小橘
1c512435de
Three-role workflow (questioner → answerer → explorer) that iterates over .knowledge/ cards to discover and fill knowledge gaps via BFS. - questioner: createLlmRole, reads card, asks 3 technical questions - answerer: spawnSafe nerve knowledge query, judges answers - explorer: reads code, writes/patches .knowledge cards, runs sync - moderator: BFS queue from message history, stagnation rule Closes #266
107 lines
3.1 KiB
TypeScript
107 lines
3.1 KiB
TypeScript
import { readFile } from "node:fs/promises";
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import { join } from "node:path";
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import type { Role, StartStep, WorkflowMessage } from "@uncaged/nerve-core";
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import type { LlmExtractorConfig } from "@uncaged/nerve-workflow-utils";
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import { createLlmRole } from "@uncaged/nerve-workflow-utils";
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import { z } from "zod";
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import { resolveQueueForQuestioner } from "../lib/knowledge-queue.js";
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import { resolveWorkdir } from "../lib/workdir.js";
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const questionerExtractSchema = z.object({
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questions: z
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.array(
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z.object({
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id: z.string(),
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question: z.string(),
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domain: z.string(),
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}),
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)
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.length(3),
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});
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export type QuestionerMeta = {
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/** Empty when no .knowledge cards and no work to do. */
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card: string;
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questions: { id: string; question: string; domain: string }[];
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remaining_queue: string[];
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};
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export type CreateQuestionerRoleDeps = {
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extract: LlmExtractorConfig;
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};
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function questionerSystem(): string {
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return `You are the **questioner** in a knowledge-extraction workflow.
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Read the given markdown knowledge card. Propose exactly **three** technical questions that are **not** already answered or covered by that card.
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Rules:
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- Questions must be concrete and technical.
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- Each question needs a stable string id (e.g. q1, q2, q3), a short domain label (e.g. routing, storage), and the question text.
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- Do not assume access to other files or tools — reason only from the card content shown.`;
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}
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function questionerUser(card: string, cardBody: string, remainingHint: string[]): string {
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return `Current card path: ${card}
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Remaining queue after this card (paths, may be empty): ${JSON.stringify(remainingHint)}
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--- Card content ---
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${cardBody}`;
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}
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export function createQuestionerRole(adapterExtract: CreateQuestionerRoleDeps): Role<QuestionerMeta> {
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const { extract } = adapterExtract;
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return async (start: StartStep, messages: WorkflowMessage[]) => {
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const cwd = resolveWorkdir(start);
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const queue = await resolveQueueForQuestioner(start, messages, cwd);
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if (queue.length === 0) {
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return {
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content:
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"questioner: no `.knowledge` markdown files found and no seed path in the trigger prompt; queue is empty.",
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meta: {
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card: "",
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questions: [],
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remaining_queue: [],
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},
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};
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}
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const card = queue[0]!;
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const remaining_queue = queue.slice(1);
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let cardBody: string;
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try {
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cardBody = await readFile(join(cwd, card), "utf8");
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} catch (e) {
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const msg = e instanceof Error ? e.message : String(e);
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throw new Error(`questioner: failed to read ${card}: ${msg}`);
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}
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const inner = createLlmRole({
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provider: extract.provider,
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prompt: async () => [
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{ role: "system", content: questionerSystem() },
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{ role: "user", content: questionerUser(card, cardBody, remaining_queue) },
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],
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extract: {
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schema: questionerExtractSchema,
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provider: extract.provider,
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},
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});
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const r = await inner(start, messages);
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return {
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content: r.content,
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meta: {
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card,
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questions: r.meta.questions,
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remaining_queue,
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},
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};
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};
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}
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