Pivot to text-first column-stack workspace + merge-with-review across AI artifacts
Workspace - Pivot from "set of open panes" to a Finder-style miller column stack: TopBar / LeftSidebar / [section → entity → entity ...] / pinned text editor. openPanesStore is now an ordered Column[] with pushFrom / closeFrom / setStack; only one top-level section is rooted at a time. - New entity column panes: Term, Block, Association, Constraint, Requirement, Finding. Click-through navigation truncates deeper columns automatically. - LeftSidebar surfaces a pending-count chip per section (single-glance navigation cue) and spins its analyze ↻ via SVG Spinner whenever the LLM is working — including server-initiated runs caught by the runs poll, not just user-triggered ones. Analyze pipeline + persistence - Unified `concepts` pass (taxonomy + glossary in one LLM call) replaces the two-pass setup. Server still accepts ?section=taxonomy|glossary and normalizes them for back-compat. - model / requirements / detection (assumptions, risks, inconsistencies) + cross-layer validation rules (X1–X4: stale term link, unlinked formalism, undefined linked term, prose-only term). - Persistence: NarrativeDocument, ModelSnapshot, ChangelogEntry, TaxonomyTerm, RequirementEntry, Finding, AnalysisRun. Re-runs MERGE instead of replace: gentle update on existing items, suggested on new, deprecated on missing — same idiom for every artifact kind. User pins preserve "kept" decisions across re-analyses. - Migrations: pivot_text_first, add_requirement_linked_term, term_review_state, review_state_for_reqs_and_findings, add_term_definition_pinned. Concept ↔ ontology integration - linkedTermId on Block / Association / Constraint / Requirement. PromoteToolbar lets the user formalize a concept inline: + Block / + Association / + Constraint / + Requirement, all routed through applyOps so undo/redo and SSE work for free. - decideElement op for in-canvas keep/discard on review-pending model elements. User-authored definitions - TermColumn definition is click-to-edit. Save (Cmd-Enter / blur), Cancel (Esc), Reset to AI suggestion when pinned. - definitionPinned flag on TaxonomyTerm: future Analyze runs leave the user's text alone. setTermDefinition repo function + POST /api/projects/[id]/terms/[termId]/definition endpoint. - mergeTaxonomySuggestion + applyGlossaryDefinitions both pin-aware. UX/UI - StatusChip: single component for all state idioms (suggested, deprecated, accepted, dismissed, resolved, severity, validation code, confidence, warn). Replaces 5+ ad-hoc badge classes. - PaneControls (PaneViewTabs + PaneFilterChip): separates view-mode toggles from filter chips so toggling Pending no longer flips you off the current view. - PaneEmpty: unified empty-state with title + hint + action. - PaneDrawer: collapsible groups for Pending / Discarded review; cards group as Kept (top) → Pending (bottom drawer) → Discarded (Findings only, hidden when empty). Restore action recovers dismissed/resolved findings. - ConceptCard unifies Tree and A–Z views in Concepts; only Tree parents carry the chevron (no empty placeholder offset). - Type + spacing tokens (--text-xs..xl, --space-1..6, --lh-tight/ui/ prose, --radius-*) replace every ad-hoc value. - Buttons standardized to body sans 500 (was a mishmash of mono / display). - Card shells unified across Concepts / Requirements / Findings. Cleanup - Removed: LeftRail, FindingsPanel, IssuesPanel, SocratesDock, ProposalCard, SlashMenu, SlashExtension, slashSuggestion, CanvasHeader, TaxonomyPane, GlossaryPane, TermDetail (popover; now TermColumn). - Section ids in openPanesStore: dropped taxonomy/glossary, added concepts. localStorage migration runs on hydrate. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
150
apps/web/lib/llm/seedInterview.ts
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150
apps/web/lib/llm/seedInterview.ts
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// Live seed interview — per-turn handler.
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//
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// Stateless on the server: the client passes the running thread + the draft
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// extracted so far, plus the user's latest reply. We call the LLM with the
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// character + interview prompts and get back { assistant turn, updated draft,
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// confidence, ready }.
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import "server-only";
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import { defaultGateway, chatJSON, type Message } from "./gateway";
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import { loadPrompt } from "./prompts";
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export interface SeedDraft {
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title: string;
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problem: string;
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targetUser: string;
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desiredOutcome: string;
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initialHypothesis?: string;
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constraints?: string[];
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}
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export interface InterviewTurn {
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role: "socrates" | "user";
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text: string;
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}
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export interface InterviewStepArgs {
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/** Running interview history. Empty array on the first call → Socrates opens. */
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history: InterviewTurn[];
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/** Latest user reply (empty string for the opening turn). */
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userText: string;
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/** Draft extracted so far. Empty on the first call. */
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draft: SeedDraft;
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}
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export interface InterviewStepResult {
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text: string;
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draft: SeedDraft;
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confidence: number;
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ready: boolean;
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inputTokens: number;
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outputTokens: number;
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provider: string;
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model: string;
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}
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const interviewJsonSchema = {
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type: "object",
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additionalProperties: false,
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required: ["text", "draft", "confidence", "ready"],
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properties: {
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text: { type: "string", minLength: 1 },
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draft: {
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type: "object",
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additionalProperties: false,
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required: ["title", "problem", "targetUser", "desiredOutcome"],
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properties: {
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title: { type: "string" },
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problem: { type: "string" },
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targetUser: { type: "string" },
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desiredOutcome: { type: "string" },
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initialHypothesis: { type: "string" },
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constraints: { type: "array", items: { type: "string" } },
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},
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},
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confidence: { type: "number", minimum: 0, maximum: 1 },
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ready: { type: "boolean" },
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},
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} as const;
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interface RawResponse {
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text?: string;
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draft?: Partial<SeedDraft>;
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confidence?: number;
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ready?: boolean;
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}
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export async function interviewStep(args: InterviewStepArgs): Promise<InterviewStepResult> {
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const character = loadPrompt("socrates/character.md");
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const interview = loadPrompt("socrates/interview.md");
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const userTurnsSoFar = args.history.filter(t => t.role === "user").length;
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const remaining = Math.max(0, 5 - userTurnsSoFar - (args.userText.trim().length > 0 ? 1 : 0));
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const messages: Message[] = [
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{
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role: "system",
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content: [
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character,
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"---",
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interview,
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"---",
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`Draft so far (your previous extraction):\n\`\`\`json\n${JSON.stringify(args.draft, null, 2)}\n\`\`\``,
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`Turns remaining before ready signal becomes mandatory: ${remaining}`,
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].join("\n\n"),
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},
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];
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// Replay history so the LLM has full context.
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for (const t of args.history) {
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messages.push({ role: t.role === "socrates" ? "assistant" : "user", content: t.text });
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}
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if (args.userText.trim().length > 0) {
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messages.push({ role: "user", content: args.userText });
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} else if (args.history.length === 0) {
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messages.push({ role: "user", content: "Begin the interview." });
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}
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const gateway = defaultGateway();
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const { value, result } = await chatJSON<RawResponse>(gateway, messages, {
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temperature: 0.4,
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maxTokens: 768,
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jsonSchema: { name: "interview_step", schema: interviewJsonSchema as Record<string, unknown> },
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jsonObjectMode: true,
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maxRepairs: 2,
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});
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// Normalize / merge — the LLM may emit a partial draft; we union with the
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// prior draft so a user backtracking doesn't wipe a previously-confirmed field.
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const incoming: Partial<SeedDraft> = value?.draft ?? {};
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const draft: SeedDraft = {
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title: nonEmpty(incoming.title) ?? args.draft.title ?? "",
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problem: nonEmpty(incoming.problem) ?? args.draft.problem ?? "",
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targetUser: nonEmpty(incoming.targetUser) ?? args.draft.targetUser ?? "",
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desiredOutcome: nonEmpty(incoming.desiredOutcome) ?? args.draft.desiredOutcome ?? "",
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initialHypothesis: nonEmpty(incoming.initialHypothesis) ?? args.draft.initialHypothesis,
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constraints: Array.isArray(incoming.constraints) ? incoming.constraints : args.draft.constraints,
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};
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return {
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text: typeof value?.text === "string" && value.text.length > 0 ? value.text : "[empty response]",
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draft,
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confidence: clamp01(value?.confidence ?? 0),
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ready: !!value?.ready,
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inputTokens: result.inputTokens,
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outputTokens: result.outputTokens,
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provider: gateway.provider,
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model: gateway.model,
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};
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}
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function nonEmpty(s: unknown): string | undefined {
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if (typeof s !== "string") return undefined;
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const t = s.trim();
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return t.length > 0 ? t : undefined;
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}
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function clamp01(n: number): number {
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if (Number.isNaN(n)) return 0;
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return Math.max(0, Math.min(1, n));
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}
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