Files
learnharness/config.ts
T
amosblomqvist f0267a5fd9 Clusters phases B+C: labeling records, the naming call, snapshot payload
Core-survival labelings (vision section 1, DECIDED 2026-09-13):
ClusterLabeling records in a new append-only ledger
(data/graph/cluster-labels.jsonl), immutable, id = clu_ + sha256 of the
sorted core so re-naming races and re-runs dedupe at the store seam.
matchClusterLabelings (core/clusters.ts, pure): a community keeps a
record's label while at least half its core survives in it; both sides
single-successor (a split keeps one name; greedy by overlap, ties to
the newer record).

The namer (src/agent/subagents/cluster-namer.ts): ONE singular cheap LLM
call (new 'labeler' role, glm-flash/low) names every new/changed
community at once, given member names + statements (capped at 12
members per cluster). refreshClusterLabels runs POST-EXTRACTION
(user-decided) from both front doors, fire-and-forget — naming failure
never fails the run; refreshes serialize behind a chain so parallel
imports never mint duplicate labels. import-notes.ts awaits
clusterNamingSettled before exit.

Payload: GraphSnapshot.clusters (>= MIN_CLUSTER_SIZE 3) joins detection
with labelings at read time — claimed clusters carry the record's label
and id; unlabeled ones carry a member-hash id and null label. Detection
is deterministic so no memo in v1 (ms at current scale; revisit at
vault scale).

Tests (372/372): matcher half-rule/split/merge/greedy/tie cases; naming
end-to-end through importNotes (one call, one record, claim on
re-refresh), naming failure non-fatal; snapshot derivation with
labeling join; 11 test pipelines now pin an explicit labeler seam.
2026-09-13 03:59:48 +03:00

87 lines
4.1 KiB
TypeScript

import path from "node:path";
import { fileURLToPath } from "node:url";
/** Repository root (this file is at <root>/config.ts). */
export const ROOT = path.dirname(fileURLToPath(import.meta.url));
export const PATHS = {
root: ROOT,
/** Teacher system prompt — the agent's instructions (src/agent/). */
teacherPrompt: path.join(ROOT, "src", "agent", "system-prompt.md"),
data: path.join(ROOT, "data"),
/** Chat sessions (AI SDK UIMessage transcripts, one JSON file per chat). */
chats: path.join(ROOT, "data", "chats"),
graph: path.join(ROOT, "data", "graph"),
} as const;
// ─── Models ────────────────────────────────────────────────────────────────
// THE single place every LLM is chosen and tuned. .env stays for secrets
// (API keys) only — model choice and thinking budget are product tuning,
// not deployment environment, and this section is the one place they grow.
// `effort` is pi's thinking-level scale (off|minimal|low|medium|high|xhigh
// |max): the per-model truth lives in pi-ai's catalog (thinkingLevelMap) —
// getSupportedThinkingLevels(model) lists what a model actually accepts,
// and streamSimple clamps any other value to the nearest supported one
// (e.g. GLM-5.3-flash supports exactly low|high|max).
export type ThinkingLevel = "off" | "minimal" | "low" | "medium" | "high" | "xhigh" | "max";
export interface ModelSpec {
/** OpenRouter model id (e.g. "z-ai/glm-5.3-flash") — must exist in pi-ai's catalog. */
model: string;
/** Thinking budget — what the model may spend on reasoning tokens. */
effort: ThinkingLevel;
}
export const MODELS = {
/** The prober (agent A) — edge-bracketing questions; keep it lively and adaptive. */
prober: { model: "z-ai/glm-5.3-flash", effort: "low" },
/** The teacher (agent B) — planning + streaming, lesson-facing: smart, but keep it lively. */
teacher: { model: "z-ai/glm-5.3-flash", effort: "low" },
/** Background sub-agents: fast and cheap beats deep thought. */
researcher: { model: "z-ai/glm-5.3-flash", effort: "low" },
demoBuilder: { model: "z-ai/glm-5.3-flash", effort: "low" },
/** Must be vision-capable: it looks at rendered SVGs. */
visualMaker: { model: "qwen/qwen3.8-flash", effort: "low" },
/** Extraction is judgment over given evidence — light thinking suffices. */
extractor: { model: "z-ai/glm-5.3-flash", effort: "low" },
/** The auditor verifies evidence exists; keep it cheap. */
auditor: { model: "z-ai/glm-5.3-flash", effort: "low" },
/** Names detected communities — one singular call, given member names
* + statements. Purely a labeling flourish; keep it cheap. */
labeler: { model: "z-ai/glm-5.3-flash", effort: "low" },
} satisfies Record<string, ModelSpec>;
export type ModelRole = keyof typeof MODELS;
/**
* The extraction pipeline (vision §1 construction): the sole graph writer.
* Triggered per chat once this many NEW quiz answers have landed since the
* chat's last extraction checkpoint. The trigger mechanism is a v1
* placeholder — deliberately isolated behind one seam
* (server/turn-service.ts) so it can be swapped without touching the
* pipeline.
*/
export const EXTRACTION = { everyNQuizAnswers: 3 } as const;
/**
* The LIVE graph store (src/store/graph.ts) — owned by the app's extraction
* pipeline. Line shapes: src/core/contracts.ts.
*/
export const LEDGER_FILES = {
nodes: path.join(PATHS.graph, "nodes.jsonl"),
edges: path.join(PATHS.graph, "edges.jsonl"),
events: path.join(PATHS.graph, "events.jsonl"),
mappings: path.join(PATHS.graph, "mappings.jsonl"),
sessions: path.join(PATHS.graph, "sessions.jsonl"),
/** LLM-named community records (contracts/graph.ts ClusterLabeling). */
clusterLabels: path.join(PATHS.graph, "cluster-labels.jsonl"),
} as const;
/**
* Notes-import bookkeeping (NOT a ledger): one JSON map, src-id → NotesSource
* record (contracts/graph.ts) — the file path + import time + the run's usage
* log. Read-modify-write like a chat file, not append-only like the ledgers.
*/
export const SOURCES_FILE = path.join(PATHS.graph, "sources.json");