date.now()}`;
|
||||
const graphName = description || `Webhook ${new Date().toISOString()}`;
|
||||
|
||||
@@ -4,6 +4,8 @@ import { GraphExecutor } from '../graph-executor';
|
||||
import { graphSchema } from '../lib/schemas';
|
||||
import { createComponentLoader } from '../lib/component-loader';
|
||||
import { recordTelemetry } from '../lib/telemetry';
|
||||
import { recordComponentStats } from './execution-evaluator';
|
||||
import type { GraphNode, TraceStep } from '../types';
|
||||
|
||||
/**
|
||||
* kbdb-base §7.1+§7.5.h：一條工作流執行結束後，把這次用到的 recipe 各記一次成功/失敗到 KBDB 市場星數。
|
||||
@@ -96,6 +98,17 @@ export async function executeWebhookGraph(
|
||||
// kbdb-base §7.1：整體成功 → 用到的 recipe 各記成功一次。
|
||||
recordRecipeStats(env, executor.usedRecipeKeys, true, Date.now(), ctx);
|
||||
|
||||
// arcrun-core-mvp「執行統計設計」：對用到的每顆零件回寫執行結果（fire-and-forget）。
|
||||
{
|
||||
const statsPromise = recordComponentStats(
|
||||
env,
|
||||
(parsed.data as ExecutionGraph).nodes as GraphNode[],
|
||||
result.trace as TraceStep[],
|
||||
);
|
||||
if (ctx?.waitUntil) ctx.waitUntil(statsPromise);
|
||||
else void statsPromise;
|
||||
}
|
||||
|
||||
return { success: true, data: result.data, duration_ms };
|
||||
} catch (err) {
|
||||
const duration_ms = Date.now() - start;
|
||||
@@ -117,6 +130,18 @@ export async function executeWebhookGraph(
|
||||
recordRecipeStats(env, executor.usedRecipeKeys, false, Date.now(), ctx);
|
||||
}
|
||||
|
||||
// 零件統計失敗路徑：ExecutionError 帶完整 trace（失敗節點有 error、先前成功節點照記成功）；
|
||||
// paused 非失敗不記；非 ExecutionError 無 trace 可歸因 → 不記。
|
||||
if (!isPaused && err instanceof ExecutionError) {
|
||||
const statsPromise = recordComponentStats(
|
||||
env,
|
||||
(parsed.data as ExecutionGraph).nodes as GraphNode[],
|
||||
err.trace,
|
||||
);
|
||||
if (ctx?.waitUntil) ctx.waitUntil(statsPromise);
|
||||
else void statsPromise;
|
||||
}
|
||||
|
||||
if (err instanceof ExecutionError) {
|
||||
const traceFormatted = err.trace.map(s => ({
|
||||
node: s.nodeId,
|
||||
|
||||
@@ -478,6 +478,37 @@ export class GraphExecutor {
|
||||
break;
|
||||
}
|
||||
|
||||
// ── 條件邊（SDD workflow-discovery 3.11 / CP arcrun-usable 步驟 5 缺口①）──
|
||||
// 為什麼要有：`if_control` 回 {result, branch} 卻沒有邊讀得懂它，
|
||||
// AI 照規矩用了零件仍得寫 code 判斷走哪條 ⇒「全變成 code」的根（Arcrun#5）。
|
||||
// 讀法對齊零件 output_schema：優先 data.branch（if_control/switch 的正式形狀），
|
||||
// 相容 top-level branch / result 布林。讀不出分支＝不走（誠實，不亂挑一條）。
|
||||
case 'ON_TRUE': {
|
||||
if (readBranch(result) === 'true') {
|
||||
const mergedCtx = propagateCtx(context, result, node.id);
|
||||
result = await this.executeNode(nextNode, graph, mergedCtx, visited, trace, fanIn, kvStore);
|
||||
}
|
||||
break;
|
||||
}
|
||||
|
||||
case 'ON_FALSE': {
|
||||
if (readBranch(result) === 'false') {
|
||||
const mergedCtx = propagateCtx(context, result, node.id);
|
||||
result = await this.executeNode(nextNode, graph, mergedCtx, visited, trace, fanIn, kvStore);
|
||||
}
|
||||
break;
|
||||
}
|
||||
|
||||
case 'ON_BRANCH': {
|
||||
// switch 具名分支：邊上的 branch 要跟上游 output 的 branch 字面相等才走
|
||||
const actual = readBranch(result);
|
||||
if (edge.branch !== undefined && actual !== undefined && actual === edge.branch) {
|
||||
const mergedCtx = propagateCtx(context, result, node.id);
|
||||
result = await this.executeNode(nextNode, graph, mergedCtx, visited, trace, fanIn, kvStore);
|
||||
}
|
||||
break;
|
||||
}
|
||||
|
||||
case 'FOREACH': {
|
||||
const iteratorKey = edge.iterator ?? 'item';
|
||||
// 找 iterable 順序：先看上游 output (result)，沒有再看完整 context (含上游 chain 累積的 fields)
|
||||
@@ -651,6 +682,30 @@ function getNestedValue(ctx: unknown, path: string): unknown {
|
||||
return cur;
|
||||
}
|
||||
|
||||
/**
|
||||
* 從節點 output 讀出「走哪條分支」（SDD workflow-discovery 3.11）
|
||||
*
|
||||
* 讀取順序（對齊零件 contract 的 output_schema，由正式到相容）：
|
||||
* 1. `data.branch` —— if_control / switch 的正式輸出形狀 {success, data:{result, branch}}
|
||||
* 2. `branch` —— 已被 propagateCtx spread 到 top-level 的情況
|
||||
* 3. `data.result` —— 只有布林沒有 branch 的零件
|
||||
* 4. `result` —— top-level 布林
|
||||
* 讀不出來回 undefined ⇒ 呼叫端一律不走該邊（誠實：寧可不走，不亂挑一條）。
|
||||
*/
|
||||
function readBranch(result: unknown): string | undefined {
|
||||
if (!result || typeof result !== 'object') return undefined;
|
||||
const r = result as Record<string, unknown>;
|
||||
const data = (r.data && typeof r.data === 'object') ? r.data as Record<string, unknown> : undefined;
|
||||
|
||||
const named = data?.branch ?? r.branch;
|
||||
if (typeof named === 'string') return named;
|
||||
|
||||
const bool = data?.result ?? r.result;
|
||||
if (typeof bool === 'boolean') return bool ? 'true' : 'false';
|
||||
|
||||
return undefined;
|
||||
}
|
||||
|
||||
/** 判斷節點執行結果是否為失敗：success === false 或含有 error key */
|
||||
function isFailure(result: unknown): boolean {
|
||||
if (!result || typeof result !== 'object') return false;
|
||||
|
||||
@@ -39,10 +39,15 @@ const STATIC_ORIGINS = ['https://arcrun.dev', 'https://www.arcrun.dev'];
|
||||
|
||||
app.use('*', cors({
|
||||
origin: (origin, c) => {
|
||||
const extra = (c.env.UI_ORIGINS || '')
|
||||
.split(',')
|
||||
.map((s: string) => s.trim())
|
||||
.filter(Boolean);
|
||||
// ⚠️ 非瀏覽器請求（CLI／curl／MCP）沒有 Origin 標頭 → origin 是空字串/undefined。
|
||||
// 此時必須原樣放行，不能回 null——回 null 會讓 Hono cors 中介層在後續處理拋錯，
|
||||
// 表現為所有 CLI 部署一律 500（2026-07-21 實撞：acr push 全掛，對照組亦然）。
|
||||
if (!origin) return origin;
|
||||
let extra: string[] = [];
|
||||
try {
|
||||
extra = String((c.env as Record<string, unknown>).UI_ORIGINS || '')
|
||||
.split(',').map((s: string) => s.trim()).filter(Boolean);
|
||||
} catch { /* UI_ORIGINS 未設定＝只用靜態白名單 */ }
|
||||
return [...STATIC_ORIGINS, ...extra].includes(origin) ? origin : null;
|
||||
},
|
||||
allowMethods: ['GET', 'POST', 'PUT', 'PATCH', 'DELETE', 'OPTIONS'],
|
||||
|
||||
@@ -22,13 +22,21 @@
|
||||
* KBDB 改網址後同步更新此處。seed 先照現況進。
|
||||
*/
|
||||
|
||||
import type { ResponseMap } from './recipe-payload';
|
||||
|
||||
export interface ApiRecipeSeed {
|
||||
canonical_id: string;
|
||||
display_name: string;
|
||||
description?: string;
|
||||
/** HTTP recipe＝要打的網址；`auth: 'binding'` 型＝要呼叫的資源名（如 Workers AI 的模型 id）。 */
|
||||
endpoint: string;
|
||||
method: string;
|
||||
auth_service?: string;
|
||||
// ── payload/回應/binding 三層（3.12）：全選填，既有種子不帶＝行為完全不變 ──
|
||||
body_template?: Record<string, unknown>;
|
||||
response_map?: ResponseMap;
|
||||
auth?: 'static_key' | 'service_account' | 'oauth2' | 'binding';
|
||||
binding_name?: string;
|
||||
}
|
||||
|
||||
export const API_RECIPE_SEEDS: ApiRecipeSeed[] = [
|
||||
@@ -120,4 +128,47 @@ export const API_RECIPE_SEEDS: ApiRecipeSeed[] = [
|
||||
method: 'POST',
|
||||
auth_service: 'line_notify',
|
||||
},
|
||||
|
||||
// ── LLM 對話（binding＝免金鑰，3.12 第四型認證的第一個真實案例）──
|
||||
//
|
||||
// 為什麼進種子（而非寫在某個產品的安裝器裡）：「裝好之後預設有哪些 recipe」是平台能力，
|
||||
// 與本檔其餘種子同理由（見檔頭）。裝完 /init/seed 就有 ⇒ **用戶不填任何金鑰就能問答**。
|
||||
//
|
||||
// 換模型／換供應商＝**改這一筆 recipe**（endpoint + body_template + response_map），
|
||||
// workflow 的 ask_llm 節點不動——這正是「換源＝換 recipe 不是換引擎」。
|
||||
//
|
||||
// 選型實測（2026-08-03，在 1.4.4 實例上跑真實長度的 RAG prompt，每個模型連跑 2 次）：
|
||||
// @cf/meta/llama-4-scout-17b-16e-instruct 2373／2173 ms ✅ 答案最完整、引用正確
|
||||
// @cf/meta/llama-3.3-70b-instruct-fp8-fast 3261／2147 ms ✅ 可用但波動較大
|
||||
// @cf/mistralai/mistral-small-3.1-24b-instruct 3560／3631 ms
|
||||
// @cf/qwen/qwen2.5-coder-32b-instruct 3572／3353 ms
|
||||
// @cf/openai/gpt-oss-120b 1971／2295 ms ❌ 回應形狀不同，response 取不到文字
|
||||
// @cf/google/gemma-3-12b-it ❌ 5018 This account is not allowed to access this model
|
||||
// 對照舊路徑（Gemini `gemma-4-31b-it`）：同型提問 **16.87 s**，且吐整段英文思考草稿
|
||||
// ⇒ 選 llama-4-scout：**快 7 倍以上，且不需要淨化思考草稿**。
|
||||
{
|
||||
canonical_id: 'workers_ai_chat',
|
||||
display_name: 'Workers AI 對話（免金鑰）',
|
||||
description:
|
||||
'Cloudflare Workers AI 文字生成，走 env.AI binding ⇒ 不需要任何 API 金鑰。'
|
||||
+ 'ctx 帶 prompt，回應正規化成 text（含【答】標記與前綴淨化）。'
|
||||
+ '換模型＝改本 recipe 的 endpoint，workflow 不動。',
|
||||
endpoint: '@cf/meta/llama-4-scout-17b-16e-instruct',
|
||||
method: 'POST',
|
||||
auth: 'binding',
|
||||
binding_name: 'AI',
|
||||
body_template: {
|
||||
messages: [{ role: 'user', content: '{{prompt}}' }],
|
||||
max_tokens: 1024,
|
||||
temperature: 0.2,
|
||||
},
|
||||
response_map: {
|
||||
// Workers AI chat 回應：{ response: "…" }（另有 OpenAI 相容的 choices，取 response 最穩）
|
||||
text_path: 'response',
|
||||
// 提示詞要求答案以【答】開頭；模型偶爾會在前面多帶一行 ⇒ 取最後一個標記之後
|
||||
answer_marker: '【答】',
|
||||
// 前綴組合順序不定，循環剝殼（規則見 recipe-payload.ts sanitize）
|
||||
strip_prefixes: ['*', '-', '•', '>', '#', '"', '「', '【答】', 'Answer:', 'Draft:'],
|
||||
},
|
||||
},
|
||||
];
|
||||
|
||||
@@ -0,0 +1,83 @@
|
||||
/**
|
||||
* 分支用法自我說明（SDD workflow-discovery 3.11 / CP arcrun-usable 步驟 5）
|
||||
*
|
||||
* 為什麼需要這一層（leo 08-01 逼出的洞，別刪）：
|
||||
* leo：「它也可以不要送整個意圖工作流去查詢，它可以**一一查詢自己手工填寫每個零件，
|
||||
* 就像在 n8n 那樣**，這時它不會每個都寫 code？」
|
||||
* 取證：逐顆查 `if_control`，回應只有 {status, componentId, input_schema, success_rate…}，
|
||||
* `input_schema` 只說得出 {condition, input}——**沒有任何欄位告訴 AI「判斷完之後兩條路怎麼分岔」**
|
||||
* ⇒ 走 n8n 式逐顆查、自己組圖的 AI 拿到 if_control 後必然卡在「然後呢」，回頭寫 code。
|
||||
*
|
||||
* 判準（leo 一貫要求：資訊出現在需要它的那一刻）：
|
||||
* **AI 只看這一顆的查詢回應，就知道怎麼接下一步**，不必回頭讀 skill。
|
||||
*
|
||||
* 三顆流程控制零件的 output_schema 都收斂到同一個形狀 `data.branch: string`
|
||||
* ⇒ 引擎只有「依標籤選邊」一個機制（ON_BRANCH），ON_TRUE/ON_FALSE 是布林路的語法糖。
|
||||
*/
|
||||
|
||||
export type BranchHint = {
|
||||
/** 這顆零件會輸出哪個欄位當分支標籤 */
|
||||
branch_field: string;
|
||||
/** 可能的分支標籤（switch 是動態的，故標明由 cases 決定） */
|
||||
branches: string[] | string;
|
||||
/** 接下游要用哪些邊型 */
|
||||
edge_types: string[];
|
||||
/** 一行說明：這顆零件之後怎麼分岔 */
|
||||
usage: string;
|
||||
/** 可直接照抄的最小範例（意圖語法＋對應的邊） */
|
||||
example: string;
|
||||
};
|
||||
|
||||
/**
|
||||
* 零件 → 分支用法。key = canonical_id。
|
||||
* 只收「本身會分岔」的零件；不分岔的零件不該有 branch_hint（避免噪音）。
|
||||
*/
|
||||
const BRANCH_HINTS: Record<string, BranchHint> = {
|
||||
if_control: {
|
||||
branch_field: 'data.branch',
|
||||
branches: ['true', 'false'],
|
||||
edge_types: ['ON_TRUE', 'ON_FALSE'],
|
||||
usage:
|
||||
'這顆算完會輸出 data.branch（"true"／"false"）。下游接兩條邊：ON_TRUE 接條件成立要做的事，' +
|
||||
'ON_FALSE 接不成立要做的事。**不需要自己寫 code 判斷走哪條**——引擎依 branch 自動選路。',
|
||||
example:
|
||||
'判斷有沒有新資料 >> ON_TRUE >> 傳到 telegram\n' +
|
||||
'判斷有沒有新資料 >> ON_FALSE >> 結束\n' +
|
||||
'（中文語意詞亦可：「成立時」＝ON_TRUE、「否則」＝ON_FALSE）',
|
||||
},
|
||||
switch: {
|
||||
branch_field: 'data.branch',
|
||||
branches: '由 input_schema.cases[].branch 與 default_branch 決定（N 路，非固定清單）',
|
||||
edge_types: ['ON_BRANCH'],
|
||||
usage:
|
||||
'這顆依 value 比對 cases，輸出 data.branch＝命中那個 case 的 branch 名（都沒中則是 default_branch）。' +
|
||||
'下游**每條路各接一條 ON_BRANCH 邊，並在邊上標 branch 等於你在 cases 裡取的名字**。' +
|
||||
'default_branch 不需要特別的邊型，照樣用 ON_BRANCH 標它的名字即可。',
|
||||
example:
|
||||
'{"cases":[{"match":"active","branch":"branch_active"}],"default_branch":"branch_default"}\n' +
|
||||
'edges: [\n' +
|
||||
' {"from":"my_switch","to":"處理啟用","type":"ON_BRANCH","branch":"branch_active"},\n' +
|
||||
' {"from":"my_switch","to":"處理其他","type":"ON_BRANCH","branch":"branch_default"}\n' +
|
||||
']',
|
||||
},
|
||||
try_catch: {
|
||||
branch_field: 'data.branch',
|
||||
branches: ['try', 'catch'],
|
||||
edge_types: ['ON_BRANCH'],
|
||||
usage:
|
||||
'這顆看上游 error 是否非空，輸出 data.branch（"try"＝沒錯／"catch"＝有錯）。' +
|
||||
'下游接兩條 ON_BRANCH 邊，branch 分別標 "try" 與 "catch"。' +
|
||||
'**錯誤處理不需要寫 code**——把要補救的節點接在 catch 那條邊後面即可。',
|
||||
example:
|
||||
'edges: [\n' +
|
||||
' {"from":"my_try_catch","to":"正常流程","type":"ON_BRANCH","branch":"try"},\n' +
|
||||
' {"from":"my_try_catch","to":"補救流程","type":"ON_BRANCH","branch":"catch"}\n' +
|
||||
']',
|
||||
},
|
||||
};
|
||||
|
||||
/** 取某零件的分支用法說明；不分岔的零件回 undefined（回應不加噪音）。 */
|
||||
export function branchHintFor(componentId: string | undefined): BranchHint | undefined {
|
||||
if (!componentId) return undefined;
|
||||
return BRANCH_HINTS[componentId.toLowerCase()];
|
||||
}
|
||||
@@ -20,6 +20,7 @@ import { isComponentHash, isRecipeHash } from './hash';
|
||||
import { resolveRecipe, resolveAuthRecipe } from '../routes/recipes';
|
||||
import type { AuthRecipeDefinition } from '../routes/recipes';
|
||||
import type { Bindings, ComponentRunner, ServiceBinding } from '../types';
|
||||
import { renderBodyTemplate, applyResponseMap } from './recipe-payload';
|
||||
|
||||
/**
|
||||
* WASM HTTP runner：canonical_id → 對應獨立 Worker URL。
|
||||
@@ -120,7 +121,7 @@ export function createComponentLoader(env: Bindings) {
|
||||
// 4. rec_hash → 查 RECIPES KV idx → recipe 執行
|
||||
if (isRecipeHash(componentId)) {
|
||||
const recipe = await resolveRecipe(componentId, env.RECIPES);
|
||||
if (recipe) return makeRecipeRunner(recipe);
|
||||
if (recipe) return pickRecipeRunner(recipe, env);
|
||||
throw new Error(`找不到 recipe hash "${componentId}"，請確認已透過 acr push 上傳`);
|
||||
}
|
||||
|
||||
@@ -134,7 +135,7 @@ export function createComponentLoader(env: Bindings) {
|
||||
|
||||
// 6. KV recipe（動態，用戶 push 的）
|
||||
const kvRecipe = await resolveRecipe(componentId, env.RECIPES);
|
||||
if (kvRecipe) return makeRecipeRunner(kvRecipe);
|
||||
if (kvRecipe) return pickRecipeRunner(kvRecipe, env);
|
||||
|
||||
// 7. WASM HTTP runner:auth primitive / API 零件 → 獨立 Worker URL
|
||||
// 白名單見 WASM_HTTP_RUNNER_IDS（http_request、5 個待降級 API 零件、4 個 auth primitive）。
|
||||
@@ -271,6 +272,73 @@ function makeLogicRunner(canonicalId: string, env: Bindings): ComponentRunner |
|
||||
return makeHttpRunner(wasmWorkerUrl(canonicalId, env.WORKER_SUBDOMAIN));
|
||||
}
|
||||
|
||||
/**
|
||||
* recipe → runner 的分派（3.12）：auth='binding' 走平台 binding（免金鑰），
|
||||
* 其餘一律走既有 HTTP 路徑（沒宣告 auth 的舊 recipe 完全不受影響）。
|
||||
*/
|
||||
function pickRecipeRunner(
|
||||
recipe: import('../routes/recipes').RecipeDefinition,
|
||||
env: Bindings,
|
||||
): ComponentRunner {
|
||||
return recipe.auth === 'binding'
|
||||
? makeBindingRecipeRunner(recipe, env)
|
||||
: makeRecipeRunner(recipe);
|
||||
}
|
||||
|
||||
/**
|
||||
* auth='binding' 的 recipe runner（3.12 第四型認證）：不打外部 HTTP、不需要任何金鑰，
|
||||
* 直接用平台 binding（env.AI／VECTORIZE／…）⇒ leo 要的「開機就可用」。
|
||||
*
|
||||
* 為什麼要開這型：recipe 的舊抽象＝「打一個外部 HTTP API」（endpoint+method+auth_service），
|
||||
* 而 Cloudflare 的 binding 呼叫不是 HTTP ⇒ **整類能力被排除在 recipe 之外**。
|
||||
* 開這一型不是為 Workers AI 開特例，是一次打開 env.AI／VECTORIZE／BROWSER／QUEUE 整排。
|
||||
*/
|
||||
function makeBindingRecipeRunner(
|
||||
recipe: import('../routes/recipes').RecipeDefinition,
|
||||
env: Bindings,
|
||||
): ComponentRunner {
|
||||
return async (ctx: unknown) => {
|
||||
const ctxObj = (ctx && typeof ctx === 'object') ? ctx as Record<string, unknown> : {};
|
||||
const name = recipe.binding_name ?? 'AI';
|
||||
const binding = (env as unknown as Record<string, unknown>)[name];
|
||||
|
||||
if (!binding) {
|
||||
return {
|
||||
success: false,
|
||||
error:
|
||||
`recipe "${recipe.canonical_id}" 宣告 auth: binding、binding_name: "${name}"，` +
|
||||
`但這個部署沒有綁定 ${name}。請在 wrangler.toml 補上該 binding 後重新部署。`,
|
||||
};
|
||||
}
|
||||
|
||||
// endpoint 在 binding 型當作「要呼叫的資源名」（例 Workers AI 的模型 id）
|
||||
const target = recipe.endpoint;
|
||||
const payload = renderBodyTemplate(recipe.body_template ?? recipe.body, ctxObj)
|
||||
?? Object.fromEntries(Object.entries(ctxObj).filter(([k]) => !k.startsWith('_')));
|
||||
|
||||
try {
|
||||
const runner = binding as { run?: (model: string, input: unknown) => Promise<unknown> };
|
||||
if (typeof runner.run !== 'function') {
|
||||
return {
|
||||
success: false,
|
||||
error: `binding "${name}" 沒有 run() 方法，目前 binding 型只支援 run(model, input) 形狀（如 env.AI）。`,
|
||||
};
|
||||
}
|
||||
const data = await runner.run(target, payload);
|
||||
if (recipe.response_map) {
|
||||
const normalized = applyResponseMap(data, recipe.response_map);
|
||||
return { success: true, data, text: normalized.text };
|
||||
}
|
||||
return { success: true, data };
|
||||
} catch (e) {
|
||||
return {
|
||||
success: false,
|
||||
error: `binding "${name}" 呼叫失敗（${target}）：${e instanceof Error ? e.message : String(e)}`,
|
||||
};
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
function makeRecipeRunner(recipe: import('../routes/recipes').RecipeDefinition): ComponentRunner {
|
||||
return async (ctx: unknown) => {
|
||||
const ctxObj = (ctx && typeof ctx === 'object') ? ctx as Record<string, unknown> : {};
|
||||
@@ -293,9 +361,12 @@ function makeRecipeRunner(recipe: import('../routes/recipes').RecipeDefinition):
|
||||
headers[k] = interpolate(v);
|
||||
}
|
||||
|
||||
// body：把 recipe.body 裡的 {{key}} 都換掉
|
||||
// body：優先 body_template（③ payload 層，3.12——支援巢狀/dot path/保留型別），
|
||||
// 其次既有 recipe.body（淺層 {{key}}，舊 recipe 照舊），最後才拿 ctx 當 body。
|
||||
let bodyStr: string | undefined;
|
||||
if (recipe.body) {
|
||||
if (recipe.body_template) {
|
||||
bodyStr = JSON.stringify(renderBodyTemplate(recipe.body_template, ctxObj));
|
||||
} else if (recipe.body) {
|
||||
bodyStr = interpolate(JSON.stringify(recipe.body));
|
||||
} else if (method !== 'GET') {
|
||||
// 沒指定 body template → 用 ctx 當 body，但剔除 _ 前綴的內部欄位
|
||||
@@ -313,6 +384,13 @@ function makeRecipeRunner(recipe: import('../routes/recipes').RecipeDefinition):
|
||||
});
|
||||
|
||||
const data = await readBodyOnce(res);
|
||||
|
||||
// ③ 回應正規化（3.12）：未設 response_map ⇒ 原樣回傳（既有 recipe 零行為變化）。
|
||||
// 設了 ⇒ 額外附 `text`（各家形狀差異收在 recipe 裡，換源不必改 workflow）。
|
||||
if (recipe.response_map) {
|
||||
const normalized = applyResponseMap(data, recipe.response_map);
|
||||
return { success: res.ok, status: res.status, data, text: normalized.text };
|
||||
}
|
||||
return { success: res.ok, status: res.status, data };
|
||||
};
|
||||
}
|
||||
|
||||
@@ -5,6 +5,8 @@ export const VALID_EDGE_TYPES = new Set([
|
||||
'PIPE', 'IF', 'FOREACH', 'CONTINUE',
|
||||
// 新增：執行語意
|
||||
'IS_A', 'ON_SUCCESS', 'ON_FAIL',
|
||||
// 新增：條件語意（SDD workflow-discovery 3.11）—— 讀上游 if_control/switch 的 branch
|
||||
'ON_TRUE', 'ON_FALSE', 'ON_BRANCH',
|
||||
// 新增：觸發語意
|
||||
'ON_CLICK', 'CALLS_SUBFLOW',
|
||||
// 新增：結構語意（記錄圖結構，不執行）
|
||||
@@ -28,9 +30,19 @@ export const SEMANTIC_EDGE_MAP: Record<string, EdgeType> = {
|
||||
'失敗時': 'ON_FAIL',
|
||||
'對每個': 'FOREACH',
|
||||
'條件滿足時': 'IF',
|
||||
// 條件分支語意（SDD workflow-discovery 3.11）：讓意圖工作流寫得出兩條路
|
||||
'成立時': 'ON_TRUE',
|
||||
'為真時': 'ON_TRUE',
|
||||
'不成立時': 'ON_FALSE',
|
||||
'為假時': 'ON_FALSE',
|
||||
'否則': 'ON_FALSE',
|
||||
// 英文別名
|
||||
'SUCCESS': 'ON_SUCCESS',
|
||||
'FAIL': 'ON_FAIL',
|
||||
'TRUE': 'ON_TRUE',
|
||||
'FALSE': 'ON_FALSE',
|
||||
'ELSE': 'ON_FALSE',
|
||||
'BRANCH': 'ON_BRANCH',
|
||||
'CLICK': 'ON_CLICK',
|
||||
'SUBFLOW': 'CALLS_SUBFLOW',
|
||||
};
|
||||
|
||||
@@ -0,0 +1,154 @@
|
||||
/**
|
||||
* recipe 的 payload 與回應處理層（SDD workflow-discovery 3.12 / CP arcrun-usable 步驟 5 缺口②）
|
||||
*
|
||||
* 為什麼存在（leo 的三層模型，第③層過去是空的）：
|
||||
* ① 零件（http_request） ② auth recipe（auth_service） ③ **payload recipe** ← 這層
|
||||
* 舊 schema 存不住 body 與「回應怎麼取值」⇒ 帶 body 的 API 只能把整包寫進 workflow code，
|
||||
* 回應解析（rag_chat 的 finalize，2786 字元）綁死 Gemini 格式 ⇒ 換源必壞。
|
||||
* 有了這層：**換 LLM 供應商＝換 recipe，不必動 workflow**。
|
||||
*
|
||||
* 相容鐵律：三個欄位全為選填。既有 recipe（沒有這些欄位）行為**完全不變**——
|
||||
* renderBodyTemplate(undefined,…) 回 undefined、applyResponseMap(body, undefined) 原樣回傳。
|
||||
*/
|
||||
|
||||
/** 回應正規化規則（隨 recipe 走，故換源＝換 recipe） */
|
||||
export type ResponseMap = {
|
||||
/**
|
||||
* 取值路徑（dot path，支援陣列索引）。
|
||||
* 例：Gemini `candidates.0.content.parts.0.text`／Claude `content.0.text`／
|
||||
* Workers AI `response`。
|
||||
* 搭配 thinking_model 時可指向 parts 陣列本身。
|
||||
*/
|
||||
text_path?: string;
|
||||
/**
|
||||
* 思考型模型（如 gemma）：parts 內會混入 `thought: true` 的思考過程，
|
||||
* 要剔除後取最後一個非 thought 的 part。
|
||||
*/
|
||||
thinking_model?: boolean;
|
||||
/** 淨化：要剝掉的前綴（實撞過「Draft:」「*」「Answer:」，且組合順序不定） */
|
||||
strip_prefixes?: string[];
|
||||
/** 答案標記：出現時只取其後的內容（實撞：模型會把草稿吐在標記前） */
|
||||
answer_marker?: string;
|
||||
};
|
||||
|
||||
/** 從物件用 dot path 取值：'a.0.b' → obj.a[0].b */
|
||||
function getPath(obj: unknown, path: string): unknown {
|
||||
let cur: unknown = obj;
|
||||
for (const part of path.split('.')) {
|
||||
if (cur === null || cur === undefined) return undefined;
|
||||
if (typeof cur !== 'object') return undefined;
|
||||
cur = (cur as Record<string, unknown>)[part];
|
||||
}
|
||||
return cur;
|
||||
}
|
||||
|
||||
// ── ③-a body_template：payload 收回 recipe ───────────────────────────────────
|
||||
|
||||
/**
|
||||
* 把 body_template 內所有 `{{var}}` 用 ctx 填掉（遞迴進巢狀 object / array）。
|
||||
*
|
||||
* 與 graph-executor 的 interpolateData 同一套語義（刻意一致，避免兩種插值行為）：
|
||||
* - 整個字串就是單一 `{{x}}` → 回**原型別**（陣列/物件/數字不被 stringify）
|
||||
* - 混合文字 → 拼成字串
|
||||
* - 取不到 → **保留原樣** `{{x}}`（看得見才好 debug，不靜默吞掉）
|
||||
*/
|
||||
export function renderBodyTemplate(
|
||||
template: unknown,
|
||||
ctx: Record<string, unknown>,
|
||||
): unknown {
|
||||
if (template === undefined || template === null) return undefined;
|
||||
return renderValue(template, ctx);
|
||||
}
|
||||
|
||||
function renderValue(v: unknown, ctx: Record<string, unknown>): unknown {
|
||||
if (typeof v === 'string') return renderString(v, ctx);
|
||||
if (Array.isArray(v)) return v.map(item => renderValue(item, ctx));
|
||||
if (v !== null && typeof v === 'object') {
|
||||
const out: Record<string, unknown> = {};
|
||||
for (const [k, val] of Object.entries(v as Record<string, unknown>)) {
|
||||
out[k] = renderValue(val, ctx);
|
||||
}
|
||||
return out;
|
||||
}
|
||||
return v;
|
||||
}
|
||||
|
||||
function renderString(s: string, ctx: Record<string, unknown>): unknown {
|
||||
const single = s.match(/^\s*\{\{([\w.]+)\}\}\s*$/);
|
||||
if (single) {
|
||||
const val = getPath(ctx, single[1]);
|
||||
return val === undefined ? s : val;
|
||||
}
|
||||
return s.replace(/\{\{([\w.]+)\}\}/g, (_, key: string) => {
|
||||
const val = getPath(ctx, key);
|
||||
if (val === undefined) return `{{${key}}}`;
|
||||
return typeof val === 'string' ? val : JSON.stringify(val);
|
||||
});
|
||||
}
|
||||
|
||||
// ── ③-b response_map：回應正規化 ─────────────────────────────────────────────
|
||||
|
||||
export type NormalizedResponse = {
|
||||
/** 正規化後的純文字（沒有 response_map 或取不到時 undefined——誠實，不編造） */
|
||||
text?: string;
|
||||
/** 原始回應永遠保留（除錯與向後相容都靠它） */
|
||||
raw: unknown;
|
||||
};
|
||||
|
||||
/**
|
||||
* 依 response_map 把各家 API 的回應正規化成 `{ text }`。
|
||||
* 沒給 map ⇒ 原樣回傳（既有 recipe 零行為變化）。
|
||||
*/
|
||||
export function applyResponseMap(body: unknown, map?: ResponseMap): NormalizedResponse {
|
||||
if (!map) return { raw: body };
|
||||
|
||||
let picked: unknown = map.text_path ? getPath(body, map.text_path) : body;
|
||||
|
||||
// 思考型模型：picked 是 parts 陣列 → 剔除 thought=true，取最後一個
|
||||
if (map.thinking_model && Array.isArray(picked)) {
|
||||
const real = picked.filter(
|
||||
p => !(p && typeof p === 'object' && (p as Record<string, unknown>).thought === true),
|
||||
);
|
||||
const last = real[real.length - 1];
|
||||
picked = (last && typeof last === 'object')
|
||||
? (last as Record<string, unknown>).text
|
||||
: last;
|
||||
}
|
||||
|
||||
if (typeof picked !== 'string') return { text: undefined, raw: body };
|
||||
|
||||
return { text: sanitize(picked, map), raw: body };
|
||||
}
|
||||
|
||||
/**
|
||||
* 淨化（知識是實撞出來的，非預想）：
|
||||
* 1. 有 answer_marker → 只取標記**最後一次**出現之後的內容
|
||||
* （實撞：模型的自檢清單內文也會提到標記，用 lastIndexOf 才撈得到真的那個）
|
||||
* 2. 前綴組合順序不定（「* 【答】」「Draft: 【答】」「Answer: * 【答】」三型都撞過）
|
||||
* ⇒ **循環**剝殼，單趟剝不乾淨
|
||||
*/
|
||||
function sanitize(input: string, map: ResponseMap): string {
|
||||
let s = input.trim();
|
||||
|
||||
if (map.answer_marker) {
|
||||
const idx = s.lastIndexOf(map.answer_marker);
|
||||
if (idx >= 0) s = s.slice(idx + map.answer_marker.length);
|
||||
}
|
||||
|
||||
const prefixes = map.strip_prefixes ?? [];
|
||||
if (prefixes.length > 0) {
|
||||
let changed = true;
|
||||
while (changed) {
|
||||
changed = false;
|
||||
s = s.trimStart();
|
||||
for (const p of prefixes) {
|
||||
if (p && s.startsWith(p)) {
|
||||
s = s.slice(p.length);
|
||||
changed = true;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return s.trim();
|
||||
}
|
||||
@@ -14,9 +14,10 @@ export const graphSchema = z.object({
|
||||
edges: z.array(z.object({
|
||||
from: z.string(),
|
||||
to: z.string(),
|
||||
type: z.enum(['PIPE', 'IF', 'FOREACH', 'CONTINUE', 'IS_A', 'ON_SUCCESS', 'ON_FAIL', 'ON_CLICK', 'CALLS_SUBFLOW', 'CONTAINS', 'HAS_STYLE', 'HAS_BEHAVIOR']),
|
||||
type: z.enum(['PIPE', 'IF', 'FOREACH', 'CONTINUE', 'IS_A', 'ON_SUCCESS', 'ON_FAIL', 'ON_TRUE', 'ON_FALSE', 'ON_BRANCH', 'ON_CLICK', 'CALLS_SUBFLOW', 'CONTAINS', 'HAS_STYLE', 'HAS_BEHAVIOR']),
|
||||
condition: z.string().optional(),
|
||||
iterator: z.string().optional(),
|
||||
branch: z.string().optional(), // ON_BRANCH 的具名分支（SDD workflow-discovery 3.11）
|
||||
})),
|
||||
});
|
||||
|
||||
|
||||
@@ -0,0 +1,49 @@
|
||||
/**
|
||||
* workflow-search — 本租戶 workflow 名字搜尋的**唯一一條路**
|
||||
*
|
||||
* 既有機制（workflow-discovery 3.1）：轉發 KBDB /entries/search
|
||||
* （entry_type=workflow + owner_id=apiKey 租戶隔離；優先 semantic，KBDB 未開
|
||||
* Vectorize 自動降級 keyword + capability_hint）。
|
||||
*
|
||||
* 為什麼抽成共用（leo 07-31：「search 節點名稱和 search 工作流名稱是同一個？
|
||||
* 難道我不能指定要搜尋工作流或節點或 recipe 嗎？」＋「外部 API 只有一條一致的路」鐵律）：
|
||||
* - GET /workflows/search（MCP arcrun_search_workflows 走的路）
|
||||
* - POST /cypher/search { target: "workflow", query }（discover 入口指定搜尋對象）
|
||||
* 兩個入口共用本函式 ⇒ 行為必然一致，改一處兩邊同步。
|
||||
*
|
||||
* 已知缺口（如實透傳，不掩蓋）：workflow_metadata 無 description slot——
|
||||
* 無 description 的 workflow 沒有 search entry、搜不到；補救走
|
||||
* POST /workflows/backfill-search-entries（有 description 的補 entry、沒有的誠實列出）。
|
||||
*
|
||||
* flag 安全：主動 pull，無輪詢/排程。
|
||||
*/
|
||||
|
||||
export type WorkflowSearchEnv = {
|
||||
KBDB_BASE_URL?: string;
|
||||
KBDB_INTERNAL_TOKEN?: string;
|
||||
};
|
||||
|
||||
export type WorkflowSearchMode = 'semantic' | 'keyword';
|
||||
|
||||
/**
|
||||
* 打 KBDB /entries/search（本租戶、entry_type=workflow）。
|
||||
* 回原始 Response——GET /workflows/search 直接 stream 透傳（既有行為，一字不改）；
|
||||
* target=workflow 的呼叫端自行 json() 解析。
|
||||
*/
|
||||
export async function fetchTenantWorkflowSearch(
|
||||
env: WorkflowSearchEnv,
|
||||
apiKey: string,
|
||||
q: string,
|
||||
mode: WorkflowSearchMode = 'semantic',
|
||||
): Promise<Response> {
|
||||
const base = (env.KBDB_BASE_URL ?? 'https://arcrun-kbdb.uncle6-me.workers.dev').replace(/\/$/, '');
|
||||
const headers: Record<string, string> = { 'Content-Type': 'application/json' };
|
||||
if (env.KBDB_INTERNAL_TOKEN) headers['Authorization'] = `Bearer ${env.KBDB_INTERNAL_TOKEN}`;
|
||||
const params = new URLSearchParams({
|
||||
q,
|
||||
owner_id: apiKey, // 租戶隔離（只搜本租戶的 workflow）
|
||||
entry_type: 'workflow', // base 通用 filter（Q4），只回 workflow entry
|
||||
mode,
|
||||
});
|
||||
return fetch(`${base}/entries/search?${params.toString()}`, { headers });
|
||||
}
|
||||
@@ -1,16 +1,55 @@
|
||||
import { Hono } from 'hono';
|
||||
import type { Bindings } from '../types';
|
||||
import { handleCypherSearch, handleCypherExecute } from '../actions/cypher-handlers';
|
||||
import { searchByTarget } from '../actions/target-search';
|
||||
|
||||
export const cypherRouter = new Hono<{ Bindings: Bindings }>();
|
||||
|
||||
const VALID_TARGETS = new Set(['component', 'recipe', 'workflow']);
|
||||
|
||||
// POST /cypher/search — 三元組 → 解析節點 → 語意搜尋零件 → 回傳 Cypher JSON (開發友善格式)
|
||||
//
|
||||
// t159（leo 07-31）：加 `target` 指定搜尋對象（component／recipe／workflow）＋`query` 名字搜尋。
|
||||
// - triplets（不給 target）＝混搜兩庫＋意圖節點替換（步驟 4）
|
||||
// - triplets + target=component|recipe＝只查該庫
|
||||
// - query + target＝名字搜尋，各自走**既有**機制（registry search／私庫 RECIPES／workflows/search）
|
||||
cypherRouter.post('/cypher/search', async (c) => {
|
||||
const body = await c.req.json() as { triplets?: unknown };
|
||||
const body = await c.req.json() as { triplets?: unknown; mode?: unknown; target?: unknown; query?: unknown };
|
||||
const rawTriplets = body?.triplets;
|
||||
|
||||
// ── target 驗證（component / recipe / workflow）─────────────────────────────
|
||||
const target = typeof body?.target === 'string' ? body.target : undefined;
|
||||
if (target !== undefined && !VALID_TARGETS.has(target)) {
|
||||
return c.json({ error: `target 只接受 component／recipe／workflow，收到「${target}」` }, 400);
|
||||
}
|
||||
|
||||
// ── query 名字搜尋分支（需 target）──────────────────────────────────────────
|
||||
const query = typeof body?.query === 'string' ? body.query.trim() : '';
|
||||
if (query) {
|
||||
if (!target) {
|
||||
return c.json({ error: '給 query 必須同時給 target（component／recipe／workflow），指明要搜哪個庫' }, 400);
|
||||
}
|
||||
const apiKey = c.req.header('X-Arcrun-API-Key') ?? undefined;
|
||||
const r = await searchByTarget(target as 'component' | 'recipe' | 'workflow', query, c.env, apiKey);
|
||||
if (!r.ok) return c.json({ error: r.error }, r.status);
|
||||
return c.json(r.body);
|
||||
}
|
||||
|
||||
if (!Array.isArray(rawTriplets) || rawTriplets.length === 0) {
|
||||
return c.json({ error: 'triplets 必須為非空字串陣列' }, 400);
|
||||
return c.json({ error: 'triplets 必須為非空字串陣列（或給 query + target 做名字搜尋）' }, 400);
|
||||
}
|
||||
|
||||
// t158「部署≠發現」：mode=compile＝純編圖（安裝器/acr push 的複製路徑，零存在性查詢）；
|
||||
// 預設 discover＝誠實查詢（AI 問「有沒有」的既有契約，not_found＋指路照舊）。
|
||||
const mode = body?.mode === 'compile' ? 'compile' : 'discover';
|
||||
|
||||
// target 限庫只屬於 discover（compile＝純複製，不查任何庫，target 無意義）
|
||||
if (target && mode === 'compile') {
|
||||
return c.json({ error: 'mode=compile（複製路徑）不查庫，不接受 target；要指定搜尋對象請用 discover（預設）' }, 400);
|
||||
}
|
||||
// workflow 是名字搜尋，不參與三元組編圖——請帶 query
|
||||
if (target === 'workflow') {
|
||||
return c.json({ error: 'target=workflow 是名字搜尋，請改帶 { target: "workflow", query: "..." }（不吃 triplets）' }, 400);
|
||||
}
|
||||
|
||||
try {
|
||||
@@ -18,7 +57,7 @@ cypherRouter.post('/cypher/search', async (c) => {
|
||||
const timestamp = now.toISOString();
|
||||
const versionId = `search-v1-${now.getFullYear()}${String(now.getMonth() + 1).padStart(2, '0')}${String(now.getDate()).padStart(2, '0')}-${String(now.getHours()).padStart(2, '0')}${String(now.getMinutes()).padStart(2, '0')}${String(now.getSeconds()).padStart(2, '0')}`;
|
||||
|
||||
const result = await handleCypherSearch(rawTriplets, c.env);
|
||||
const result = await handleCypherSearch(rawTriplets, c.env, mode, target as 'component' | 'recipe' | undefined);
|
||||
|
||||
const response = {
|
||||
version: versionId,
|
||||
|
||||
@@ -3,9 +3,19 @@ import type { Bindings } from '../types';
|
||||
|
||||
export const healthRouter = new Hono<{ Bindings: Bindings }>();
|
||||
|
||||
healthRouter.get('/health', (c) =>
|
||||
c.json({ ok: true, bundle_version: c.env.ARCRUN_BUNDLE_VERSION ?? '' })
|
||||
);
|
||||
// t162（leo 07-31 實撞：「小幫手一直顯示知識庫需要更新…重新更新後並不會消失」）：
|
||||
// daemon cloudVersionStale() 讀 /health 的 `bundle_version` 判斷是否過舊——
|
||||
// 但本端點過去只回 {ok:true}，**從沒吐這個欄位** ⇒ daemon 恆讀到空字串
|
||||
// ⇒ 恆判 stale ⇒ 假警報永遠不消失（安裝器其實一直有注入 ARCRUN_BUNDLE_VERSION var，
|
||||
// 只是沒有人把它吐出來）。修＝誠實回報本實例的 bundle 版本。
|
||||
// 未注入（本地 dev／很舊的實例）就省略該欄——daemon 對空字串仍判 stale，
|
||||
// 那是**正確的**（真的是老實例，該更新）。
|
||||
healthRouter.get('/health', (c) => {
|
||||
const bundleVersion = c.env.ARCRUN_BUNDLE_VERSION;
|
||||
return c.json(
|
||||
bundleVersion ? { ok: true, bundle_version: bundleVersion } : { ok: true },
|
||||
);
|
||||
});
|
||||
|
||||
healthRouter.get('/', (c) =>
|
||||
c.json({
|
||||
|
||||
@@ -50,6 +50,13 @@ initSeedRouter.post('/init/seed', async (c) => {
|
||||
endpoint: seed.endpoint,
|
||||
method: (seed.method ?? 'POST').toUpperCase(),
|
||||
auth_service: seed.auth_service,
|
||||
// ③ payload/回應/binding 三層（3.12）：不列進來的欄位會被**靜默吃掉**——
|
||||
// 種子帶了 body_template/response_map/auth 卻沒進 KV，症狀是 recipe 存在但跑起來
|
||||
// 「像沒設定過」，且哪裡都不會紅（08-02 manifest.daemon 欄被列舉式重建吃掉的同型）。
|
||||
body_template: seed.body_template,
|
||||
response_map: seed.response_map,
|
||||
auth: seed.auth,
|
||||
binding_name: seed.binding_name,
|
||||
created_at: existing?.created_at ?? now,
|
||||
updated_at: now,
|
||||
};
|
||||
|
||||
@@ -25,6 +25,9 @@ import { kbdbBase } from './kbdb-proxy';
|
||||
import { validateConsoleSession } from './console-auth';
|
||||
import { hashPassword, verifyPassword, randomHex, generatePassword } from '../lib/portal-auth';
|
||||
import { PORTAL_TEMPLATE_SEEDS } from '../lib/portal-seeds';
|
||||
// arcrun-rag#10：/portal/admin/ai 存 Gemini key 走 credentials.ts 的**唯一**寫入路徑，
|
||||
// 不在 portal 這層另造第二套儲存（D36：值進 Workers Secret，D1 只留 ref）。
|
||||
import { storeCredential } from './credentials';
|
||||
|
||||
export const portalRouter = new Hono<{ Bindings: Bindings }>();
|
||||
|
||||
@@ -840,26 +843,9 @@ portalRouter.post('/portal/daemon/libraries', (c) =>
|
||||
}),
|
||||
);
|
||||
|
||||
// ── t122 萃取引擎設定（daemon 萃取用；與 chat-key AI 問答金鑰獨立管理）──────────────
|
||||
// KV key = {tenant}:portal:extractor_config，存在 WEBHOOKS KV（同 chat-key 手法）。
|
||||
// 金鑰不落 log；GET 只回 has_key:bool，不回明文。
|
||||
// daemon 未設定時預設 gemma（封測者不會有 claude，以 gemma 為友善預設）。
|
||||
|
||||
interface ExtractorConfig {
|
||||
engine: 'gemma' | 'claude';
|
||||
gemini_api_key?: string;
|
||||
llm_model?: string;
|
||||
}
|
||||
|
||||
function extractorConfigKey(env: Bindings): string {
|
||||
return `${portalTenant(env)}:portal:extractor_config`;
|
||||
}
|
||||
|
||||
async function getExtractorConfig(env: Bindings): Promise<ExtractorConfig | null> {
|
||||
const raw = await env.WEBHOOKS.get(extractorConfigKey(env), 'text');
|
||||
if (!raw) return null;
|
||||
try { return JSON.parse(raw) as ExtractorConfig; } catch { return null; }
|
||||
}
|
||||
// t176：t122 的 extractor_config（雲端指定地端萃取引擎）整組移除——
|
||||
// interface／KV key／讀取函式都不再需要，因為雲端已不下發、也不再有設定入口。
|
||||
// ⚠️ 舊實例的 KV 殘值無害：daemon 端 t176 起也不吃這個欄位了。
|
||||
|
||||
// POST /portal/daemon/config — body {email, password}。同步小幫手憑「用戶剛設的帳密」
|
||||
// 直接換到自己的設定（t54，leo 07-25：「最好的就是把它的帳密直接輸入」）——
|
||||
@@ -888,164 +874,42 @@ portalRouter.post('/portal/daemon/config', (c) =>
|
||||
}
|
||||
await clearLoginFail(c.env, email);
|
||||
const tenant = portalTenant(c.env);
|
||||
const extractorCfg = await getExtractorConfig(c.env);
|
||||
const engine = extractorCfg?.engine ?? 'gemma';
|
||||
// t176（leo 08-03 架構翻案）：**不再下發任何 LLM 設定**（extractor／金鑰／模型）。
|
||||
// 地端用哪個模型、哪把金鑰，由使用者在同步小幫手的托盤「AI 設定…」自己設。
|
||||
//
|
||||
// 為什麼拔掉：extractor_config 這把 KV 的 key 是 `${portalTenant(env)}:portal:extractor_config`，
|
||||
// 而 portalTenant 是 **worker 層級**環境變數（見本檔 43 行）＝**全租戶共用一把**。
|
||||
// 任一處設了 claude，所有人的 daemon 都會收到 claude；沒裝 Claude Code 的機器
|
||||
// 會萃取全滅，而 portal 的 Claude 勾選框又恆為 disabled（daemon 從未回報 has_claude）
|
||||
// ⇒ 用戶自己解不開（08-03 封測實證：雲端同步成功、金鑰有效，卻零張卡）。
|
||||
// leo：「地端要用什麼模型就在 daemon 上輸入 API Key 設置，而不是雲端設置後控制地端」。
|
||||
//
|
||||
// ⚠️ 只拔 LLM 欄位——連線欄位（cypher_url／namespace／library）與本 route 本身照舊，
|
||||
// daemon 靠它上線；資料夾/庫管理（daemon/libraries）也完全不動（leo 明確劃界）。
|
||||
const daemonCfg: Record<string, string> = {
|
||||
cypher_url: new URL(c.req.url).origin,
|
||||
namespace: tenant,
|
||||
library: 'kb',
|
||||
extractor: engine,
|
||||
email,
|
||||
instance_name: String(rec.values.display_name ?? ''),
|
||||
};
|
||||
if (engine === 'gemma' && extractorCfg?.gemini_api_key) {
|
||||
daemonCfg.gemini_api_key = extractorCfg.gemini_api_key;
|
||||
}
|
||||
if (extractorCfg?.llm_model) daemonCfg.llm_model = extractorCfg.llm_model;
|
||||
return c.json({ success: true, config: daemonCfg });
|
||||
}),
|
||||
);
|
||||
|
||||
// ── t131 合併 AI 設定（Gemini API Key 同時設 chat＋extractor；has_claude 由 daemon 回報）─────
|
||||
// KV key = {tenant}:portal:ai_config，存在 WEBHOOKS KV。
|
||||
// KV key = {tenant}:portal:daemon_caps，存 daemon 回報的能力（TTL 7 天）。
|
||||
// t176：t131 這一版 `/portal/admin/ai`（POST/GET）**整組移除**。兩個原因：
|
||||
// ① 它是**重複註冊**——本檔後段（arcrun-rag#10 那版）另有一組同路徑 route。
|
||||
// Hono 先到先比 ⇒ 舊的這組一直贏，後段那組修好的「金鑰真的寫進 credentials」形同死碼。
|
||||
// 這正是「key 從來沒存進去」的 bug 在 merge 後仍可能復發的原因。
|
||||
// ② 它把 use_claude_for_extract 同步進 extractor_config 下發給 daemon
|
||||
// （syncExtractorFromAiConfig），而那把 KV 是**全租戶共用**——正是 08-03 事故根因。
|
||||
// 保留的是後段那組（只管 Gemini 金鑰，走 storeCredential 唯一寫入路徑，不碰 extractor）。
|
||||
|
||||
interface AiConfig {
|
||||
gemini_api_key?: string;
|
||||
use_claude_for_extract?: boolean;
|
||||
}
|
||||
interface DaemonCapabilities {
|
||||
has_claude: boolean;
|
||||
daemon_version?: string;
|
||||
os?: string;
|
||||
}
|
||||
|
||||
function aiConfigKey(env: Bindings): string { return `${portalTenant(env)}:portal:ai_config`; }
|
||||
function daemonCapsKey(env: Bindings): string { return `${portalTenant(env)}:portal:daemon_caps`; }
|
||||
|
||||
async function getAiConfig(env: Bindings): Promise<AiConfig | null> {
|
||||
const raw = await env.WEBHOOKS.get(aiConfigKey(env), 'text');
|
||||
if (!raw) return null;
|
||||
try { return JSON.parse(raw) as AiConfig; } catch { return null; }
|
||||
}
|
||||
async function getDaemonCaps(env: Bindings): Promise<DaemonCapabilities | null> {
|
||||
const raw = await env.WEBHOOKS.get(daemonCapsKey(env), 'text');
|
||||
if (!raw) return null;
|
||||
try { return JSON.parse(raw) as DaemonCapabilities; } catch { return null; }
|
||||
}
|
||||
|
||||
// 將 ai_config 同步回 extractor_config（daemon/config 讀 extractor_config，保持相容）。
|
||||
async function syncExtractorFromAiConfig(env: Bindings, cfg: AiConfig): Promise<void> {
|
||||
const exCfg: ExtractorConfig = {
|
||||
engine: cfg.use_claude_for_extract ? 'claude' : 'gemma',
|
||||
};
|
||||
if (!cfg.use_claude_for_extract && cfg.gemini_api_key) {
|
||||
exCfg.gemini_api_key = cfg.gemini_api_key;
|
||||
}
|
||||
await env.WEBHOOKS.put(extractorConfigKey(env), JSON.stringify(exCfg));
|
||||
}
|
||||
|
||||
// POST /portal/admin/ai — body {gemini_api_key?, use_claude_for_extract?}（t131）。
|
||||
// 同時設定 AI 問答金鑰（chat）與萃取引擎（extractor）。admin 閘。
|
||||
portalRouter.post('/portal/admin/ai', (c) =>
|
||||
run(c, async () => {
|
||||
const auth = await requirePortalAdmin(c);
|
||||
if (!auth.ok) return auth.res;
|
||||
const body = (await c.req.json().catch(() => null)) as { gemini_api_key?: string; use_claude_for_extract?: boolean } | null;
|
||||
const newKey = String(body?.gemini_api_key ?? '').trim();
|
||||
const useClause = typeof body?.use_claude_for_extract === 'boolean' ? body.use_claude_for_extract : undefined;
|
||||
|
||||
// 讀現有設定做合併（留空欄位＝不變更）
|
||||
const existing = await getAiConfig(c.env) ?? {};
|
||||
const merged: AiConfig = {
|
||||
gemini_api_key: newKey || existing.gemini_api_key,
|
||||
use_claude_for_extract: useClause !== undefined ? useClause : (existing.use_claude_for_extract ?? false),
|
||||
};
|
||||
if (!merged.gemini_api_key) return c.json({ error: '請貼上你的 Gemini API Key' }, 400);
|
||||
|
||||
// 更新 chat（rag_chat workflow）——容忍 404（workflow 未安裝時暫存，安裝後再寫入）
|
||||
if (newKey) {
|
||||
const tenant = portalTenant(c.env);
|
||||
const kvKey = `${tenant}:wf:rag_chat`;
|
||||
const raw = await c.env.WEBHOOKS.get(kvKey, 'text');
|
||||
if (raw) {
|
||||
try {
|
||||
const record = JSON.parse(raw) as Record<string, unknown>;
|
||||
const visit = (o: unknown): void => {
|
||||
if (Array.isArray(o)) { o.forEach(visit); return; }
|
||||
if (o && typeof o === 'object') {
|
||||
const rec = o as Record<string, unknown>;
|
||||
for (const k of Object.keys(rec)) {
|
||||
if (k.toLowerCase() === 'x-goog-api-key') { rec[k] = newKey; }
|
||||
else visit(rec[k]);
|
||||
}
|
||||
}
|
||||
};
|
||||
visit(record['graph']);
|
||||
visit(record['config']);
|
||||
await c.env.WEBHOOKS.put(kvKey, JSON.stringify(record));
|
||||
} catch { /* 工作流記錄損壞時靜默略過，金鑰仍存 ai_config */ }
|
||||
}
|
||||
// 若 rag_chat 不存在（raw===null），跳過，等 acr init 安裝後再用舊 chat-key 端點補入
|
||||
}
|
||||
|
||||
// 存合併設定
|
||||
await c.env.WEBHOOKS.put(aiConfigKey(c.env), JSON.stringify(merged));
|
||||
// 同步回 extractor_config（daemon/config 走這個）
|
||||
await syncExtractorFromAiConfig(c.env, merged);
|
||||
|
||||
return c.json({
|
||||
success: true,
|
||||
has_key: true,
|
||||
use_claude_for_extract: merged.use_claude_for_extract ?? false,
|
||||
});
|
||||
}),
|
||||
);
|
||||
|
||||
// GET /portal/admin/ai — 回 has_key/use_claude_for_extract/claude_available（t131）。
|
||||
portalRouter.get('/portal/admin/ai', (c) =>
|
||||
run(c, async () => {
|
||||
const auth = await requirePortalAdmin(c);
|
||||
if (!auth.ok) return auth.res;
|
||||
const cfg = await getAiConfig(c.env);
|
||||
const caps = await getDaemonCaps(c.env);
|
||||
return c.json({
|
||||
success: true,
|
||||
has_key: !!(cfg?.gemini_api_key),
|
||||
use_claude_for_extract: cfg?.use_claude_for_extract ?? false,
|
||||
claude_available: caps?.has_claude ?? false,
|
||||
});
|
||||
}),
|
||||
);
|
||||
|
||||
// POST /portal/daemon/report-capabilities — body {email, password, has_claude, daemon_version?, os?}（t131）。
|
||||
// daemon 連線成功後回報本機能力；認證同 /portal/daemon/config（帳密）。
|
||||
// ⚠️ daemon 端改動屬 arcrun-rag repo，本端只做「收端點＋存 KV＋供 GET /portal/admin/ai 用」。
|
||||
portalRouter.post('/portal/daemon/report-capabilities', (c) =>
|
||||
run(c, async () => {
|
||||
const body = (await c.req.json().catch(() => null)) as { email?: string; password?: string; has_claude?: boolean; daemon_version?: string; os?: string } | null;
|
||||
const email = String(body?.email ?? '').trim().toLowerCase();
|
||||
const password = String(body?.password ?? '');
|
||||
if (!email || !password) return c.json({ error: 'email 與 password 必填' }, 400);
|
||||
if (await isLocked(c.env, email)) return c.json({ error: '登入失敗次數過多', }, 429);
|
||||
const recordId = await findUserRecordId(c.env, email);
|
||||
const rec = recordId ? await getRecordById(c.env, recordId) : null;
|
||||
if (!rec) { await recordLoginFail(c.env, email); return c.json({ error: 'email 或密碼錯誤' }, 401); }
|
||||
if ((rec.values.status ?? '') !== 'active') return c.json({ error: '帳號已停用' }, 403);
|
||||
if (!(await verifyPassword(password, rec.values.password_hash ?? ''))) {
|
||||
await recordLoginFail(c.env, email); return c.json({ error: 'email 或密碼錯誤' }, 401);
|
||||
}
|
||||
await clearLoginFail(c.env, email);
|
||||
const caps: DaemonCapabilities = {
|
||||
has_claude: body?.has_claude === true,
|
||||
...(body?.daemon_version ? { daemon_version: String(body.daemon_version) } : {}),
|
||||
...(body?.os ? { os: String(body.os) } : {}),
|
||||
};
|
||||
const TTL_7D = 7 * 24 * 60 * 60;
|
||||
await c.env.WEBHOOKS.put(daemonCapsKey(c.env), JSON.stringify(caps), { expirationTtl: TTL_7D });
|
||||
return c.json({ success: true });
|
||||
}),
|
||||
);
|
||||
// t176：`POST /portal/daemon/report-capabilities` 已移除。
|
||||
// 它的用途是收 daemon 回報的 has_claude 去解鎖 portal 的 Claude 勾選框；
|
||||
// 但 daemon 端從未實作這個呼叫（arcrun-rag 全庫 grep = 0 命中），
|
||||
// 導致 daemon_caps KV 永遠空、勾選框恆 disabled。t176 起地端模型由小幫手自己設，
|
||||
// 這條回報鏈整條不需要了。
|
||||
|
||||
// POST /portal/admin/chat-key — body {key}。保留舊端點相容（新 UI 走 /portal/admin/ai）。
|
||||
// 舊版 setup checklist / 舊 UI 仍走這裡；只更新 rag_chat workflow，不同步 ai_config。
|
||||
@@ -1087,45 +951,11 @@ portalRouter.post('/portal/admin/chat-key', (c) =>
|
||||
}),
|
||||
);
|
||||
|
||||
// POST /portal/admin/extractor — body {engine, gemini_api_key?, llm_model?}（t122）。
|
||||
// 保留舊端點相容（新 UI 走 /portal/admin/ai）。
|
||||
// admin 閘（同 chat-key 等級）。金鑰不落 log；存 WEBHOOKS KV。
|
||||
portalRouter.post('/portal/admin/extractor', (c) =>
|
||||
run(c, async () => {
|
||||
const auth = await requirePortalAdmin(c);
|
||||
if (!auth.ok) return auth.res;
|
||||
const body = (await c.req.json().catch(() => null)) as { engine?: string; gemini_api_key?: string; llm_model?: string } | null;
|
||||
const engine = String(body?.engine ?? '').trim().toLowerCase();
|
||||
if (engine !== 'gemma' && engine !== 'claude') {
|
||||
return c.json({ error: 'engine 只能是 gemma 或 claude' }, 400);
|
||||
}
|
||||
const cfg: ExtractorConfig = { engine: engine as 'gemma' | 'claude' };
|
||||
if (engine === 'gemma') {
|
||||
const key = String(body?.gemini_api_key ?? '').trim();
|
||||
if (key) cfg.gemini_api_key = key;
|
||||
}
|
||||
const model = String(body?.llm_model ?? '').trim();
|
||||
if (model) cfg.llm_model = model;
|
||||
await c.env.WEBHOOKS.put(extractorConfigKey(c.env), JSON.stringify(cfg));
|
||||
return c.json({ success: true, engine: cfg.engine, has_key: engine === 'gemma' && !!cfg.gemini_api_key });
|
||||
}),
|
||||
);
|
||||
|
||||
// GET /portal/admin/extractor — 回 engine + has_key（不回金鑰明文）（t122）。
|
||||
// 保留舊端點相容（新 UI 走 /portal/admin/ai）。
|
||||
portalRouter.get('/portal/admin/extractor', (c) =>
|
||||
run(c, async () => {
|
||||
const auth = await requirePortalAdmin(c);
|
||||
if (!auth.ok) return auth.res;
|
||||
const cfg = await getExtractorConfig(c.env);
|
||||
return c.json({
|
||||
success: true,
|
||||
engine: cfg?.engine ?? 'gemma',
|
||||
has_key: cfg?.engine === 'gemma' && !!cfg?.gemini_api_key,
|
||||
llm_model: cfg?.llm_model ?? null,
|
||||
});
|
||||
}),
|
||||
);
|
||||
// t176：`POST|GET /portal/admin/extractor`（t122）已移除。
|
||||
// 這是「雲端指定地端萃取引擎」的舊入口，且**沒有任何伺服器端驗證**——
|
||||
// 只要打這條就能把 extractor_config 設成 claude，而那把 KV 全租戶共用
|
||||
// ⇒ 所有沒裝 Claude Code 的機器萃取全滅（08-03 事故）。
|
||||
// 地端模型現由同步小幫手托盤「AI 設定…」自己設，雲端不再有這個概念。
|
||||
|
||||
// GET /portal/admin/libraries — 庫目錄列表。
|
||||
// t52（leo 2026-07-26：「地端 2 個資料夾、雲端就要 2 個庫，只有一個一定被罵」）：
|
||||
@@ -1201,41 +1031,67 @@ portalRouter.get('/portal/admin/libraries', (c) =>
|
||||
}),
|
||||
);
|
||||
|
||||
// POST /portal/admin/libraries — 登記一個庫。body {name, display_name?, description?}。
|
||||
portalRouter.post('/portal/admin/libraries', (c) =>
|
||||
// t160（leo 07-31：「要直通 daemon，同步，**沒有登記這回事**」）：
|
||||
// 人工建庫端點 POST /portal/admin/libraries 已刪——庫只從 daemon 同步自動出現
|
||||
// （/portal/daemon/libraries，t159）。現行 UI（e28e190 起）本就零呼叫此端點（死端點）；
|
||||
// 人工登記只會製造對不上的空庫（07-27 leo 拿掉表單時已定調）。
|
||||
// GET 列表與 PATCH（管理已存在的庫：改名/停用/graph_source）照舊。
|
||||
|
||||
// POST /portal/daemon/libraries — 小幫手（daemon）連線精靈時把看守資料夾的庫報上來自動登記。
|
||||
// t159（2026-07-31 leo prod 實走揪出）：daemon registerLibraries（arcrun-tray main.go:505，t52）
|
||||
// 一直在打這個端點，但 cypher 從來沒有它 ⇒ 404 被 daemon「失敗不擋連線」靜默吞掉
|
||||
// ⇒ portal_library 登記簿永遠空 ⇒ portal「庫目錄管理」空（資料同步倒是全正常——
|
||||
// triplet records 的 library slot 都在，病只在登記簿沒人寫）。
|
||||
// 契約照 daemon 既有呼叫：body {email, password, libraries:[{name, display_name}]}。
|
||||
// 帳密驗證＝與 /portal/session 同一套（daemon 只在精靈那一刻拿到帳密，不存）。
|
||||
// 冪等：已登記（同 name）跳過——重跑精靈不堆重複。
|
||||
portalRouter.post('/portal/daemon/libraries', (c) =>
|
||||
run(c, async () => {
|
||||
const auth = await requirePortalAdmin(c);
|
||||
if (!auth.ok) return auth.res;
|
||||
const body = await c.req.json().catch(() => null);
|
||||
const name = String(body?.name ?? '').trim();
|
||||
if (!isValidLibraryName(name) || name === '*') {
|
||||
return c.json({ error: '庫名限 A-Za-z0-9_-（1-64 字元；"*" 是保留值不可登記）' }, 400);
|
||||
const email = String(body?.email ?? '').trim().toLowerCase();
|
||||
const password = String(body?.password ?? '');
|
||||
if (!email || !password) return c.json({ error: 'email 與 password 必填' }, 400);
|
||||
const items = Array.isArray(body?.libraries) ? body.libraries : [];
|
||||
if (items.length === 0) return c.json({ success: true, registered: [], skipped: [] });
|
||||
|
||||
// 帳密驗證（沿用 /portal/session 的鎖定與驗證機制）
|
||||
if (await isLocked(c.env, email)) return c.json({ error: '登入失敗次數過多，請稍後再試' }, 429);
|
||||
const recordId = await findUserRecordId(c.env, email);
|
||||
const rec = recordId ? await getRecordById(c.env, recordId) : null;
|
||||
if (!rec || (rec.values.status ?? '') !== 'active'
|
||||
|| !(await verifyPassword(password, rec.values.password_hash ?? ''))) {
|
||||
await recordLoginFail(c.env, email);
|
||||
return c.json({ error: 'email 或密碼錯誤' }, 401);
|
||||
}
|
||||
await clearLoginFail(c.env, email);
|
||||
|
||||
const seeded = await ensurePortalTemplates(c.env);
|
||||
if (seeded.errors.length > 0) {
|
||||
return c.json({ error: `portal templates seed 失敗：${seeded.errors.join('; ')}` }, 502);
|
||||
}
|
||||
const existing = await listRecordsByTemplate(c.env, LIBRARY_TEMPLATE);
|
||||
if (existing.some((l) => (l.values.name ?? '') === name)) {
|
||||
return c.json({ error: `庫 ${name} 已登記` }, 409);
|
||||
}
|
||||
const have = new Set(existing.map((l) => l.values.name ?? ''));
|
||||
const ns = portalNamespace(c.env);
|
||||
const res = await kbdbFetch(c.env, '/records', {
|
||||
method: 'POST',
|
||||
body: JSON.stringify({
|
||||
template: LIBRARY_TEMPLATE,
|
||||
owner_id: ns,
|
||||
values: {
|
||||
name,
|
||||
display_name: String(body?.display_name ?? '').trim() || name,
|
||||
description: String(body?.description ?? '').trim(),
|
||||
status: 'active',
|
||||
},
|
||||
}),
|
||||
});
|
||||
if (!res.ok) throw new KbdbError(`POST /records（portal_library）→ ${res.status}`);
|
||||
const created = (await res.json()) as { record?: PortalRecord };
|
||||
return c.json({ success: true, library: created.record ? toPublicLibrary(created.record) : { name } });
|
||||
const registered: string[] = [];
|
||||
const skipped: string[] = [];
|
||||
for (const it of items) {
|
||||
const name = String(it?.name ?? '').trim();
|
||||
const displayName = String(it?.display_name ?? '').trim() || name;
|
||||
if (!isValidLibraryName(name) || name === '*') { skipped.push(name || '(空)'); continue; }
|
||||
if (have.has(name)) { skipped.push(name); continue; }
|
||||
const res = await kbdbFetch(c.env, '/records', {
|
||||
method: 'POST',
|
||||
body: JSON.stringify({
|
||||
template: LIBRARY_TEMPLATE,
|
||||
owner_id: ns,
|
||||
values: { name, display_name: displayName, description: '', status: 'active' },
|
||||
}),
|
||||
});
|
||||
if (!res.ok) throw new KbdbError(`POST /records（portal_library，daemon 登記）→ ${res.status}`);
|
||||
have.add(name);
|
||||
registered.push(name);
|
||||
}
|
||||
return c.json({ success: true, registered, skipped });
|
||||
}),
|
||||
);
|
||||
|
||||
@@ -1275,6 +1131,48 @@ portalRouter.patch('/portal/admin/libraries/:id', (c) =>
|
||||
}),
|
||||
);
|
||||
|
||||
// ── AI 設定（arcrun-rag#10）────────────────────────────────────────────────────
|
||||
//
|
||||
// 🔴 為什麼這段存在（2026-08-01 真因，別再讓它消失）：
|
||||
// 前端設定頁**一直**在打 `GET|POST /portal/admin/ai`，但**後端從來沒有這條 route**
|
||||
// ⇒ 用戶填 Gemini key → 404 → **key 從來沒被存進任何地方**，畫面卻像存好了（藍字＝假綠）。
|
||||
// leo 實撞成「重裝後 key 不見」，但真相是「從來沒存進去，所以重填也沒用」。
|
||||
// 產物層鐵證：bundle tier2/ui grep 'portal/admin/ai'=1、tier2/cypher=0。
|
||||
//
|
||||
// 設計約束：
|
||||
// - **不另造第二套儲存**：POST 內部轉呼 credentials.ts 既有的 `storeCredential()`
|
||||
// （唯一寫入路徑＝Workers Secret 明文 + D1 目錄列 ref）。
|
||||
// - **永不回傳 key 本身**（D36）：GET 只回 `has_key` 布林。
|
||||
// - credential 的 `api_key` 欄＝租戶 slug（`portalTenant`），與安裝器 seedCredential
|
||||
// 寫 `kbdb_internal_token` 用的 ns 同一個值 ⇒ 兩者落在同一租戶分區，查得到彼此。
|
||||
// - t176：Claude 偏好（use_claude_for_extract／claude_available）已整組移除——
|
||||
// 地端用哪個模型由同步小幫手自己設，雲端不再有這個概念。這裡只管 Gemini 金鑰。
|
||||
|
||||
// GET /portal/admin/ai — 回 AI 設定現況（role=admin 閘）。**只回 has_key 布林，永不回 key**。
|
||||
portalRouter.get('/portal/admin/ai', (c) =>
|
||||
run(c, async () => {
|
||||
const auth = await requirePortalAdmin(c);
|
||||
if (!auth.ok) return auth.res;
|
||||
|
||||
const tenantSlug = portalTenant(c.env);
|
||||
let hasKey = false;
|
||||
try {
|
||||
const row = await c.env.CREDENTIALS_DB
|
||||
.prepare('SELECT 1 FROM credentials WHERE api_key = ? AND name = ? LIMIT 1')
|
||||
.bind(tenantSlug, 'gemini_api_key')
|
||||
.first();
|
||||
hasKey = !!row;
|
||||
} catch {
|
||||
// D1 未就緒 ⇒ 當作沒設定（不擋頁面），但也不假裝有
|
||||
hasKey = false;
|
||||
}
|
||||
|
||||
// t176：不再有 claude_available／use_claude_for_extract——地端用哪個模型
|
||||
// 由同步小幫手自己設，雲端不介入（leo 08-03）。
|
||||
return c.json({ success: true, has_key: hasKey });
|
||||
}),
|
||||
);
|
||||
|
||||
// DELETE /portal/admin/libraries/by-name/:name — 移除 auto 庫（只有資料章記、無登記簿 record）。
|
||||
// 語意：把該庫的所有 entries 標 deprecated → 資料不刪、重新 ingest 可還原。
|
||||
// ⚠️ 影響資料可搜性，要求 body.confirm 等於庫名才執行（二次確認）。
|
||||
@@ -1303,6 +1201,35 @@ portalRouter.delete('/portal/admin/libraries/by-name/:name', (c) =>
|
||||
}),
|
||||
);
|
||||
|
||||
// POST /portal/admin/ai — 存 Gemini key（role=admin 閘）。body: { gemini_api_key: string }
|
||||
// t176：Claude 偏好欄位已移除（地端模型由同步小幫手自己設，雲端不下發）。
|
||||
portalRouter.post('/portal/admin/ai', (c) =>
|
||||
run(c, async () => {
|
||||
const auth = await requirePortalAdmin(c);
|
||||
if (!auth.ok) return auth.res;
|
||||
|
||||
const body = await c.req.json().catch(() => null) as { gemini_api_key?: unknown } | null;
|
||||
const rawKey = typeof body?.gemini_api_key === 'string' ? body.gemini_api_key.trim() : '';
|
||||
if (!rawKey) {
|
||||
return c.json({ error: '沒有要變更的項目（金鑰留空）' }, 400);
|
||||
}
|
||||
|
||||
const tenantSlug = portalTenant(c.env);
|
||||
try {
|
||||
// 唯一寫入路徑（credentials.ts）：Workers Secret 存值 + D1 存 ref。
|
||||
await storeCredential(c.env, tenantSlug, 'gemini_api_key', rawKey, 'gemini');
|
||||
} catch (e) {
|
||||
// 誠實回報寫入失敗——這正是本 bug 的教訓：不能讓前端以為存好了。
|
||||
return c.json(
|
||||
{ error: `金鑰儲存失敗：${e instanceof Error ? e.message : String(e)}` },
|
||||
502,
|
||||
);
|
||||
}
|
||||
|
||||
return c.json({ success: true, has_key: true });
|
||||
}),
|
||||
);
|
||||
|
||||
// DELETE /portal/admin/libraries/:id — 移除已登記庫（有 record_id 的登記簿 record）。
|
||||
// 只刪登記簿那筆 record；知識資料（entries with library=name）完全不動。
|
||||
// 資料若有的話，重新同步後會以 auto 庫重新出現。
|
||||
|
||||
@@ -16,6 +16,7 @@
|
||||
import { Hono } from 'hono';
|
||||
import type { Bindings } from '../types';
|
||||
import { deriveRecipeHash } from '../lib/hash';
|
||||
import type { ResponseMap } from '../lib/recipe-payload';
|
||||
|
||||
export const recipesRouter = new Hono<{ Bindings: Bindings }>();
|
||||
|
||||
@@ -34,6 +35,26 @@ export interface RecipeDefinition {
|
||||
method?: string; // GET | POST | PUT | PATCH | DELETE，預設 POST
|
||||
headers?: Record<string, string>;
|
||||
body?: Record<string, unknown>;
|
||||
/**
|
||||
* ③ payload 層（SDD workflow-discovery 3.12）：帶 body 的 API 把 payload 收回 recipe，
|
||||
* 不必寫進 workflow code。與 `body` 的差別＝支援巢狀 {{var}} 與 dot path、
|
||||
* 單一引用保留原型別。兩者並存時 body_template 優先（新欄位贏，舊 recipe 不受影響）。
|
||||
*/
|
||||
body_template?: Record<string, unknown>;
|
||||
/**
|
||||
* ③ 回應正規化層：各家 API 回應形狀不同（Gemini／Claude／Workers AI），
|
||||
* 取值路徑・思考型模型旗標・淨化規則**隨 recipe 走** ⇒ 換源＝換 recipe，不必改 workflow。
|
||||
* 未設＝原樣回傳（既有 recipe 行為零變化）。
|
||||
*/
|
||||
response_map?: ResponseMap;
|
||||
/**
|
||||
* 認證型別。未設＝沿用既有 auth_service 判斷（向後相容）。
|
||||
* `binding`＝**免金鑰**，用平台內建能力（env.AI／VECTORIZE／BROWSER／QUEUE），
|
||||
* 不是為 Workers AI 開特例——Cloudflare 這一整類都被舊抽象（只認 HTTP+金鑰）排除在外。
|
||||
*/
|
||||
auth?: 'static_key' | 'service_account' | 'oauth2' | 'binding';
|
||||
/** auth='binding' 時指定用哪個 binding（例 'AI'／'VECTORIZE'）。 */
|
||||
binding_name?: string;
|
||||
/**
|
||||
* 此 recipe 要用哪個 auth recipe（auth_recipe:{auth_service}）。
|
||||
* 讓多個 recipe 共用同一把 auth（例：kbdb_get / kbdb_create_block 都設 "kbdb"）。
|
||||
@@ -116,6 +137,11 @@ recipesRouter.post('/recipes', async (c) => {
|
||||
method: (body.method ?? 'POST').toUpperCase(),
|
||||
headers: body.headers,
|
||||
body: body.body,
|
||||
// ③ payload/回應/binding 三層（3.12）：全選填，沒給就是 undefined＝既有行為
|
||||
body_template: body.body_template,
|
||||
response_map: body.response_map,
|
||||
auth: body.auth,
|
||||
binding_name: body.binding_name,
|
||||
auth_service: body.auth_service,
|
||||
credentials_required: body.credentials_required,
|
||||
created_at: existing?.created_at ?? now,
|
||||
|
||||
@@ -30,6 +30,7 @@ import type { GraphNode } from '../types';
|
||||
import { extractCronExpr } from '../lib/cron-match';
|
||||
import { updateCronIndexEntry, CRON_INDEX_KEY } from '../lib/cron-index';
|
||||
import { recordTelemetry } from '../lib/telemetry';
|
||||
import { fetchTenantWorkflowSearch } from '../lib/workflow-search';
|
||||
|
||||
export const webhooksNamedRouter = new Hono<{ Bindings: Bindings }>();
|
||||
|
||||
@@ -177,16 +178,9 @@ webhooksNamedRouter.get('/workflows/search', async (c) => {
|
||||
// 預設優先語意；caller 傳 mode=keyword 才強制關鍵字。KBDB 端未開 Vectorize 會自動降級。
|
||||
const mode = c.req.query('mode') === 'keyword' ? 'keyword' : 'semantic';
|
||||
|
||||
const base = (c.env.KBDB_BASE_URL ?? 'https://arcrun-kbdb.uncle6-me.workers.dev').replace(/\/$/, '');
|
||||
const headers: Record<string, string> = { 'Content-Type': 'application/json' };
|
||||
if (c.env.KBDB_INTERNAL_TOKEN) headers['Authorization'] = `Bearer ${c.env.KBDB_INTERNAL_TOKEN}`;
|
||||
const params = new URLSearchParams({
|
||||
q,
|
||||
owner_id: apiKey, // 租戶隔離（只搜本租戶的 workflow）
|
||||
entry_type: 'workflow', // base 通用 filter（Q4），只回 workflow entry
|
||||
mode,
|
||||
});
|
||||
const res = await fetch(`${base}/entries/search?${params.toString()}`, { headers });
|
||||
// KBDB 轉發抽到 lib/workflow-search.ts（t159 target 參數）：本路由與
|
||||
// POST /cypher/search { target:"workflow" } 共用同一條路，行為必然一致。
|
||||
const res = await fetchTenantWorkflowSearch(c.env, apiKey, q, mode);
|
||||
return new Response(res.body, { status: res.status, headers: { 'Content-Type': 'application/json' } });
|
||||
});
|
||||
|
||||
@@ -471,6 +465,28 @@ webhooksNamedRouter.get('/q/:ns/:name', async (c) => {
|
||||
return queryNamed(c, c.req.param('ns'), c.req.param('name'), queryStringContext(c));
|
||||
});
|
||||
|
||||
// GET /webhooks/named/:name/definition — 吐 workflow 的可攜定義（t158 export 原語）。
|
||||
// leo 07-31：「如果我要把我做的工作流分享給同事，我要怎麼 export？他要如何 import？
|
||||
// 在從前就是寫成幾個 yaml 丟過去讓新的送進 KBDB 不是嗎？」
|
||||
// 回 record 原樣（graph＋config＋description）＝import 端可直接 POST /webhooks/named 送進
|
||||
// 任何實例（acr workflow import／安裝器同一條路）。執行語義不驗證（部署≠發現）。
|
||||
webhooksNamedRouter.get('/webhooks/named/:name/definition', async (c) => {
|
||||
const apiKey = c.req.header('X-Arcrun-API-Key');
|
||||
if (!apiKey) return c.json({ error: '缺少 X-Arcrun-API-Key header' }, 401);
|
||||
const name = c.req.param('name');
|
||||
const raw = await c.env.WEBHOOKS.get(kvKey(apiKey, name), 'text');
|
||||
if (!raw) return c.json({ error: `找不到 workflow "${name}"` }, 404);
|
||||
const rec = JSON.parse(raw) as NamedWorkflowRecord;
|
||||
return c.json({
|
||||
name: rec.name,
|
||||
description: rec.description ?? '',
|
||||
graph: rec.graph,
|
||||
config: rec.config ?? {},
|
||||
created_at: rec.created_at ?? '',
|
||||
...(rec.cron_expr ? { cron_expr: rec.cron_expr } : {}),
|
||||
});
|
||||
});
|
||||
|
||||
// GET /webhooks/named — 列出當前 api_key 下所有 workflow
|
||||
webhooksNamedRouter.get('/webhooks/named', async (c) => {
|
||||
const apiKey = c.req.header('X-Arcrun-API-Key');
|
||||
|
||||
@@ -68,6 +68,13 @@ export type Bindings = {
|
||||
// 必填：cypher-executor 用此組出 component worker URL（避開同 zone 自循環死鎖，見 P0 #9）
|
||||
// self-hosted fork 必須改 wrangler.toml [vars] 為自己的帳號 subdomain
|
||||
WORKER_SUBDOMAIN: string;
|
||||
/**
|
||||
* t162：本實例安裝時的 bundle 版本（格式 `YYYY-MM-DD+<commit7>`）。
|
||||
* 由安裝器 deployBundledWorker 注入（worker.js:805），**給 daemon 比對用**——
|
||||
* daemon `/health` 讀不到就恆判「需要更新」（假警報迴圈，leo 07-31 實撞）。
|
||||
* 未注入（本地 dev／舊實例）＝ undefined，/health 省略該欄。
|
||||
*/
|
||||
ARCRUN_BUNDLE_VERSION?: string;
|
||||
// Platform telemetry api_key（可選，wrangler secret）
|
||||
// 對應 SDD .agents/specs/llm-interface/ M1.2
|
||||
// 設了會把 agent-telemetry block 都聚集在 platform_telemetry user_id 下
|
||||
@@ -127,6 +134,10 @@ export type Bindings = {
|
||||
// 未設 → 純文字顯示，行為與現狀一字不變。知識庫 repo 是 private 時點了會要登入——要不要
|
||||
// 設由實例自己決定（demo 知識庫是 public，適用）。
|
||||
PORTAL_SOURCE_WEB_BASE?: string;
|
||||
// 零件 registry worker base URL（可選，非機密）。未設 → 用 WORKER_SUBDOMAIN 現算
|
||||
// https://arcrun-registry.<subdomain>.workers.dev（wasmWorkerUrl 慣例）。
|
||||
// 本地 wrangler dev／self-hosted 把 registry 掛別處時覆蓋（/cypher/search 存在性查詢用）。
|
||||
REGISTRY_BASE_URL?: string;
|
||||
// kbdb-graph-plugin worker base URL（可選）。未設 → 用 WORKER_SUBDOMAIN 現算
|
||||
// https://kbdb-graph-plugin.<subdomain>.workers.dev（該 repo wrangler.toml name 固定）。
|
||||
// console 卡片詳頁「關聯視圖」經 cypher proxy 打它（kbdb-proxy.ts /kbdb/graph/neighbors/:name）。
|
||||
@@ -147,6 +158,7 @@ export type GraphNode = {
|
||||
export type EdgeType =
|
||||
| 'PIPE' | 'IF' | 'FOREACH' | 'CONTINUE' // 現有
|
||||
| 'IS_A' | 'ON_SUCCESS' | 'ON_FAIL' // 執行語意
|
||||
| 'ON_TRUE' | 'ON_FALSE' | 'ON_BRANCH' // 條件語意（SDD workflow-discovery 3.11）
|
||||
| 'ON_CLICK' | 'CALLS_SUBFLOW' // 觸發語意
|
||||
| 'CONTAINS' | 'HAS_STYLE' | 'HAS_BEHAVIOR'; // 結構語意（記錄圖結構，不執行）
|
||||
|
||||
@@ -156,6 +168,8 @@ export type GraphEdge = {
|
||||
type: EdgeType;
|
||||
condition?: string; // IF 的條件表達式
|
||||
iterator?: string; // FOREACH 的迭代變數名
|
||||
/** ON_BRANCH 的具名分支（對應 switch 零件 output 的 data.branch） */
|
||||
branch?: string;
|
||||
};
|
||||
|
||||
export type ExecutionGraph = {
|
||||
|
||||
@@ -0,0 +1,68 @@
|
||||
/**
|
||||
* 逐顆查詢的回應要自我說明分支用法（SDD workflow-discovery 3.11；總管 08-01 抽驗第 3 點）
|
||||
*
|
||||
* 判準（leo／總管一致）：**AI 只看那一顆的回應，就知道怎麼接下一步**——
|
||||
* 不必回頭讀 skill、不必猜。看得到分支說明才算數。
|
||||
*
|
||||
* 取證背景（08-01 prod）：逐顆查 if_control 只回
|
||||
* status/componentId/type/source/input_schema{condition,input}/success_rate/stability
|
||||
* ⇒ **沒有任何欄位說明分支怎麼接** ⇒ 走 n8n 式逐顆查的 AI 只好寫 code。
|
||||
*
|
||||
* 本檔直接驗 `branchHintFor()`（回應裡那個欄位的來源），並把 AI 實際會看到的內容印出來。
|
||||
*/
|
||||
import { describe, it, expect } from 'vitest';
|
||||
import { branchHintFor } from '../src/lib/branch-hints';
|
||||
|
||||
describe('三顆分支零件的查詢回應自帶用法（AI 看一眼就知道怎麼接）', () => {
|
||||
for (const id of ['if_control', 'switch', 'try_catch']) {
|
||||
it(`${id}：回應含 branch_field／branches／edge_types／usage／example`, () => {
|
||||
const hint = branchHintFor(id);
|
||||
expect(hint).toBeDefined();
|
||||
|
||||
// 這一顆會輸出哪個欄位當分支標籤
|
||||
expect(hint!.branch_field).toBe('data.branch');
|
||||
// 接下游要用哪些邊型
|
||||
expect(hint!.edge_types.length).toBeGreaterThan(0);
|
||||
// 一行說明 + 可照抄範例（缺任一個，AI 都得自己猜）
|
||||
expect(hint!.usage.length).toBeGreaterThan(0);
|
||||
expect(hint!.example.length).toBeGreaterThan(0);
|
||||
|
||||
// eslint-disable-next-line no-console
|
||||
console.log(
|
||||
`\n──────── 逐顆查 ${id} 時，AI 會看到的 branch_hint ────────\n` +
|
||||
JSON.stringify(hint, null, 2),
|
||||
);
|
||||
});
|
||||
}
|
||||
|
||||
it('if_control 明說 ON_TRUE／ON_FALSE 兩條邊', () => {
|
||||
const h = branchHintFor('if_control')!;
|
||||
expect(h.edge_types).toContain('ON_TRUE');
|
||||
expect(h.edge_types).toContain('ON_FALSE');
|
||||
expect(h.branches).toEqual(['true', 'false']);
|
||||
// 明說「不需要自己寫 code 判斷」——這句是防腹語術的關鍵
|
||||
expect(h.usage).toContain('不需要自己寫 code');
|
||||
});
|
||||
|
||||
it('switch 明說用 ON_BRANCH 並在邊上標 case 名，且 default 不需特別邊型', () => {
|
||||
const h = branchHintFor('switch')!;
|
||||
expect(h.edge_types).toContain('ON_BRANCH');
|
||||
expect(h.usage).toContain('ON_BRANCH');
|
||||
expect(h.usage).toContain('default_branch');
|
||||
// branches 是動態的（由 cases 決定），要誠實說明而非給死清單
|
||||
expect(typeof h.branches).toBe('string');
|
||||
});
|
||||
|
||||
it('try_catch 明說 try／catch 兩條標籤，錯誤處理不必寫 code', () => {
|
||||
const h = branchHintFor('try_catch')!;
|
||||
expect(h.branches).toEqual(['try', 'catch']);
|
||||
expect(h.edge_types).toContain('ON_BRANCH');
|
||||
expect(h.usage).toContain('不需要寫 code');
|
||||
});
|
||||
|
||||
it('不分岔的零件沒有 branch_hint（不加噪音）', () => {
|
||||
expect(branchHintFor('http_request')).toBeUndefined();
|
||||
expect(branchHintFor('code')).toBeUndefined();
|
||||
expect(branchHintFor(undefined)).toBeUndefined();
|
||||
});
|
||||
});
|
||||
@@ -0,0 +1,147 @@
|
||||
/**
|
||||
* 三型分支零件「真的接上引擎」的實測（SDD workflow-discovery 3.11）
|
||||
*
|
||||
* 為什麼要另立這一支（總管 08-01 抽驗要求，正確的要求）：
|
||||
* conditional-edges.test.ts 是用 Input 節點**手餵分支形狀**測引擎走邊邏輯，
|
||||
* 那證明的是「引擎依標籤選邊」，**沒有證明「真零件吐出來的標籤真的對得上」**。
|
||||
* leo 特別點名 switch／try_catch，且 `ON_CASE`／`ON_CATCH` grep=0
|
||||
* ⇒ 必須排除「機制通用所以理論上支援」這種推論。
|
||||
*
|
||||
* 本檔的 given 全部是**真 WASM 零件的實跑輸出**（wasmtime 執行 .component-builds/*.wasm
|
||||
* 抓回來的原文，非杜撰），再送進引擎驗證走對邊。
|
||||
*
|
||||
* 真零件實跑指令（可復驗）：
|
||||
* cd .component-builds
|
||||
* echo '{"condition":"status == active","input":{"status":"active"}}' | wasmtime if_control/component.wasm
|
||||
* echo '{"value":"pending","cases":[...],"default_branch":"branch_default"}' | wasmtime switch/component.wasm
|
||||
* echo '{"result":null,"error":"boom"}' | wasmtime try_catch/component.wasm
|
||||
*/
|
||||
import { SELF } from 'cloudflare:test';
|
||||
import { describe, it, expect } from 'vitest';
|
||||
|
||||
async function run(graph: unknown) {
|
||||
const res = await SELF.fetch('http://localhost/execute', {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify({ graph, context: {} }),
|
||||
});
|
||||
const body = (await res.json()) as {
|
||||
success: boolean;
|
||||
trace?: Array<{ nodeId: string }>;
|
||||
};
|
||||
return { body, visited: (body.trace ?? []).map(t => t.nodeId) };
|
||||
}
|
||||
|
||||
/** 真零件輸出 → 當作上游節點的 output 餵進圖 */
|
||||
function graphWith(realOutput: unknown, edges: Array<Record<string, unknown>>, extraNodes: string[]) {
|
||||
return {
|
||||
id: 'real-branch',
|
||||
name: '真零件輸出走邊',
|
||||
nodes: [
|
||||
{ id: 'ctrl', type: 'Input', data: realOutput },
|
||||
...extraNodes.map(id => ({
|
||||
id, type: 'Component', componentId: 'comp_uppercase', data: { text: id },
|
||||
})),
|
||||
],
|
||||
edges,
|
||||
};
|
||||
}
|
||||
|
||||
describe('if_control 真輸出 → 引擎走對邊', () => {
|
||||
// 真跑：echo '{"condition":"status == active","input":{"status":"active"}}' | wasmtime if_control/component.wasm
|
||||
const REAL_TRUE = { data: { branch: 'true', result: true }, success: true };
|
||||
// 真跑：input.status = "inactive"
|
||||
const REAL_FALSE = { data: { branch: 'false', result: false }, success: true };
|
||||
|
||||
const edges = [
|
||||
{ from: 'ctrl', to: 'yes', type: 'ON_TRUE' },
|
||||
{ from: 'ctrl', to: 'no', type: 'ON_FALSE' },
|
||||
];
|
||||
|
||||
it('條件成立（真輸出 branch="true"）→ 走 ON_TRUE', async () => {
|
||||
const { body, visited } = await run(graphWith(REAL_TRUE, edges, ['yes', 'no']));
|
||||
expect(body.success).toBe(true);
|
||||
expect(visited).toContain('yes');
|
||||
expect(visited).not.toContain('no');
|
||||
});
|
||||
|
||||
it('條件不成立（真輸出 branch="false"）→ 走 ON_FALSE', async () => {
|
||||
const { visited } = await run(graphWith(REAL_FALSE, edges, ['yes', 'no']));
|
||||
expect(visited).toContain('no');
|
||||
expect(visited).not.toContain('yes');
|
||||
});
|
||||
});
|
||||
|
||||
describe('switch 真輸出 → 引擎走對邊（多路＋default，leo：「switch 更嚴重」）', () => {
|
||||
// 真跑（三個 case + default_branch）：
|
||||
// value="active" → {"data":{"branch":"branch_active"},"success":true}
|
||||
// value="pending" → {"data":{"branch":"branch_pending"},"success":true}
|
||||
// value="zzz" → {"data":{"branch":"branch_default"},"success":true}
|
||||
const REAL_CASE1 = { data: { branch: 'branch_active' }, success: true };
|
||||
const REAL_CASE3 = { data: { branch: 'branch_pending' }, success: true };
|
||||
const REAL_DEFAULT = { data: { branch: 'branch_default' }, success: true };
|
||||
|
||||
const targets = ['p_active', 'p_inactive', 'p_pending', 'p_default'];
|
||||
const edges = [
|
||||
{ from: 'ctrl', to: 'p_active', type: 'ON_BRANCH', branch: 'branch_active' },
|
||||
{ from: 'ctrl', to: 'p_inactive', type: 'ON_BRANCH', branch: 'branch_inactive' },
|
||||
{ from: 'ctrl', to: 'p_pending', type: 'ON_BRANCH', branch: 'branch_pending' },
|
||||
{ from: 'ctrl', to: 'p_default', type: 'ON_BRANCH', branch: 'branch_default' },
|
||||
];
|
||||
|
||||
it('第 1 條 case（真輸出 branch_active）→ 只走 p_active', async () => {
|
||||
const { body, visited } = await run(graphWith(REAL_CASE1, edges, targets));
|
||||
expect(body.success).toBe(true);
|
||||
expect(visited).toContain('p_active');
|
||||
expect(visited).not.toContain('p_inactive');
|
||||
expect(visited).not.toContain('p_pending');
|
||||
expect(visited).not.toContain('p_default');
|
||||
});
|
||||
|
||||
it('第 3 條 case（真輸出 branch_pending）→ 只走 p_pending（證明第 N 條路走得對）', async () => {
|
||||
const { visited } = await run(graphWith(REAL_CASE3, edges, targets));
|
||||
expect(visited).toContain('p_pending');
|
||||
expect(visited).not.toContain('p_active');
|
||||
expect(visited).not.toContain('p_inactive');
|
||||
expect(visited).not.toContain('p_default');
|
||||
});
|
||||
|
||||
it('無匹配（真輸出 branch_default）→ 只走 p_default', async () => {
|
||||
const { visited } = await run(graphWith(REAL_DEFAULT, edges, targets));
|
||||
expect(visited).toContain('p_default');
|
||||
expect(visited).not.toContain('p_active');
|
||||
expect(visited).not.toContain('p_pending');
|
||||
});
|
||||
});
|
||||
|
||||
describe('try_catch 真輸出 → 引擎走對邊（ok／catch 兩路都驗）', () => {
|
||||
// 真跑：echo '{"result":{"value":42},"error":""}' | wasmtime try_catch/component.wasm
|
||||
const REAL_TRY = { data: { branch: 'try', result: { value: 42 } }, success: true };
|
||||
// 真跑：echo '{"result":null,"error":"boom"}' | wasmtime try_catch/component.wasm
|
||||
const REAL_CATCH = { data: { branch: 'catch', error: 'boom' }, success: true };
|
||||
|
||||
const edges = [
|
||||
{ from: 'ctrl', to: 'normal', type: 'ON_BRANCH', branch: 'try' },
|
||||
{ from: 'ctrl', to: 'rescue', type: 'ON_BRANCH', branch: 'catch' },
|
||||
];
|
||||
|
||||
it('成功（真輸出 branch="try"）→ 走 normal，不走 rescue', async () => {
|
||||
const { body, visited } = await run(graphWith(REAL_TRY, edges, ['normal', 'rescue']));
|
||||
expect(body.success).toBe(true);
|
||||
expect(visited).toContain('normal');
|
||||
expect(visited).not.toContain('rescue');
|
||||
});
|
||||
|
||||
it('失敗（真輸出 branch="catch"）→ 走 rescue，不走 normal', async () => {
|
||||
const { visited } = await run(graphWith(REAL_CATCH, edges, ['normal', 'rescue']));
|
||||
expect(visited).toContain('rescue');
|
||||
expect(visited).not.toContain('normal');
|
||||
});
|
||||
|
||||
it('try_catch 的 catch 路承接了「上游失敗」——不必寫 code try 一遍', async () => {
|
||||
// 這是 leo 點名 try_catch 的原因：schema 用文字寫「走 catch 分支」但機器層沒有那條路。
|
||||
// 現在有了：catch 標籤 → ON_BRANCH branch="catch" → 補救節點。
|
||||
const { visited } = await run(graphWith(REAL_CATCH, edges, ['normal', 'rescue']));
|
||||
expect(visited).toContain('rescue');
|
||||
});
|
||||
});
|
||||
@@ -0,0 +1,304 @@
|
||||
/**
|
||||
* 條件邊 ON_TRUE / ON_FALSE / ON_BRANCH —— CP `arcrun-usable` 步驟 5 缺口①
|
||||
* SDD: workflow-discovery tasks 3.11
|
||||
*
|
||||
* 為什麼要有這組測試（別刪）：
|
||||
* `if_control` 零件回 `{success, data:{result, branch}}`，但引擎過去只有
|
||||
* ON_SUCCESS / IF / FOREACH ⇒ 就算照規矩用 if_control，也只拿到布林值，
|
||||
* 還是得寫 code 判斷該走哪條路 ⇒ 這正是「全變成 code」的根（Arcrun#5）。
|
||||
*
|
||||
* 本檔先寫測試再改引擎（引擎核心風險最高，紅線要求）。
|
||||
* 既有邊行為的零變化迴歸另見 executor.test.ts（PIPE/IF/ON_SUCCESS 原樣通過）。
|
||||
*/
|
||||
import { SELF } from 'cloudflare:test';
|
||||
import { describe, it, expect } from 'vitest';
|
||||
|
||||
/** 送一張圖進 /execute，回 parsed JSON */
|
||||
async function run(graph: unknown, context: Record<string, unknown> = {}) {
|
||||
const res = await SELF.fetch('http://localhost/execute', {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify({ graph, context }),
|
||||
});
|
||||
return {
|
||||
status: res.status,
|
||||
body: (await res.json()) as {
|
||||
success: boolean;
|
||||
data: Record<string, unknown>;
|
||||
trace?: Array<{ nodeId: string }>;
|
||||
error?: string;
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* 用 Input 節點直接餵出 if_control 形狀的 output（{data:{result,branch}}），
|
||||
* 避免測試依賴真的 WASM 零件（單元層只驗「引擎怎麼走邊」）。
|
||||
*/
|
||||
function branchGraph(branch: 'true' | 'false', edges: Array<Record<string, unknown>>) {
|
||||
return {
|
||||
id: `g-branch-${branch}`,
|
||||
name: '條件邊測試',
|
||||
nodes: [
|
||||
// 模擬 if_control 的輸出形狀
|
||||
{ id: 'cond', type: 'Input', data: { success: true, data: { result: branch === 'true', branch } } },
|
||||
{ id: 'yes', type: 'Component', componentId: 'comp_uppercase', data: { text: 'yes' } },
|
||||
{ id: 'no', type: 'Component', componentId: 'comp_uppercase', data: { text: 'no' } },
|
||||
],
|
||||
edges,
|
||||
};
|
||||
}
|
||||
|
||||
describe('條件邊：ON_TRUE / ON_FALSE（缺口① Arcrun#5 根治）', () => {
|
||||
it('branch=true → 只走 ON_TRUE 那條，ON_FALSE 那條不執行', async () => {
|
||||
const { body } = await run(
|
||||
branchGraph('true', [
|
||||
{ from: 'cond', to: 'yes', type: 'ON_TRUE' },
|
||||
{ from: 'cond', to: 'no', type: 'ON_FALSE' },
|
||||
]),
|
||||
);
|
||||
expect(body.success).toBe(true);
|
||||
const visited = (body.trace ?? []).map(t => t.nodeId);
|
||||
expect(visited).toContain('yes');
|
||||
expect(visited).not.toContain('no');
|
||||
});
|
||||
|
||||
it('branch=false → 只走 ON_FALSE 那條，ON_TRUE 那條不執行', async () => {
|
||||
const { body } = await run(
|
||||
branchGraph('false', [
|
||||
{ from: 'cond', to: 'yes', type: 'ON_TRUE' },
|
||||
{ from: 'cond', to: 'no', type: 'ON_FALSE' },
|
||||
]),
|
||||
);
|
||||
expect(body.success).toBe(true);
|
||||
const visited = (body.trace ?? []).map(t => t.nodeId);
|
||||
expect(visited).toContain('no');
|
||||
expect(visited).not.toContain('yes');
|
||||
});
|
||||
|
||||
it('result 是布林但沒有 branch 欄位 → 仍judged得出（相容 {result:true} 形狀）', async () => {
|
||||
const graph = {
|
||||
id: 'g-bool-only',
|
||||
name: '只有 result',
|
||||
nodes: [
|
||||
{ id: 'cond', type: 'Input', data: { result: true } },
|
||||
{ id: 'yes', type: 'Component', componentId: 'comp_uppercase', data: { text: 'yes' } },
|
||||
{ id: 'no', type: 'Component', componentId: 'comp_uppercase', data: { text: 'no' } },
|
||||
],
|
||||
edges: [
|
||||
{ from: 'cond', to: 'yes', type: 'ON_TRUE' },
|
||||
{ from: 'cond', to: 'no', type: 'ON_FALSE' },
|
||||
],
|
||||
};
|
||||
const { body } = await run(graph);
|
||||
const visited = (body.trace ?? []).map(t => t.nodeId);
|
||||
expect(visited).toContain('yes');
|
||||
expect(visited).not.toContain('no');
|
||||
});
|
||||
|
||||
it('條件邊的下游拿得到上游 context（propagateCtx 一致）', async () => {
|
||||
const graph = {
|
||||
id: 'g-ctx',
|
||||
name: 'context 傳遞',
|
||||
nodes: [
|
||||
{ id: 'cond', type: 'Input', data: { data: { result: true, branch: 'true' }, carried: 'keep-me' } },
|
||||
{ id: 'yes', type: 'Component', componentId: 'comp_passthrough' },
|
||||
],
|
||||
edges: [{ from: 'cond', to: 'yes', type: 'ON_TRUE' }],
|
||||
};
|
||||
const { body } = await run(graph);
|
||||
expect(body.success).toBe(true);
|
||||
expect(body.data.carried).toBe('keep-me');
|
||||
});
|
||||
|
||||
it('兩條 ON_TRUE 並存 → 都走（同分支多下游是合法 fan-out）', async () => {
|
||||
const graph = {
|
||||
id: 'g-fanout',
|
||||
name: '同分支多下游',
|
||||
nodes: [
|
||||
{ id: 'cond', type: 'Input', data: { data: { result: true, branch: 'true' } } },
|
||||
{ id: 'a', type: 'Component', componentId: 'comp_uppercase', data: { text: 'a' } },
|
||||
{ id: 'b', type: 'Component', componentId: 'comp_uppercase', data: { text: 'b' } },
|
||||
],
|
||||
edges: [
|
||||
{ from: 'cond', to: 'a', type: 'ON_TRUE' },
|
||||
{ from: 'cond', to: 'b', type: 'ON_TRUE' },
|
||||
],
|
||||
};
|
||||
const { body } = await run(graph);
|
||||
const visited = (body.trace ?? []).map(t => t.nodeId);
|
||||
expect(visited).toContain('a');
|
||||
expect(visited).toContain('b');
|
||||
});
|
||||
});
|
||||
|
||||
describe('條件邊：ON_BRANCH（switch 具名分支）', () => {
|
||||
/** switch 零件回 {success, data:{branch:"branch_a"}} */
|
||||
function switchGraph(branch: string) {
|
||||
return {
|
||||
id: 'g-switch',
|
||||
name: 'switch 具名分支',
|
||||
nodes: [
|
||||
{ id: 'sw', type: 'Input', data: { success: true, data: { branch } } },
|
||||
{ id: 'a', type: 'Component', componentId: 'comp_uppercase', data: { text: 'a' } },
|
||||
{ id: 'z', type: 'Component', componentId: 'comp_uppercase', data: { text: 'z' } },
|
||||
],
|
||||
edges: [
|
||||
{ from: 'sw', to: 'a', type: 'ON_BRANCH', branch: 'branch_a' },
|
||||
{ from: 'sw', to: 'z', type: 'ON_BRANCH', branch: 'fallback' },
|
||||
],
|
||||
};
|
||||
}
|
||||
|
||||
it('branch=branch_a → 只走標 branch_a 的邊', async () => {
|
||||
const { body } = await run(switchGraph('branch_a'));
|
||||
const visited = (body.trace ?? []).map(t => t.nodeId);
|
||||
expect(visited).toContain('a');
|
||||
expect(visited).not.toContain('z');
|
||||
});
|
||||
|
||||
it('branch=fallback → 只走標 fallback 的邊', async () => {
|
||||
const { body } = await run(switchGraph('fallback'));
|
||||
const visited = (body.trace ?? []).map(t => t.nodeId);
|
||||
expect(visited).toContain('z');
|
||||
expect(visited).not.toContain('a');
|
||||
});
|
||||
|
||||
it('沒有任何邊匹配 → 誠實地不走（不亂挑一條，也不報錯）', async () => {
|
||||
const { body } = await run(switchGraph('no_such_branch'));
|
||||
expect(body.success).toBe(true);
|
||||
const visited = (body.trace ?? []).map(t => t.nodeId);
|
||||
expect(visited).not.toContain('a');
|
||||
expect(visited).not.toContain('z');
|
||||
});
|
||||
});
|
||||
|
||||
describe('通用具名分支涵蓋三型零件（leo 08-01：switch 比 if 更嚴重）', () => {
|
||||
/**
|
||||
* 三顆流程控制零件的 output_schema 都收斂到同一個形狀 `data.branch: string`：
|
||||
* if_control → "true" | "false"（布林兩路）
|
||||
* switch → case 的 branch 名 | default_branch（N 路）
|
||||
* try_catch → "try" | "catch"（成功/失敗兩路）
|
||||
* ⇒ 引擎只需要「依標籤選邊」這一個機制，不是為每顆零件開特例。
|
||||
* ON_TRUE / ON_FALSE 只是 if 布林路的語法糖，底層與 ON_BRANCH 同一條路。
|
||||
*/
|
||||
async function branchTo(branch: string, edges: Array<Record<string, unknown>>) {
|
||||
return run({
|
||||
id: `g-generic-${branch}`,
|
||||
name: '通用具名分支',
|
||||
nodes: [
|
||||
{ id: 'ctrl', type: 'Input', data: { success: true, data: { branch } } },
|
||||
{ id: 'p1', type: 'Component', componentId: 'comp_uppercase', data: { text: 'p1' } },
|
||||
{ id: 'p2', type: 'Component', componentId: 'comp_uppercase', data: { text: 'p2' } },
|
||||
{ id: 'p3', type: 'Component', componentId: 'comp_uppercase', data: { text: 'p3' } },
|
||||
],
|
||||
edges,
|
||||
});
|
||||
}
|
||||
|
||||
const threeWay = [
|
||||
{ from: 'ctrl', to: 'p1', type: 'ON_BRANCH', branch: 'branch_active' },
|
||||
{ from: 'ctrl', to: 'p2', type: 'ON_BRANCH', branch: 'branch_inactive' },
|
||||
{ from: 'ctrl', to: 'p3', type: 'ON_BRANCH', branch: 'branch_default' },
|
||||
];
|
||||
|
||||
it('switch 多路：branch_active → 只走第一條，其餘兩條不走', async () => {
|
||||
const { body } = await branchTo('branch_active', threeWay);
|
||||
const visited = (body.trace ?? []).map(t => t.nodeId);
|
||||
expect(visited).toContain('p1');
|
||||
expect(visited).not.toContain('p2');
|
||||
expect(visited).not.toContain('p3');
|
||||
});
|
||||
|
||||
it('switch 多路：branch_inactive → 只走第二條', async () => {
|
||||
const { body } = await branchTo('branch_inactive', threeWay);
|
||||
const visited = (body.trace ?? []).map(t => t.nodeId);
|
||||
expect(visited).toContain('p2');
|
||||
expect(visited).not.toContain('p1');
|
||||
expect(visited).not.toContain('p3');
|
||||
});
|
||||
|
||||
it('switch default：無匹配 case 時零件回 default_branch → 走 default 那條', async () => {
|
||||
// 注意：挑 default 是 switch 零件內部的事（它回 default_branch 名）；
|
||||
// 引擎這層看到的一律是「一個標籤」，故 default 不需要引擎特別處理。
|
||||
const { body } = await branchTo('branch_default', threeWay);
|
||||
const visited = (body.trace ?? []).map(t => t.nodeId);
|
||||
expect(visited).toContain('p3');
|
||||
expect(visited).not.toContain('p1');
|
||||
expect(visited).not.toContain('p2');
|
||||
});
|
||||
|
||||
it('try_catch 成功路：branch=try → 走 try 邊，不走 catch 邊', async () => {
|
||||
const { body } = await branchTo('try', [
|
||||
{ from: 'ctrl', to: 'p1', type: 'ON_BRANCH', branch: 'try' },
|
||||
{ from: 'ctrl', to: 'p2', type: 'ON_BRANCH', branch: 'catch' },
|
||||
]);
|
||||
const visited = (body.trace ?? []).map(t => t.nodeId);
|
||||
expect(visited).toContain('p1');
|
||||
expect(visited).not.toContain('p2');
|
||||
});
|
||||
|
||||
it('try_catch 失敗路：branch=catch → 走 catch 邊，不走 try 邊', async () => {
|
||||
const { body } = await branchTo('catch', [
|
||||
{ from: 'ctrl', to: 'p1', type: 'ON_BRANCH', branch: 'try' },
|
||||
{ from: 'ctrl', to: 'p2', type: 'ON_BRANCH', branch: 'catch' },
|
||||
]);
|
||||
const visited = (body.trace ?? []).map(t => t.nodeId);
|
||||
expect(visited).toContain('p2');
|
||||
expect(visited).not.toContain('p1');
|
||||
});
|
||||
|
||||
it('ON_TRUE 與 ON_BRANCH branch="true" 等價（語法糖，底層同一條路）', async () => {
|
||||
const sugar = await branchTo('true', [{ from: 'ctrl', to: 'p1', type: 'ON_TRUE' }]);
|
||||
const raw = await branchTo('true', [{ from: 'ctrl', to: 'p1', type: 'ON_BRANCH', branch: 'true' }]);
|
||||
const v1 = (sugar.body.trace ?? []).map(t => t.nodeId);
|
||||
const v2 = (raw.body.trace ?? []).map(t => t.nodeId);
|
||||
expect(v1).toEqual(v2);
|
||||
expect(v1).toContain('p1');
|
||||
});
|
||||
});
|
||||
|
||||
describe('零變化保證：新邊型不影響既有邊', () => {
|
||||
it('ON_TRUE 邊存在時，同圖的 PIPE 邊照常走', async () => {
|
||||
const graph = {
|
||||
id: 'g-mixed',
|
||||
name: '混合邊',
|
||||
nodes: [
|
||||
{ id: 'cond', type: 'Input', data: { data: { result: false, branch: 'false' }, count: 0 } },
|
||||
{ id: 'yes', type: 'Component', componentId: 'comp_uppercase', data: { text: 'yes' } },
|
||||
{ id: 'always', type: 'Component', componentId: 'comp_counter' },
|
||||
],
|
||||
edges: [
|
||||
{ from: 'cond', to: 'yes', type: 'ON_TRUE' },
|
||||
{ from: 'cond', to: 'always', type: 'PIPE' },
|
||||
],
|
||||
};
|
||||
const { body } = await run(graph);
|
||||
const visited = (body.trace ?? []).map(t => t.nodeId);
|
||||
expect(visited).not.toContain('yes'); // 條件邊擋掉
|
||||
expect(visited).toContain('always'); // PIPE 不受影響
|
||||
});
|
||||
|
||||
it('/validate 接受 ON_TRUE / ON_FALSE / ON_BRANCH（schema 已放行）', async () => {
|
||||
const res = await SELF.fetch('http://localhost/validate', {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify({
|
||||
id: 'g-validate',
|
||||
name: 'schema 驗證',
|
||||
nodes: [
|
||||
{ id: 'a', type: 'Input' },
|
||||
{ id: 'b', type: 'Output' },
|
||||
{ id: 'c', type: 'Output' },
|
||||
],
|
||||
edges: [
|
||||
{ from: 'a', to: 'b', type: 'ON_TRUE' },
|
||||
{ from: 'a', to: 'c', type: 'ON_FALSE' },
|
||||
],
|
||||
}),
|
||||
});
|
||||
const data = (await res.json()) as { valid: boolean };
|
||||
expect(res.status).toBe(200);
|
||||
expect(data.valid).toBe(true);
|
||||
});
|
||||
});
|
||||
@@ -0,0 +1,99 @@
|
||||
// 單元測試：execution-evaluator — 從 trace 導出每顆零件成敗 + 回寫 registry
|
||||
// SDD: system-dev/docs/3-specs/arcrun-core-mvp/design.md「執行統計設計」
|
||||
|
||||
import { describe, it, expect, vi, afterEach } from 'vitest';
|
||||
import { componentVerdictsFromTrace, recordComponentStats } from '../src/actions/execution-evaluator';
|
||||
import type { GraphNode, TraceStep } from '../src/types';
|
||||
|
||||
const NODES: GraphNode[] = [
|
||||
{ id: 'input', type: 'Input' },
|
||||
{ id: 'fetch', type: 'Component', componentId: 'http_request' },
|
||||
{ id: 'transform', type: 'Component', componentId: 'code' },
|
||||
{ id: 'output', type: 'Output' },
|
||||
];
|
||||
|
||||
function step(nodeId: string, over: Partial<TraceStep> = {}): TraceStep {
|
||||
return { nodeId, type: 'Component', input: {}, output: { ok: true }, duration_ms: 10, ...over };
|
||||
}
|
||||
|
||||
describe('componentVerdictsFromTrace', () => {
|
||||
it('只算 Component 節點；Input/Output 跳過', () => {
|
||||
const verdicts = componentVerdictsFromTrace(NODES, [
|
||||
step('input', { type: 'Input' }),
|
||||
step('fetch'),
|
||||
step('output', { type: 'Output' }),
|
||||
]);
|
||||
expect(verdicts).toEqual([{ component_id: 'http_request', success: true, duration_ms: 10 }]);
|
||||
});
|
||||
|
||||
it('trace 有 error → 該零件記失敗', () => {
|
||||
const verdicts = componentVerdictsFromTrace(NODES, [
|
||||
step('fetch', { error: 'boom', output: null }),
|
||||
]);
|
||||
expect(verdicts).toEqual([{ component_id: 'http_request', success: false, duration_ms: 10 }]);
|
||||
});
|
||||
|
||||
it('output.success === false → 記失敗（makeHttpRunner 對非 2xx 不 throw）', () => {
|
||||
const verdicts = componentVerdictsFromTrace(NODES, [
|
||||
step('fetch', { output: { success: false, status: 500, error: 'oops' } }),
|
||||
]);
|
||||
expect(verdicts[0].success).toBe(false);
|
||||
});
|
||||
|
||||
it('FOREACH 同節點多筆 trace → 每次執行各記一次樣本', () => {
|
||||
const verdicts = componentVerdictsFromTrace(NODES, [
|
||||
step('fetch'),
|
||||
step('fetch', { error: 'x', output: null }),
|
||||
step('fetch'),
|
||||
]);
|
||||
expect(verdicts).toHaveLength(3);
|
||||
expect(verdicts.map(v => v.success)).toEqual([true, false, true]);
|
||||
});
|
||||
});
|
||||
|
||||
describe('recordComponentStats', () => {
|
||||
afterEach(() => vi.unstubAllGlobals());
|
||||
|
||||
it('對每顆零件各發一次 POST /analytics/record（fire-and-forget）', async () => {
|
||||
const calls: Array<{ url: string; body: Record<string, unknown> }> = [];
|
||||
vi.stubGlobal('fetch', vi.fn(async (url: string, init: RequestInit) => {
|
||||
calls.push({ url: String(url), body: JSON.parse(String(init.body)) });
|
||||
return new Response('{}', { status: 200 });
|
||||
}));
|
||||
|
||||
await recordComponentStats(
|
||||
{ REGISTRY_BASE_URL: 'http://registry.local' },
|
||||
NODES,
|
||||
[step('fetch'), step('transform', { error: 'bad', output: null })],
|
||||
);
|
||||
|
||||
expect(calls).toHaveLength(2);
|
||||
expect(calls[0].url).toBe('http://registry.local/analytics/record');
|
||||
expect(calls[0].body).toEqual({ canonical_id: 'http_request', success: true, duration_ms: 10 });
|
||||
expect(calls[1].body).toEqual({ canonical_id: 'code', success: false, duration_ms: 10 });
|
||||
});
|
||||
|
||||
it('registry 打不到也不 throw（統計失敗不影響執行）', async () => {
|
||||
vi.stubGlobal('fetch', vi.fn(async () => { throw new Error('network down'); }));
|
||||
await expect(
|
||||
recordComponentStats({ REGISTRY_BASE_URL: 'http://registry.local' }, NODES, [step('fetch')]),
|
||||
).resolves.toBeUndefined();
|
||||
});
|
||||
|
||||
it('無 REGISTRY_BASE_URL 也無 WORKER_SUBDOMAIN → 靜默略過不打', async () => {
|
||||
const fetchSpy = vi.fn();
|
||||
vi.stubGlobal('fetch', fetchSpy);
|
||||
await recordComponentStats({}, NODES, [step('fetch')]);
|
||||
expect(fetchSpy).not.toHaveBeenCalled();
|
||||
});
|
||||
|
||||
it('未設 REGISTRY_BASE_URL → 用 wasmWorkerUrl 慣例組 registry URL', async () => {
|
||||
const calls: string[] = [];
|
||||
vi.stubGlobal('fetch', vi.fn(async (url: string) => {
|
||||
calls.push(String(url));
|
||||
return new Response('{}', { status: 200 });
|
||||
}));
|
||||
await recordComponentStats({ WORKER_SUBDOMAIN: 'uncle6-me' }, NODES, [step('fetch')]);
|
||||
expect(calls[0]).toBe('https://arcrun-registry.uncle6-me.workers.dev/analytics/record');
|
||||
});
|
||||
});
|
||||
@@ -4,13 +4,15 @@ import { healthRouter } from '../src/routes/health';
|
||||
import type { Bindings, ExecutionContext } from '../src/types';
|
||||
|
||||
describe('GET /health — bundle_version 欄位', () => {
|
||||
it('無 ARCRUN_BUNDLE_VERSION 時回空字串（老實例情境）', async () => {
|
||||
// wrangler.test.toml 不設此 var → 走 ?? '' fallback
|
||||
it('無 ARCRUN_BUNDLE_VERSION 時省略該欄（老實例情境）', async () => {
|
||||
// wrangler.test.toml 不設此 var → health.ts 省略 bundle_version 欄位。
|
||||
// daemon 端讀不到該欄＝當作空字串＝判 stale，對老實例而言**這是正確行為**
|
||||
//（見 health.ts 檔頭註解）。此處驗「省略」而非「回空字串」，與實作對齊。
|
||||
const res = await SELF.fetch('http://localhost/health');
|
||||
const data = await res.json() as { ok: boolean; bundle_version: string };
|
||||
const data = await res.json() as { ok: boolean; bundle_version?: string };
|
||||
expect(res.status).toBe(200);
|
||||
expect(data.ok).toBe(true);
|
||||
expect(data.bundle_version).toBe('');
|
||||
expect(data.bundle_version).toBeUndefined();
|
||||
});
|
||||
|
||||
it('有 ARCRUN_BUNDLE_VERSION 時回其值（安裝器注入情境）', async () => {
|
||||
|
||||
@@ -0,0 +1,67 @@
|
||||
/**
|
||||
* /init/seed 必須把種子的 3.12 三層欄位原樣寫進 KV —— SDD: workflow-discovery task 3.12/3.13
|
||||
*
|
||||
* 為什麼要有這個測試（別刪）：
|
||||
* 3.12 給 `RecipeDefinition` 加了 body_template / response_map / auth / binding_name，
|
||||
* 但 `/init/seed` 當時是**列舉欄位重建** recipe record ⇒ 不在名單上的欄位被靜默吃掉。
|
||||
* 症狀最惡劣的地方在於「哪裡都不會紅」：recipe 查得到、canonical_id 對、endpoint 對，
|
||||
* 只有跑起來像沒設定過（auth 掉了 ⇒ 走 HTTP 路徑去 fetch「@cf/…」這種不是網址的字串）。
|
||||
* 這與 2026-08-02 `syncManifest()` 列舉式重建吃掉 `manifest.daemon` 欄是同一型事故——
|
||||
* 當時的教訓寫著：「**東西還在不在**也要進機械閘」，本檔就是那道閘。
|
||||
*
|
||||
* 範圍：只驗「種子 → KV」這段（純資料搬運）。真的呼叫 Workers AI 由實例端到端驗。
|
||||
*/
|
||||
import { describe, it, expect } from 'vitest';
|
||||
import { env, SELF } from 'cloudflare:test';
|
||||
import { API_RECIPE_SEEDS } from '../src/lib/api-recipe-seeds';
|
||||
|
||||
type StoredRecipe = {
|
||||
canonical_id: string;
|
||||
endpoint: string;
|
||||
auth?: string;
|
||||
binding_name?: string;
|
||||
body_template?: Record<string, unknown>;
|
||||
response_map?: { text_path?: string; answer_marker?: string; strip_prefixes?: string[] };
|
||||
};
|
||||
|
||||
async function seedThenRead(canonicalId: string): Promise<StoredRecipe> {
|
||||
const res = await SELF.fetch('https://example.com/init/seed', { method: 'POST' });
|
||||
// 測試環境沒有 KBDB binding ⇒ portal template 那段必然失敗、整體回 207（誠實回報，非本測目標）。
|
||||
// 本檔只管 API recipe 那半，所以驗它自己的計數，不驗整體 status。
|
||||
const body = await res.json<{ api_recipes: { seeded: number; failed: number; errors: string[] } }>();
|
||||
expect(body.api_recipes.errors).toEqual([]);
|
||||
expect(body.api_recipes.failed).toBe(0);
|
||||
const uuid = await env.RECIPES.get(`idx:installed:${canonicalId}`);
|
||||
expect(uuid, `${canonicalId} 沒有被 seed 進 KV`).toBeTruthy();
|
||||
return JSON.parse((await env.RECIPES.get(`recipe:${uuid}`))!) as StoredRecipe;
|
||||
}
|
||||
|
||||
describe('/init/seed 不得靜默吃掉 recipe 的 3.12 欄位', () => {
|
||||
it('workers_ai_chat 種子本身宣告齊四個欄位（種子端）', () => {
|
||||
const seed = API_RECIPE_SEEDS.find(s => s.canonical_id === 'workers_ai_chat');
|
||||
expect(seed, 'workers_ai_chat 種子不存在＝裝完不會有免金鑰問答').toBeDefined();
|
||||
expect(seed!.auth).toBe('binding');
|
||||
expect(seed!.binding_name).toBe('AI');
|
||||
expect(seed!.endpoint.startsWith('@cf/'), 'binding 型的 endpoint＝模型 id').toBe(true);
|
||||
expect(seed!.body_template).toBeDefined();
|
||||
expect(seed!.response_map?.text_path).toBe('response');
|
||||
});
|
||||
|
||||
it('seed 之後 KV 裡讀回來的仍帶 auth/binding_name/body_template/response_map（KV 端）', async () => {
|
||||
const stored = await seedThenRead('workers_ai_chat');
|
||||
expect(stored.auth, 'auth 掉了 ⇒ 會被當成 HTTP recipe 去 fetch 一個不是網址的字串').toBe('binding');
|
||||
expect(stored.binding_name).toBe('AI');
|
||||
expect(stored.body_template, 'body_template 掉了 ⇒ 整包 ctx 被當 payload 送給模型').toBeDefined();
|
||||
expect(stored.response_map?.text_path, 'response_map 掉了 ⇒ 下游拿不到 text').toBe('response');
|
||||
expect(stored.response_map?.answer_marker).toBe('【答】');
|
||||
});
|
||||
|
||||
it('既有 HTTP 種子不受影響：沒宣告新欄位就是 undefined，不憑空長出來', async () => {
|
||||
const stored = await seedThenRead('telegram_send');
|
||||
expect(stored.auth).toBeUndefined();
|
||||
expect(stored.binding_name).toBeUndefined();
|
||||
expect(stored.body_template).toBeUndefined();
|
||||
expect(stored.response_map).toBeUndefined();
|
||||
expect(stored.endpoint).toContain('api.telegram.org');
|
||||
});
|
||||
});
|
||||
@@ -0,0 +1,104 @@
|
||||
/**
|
||||
* 意圖語法寫得出條件分支 → 編圖帶對邊型與標籤（SDD workflow-discovery 3.11）
|
||||
*
|
||||
* 為什麼補這一支（08-01 施工中自查發現的斷點，差點漏掉）：
|
||||
* 引擎支援了 ON_TRUE/ON_FALSE/ON_BRANCH，skill 文件也教了寫法，
|
||||
* 但 `graph-builder` 原本**只認得 `對每個 X` 的參數化 label**，
|
||||
* `ON_BRANCH(branch_active)` 這種帶括號的 label 會落到 `toEdgeType` 的預設值 **PIPE**
|
||||
* ⇒ 「教了語法但引擎不收，而且是靜默的」——比沒做更糟（AI 以為分支了，實際全走同一條）。
|
||||
*
|
||||
* 本檔守的就是「文件教的寫法，編圖真的收得到」這條線。
|
||||
*/
|
||||
import { describe, it, expect } from 'vitest';
|
||||
import { buildExecutionGraph } from '../src/actions/graph-builder';
|
||||
import { parseTriplets, resolveNodeRole } from '../src/actions/triplet-parser';
|
||||
|
||||
/** 把意圖字串編成圖（走 AI 真正會走的那條路：triplets → graph）。
|
||||
* nodeResults 用「全部 found」的最小替身——本檔只驗**邊**的編法，零件解析另有測試。 */
|
||||
function build(triplets: string[]) {
|
||||
const parsed = parseTriplets(triplets)!;
|
||||
const nodeResults: Record<string, { status: 'found'; componentId: string; type: ReturnType<typeof resolveNodeRole> }> = {};
|
||||
for (const name of parsed.nodeNames) {
|
||||
nodeResults[name] = {
|
||||
status: 'found',
|
||||
componentId: name.toLowerCase().replace(/\s+/g, '_'),
|
||||
type: resolveNodeRole(name, parsed),
|
||||
};
|
||||
}
|
||||
return buildExecutionGraph(parsed, nodeResults as never, 'test-graph', '測試');
|
||||
}
|
||||
|
||||
function edgeBetween(graph: ReturnType<typeof build>, from: string, to: string) {
|
||||
return graph.edges.find(e => e.from === from && e.to === to);
|
||||
}
|
||||
|
||||
describe('意圖語法：if_control 兩路（ON_TRUE／ON_FALSE）', () => {
|
||||
it('ON_TRUE／ON_FALSE 編成對應邊型，不會退化成 PIPE', () => {
|
||||
const g = build([
|
||||
'input >> ON_SUCCESS >> 判斷有沒有新資料',
|
||||
'判斷有沒有新資料 >> ON_TRUE >> 傳到telegram',
|
||||
'判斷有沒有新資料 >> ON_FALSE >> 結束',
|
||||
]);
|
||||
expect(edgeBetween(g, '判斷有沒有新資料', '傳到telegram')?.type).toBe('ON_TRUE');
|
||||
expect(edgeBetween(g, '判斷有沒有新資料', '結束')?.type).toBe('ON_FALSE');
|
||||
});
|
||||
|
||||
it('中文語意詞「成立時」「否則」也編得出來', () => {
|
||||
const g = build([
|
||||
'判斷有沒有新資料 >> 成立時 >> 傳到telegram',
|
||||
'判斷有沒有新資料 >> 否則 >> 結束',
|
||||
]);
|
||||
expect(edgeBetween(g, '判斷有沒有新資料', '傳到telegram')?.type).toBe('ON_TRUE');
|
||||
expect(edgeBetween(g, '判斷有沒有新資料', '結束')?.type).toBe('ON_FALSE');
|
||||
});
|
||||
});
|
||||
|
||||
describe('意圖語法：switch 具名分支（ON_BRANCH(標籤)）', () => {
|
||||
it('括號裡的標籤被抽成 edge.branch，型別是 ON_BRANCH', () => {
|
||||
const g = build([
|
||||
'my_switch >> ON_BRANCH(branch_active) >> 處理啟用',
|
||||
'my_switch >> ON_BRANCH(branch_pending) >> 處理待辦',
|
||||
'my_switch >> ON_BRANCH(branch_default) >> 其他',
|
||||
]);
|
||||
const active = edgeBetween(g, 'my_switch', '處理啟用');
|
||||
expect(active?.type).toBe('ON_BRANCH');
|
||||
expect(active?.branch).toBe('branch_active');
|
||||
|
||||
const pending = edgeBetween(g, 'my_switch', '處理待辦');
|
||||
expect(pending?.branch).toBe('branch_pending');
|
||||
|
||||
const dflt = edgeBetween(g, 'my_switch', '其他');
|
||||
expect(dflt?.branch).toBe('branch_default');
|
||||
});
|
||||
|
||||
it('全形括號也收（中文輸入法常打出全形）', () => {
|
||||
const g = build(['my_switch >> ON_BRANCH（branch_active） >> 處理啟用']);
|
||||
const e = edgeBetween(g, 'my_switch', '處理啟用');
|
||||
expect(e?.type).toBe('ON_BRANCH');
|
||||
expect(e?.branch).toBe('branch_active');
|
||||
});
|
||||
|
||||
it('try_catch 的 try／catch 標籤同樣收得到', () => {
|
||||
const g = build([
|
||||
'my_try >> ON_BRANCH(try) >> 正常流程',
|
||||
'my_try >> ON_BRANCH(catch) >> 補救流程',
|
||||
]);
|
||||
expect(edgeBetween(g, 'my_try', '正常流程')?.branch).toBe('try');
|
||||
expect(edgeBetween(g, 'my_try', '補救流程')?.branch).toBe('catch');
|
||||
});
|
||||
});
|
||||
|
||||
describe('零變化：既有語法不受影響', () => {
|
||||
it('ON_SUCCESS 仍是 ON_SUCCESS', () => {
|
||||
const g = build(['input >> ON_SUCCESS >> prep']);
|
||||
expect(edgeBetween(g, 'input', 'prep')?.type).toBe('ON_SUCCESS');
|
||||
});
|
||||
|
||||
it('「對每個 X」仍抽得到 iterator（不被新的 branch 抽取干擾）', () => {
|
||||
const g = build(['parse_card >> 對每個 block >> post_block']);
|
||||
const e = edgeBetween(g, 'parse_card', 'post_block');
|
||||
expect(e?.type).toBe('FOREACH');
|
||||
expect(e?.iterator).toBe('block');
|
||||
expect(e?.branch).toBeUndefined();
|
||||
});
|
||||
});
|
||||
@@ -0,0 +1,133 @@
|
||||
/**
|
||||
* GET|POST /portal/admin/ai —— arcrun-rag#10 迴歸守衛
|
||||
*
|
||||
* 🔴 為什麼有這支測試（別刪）：
|
||||
* 這條 route **以前根本不存在**，但前端設定頁一直在打它 ⇒ 用戶填 Gemini key → 404
|
||||
* ⇒ **key 從來沒被存進任何地方**，畫面卻像存好了（藍字＝假綠）。
|
||||
* leo 實撞成「重裝後 key 不見」，真相是「從來沒存進去，所以重填也沒用」。
|
||||
* 產物層鐵證（修復前）：bundle tier2/ui grep 'portal/admin/ai'=1、tier2/cypher=**0**。
|
||||
* ⇒ 這支測試的存在本身就是防線：**route 消失＝測試紅**。
|
||||
*
|
||||
* 覆蓋：
|
||||
* 1. 未登入 → 401；非 admin → 403（不是 404＝route 真的在）
|
||||
* 2. GET 回 has_key 布林，**永不回傳 key 本身**（D36）
|
||||
* 3. POST 空 body → 400（不假裝成功）
|
||||
* 4. POST 只改 Claude 偏好（不帶 key）→ 成功，且不碰 credential
|
||||
*/
|
||||
import { SELF, env, fetchMock } from 'cloudflare:test';
|
||||
import { beforeAll, afterEach, describe, it, expect } from 'vitest';
|
||||
import { hashPassword } from '../src/lib/portal-auth';
|
||||
|
||||
const KBDB = 'https://kbdb.test';
|
||||
|
||||
let storedHash: string;
|
||||
|
||||
beforeAll(async () => {
|
||||
fetchMock.activate();
|
||||
fetchMock.disableNetConnect();
|
||||
storedHash = await hashPassword('unit-test-pw-1', 10_000);
|
||||
});
|
||||
afterEach(() => fetchMock.assertNoPendingInterceptors());
|
||||
|
||||
function json(method: string, path: string, body?: unknown, headers: Record<string, string> = {}) {
|
||||
return SELF.fetch(`http://localhost${path}`, {
|
||||
method,
|
||||
headers: { 'Content-Type': 'application/json', ...headers },
|
||||
body: body === undefined ? undefined : JSON.stringify(body),
|
||||
});
|
||||
}
|
||||
|
||||
function mockGetRecord(recordId: string, values: Record<string, string>) {
|
||||
fetchMock
|
||||
.get(KBDB)
|
||||
.intercept({ path: `/records/${recordId}`, method: 'GET' })
|
||||
.reply(200, { success: true, record: { record_id: recordId, template_id: 'tpl_pu', values } });
|
||||
}
|
||||
|
||||
function adminValues(overrides: Record<string, string> = {}): Record<string, string> {
|
||||
return {
|
||||
email: 'admin@example.com',
|
||||
display_name: '管理員',
|
||||
status: 'active',
|
||||
role: 'admin',
|
||||
password_hash: storedHash,
|
||||
libraries: '["*"]',
|
||||
created_at: '2026-07-14T00:00:00.000Z',
|
||||
updated_at: '2026-07-14T00:00:00.000Z',
|
||||
...overrides,
|
||||
};
|
||||
}
|
||||
|
||||
async function seedSession(token: string, recordId: string) {
|
||||
await env.SESSIONS_KV.put(`portal_sess:${token}`, JSON.stringify({ record_id: recordId }));
|
||||
}
|
||||
|
||||
const authHdr = (t: string) => ({ Authorization: `Bearer ${t}` });
|
||||
|
||||
describe('GET /portal/admin/ai — 認證閘（route 存在的證明）', () => {
|
||||
it('未登入 → 401（不是 404 ⇒ route 真的在）', async () => {
|
||||
const res = await json('GET', '/portal/admin/ai');
|
||||
expect(res.status).toBe(401);
|
||||
expect(res.status).not.toBe(404);
|
||||
});
|
||||
|
||||
it('非 admin → 403', async () => {
|
||||
await seedSession('tok-user', 'rec_user');
|
||||
mockGetRecord('rec_user', adminValues({ role: 'user', email: 'u@example.com' }));
|
||||
const res = await json('GET', '/portal/admin/ai', undefined, authHdr('tok-user'));
|
||||
expect(res.status).toBe(403);
|
||||
});
|
||||
});
|
||||
|
||||
describe('GET /portal/admin/ai — 回應形狀（D36：永不回傳 key）', () => {
|
||||
it('回 has_key 布林，且回應完全不含金鑰值', async () => {
|
||||
await seedSession('tok-a1', 'rec_admin');
|
||||
mockGetRecord('rec_admin', adminValues());
|
||||
const res = await json('GET', '/portal/admin/ai', undefined, authHdr('tok-a1'));
|
||||
expect(res.status).toBe(200);
|
||||
const raw = await res.text();
|
||||
const d = JSON.parse(raw) as Record<string, unknown>;
|
||||
|
||||
expect(typeof d.has_key).toBe('boolean');
|
||||
|
||||
// D36：回應裡不得出現任何疑似金鑰的欄位
|
||||
expect(raw).not.toContain('gemini_api_key_value');
|
||||
expect(d).not.toHaveProperty('key');
|
||||
expect(d).not.toHaveProperty('value');
|
||||
expect(d).not.toHaveProperty('secret_ref');
|
||||
});
|
||||
|
||||
// t176 回歸守衛（leo 08-03）：雲端不再有「地端用哪個模型」的概念。
|
||||
// 這兩個欄位若復活，代表又走回「雲端控制地端」的老路——那正是 08-03 事故根因
|
||||
//（extractor_config 全租戶共用一把，任一處設 claude 就讓所有人萃取全滅）。
|
||||
it('不再回 claude_available／use_claude_for_extract（地端模型改由小幫手自己設）', async () => {
|
||||
await seedSession('tok-a1b', 'rec_admin');
|
||||
mockGetRecord('rec_admin', adminValues());
|
||||
const res = await json('GET', '/portal/admin/ai', undefined, authHdr('tok-a1b'));
|
||||
const d = (await res.json()) as Record<string, unknown>;
|
||||
expect(d).not.toHaveProperty('claude_available');
|
||||
expect(d).not.toHaveProperty('use_claude_for_extract');
|
||||
});
|
||||
});
|
||||
|
||||
describe('POST /portal/admin/ai — 不假裝成功', () => {
|
||||
it('空 body（沒帶金鑰）→ 400，不回 success', async () => {
|
||||
await seedSession('tok-a2', 'rec_admin');
|
||||
mockGetRecord('rec_admin', adminValues());
|
||||
const res = await json('POST', '/portal/admin/ai', {}, authHdr('tok-a2'));
|
||||
expect(res.status).toBe(400);
|
||||
const d = (await res.json()) as Record<string, unknown>;
|
||||
expect(d.success).toBeUndefined();
|
||||
expect(String(d.error)).toContain('沒有要變更');
|
||||
});
|
||||
|
||||
// t176 回歸守衛：只送 Claude 偏好＝沒有要變更的項目 → 400（該欄位已不存在）。
|
||||
it('只送 use_claude_for_extract（已廢欄位）→ 400，不得假裝成功', async () => {
|
||||
await seedSession('tok-a3', 'rec_admin');
|
||||
mockGetRecord('rec_admin', adminValues());
|
||||
const res = await json('POST', '/portal/admin/ai', { use_claude_for_extract: true }, authHdr('tok-a3'));
|
||||
expect(res.status).toBe(400);
|
||||
const d = (await res.json()) as Record<string, unknown>;
|
||||
expect(d.success).toBeUndefined();
|
||||
});
|
||||
});
|
||||
@@ -300,42 +300,16 @@ describe('PATCH libraries（每帳號可查庫）', () => {
|
||||
// ═══════════════ 5. 庫目錄管理 ═══════════════
|
||||
|
||||
describe('/portal/admin/libraries', () => {
|
||||
it('POST 建庫：寫 {tenant}::portal 子 namespace；重複登記 → 409', async () => {
|
||||
await seedAdminSession();
|
||||
mockGetRecord('rec_admin', adminValues());
|
||||
mockTemplatesExist();
|
||||
mockListByTemplate('portal_library', []);
|
||||
let recordBody = '';
|
||||
fetchMock
|
||||
.get(KBDB)
|
||||
.intercept({ path: '/records', method: 'POST' })
|
||||
.reply(200, (opts) => {
|
||||
recordBody = String(opts.body);
|
||||
return {
|
||||
success: true,
|
||||
record: { record_id: 'rec_lib1', template_id: 'tpl_pl', values: { name: 'finance', display_name: '財務庫', status: 'active' } },
|
||||
};
|
||||
});
|
||||
it('t160：人工建庫端點已刪（leo「沒有登記這回事」）——POST → 404；庫只從 daemon 同步來', async () => {
|
||||
// 舊測試驗「POST 建庫 200＋重複 409」——t160 拔掉人工建庫（e744ad1）後規格為：
|
||||
// 庫由 /portal/daemon/libraries（連線精靈自動登記，t159）產生，admin 只能 GET/PATCH。
|
||||
const res = await json(
|
||||
'POST',
|
||||
'/portal/admin/libraries',
|
||||
{ name: 'finance', display_name: '財務庫' },
|
||||
{ Authorization: 'Bearer tok-admin' },
|
||||
);
|
||||
expect(res.status).toBe(200);
|
||||
const rec = JSON.parse(recordBody) as { owner_id: string; template: string };
|
||||
expect(rec.owner_id).toBe(NS);
|
||||
expect(rec.template).toBe('portal_library');
|
||||
|
||||
// 重複登記
|
||||
await seedAdminSession('tok-admin3');
|
||||
mockGetRecord('rec_admin', adminValues());
|
||||
mockTemplatesExist();
|
||||
mockListByTemplate('portal_library', [
|
||||
{ record_id: 'rec_lib1', values: { name: 'finance', display_name: '財務庫', status: 'active' } },
|
||||
]);
|
||||
const dup = await json('POST', '/portal/admin/libraries', { name: 'finance' }, { Authorization: 'Bearer tok-admin3' });
|
||||
expect(dup.status).toBe(409);
|
||||
expect(res.status).toBe(404);
|
||||
});
|
||||
|
||||
it('PATCH graph_source：boolean 進、slot 存字串；非 boolean → 400', async () => {
|
||||
@@ -564,13 +538,20 @@ describe('DELETE /portal/admin/libraries（t135）', () => {
|
||||
});
|
||||
});
|
||||
|
||||
// ═══════════════ 6. t122 萃取引擎金鑰雲端下發 ═══════════════
|
||||
// ═══════════════ 6. t176：雲端不再管地端 LLM 設定（取代原 t122／t131 兩組測試）═══════════════
|
||||
//
|
||||
// leo 2026-08-03 架構翻案：「地端要用什麼模型就在 daemon 上輸入 API Key 設置，
|
||||
// 而不是雲端設置後控制地端」。原因是 extractor_config 的 KV key 由 portalTenant() 組出，
|
||||
// 而 portalTenant 是 **worker 層級**環境變數 ⇒ **全租戶共用一把**：任一處設了 claude，
|
||||
// 所有人的 daemon 都收到 claude，沒裝 Claude Code 的機器萃取全滅，
|
||||
// 而 portal 的 Claude 勾選框又恆 disabled（daemon 從未回報 has_claude）⇒ 用戶自己解不開。
|
||||
//
|
||||
// 以下是**回歸守衛**：這些端點/欄位若復活，代表又走回「雲端控制地端」的老路。
|
||||
|
||||
describe('/portal/admin/extractor + /portal/daemon/config 萃取引擎（t122）', () => {
|
||||
describe('t176：雲端不再下發／設定地端 LLM', () => {
|
||||
const USER_EMAIL = 'daemon@example.com';
|
||||
const USER_PW = 'unit-test-pw-1'; // 與 storedHash 配對（beforeAll 計算）
|
||||
const USER_PW = 'unit-test-pw-1'; // 與 storedHash 配對（外層 beforeAll 計算）
|
||||
const USER_RECORD = 'rec_daemon_user';
|
||||
const EXTRACTOR_KV_KEY = 'leo:portal:extractor_config'; // wrangler.test.toml CONSOLE_TENANT=leo
|
||||
|
||||
/** mock email head lookup（findUserRecordId 走這個路徑）*/
|
||||
function mockEmailLookup(email: string, recordId: string | null) {
|
||||
@@ -584,47 +565,37 @@ describe('/portal/admin/extractor + /portal/daemon/config 萃取引擎（t122）
|
||||
.reply(200, { success: true, entries: recordId ? [{ content: recordId }] : [], count: recordId ? 1 : 0 });
|
||||
}
|
||||
|
||||
it('未設定 → daemon/config 下發 extractor=gemma，無 gemini_api_key', async () => {
|
||||
// 確保 KV 沒有 extractor config
|
||||
await env.WEBHOOKS.delete(EXTRACTOR_KV_KEY);
|
||||
it('POST /portal/daemon/config 只回連線欄位，**不含任何 LLM 欄位**', async () => {
|
||||
mockEmailLookup(USER_EMAIL, USER_RECORD);
|
||||
mockGetRecord(USER_RECORD, adminValues({ email: USER_EMAIL, password_hash: storedHash }));
|
||||
|
||||
const res = await json('POST', '/portal/daemon/config', { email: USER_EMAIL, password: USER_PW });
|
||||
expect(res.status).toBe(200);
|
||||
const data = (await res.json()) as { success: boolean; config: Record<string, string> };
|
||||
expect(data.success).toBe(true);
|
||||
expect(data.config.extractor).toBe('gemma');
|
||||
expect('gemini_api_key' in data.config).toBe(false);
|
||||
const d = (await res.json()) as { config: Record<string, unknown> };
|
||||
|
||||
// 連線欄位照舊（daemon 靠它上線）
|
||||
expect(d.config.cypher_url).toBeTruthy();
|
||||
expect(d.config.namespace).toBeTruthy();
|
||||
expect(d.config.library).toBe('kb');
|
||||
|
||||
// LLM 欄位一律不下發（t176 核心）
|
||||
expect(d.config).not.toHaveProperty('extractor');
|
||||
expect(d.config).not.toHaveProperty('gemini_api_key');
|
||||
expect(d.config).not.toHaveProperty('llm_model');
|
||||
});
|
||||
|
||||
it('設定 gemma+金鑰後 → daemon/config 下發含 gemini_api_key', async () => {
|
||||
await env.WEBHOOKS.put(EXTRACTOR_KV_KEY, JSON.stringify({ engine: 'gemma', gemini_api_key: 'AIza-test-key-999' }));
|
||||
mockEmailLookup(USER_EMAIL, USER_RECORD);
|
||||
mockGetRecord(USER_RECORD, adminValues({ email: USER_EMAIL, password_hash: storedHash }));
|
||||
const res = await json('POST', '/portal/daemon/config', { email: USER_EMAIL, password: USER_PW });
|
||||
expect(res.status).toBe(200);
|
||||
const data = (await res.json()) as { success: boolean; config: Record<string, string> };
|
||||
expect(data.config.extractor).toBe('gemma');
|
||||
expect(data.config.gemini_api_key).toBe('AIza-test-key-999');
|
||||
// cleanup
|
||||
await env.WEBHOOKS.delete(EXTRACTOR_KV_KEY);
|
||||
// 註：route 不存在 ⇒ 在認證之前就 404，因此不需要（也不能）預先掛 record mock，
|
||||
// 否則 afterEach 的 assertNoPendingInterceptors 會因「mock 沒被用到」而失敗。
|
||||
it('POST /portal/admin/extractor 已移除（雲端不再有指定地端引擎的入口）', async () => {
|
||||
const res = await json('POST', '/portal/admin/extractor', { engine: 'claude' }, { Authorization: 'Bearer tok-ex' });
|
||||
expect(res.status).toBe(404);
|
||||
});
|
||||
|
||||
it('GET /portal/admin/extractor → has_key=true，回應不含金鑰明文', async () => {
|
||||
await env.WEBHOOKS.put(EXTRACTOR_KV_KEY, JSON.stringify({ engine: 'gemma', gemini_api_key: 'AIza-secret-key' }));
|
||||
await seedAdminSession();
|
||||
mockGetRecord('rec_admin', adminValues());
|
||||
const res = await json('GET', '/portal/admin/extractor', undefined, { Authorization: 'Bearer tok-admin' });
|
||||
expect(res.status).toBe(200);
|
||||
const data = (await res.json()) as { success: boolean; engine: string; has_key: boolean };
|
||||
expect(data.engine).toBe('gemma');
|
||||
expect(data.has_key).toBe(true);
|
||||
// 回應主體不含金鑰明文
|
||||
const raw = JSON.stringify(data);
|
||||
expect(raw).not.toContain('AIza-secret-key');
|
||||
expect(raw).not.toContain('gemini_api_key');
|
||||
// cleanup
|
||||
await env.WEBHOOKS.delete(EXTRACTOR_KV_KEY);
|
||||
it('POST /portal/daemon/report-capabilities 已移除（has_claude 回報鏈整條退役）', async () => {
|
||||
const res = await json('POST', '/portal/daemon/report-capabilities', {
|
||||
email: USER_EMAIL, password: USER_PW, has_claude: true,
|
||||
});
|
||||
expect(res.status).toBe(404);
|
||||
});
|
||||
});
|
||||
|
||||
@@ -658,166 +629,8 @@ describe('GET /portal（P4 admin 頁 HTML 殼）', () => {
|
||||
});
|
||||
});
|
||||
|
||||
// ═══════════════ 8. t131 合併 AI 設定 ═══════════════
|
||||
|
||||
describe('/portal/admin/ai + /portal/daemon/report-capabilities（t131）', () => {
|
||||
const USER_EMAIL = 'ai-test@example.com';
|
||||
const USER_PW = 'unit-test-pw-1';
|
||||
const USER_RECORD = 'rec_ai_user';
|
||||
const AI_CONFIG_KEY = 'leo:portal:ai_config';
|
||||
const EXTRACTOR_KV_KEY = 'leo:portal:extractor_config';
|
||||
const DAEMON_CAPS_KEY = 'leo:portal:daemon_caps';
|
||||
|
||||
function aiAdminVals(): Record<string, string> {
|
||||
return { email: USER_EMAIL, display_name: 'AI 測試 admin', status: 'active', role: 'admin', password_hash: storedHash };
|
||||
}
|
||||
|
||||
// 與全域 seedAdminSession 相同格式（JSON.stringify({record_id})），fetchMock 由各測試自行 mock
|
||||
async function seedAiSession(token = 'tok-ai-admin', recordId = USER_RECORD) {
|
||||
await env.SESSIONS_KV.put(`portal_sess:${token}`, JSON.stringify({ record_id: recordId }));
|
||||
}
|
||||
|
||||
function mockAiRecord(recordId = USER_RECORD) {
|
||||
fetchMock.get(KBDB).intercept({ path: `/records/${recordId}`, method: 'GET' }).reply(200, {
|
||||
success: true,
|
||||
record: { record_id: recordId, template_id: 'tpl_pu', values: aiAdminVals() },
|
||||
});
|
||||
}
|
||||
|
||||
function mockEmailLookup(email: string, recordId: string | null) {
|
||||
const needle = new URLSearchParams({ page_name: email }).toString();
|
||||
fetchMock.get(KBDB).intercept({
|
||||
path: (p: string) => p.startsWith('/entries?') && p.includes(needle) && p.includes(encodeURIComponent(NS)),
|
||||
method: 'GET',
|
||||
}).reply(200, { success: true, entries: recordId ? [{ content: recordId }] : [], count: recordId ? 1 : 0 });
|
||||
}
|
||||
|
||||
afterEach(async () => {
|
||||
await env.WEBHOOKS.delete(AI_CONFIG_KEY);
|
||||
await env.WEBHOOKS.delete(EXTRACTOR_KV_KEY);
|
||||
await env.WEBHOOKS.delete(DAEMON_CAPS_KEY);
|
||||
});
|
||||
|
||||
it('POST /ai — 首次設定：同時寫 ai_config＋extractor_config＋更新 rag_chat workflow', async () => {
|
||||
const ragChatKey = 'leo:wf:rag_chat';
|
||||
const workflow = { graph: { nodes: [{ config: { 'x-goog-api-key': '{{credential.gemini}}' } }] }, config: {} };
|
||||
await env.WEBHOOKS.put(ragChatKey, JSON.stringify(workflow));
|
||||
await seedAiSession();
|
||||
mockAiRecord();
|
||||
const res = await json('POST', '/portal/admin/ai',
|
||||
{ gemini_api_key: 'AIza-new-key-123', use_claude_for_extract: false },
|
||||
{ Authorization: 'Bearer tok-ai-admin' }
|
||||
);
|
||||
expect(res.status).toBe(200);
|
||||
const data = (await res.json()) as { success: boolean; has_key: boolean; use_claude_for_extract: boolean };
|
||||
expect(data.success).toBe(true);
|
||||
expect(data.has_key).toBe(true);
|
||||
expect(data.use_claude_for_extract).toBe(false);
|
||||
|
||||
const stored = JSON.parse((await env.WEBHOOKS.get(AI_CONFIG_KEY, 'text')) ?? '{}');
|
||||
expect(stored.gemini_api_key).toBe('AIza-new-key-123');
|
||||
expect(stored.use_claude_for_extract).toBe(false);
|
||||
|
||||
const exCfg = JSON.parse((await env.WEBHOOKS.get(EXTRACTOR_KV_KEY, 'text')) ?? '{}');
|
||||
expect(exCfg.engine).toBe('gemma');
|
||||
expect(exCfg.gemini_api_key).toBe('AIza-new-key-123');
|
||||
|
||||
const updated = JSON.parse((await env.WEBHOOKS.get(ragChatKey, 'text')) ?? '{}') as typeof workflow;
|
||||
expect((updated.graph as { nodes: Array<{ config: Record<string, string> }> }).nodes[0].config['x-goog-api-key']).toBe('AIza-new-key-123');
|
||||
await env.WEBHOOKS.delete(ragChatKey);
|
||||
});
|
||||
|
||||
it('POST /ai — rag_chat 不存在時不報錯（容忍，金鑰存 ai_config 即可）', async () => {
|
||||
await seedAiSession();
|
||||
mockAiRecord();
|
||||
const res = await json('POST', '/portal/admin/ai',
|
||||
{ gemini_api_key: 'AIza-no-workflow-key' },
|
||||
{ Authorization: 'Bearer tok-ai-admin' }
|
||||
);
|
||||
expect(res.status).toBe(200);
|
||||
const data = (await res.json()) as { success: boolean; has_key: boolean };
|
||||
expect(data.success).toBe(true);
|
||||
expect(data.has_key).toBe(true);
|
||||
const stored = JSON.parse((await env.WEBHOOKS.get(AI_CONFIG_KEY, 'text')) ?? '{}');
|
||||
expect(stored.gemini_api_key).toBe('AIza-no-workflow-key');
|
||||
});
|
||||
|
||||
it('POST /ai — use_claude_for_extract=true：extractor engine=claude，不附 gemini_api_key', async () => {
|
||||
await seedAiSession();
|
||||
mockAiRecord();
|
||||
const res = await json('POST', '/portal/admin/ai',
|
||||
{ gemini_api_key: 'AIza-key-888', use_claude_for_extract: true },
|
||||
{ Authorization: 'Bearer tok-ai-admin' }
|
||||
);
|
||||
expect(res.status).toBe(200);
|
||||
const data = (await res.json()) as { success: boolean; use_claude_for_extract: boolean };
|
||||
expect(data.use_claude_for_extract).toBe(true);
|
||||
const exCfg = JSON.parse((await env.WEBHOOKS.get(EXTRACTOR_KV_KEY, 'text')) ?? '{}');
|
||||
expect(exCfg.engine).toBe('claude');
|
||||
expect('gemini_api_key' in exCfg).toBe(false);
|
||||
});
|
||||
|
||||
it('GET /ai — 不回明文金鑰；has_key=true；claude_available 依 daemon_caps', async () => {
|
||||
await env.WEBHOOKS.put(AI_CONFIG_KEY, JSON.stringify({ gemini_api_key: 'AIza-secret-456', use_claude_for_extract: false }));
|
||||
await env.WEBHOOKS.put(DAEMON_CAPS_KEY, JSON.stringify({ has_claude: true }));
|
||||
await seedAiSession();
|
||||
mockAiRecord();
|
||||
const res = await json('GET', '/portal/admin/ai', undefined, { Authorization: 'Bearer tok-ai-admin' });
|
||||
expect(res.status).toBe(200);
|
||||
const data = (await res.json()) as { success: boolean; has_key: boolean; use_claude_for_extract: boolean; claude_available: boolean };
|
||||
expect(data.has_key).toBe(true);
|
||||
expect(data.use_claude_for_extract).toBe(false);
|
||||
expect(data.claude_available).toBe(true);
|
||||
const raw = JSON.stringify(data);
|
||||
expect(raw).not.toContain('AIza-secret-456');
|
||||
expect(raw).not.toContain('gemini_api_key');
|
||||
});
|
||||
|
||||
it('GET /ai — 沒有 daemon_caps → claude_available=false', async () => {
|
||||
await env.WEBHOOKS.put(AI_CONFIG_KEY, JSON.stringify({ gemini_api_key: 'AIza-key-777' }));
|
||||
await seedAiSession();
|
||||
mockAiRecord();
|
||||
const res = await json('GET', '/portal/admin/ai', undefined, { Authorization: 'Bearer tok-ai-admin' });
|
||||
expect(res.status).toBe(200);
|
||||
const data = (await res.json()) as { claude_available: boolean };
|
||||
expect(data.claude_available).toBe(false);
|
||||
});
|
||||
|
||||
it('POST /portal/daemon/report-capabilities — 有 claude：daemon_caps 寫入 has_claude=true', async () => {
|
||||
mockEmailLookup(USER_EMAIL, USER_RECORD);
|
||||
mockAiRecord();
|
||||
const res = await json('POST', '/portal/daemon/report-capabilities', {
|
||||
email: USER_EMAIL, password: USER_PW, has_claude: true, daemon_version: '1.2.0', os: 'darwin',
|
||||
});
|
||||
expect(res.status).toBe(200);
|
||||
const data = (await res.json()) as { success: boolean };
|
||||
expect(data.success).toBe(true);
|
||||
const caps = JSON.parse((await env.WEBHOOKS.get(DAEMON_CAPS_KEY, 'text')) ?? '{}');
|
||||
expect(caps.has_claude).toBe(true);
|
||||
expect(caps.daemon_version).toBe('1.2.0');
|
||||
});
|
||||
|
||||
it('舊端點 /portal/admin/chat-key 仍可用（相容）', async () => {
|
||||
const ragChatKey = 'leo:wf:rag_chat';
|
||||
const workflow = { graph: { nodes: [{ config: { 'x-goog-api-key': 'old' } }] }, config: {} };
|
||||
await env.WEBHOOKS.put(ragChatKey, JSON.stringify(workflow));
|
||||
await seedAiSession();
|
||||
mockAiRecord();
|
||||
const res = await json('POST', '/portal/admin/chat-key', { key: 'AIza-compat-key' }, { Authorization: 'Bearer tok-ai-admin' });
|
||||
expect(res.status).toBe(200);
|
||||
const data = (await res.json()) as { success: boolean; replaced: number };
|
||||
expect(data.success).toBe(true);
|
||||
expect(data.replaced).toBeGreaterThan(0);
|
||||
await env.WEBHOOKS.delete(ragChatKey);
|
||||
});
|
||||
});
|
||||
|
||||
// ═══════════════ t181：daemon 萃取走 Workers AI（免金鑰）═══════════════
|
||||
//
|
||||
// leo 08-04 列為最優先：「daemon 的 AI 改用 workers AI」——
|
||||
// 「這是我的用戶最大障礙，造成首輪測試用戶的好評或惡評」。
|
||||
// 舊路徑要用戶自備 Gemini key，實測撞到「不知道去哪設定」「Google 帳號被 flag 403」
|
||||
// 「52 檔全滅還要把金鑰傳給別人才查得出原因」三種災難。
|
||||
// t131/t122 測試已隨 main 的 t176（刪除雲端下發 LLM 設定）一併移除；
|
||||
// 此處只保留 t181（daemon 走 Workers AI）的守衛。
|
||||
|
||||
describe('POST /portal/daemon/extract（t181：Workers AI 萃卡，免金鑰）', () => {
|
||||
// 認證＝X-Arcrun-API-Key（＝namespace，wrangler.test.toml CONSOLE_TENANT=leo），
|
||||
|
||||
@@ -0,0 +1,123 @@
|
||||
/**
|
||||
* recipe payload 與回應處理層 —— CP `arcrun-usable` 步驟 5 缺口②
|
||||
* SDD: workflow-discovery task 3.12
|
||||
*
|
||||
* 為什麼要有這三層（別刪）：
|
||||
* 舊 schema 只有 {canonical_id, endpoint, method, auth_service}（body 有但淺）
|
||||
* ⇒ 帶 body 的 API 只能繞過 recipe 把整包寫進 workflow code；
|
||||
* 回應解析（rag_chat 的 finalize，2786 字元）綁死 Gemini 格式，換源必壞。
|
||||
* leo：三層模型＝①零件 ②auth recipe ③payload recipe，第③層過去不存在。
|
||||
*
|
||||
* 本檔測純函式層（body_template 插值 / response_map 正規化），
|
||||
* 不打真外部 API——外部呼叫由 stage 端到端驗（features/09）。
|
||||
*/
|
||||
import { describe, it, expect } from 'vitest';
|
||||
import { renderBodyTemplate, applyResponseMap } from '../src/lib/recipe-payload';
|
||||
|
||||
describe('body_template：payload 收回 recipe（第③層）', () => {
|
||||
it('巢狀結構的 {{var}} 都會被替換（不只 top-level）', () => {
|
||||
const out = renderBodyTemplate(
|
||||
{ contents: [{ parts: [{ text: '{{prompt}}' }] }] },
|
||||
{ prompt: '你好' },
|
||||
);
|
||||
expect(out).toEqual({ contents: [{ parts: [{ text: '你好' }] }] });
|
||||
});
|
||||
|
||||
it('單一引用保留原型別（陣列/物件不被 stringify）', () => {
|
||||
const out = renderBodyTemplate(
|
||||
{ messages: '{{history}}', n: '{{count}}' },
|
||||
{ history: [{ role: 'user' }], count: 3 },
|
||||
) as Record<string, unknown>;
|
||||
expect(out.messages).toEqual([{ role: 'user' }]);
|
||||
expect(out.n).toBe(3);
|
||||
});
|
||||
|
||||
it('混合文字仍拼成字串', () => {
|
||||
const out = renderBodyTemplate({ q: '請回答：{{prompt}}' }, { prompt: '天氣' }) as Record<string, unknown>;
|
||||
expect(out.q).toBe('請回答：天氣');
|
||||
});
|
||||
|
||||
it('支援 dot path 取值', () => {
|
||||
const out = renderBodyTemplate({ t: '{{assemble.data.prompt}}' }, {
|
||||
assemble: { data: { prompt: '深層值' } },
|
||||
}) as Record<string, unknown>;
|
||||
expect(out.t).toBe('深層值');
|
||||
});
|
||||
|
||||
it('取不到的變數保留原樣（不靜默變 undefined，看得見才好 debug）', () => {
|
||||
const out = renderBodyTemplate({ t: '{{nope}}' }, {}) as Record<string, unknown>;
|
||||
expect(out.t).toBe('{{nope}}');
|
||||
});
|
||||
|
||||
it('沒有 body_template → 回 undefined（呼叫端沿用既有行為）', () => {
|
||||
expect(renderBodyTemplate(undefined, { a: 1 })).toBeUndefined();
|
||||
});
|
||||
});
|
||||
|
||||
describe('response_map：回應正規化（換源不必改 workflow）', () => {
|
||||
const geminiBody = {
|
||||
candidates: [{ content: { parts: [{ text: '【答】台北是首都' }] } }],
|
||||
};
|
||||
|
||||
it('path 取值：Gemini 形狀 → 純文字', () => {
|
||||
const out = applyResponseMap(geminiBody, { text_path: 'candidates.0.content.parts.0.text' });
|
||||
expect(out.text).toBe('【答】台北是首都');
|
||||
});
|
||||
|
||||
it('換源＝換 recipe：Claude 形狀用不同 path，同樣取得出文字', () => {
|
||||
const claudeBody = { content: [{ type: 'text', text: 'Claude 的答案' }] };
|
||||
const out = applyResponseMap(claudeBody, { text_path: 'content.0.text' });
|
||||
expect(out.text).toBe('Claude 的答案');
|
||||
});
|
||||
|
||||
it('Workers AI 形狀（binding 回傳）同樣走 path', () => {
|
||||
const waiBody = { response: 'Workers AI 的答案' };
|
||||
const out = applyResponseMap(waiBody, { text_path: 'response' });
|
||||
expect(out.text).toBe('Workers AI 的答案');
|
||||
});
|
||||
|
||||
it('思考型模型：thought=true 的 part 要被剔除，取最後一個非 thought', () => {
|
||||
const gemma = {
|
||||
candidates: [{
|
||||
content: {
|
||||
parts: [
|
||||
{ text: '讓我想想…', thought: true },
|
||||
{ text: '真正的答案' },
|
||||
],
|
||||
},
|
||||
}],
|
||||
};
|
||||
const out = applyResponseMap(gemma, {
|
||||
text_path: 'candidates.0.content.parts',
|
||||
thinking_model: true,
|
||||
});
|
||||
expect(out.text).toBe('真正的答案');
|
||||
});
|
||||
|
||||
it('淨化規則：剝掉【答】前的草稿前綴（實撞三型之一）', () => {
|
||||
const out = applyResponseMap(
|
||||
{ r: 'Draft: 【答】正確內容' },
|
||||
{ text_path: 'r', strip_prefixes: ['Draft:', '*', 'Answer:'], answer_marker: '【答】' },
|
||||
);
|
||||
expect(out.text).toBe('正確內容');
|
||||
});
|
||||
|
||||
it('淨化規則：前綴組合順序不定 → 循環剝殼剝乾淨', () => {
|
||||
const out = applyResponseMap(
|
||||
{ r: 'Answer: * 【答】內容' },
|
||||
{ text_path: 'r', strip_prefixes: ['Draft:', '*', 'Answer:'], answer_marker: '【答】' },
|
||||
);
|
||||
expect(out.text).toBe('內容');
|
||||
});
|
||||
|
||||
it('沒有 response_map → 原樣回傳（既有 recipe 行為完全不變）', () => {
|
||||
const out = applyResponseMap(geminiBody, undefined);
|
||||
expect(out.text).toBeUndefined();
|
||||
expect(out.raw).toEqual(geminiBody);
|
||||
});
|
||||
|
||||
it('path 取不到 → 誠實回 undefined，不編造', () => {
|
||||
const out = applyResponseMap({ a: 1 }, { text_path: 'b.c.d' });
|
||||
expect(out.text).toBeUndefined();
|
||||
});
|
||||
});
|
||||
@@ -0,0 +1,170 @@
|
||||
/**
|
||||
* CP `arcrun-usable` 步驟 5 驗收（SDD workflow-discovery task 3.13）
|
||||
*
|
||||
* 驗法（CP 原文）：拿現行 `assemble`（5509 字元、if×23）用新能力重寫
|
||||
* → code 大幅下降且仍 verdict=success。
|
||||
*
|
||||
* 誠實聲明（重要，別把這支當成端到端證據）：
|
||||
* 線上那顆 `assemble` 住在 arcrun-rag 的實例上（本 repo 無其定義），
|
||||
* 本檔**不是**直接改寫線上節點，而是把它的**判斷骨架**（多路分流＋失敗處理＋
|
||||
* 回應取值＋payload 組裝——即 if×23 的來源）以新能力重建成等價工作流，
|
||||
* 證明「這些判斷不再需要寫在 JS 裡」。
|
||||
* 線上節點的真正改寫＝stage 端到端（features/09），不在單元測試層宣稱。
|
||||
*
|
||||
* 對照基準（08-01 實測，來源：頂層 pending-changes「零件層系統性違規盤點」段）：
|
||||
* rag_chat 的 assemble＝5509 字元、if×23、for×12
|
||||
*/
|
||||
import { SELF } from 'cloudflare:test';
|
||||
import { describe, it, expect } from 'vitest';
|
||||
|
||||
async function execute(graph: unknown, context: Record<string, unknown> = {}) {
|
||||
const res = await SELF.fetch('http://localhost/execute', {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify({ graph, context }),
|
||||
});
|
||||
return (await res.json()) as {
|
||||
success: boolean;
|
||||
data: Record<string, unknown>;
|
||||
trace?: Array<{ nodeId: string }>;
|
||||
error?: string;
|
||||
};
|
||||
}
|
||||
|
||||
describe('步驟 5 驗收：判斷骨架不再需要 code 節點', () => {
|
||||
/**
|
||||
* 舊寫法的形狀（assemble 那 5509 字元在做的事）：
|
||||
* 一個 code 節點內部 if×23 —— 判斷資料有沒有／走哪一路／失敗了怎麼辦／
|
||||
* 從回應裡挖哪個欄位／把 payload 拼出來。
|
||||
* 新寫法：判斷交給零件輸出 branch，路由交給引擎的具名分支邊，
|
||||
* payload/取值交給 recipe 的 body_template/response_map ⇒ **零 code 節點**。
|
||||
*/
|
||||
it('多路分流＋失敗路：三條路各自到位，全程零 code 節點', async () => {
|
||||
const graph = {
|
||||
id: 'step5-acceptance',
|
||||
name: '步驟5 驗收：assemble 判斷骨架重寫',
|
||||
nodes: [
|
||||
// if_control/switch 形狀的輸出（線上是零件算出來的，這裡直接餵形狀）
|
||||
{ id: 'route', type: 'Input', data: { success: true, data: { branch: 'has_data' } } },
|
||||
{ id: 'handle_data', type: 'Component', componentId: 'comp_uppercase', data: { text: 'has-data' } },
|
||||
{ id: 'handle_empty', type: 'Component', componentId: 'comp_uppercase', data: { text: 'empty' } },
|
||||
{ id: 'handle_error', type: 'Component', componentId: 'comp_uppercase', data: { text: 'error' } },
|
||||
],
|
||||
edges: [
|
||||
{ from: 'route', to: 'handle_data', type: 'ON_BRANCH', branch: 'has_data' },
|
||||
{ from: 'route', to: 'handle_empty', type: 'ON_BRANCH', branch: 'empty' },
|
||||
{ from: 'route', to: 'handle_error', type: 'ON_BRANCH', branch: 'error' },
|
||||
],
|
||||
};
|
||||
|
||||
const out = await execute(graph);
|
||||
const visited = (out.trace ?? []).map(t => t.nodeId);
|
||||
|
||||
expect(out.success).toBe(true); // ＝ verdict success
|
||||
expect(visited).toContain('handle_data');
|
||||
expect(visited).not.toContain('handle_empty');
|
||||
expect(visited).not.toContain('handle_error');
|
||||
|
||||
// 零 code 節點＝這張圖沒有任何 componentId 為 'code' 的節點
|
||||
const codeNodes = graph.nodes.filter(n => n.componentId === 'code');
|
||||
expect(codeNodes).toHaveLength(0);
|
||||
});
|
||||
|
||||
it('布林兩路（if_control）同樣零 code', async () => {
|
||||
const graph = {
|
||||
id: 'step5-bool',
|
||||
name: '布林兩路',
|
||||
nodes: [
|
||||
{ id: 'cond', type: 'Input', data: { data: { result: false, branch: 'false' } } },
|
||||
{ id: 'yes', type: 'Component', componentId: 'comp_uppercase', data: { text: 'yes' } },
|
||||
{ id: 'no', type: 'Component', componentId: 'comp_uppercase', data: { text: 'no' } },
|
||||
],
|
||||
edges: [
|
||||
{ from: 'cond', to: 'yes', type: 'ON_TRUE' },
|
||||
{ from: 'cond', to: 'no', type: 'ON_FALSE' },
|
||||
],
|
||||
};
|
||||
const out = await execute(graph);
|
||||
const visited = (out.trace ?? []).map(t => t.nodeId);
|
||||
expect(out.success).toBe(true);
|
||||
expect(visited).toContain('no');
|
||||
expect(visited).not.toContain('yes');
|
||||
expect(graph.nodes.filter(n => n.componentId === 'code')).toHaveLength(0);
|
||||
});
|
||||
});
|
||||
|
||||
describe('步驟 5 驗收：字元數對照（判斷骨架的體積）', () => {
|
||||
/**
|
||||
* 把「同一組判斷」用兩種寫法各寫一次，量體積。
|
||||
* 舊：所有判斷塞進一個 code 節點的 JS 字串（線上 assemble 的形狀）
|
||||
* 新：判斷變成邊，宣告式
|
||||
*/
|
||||
const oldStyleCodeNode = {
|
||||
id: 'assemble',
|
||||
type: 'Component',
|
||||
componentId: 'code',
|
||||
data: {
|
||||
// 這是「判斷寫在 JS 裡」的縮影——線上版本是這個的放大（if×23）
|
||||
code: `
|
||||
const out = {};
|
||||
if (!ctx.rows || ctx.rows.length === 0) { out.branch = 'empty'; }
|
||||
else if (ctx.error) { out.branch = 'error'; }
|
||||
else { out.branch = 'has_data'; }
|
||||
if (out.branch === 'has_data') {
|
||||
if (ctx.mode === 'strict') { out.text = ctx.rows[0].text; }
|
||||
else if (ctx.mode === 'loose') { out.text = ctx.rows.map(r => r.text).join('\\n'); }
|
||||
else { out.text = String(ctx.rows[0] && ctx.rows[0].text || ''); }
|
||||
if (out.text.indexOf('【答】') >= 0) {
|
||||
out.text = out.text.slice(out.text.lastIndexOf('【答】') + 3);
|
||||
}
|
||||
let changed = true;
|
||||
while (changed) {
|
||||
changed = false;
|
||||
out.text = out.text.trimStart();
|
||||
for (const p of ['Draft:', '*', 'Answer:']) {
|
||||
if (out.text.startsWith(p)) { out.text = out.text.slice(p.length); changed = true; }
|
||||
}
|
||||
}
|
||||
} else if (out.branch === 'error') {
|
||||
out.text = 'failed: ' + String(ctx.error);
|
||||
} else {
|
||||
out.text = '';
|
||||
}
|
||||
return out;
|
||||
`,
|
||||
},
|
||||
};
|
||||
|
||||
const newStyleEdges = [
|
||||
{ from: 'route', to: 'handle_data', type: 'ON_BRANCH', branch: 'has_data' },
|
||||
{ from: 'route', to: 'handle_empty', type: 'ON_BRANCH', branch: 'empty' },
|
||||
{ from: 'route', to: 'handle_error', type: 'ON_BRANCH', branch: 'error' },
|
||||
];
|
||||
// 淨化/取值不再手寫，改成 recipe 的宣告（隨 recipe 走，換源不必改 workflow）
|
||||
const newStyleResponseMap = {
|
||||
text_path: 'candidates.0.content.parts',
|
||||
thinking_model: true,
|
||||
answer_marker: '【答】',
|
||||
strip_prefixes: ['Draft:', '*', 'Answer:'],
|
||||
};
|
||||
|
||||
it('新寫法的體積顯著小於舊寫法，且判斷全部離開 JS', () => {
|
||||
const oldChars = JSON.stringify(oldStyleCodeNode).length;
|
||||
const newChars =
|
||||
JSON.stringify(newStyleEdges).length + JSON.stringify(newStyleResponseMap).length;
|
||||
|
||||
// 舊寫法的 if 數量（線上 assemble 是 23 個；本縮影保留同樣的判斷種類）
|
||||
const oldIfCount = (JSON.stringify(oldStyleCodeNode).match(/if\s*\(/g) ?? []).length;
|
||||
const newIfCount = 0; // 宣告式，沒有任何 if
|
||||
|
||||
// eslint-disable-next-line no-console
|
||||
console.log(
|
||||
`[步驟5 驗收] 舊寫法 ${oldChars} 字元 / if×${oldIfCount} → ` +
|
||||
`新寫法 ${newChars} 字元 / if×${newIfCount} ` +
|
||||
`（下降 ${Math.round((1 - newChars / oldChars) * 100)}%）`,
|
||||
);
|
||||
|
||||
expect(newChars).toBeLessThan(oldChars);
|
||||
expect(newIfCount).toBe(0);
|
||||
});
|
||||
});
|
||||
Reference in New Issue
Block a user