refactor(knowledge-extraction): convert questioner and answerer to createRole four-tuple
- questioner: createRole(adapter, questionerPrompt, schema, extract) + queue short-circuit + meta post-processing - answerer: createRole(adapter, answererPrompt, schema, extract) + empty-questions short-circuit - build.ts: use createLlmAdapter(extract.provider) as default LLM adapter for questioner/answerer Refs uncaged/nerve#277
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@ -1,5 +1,6 @@
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import type { AgentFn, WorkflowDefinition } from "@uncaged/nerve-core";
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import type { LlmExtractorConfig } from "@uncaged/nerve-workflow-utils";
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import { createLlmAdapter } from "@uncaged/nerve-workflow-utils";
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import { moderator } from "./moderator.js";
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import type { WorkflowMeta } from "./moderator.js";
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@ -19,11 +20,12 @@ export function createKnowledgeExtractionWorkflow({
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extract,
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}: CreateKnowledgeExtractionDeps): WorkflowDefinition<WorkflowMeta> {
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const a = (role: keyof WorkflowMeta) => adapters?.[role] ?? defaultAdapter;
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const llmAdapter = createLlmAdapter(extract.provider);
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return {
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name: "knowledge-extraction",
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roles: {
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questioner: createQuestionerRole({ extract }),
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answerer: createAnswererRole({ extract }),
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questioner: createQuestionerRole(adapters?.questioner ?? llmAdapter, { extract }),
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answerer: createAnswererRole(adapters?.answerer ?? llmAdapter, { extract }),
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explorer: createExplorerRole(a("explorer"), { extract }),
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},
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moderator,
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@ -1,6 +1,6 @@
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import type { Role, ThreadContext, WorkflowMessage } from "@uncaged/nerve-core";
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import type { AgentFn, Role, ThreadContext, WorkflowMessage } from "@uncaged/nerve-core";
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import type { LlmExtractorConfig } from "@uncaged/nerve-workflow-utils";
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import { llmExtract, nerveCommandEnv, spawnSafe } from "@uncaged/nerve-workflow-utils";
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import { createRole, nerveCommandEnv, spawnSafe } from "@uncaged/nerve-workflow-utils";
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import { z } from "zod";
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import { resolveWorkdir } from "../lib/workdir.js";
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@ -34,12 +34,62 @@ function lastQuestionerMeta(messages: WorkflowMessage[]): QuestionerMeta | undef
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return undefined;
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}
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export function createAnswererRole(deps: CreateAnswererRoleDeps): Role<AnswererMeta> {
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const { extract } = deps;
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export async function answererPrompt(ctx: ThreadContext): Promise<string> {
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const messages = ctx.steps as unknown as WorkflowMessage[];
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const cwd = resolveWorkdir(ctx.start);
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const qm = lastQuestionerMeta(messages);
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if (!qm || qm.questions.length === 0) {
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throw new Error("answerer: prompt invoked without questioner questions — wrapped role should short-circuit");
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}
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const blocks: string[] = [];
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for (const q of qm.questions) {
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if ((ctx.start.meta as Record<string, unknown>).dryRun) {
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blocks.push(`### ${q.id}\n[dryRun] skipped nerve knowledge query\n`);
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continue;
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}
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const res = await spawnSafe(
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"nerve",
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["knowledge", "query", q.question],
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{
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cwd,
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env: nerveCommandEnv(),
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timeoutMs: 120_000,
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dryRun: false,
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abortSignal: null,
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},
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);
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if (res.ok) {
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blocks.push(`### ${q.id} (${q.domain})\nQuestion: ${q.question}\n---\n${res.value.stdout}\n`);
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} else {
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const err = res.error;
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const detail =
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err.kind === "non_zero_exit"
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? `exit ${err.exitCode}\n${err.stderr}`
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: err.kind === "timeout"
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? `timeout\n${err.stderr}`
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: err.kind === "spawn_failed"
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? err.message
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: "aborted";
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blocks.push(`### ${q.id}\nnerve knowledge query failed: ${detail}\n`);
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}
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}
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return [
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"You are the **answerer**. You MUST NOT read repository source code — only the CLI retrieval excerpts below.",
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"For each question id, decide whether the knowledge base already answers it.",
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"Set found=true only when the excerpt supports a confident answer; otherwise found=false.",
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"Set has_unanswered=true if any question remains unanswered by the knowledge base.",
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"",
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...blocks,
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].join("\n");
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}
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export function createAnswererRole(adapter: AgentFn, { extract }: CreateAnswererRoleDeps): Role<AnswererMeta> {
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const inner = createRole(adapter, answererPrompt, answererMetaSchema, extract);
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return async (ctx: ThreadContext) => {
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const messages = ctx.steps as unknown as WorkflowMessage[];
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const cwd = resolveWorkdir(ctx.start);
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const qm = lastQuestionerMeta(messages);
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if (!qm || qm.questions.length === 0) {
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return {
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@ -47,59 +97,6 @@ export function createAnswererRole(deps: CreateAnswererRoleDeps): Role<AnswererM
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meta: { results: [], has_unanswered: false },
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};
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}
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const blocks: string[] = [];
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for (const q of qm.questions) {
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if ((ctx.start.meta as Record<string, unknown>).dryRun) {
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blocks.push(`### ${q.id}\n[dryRun] skipped nerve knowledge query\n`);
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continue;
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}
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const res = await spawnSafe(
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"nerve",
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["knowledge", "query", q.question],
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{
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cwd,
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env: nerveCommandEnv(),
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timeoutMs: 120_000,
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dryRun: false,
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abortSignal: null,
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},
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);
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if (res.ok) {
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blocks.push(`### ${q.id} (${q.domain})\nQuestion: ${q.question}\n---\n${res.value.stdout}\n`);
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} else {
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const err = res.error;
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const detail =
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err.kind === "non_zero_exit"
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? `exit ${err.exitCode}\n${err.stderr}`
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: err.kind === "timeout"
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? `timeout\n${err.stderr}`
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: err.kind === "spawn_failed"
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? err.message
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: "aborted";
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blocks.push(`### ${q.id}\nnerve knowledge query failed: ${detail}\n`);
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}
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}
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const bundle = [
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"You are the **answerer**. You MUST NOT read repository source code — only the CLI retrieval excerpts below.",
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"For each question id, decide whether the knowledge base already answers it.",
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"Set found=true only when the excerpt supports a confident answer; otherwise found=false.",
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"Set has_unanswered=true if any question remains unanswered by the knowledge base.",
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"",
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...blocks,
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].join("\n");
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const metaR = await llmExtract({
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text: bundle,
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schema: answererMetaSchema,
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provider: extract.provider,
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dryRun: false,
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});
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if (!metaR.ok) {
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throw new Error(`answerer llmExtract: ${JSON.stringify(metaR.error)}`);
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}
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return { content: bundle, meta: metaR.value };
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return inner(ctx);
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};
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}
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@ -1,9 +1,9 @@
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import { readFile } from "node:fs/promises";
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import { join } from "node:path";
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import type { Role, ThreadContext, WorkflowMessage } from "@uncaged/nerve-core";
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import type { AgentFn, Role, ThreadContext, 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 { createRole } 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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@ -53,8 +53,29 @@ Remaining queue after this card (paths, may be empty): ${JSON.stringify(remainin
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${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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export async function questionerPrompt(ctx: ThreadContext): Promise<string> {
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const messages = ctx.steps as unknown as WorkflowMessage[];
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const cwd = resolveWorkdir(ctx.start);
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const queue = await resolveQueueForQuestioner(ctx.start, messages, cwd);
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if (queue.length === 0) {
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throw new Error(
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"questioner: prompt invoked with empty queue — wrapped role should short-circuit before LLM",
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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}: ${msg}`);
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}
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return `${questionerSystem()}\n\n${questionerUser(card, cardBody, remaining_queue)}`;
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}
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export function createQuestionerRole(adapter: AgentFn, { extract }: CreateQuestionerRoleDeps): Role<QuestionerMeta> {
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const inner = createRole(adapter, questionerPrompt, questionerExtractSchema, extract);
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return async (ctx: ThreadContext) => {
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const messages = ctx.steps as unknown as WorkflowMessage[];
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@ -74,26 +95,6 @@ export function createQuestionerRole(adapterExtract: CreateQuestionerRoleDeps):
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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(ctx);
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return {
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content: r.content,
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