71 lines
3.1 KiB
TypeScript
71 lines
3.1 KiB
TypeScript
import path from "node:path";
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import { fileURLToPath } from "node:url";
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/** Repository root (this file is at <root>/config.ts). */
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export const ROOT = path.dirname(fileURLToPath(import.meta.url));
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export const PATHS = {
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root: ROOT,
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/** Teacher system prompt — the agent's instructions (src/agent/). */
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teacherPrompt: path.join(ROOT, "src", "agent", "system-prompt.md"),
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data: path.join(ROOT, "data"),
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/** Chat sessions (AI SDK UIMessage transcripts, one JSON file per chat). */
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chats: path.join(ROOT, "data", "chats"),
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graph: path.join(ROOT, "data", "graph"),
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} as const;
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// ─── Models ────────────────────────────────────────────────────────────────
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// THE single place every LLM is chosen and tuned. .env stays for secrets
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// (API keys) only — model choice and thinking budget are product tuning,
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// not deployment environment, and this section is the one place they grow.
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// `effort` is OpenRouter's reasoning scale (docs: reasoning-tokens):
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// "none" | "minimal" | "low" | "medium" | "high" | "xhigh".
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// "none" disables reasoning tokens entirely.
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export type ReasoningEffort = "none" | "minimal" | "low" | "medium" | "high" | "xhigh";
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export interface ModelSpec {
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/** OpenRouter model id (e.g. "z-ai/glm-5.3-flash"). */
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model: string;
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/** Thinking budget — what the model may spend on reasoning tokens. */
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effort: ReasoningEffort;
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}
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export const MODELS = {
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/** The teacher — streaming, lesson-facing: smart, but keep it lively. */
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teacher: { model: "z-ai/glm-5.3-flash", effort: "minimal" },
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/** Background sub-agents: fast and cheap beats deep thought. */
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researcher: { model: "z-ai/glm-5.3-flash", effort: "minimal" },
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demoBuilder: { model: "z-ai/glm-5.3-flash", effort: "minimal" },
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/** Must be vision-capable: it looks at rendered SVGs. */
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visualMaker: { model: "qwen/qwen3.8-max", effort: "minimal" },
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/** Extraction is judgment over given evidence — light thinking suffices. */
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extractor: { model: "z-ai/glm-5.3-flash", effort: "minimal" },
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/** The auditor verifies evidence exists; keep it cheap. */
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auditor: { model: "z-ai/glm-5.3-flash", effort: "minimal" },
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} satisfies Record<string, ModelSpec>;
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export type ModelRole = keyof typeof MODELS;
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/**
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* The extraction pipeline (vision §1 construction): the sole graph writer.
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* Triggered per chat once this many NEW quiz answers have landed since the
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* chat's last extraction checkpoint. The trigger mechanism is a v1
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* placeholder — deliberately isolated behind one seam
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* (server/turn-service.ts) so it can be swapped without touching the
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* pipeline.
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*/
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export const EXTRACTION = { everyNQuizAnswers: 3 } as const;
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/**
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* The LIVE graph store (src/store/graph.ts) — owned by the app's extraction
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* pipeline. Line shapes: src/core/contracts.ts.
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*/
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export const LEDGER_FILES = {
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nodes: path.join(PATHS.graph, "nodes.jsonl"),
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edges: path.join(PATHS.graph, "edges.jsonl"),
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events: path.join(PATHS.graph, "events.jsonl"),
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mappings: path.join(PATHS.graph, "mappings.jsonl"),
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sessions: path.join(PATHS.graph, "sessions.jsonl"),
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} as const;
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