auditor: inference curation layer + llm_team fact extraction → KB
Closes the cycle J asked for: curated cloud output lands structured knowledge in the KB so future audits have architectural context, not just a log of per-finding signatures. Three pieces: 1. Inference curation (tree-split) — when diff > 30KB, shard at 4.5KB, summarize each shard via cloud (temp=0, think=false on small shards; think=true on main call). Merge into scratchpad. The cloud verification then runs against the scratchpad, not truncated raw. Eliminates the 40KB MAX_DIFF_CHARS truncation path for large PRs (PR #8 is 102KB — was losing 62KB). Anti-false-positive guard in the prompt: cloud is told scratchpad absence is NOT diff absence, so it doesn't flag curated-out symbols as missing. unflagged_gaps section is dropped entirely when curated (scratchpad can't ground them). 2. fact_extractor — TS client for llm_team_ui's extract-facts mode at localhost:5000/api/run. Sends curated scratchpad through qwen2.5 extractor + gemma2 verifier, parses SSE stream, returns structured {facts, entities, relationships, verification, llm_team_run_id}. Best-effort: if llm_team is down, extraction fails silently and the audit still completes. AWAITED so CLI tools (audit_one.ts) don't exit before extraction lands — the systemd poller has 90s headroom so the extra ~15s doesn't matter. 3. audit_facts.jsonl + checkAuditFacts() — one row per curated audit with the extraction result. kb_query tails the jsonl, explodes entity rows, aggregates by entity name with distinct-PR counting, surfaces entities recurring in 2+ PRs as info findings. Filters out short names (<3 chars, extractor truncation artifacts) and generic types (string/number/etc.) so signal isn't drowned. Verified end-to-end on PR #8: 102KB diff → 23 shards → 1KB scratchpad → qwen2.5 extracted 4 facts + 6 entities + 6 relationships (real code-level knowledge: AggregateOptions<T> type, aggregate<T> async function with real signature, typed relationships). llm_team_run_id cross-references to llm_team's own team_runs table. Also: audit.ts passes (pr_number, head_sha) as InferenceContext so extracted facts are scope-tagged for the KB index.
This commit is contained in:
profit
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@ -56,7 +56,7 @@ export async function auditPr(pr: PrSnapshot, opts: AuditOptions = {}): Promise<
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const [staticFindings, dynamicFindings, inferenceFindings, kbFindings] = await Promise.all([
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runStaticCheck(diff),
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opts.skip_dynamic ? Promise.resolve(stubFinding("dynamic", "skipped by options")) : runDynamicCheck(),
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opts.skip_inference ? Promise.resolve(stubFinding("inference", "skipped by options")) : runInferenceCheck(claims, diff),
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opts.skip_inference ? Promise.resolve(stubFinding("inference", "skipped by options")) : runInferenceCheck(claims, diff, { pr_number: pr.number, head_sha: pr.head_sha }),
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runKbCheck(claims, pr.files.map(f => f.path)),
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]);
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@ -14,7 +14,8 @@
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import type { Claim, Finding } from "../types.ts";
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import { Glob } from "bun";
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import { readFile } from "node:fs/promises";
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import { readFile, mkdir, appendFile } from "node:fs/promises";
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import { extractFacts } from "../fact_extractor.ts";
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const GATEWAY = process.env.LH_GATEWAY_URL ?? "http://localhost:3100";
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const MODEL = process.env.LH_AUDITOR_REVIEW_MODEL ?? "gpt-oss:120b";
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@ -22,11 +23,36 @@ const MODEL = process.env.LH_AUDITOR_REVIEW_MODEL ?? "gpt-oss:120b";
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// previously truncated at 15KB causing the reviewer to miss later
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// files (gitea.ts, policy.ts) and flag "no Gitea client present" as a
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// block finding when the file was simply outside the truncation window.
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//
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// Above this threshold we curate via tree-split rather than truncate,
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// following the scrum_master pattern: shard the diff, summarize each
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// shard against the claim-verification task, merge into a compact
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// scratchpad, then ask the cloud to verify claims against the
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// scratchpad. This gives the cloud full-PR fidelity without bursting
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// its context window (observed failure mode: empty response or
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// unparseable output when prompt exceeds model's comfortable range).
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const MAX_DIFF_CHARS = 40000;
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// Tree-split kicks in above this. 30KB is below MAX_DIFF_CHARS so we
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// curate BEFORE truncation would happen — never lose signal to a hard
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// cut. Shard size is chosen so ~10 shards cover PR #8-size diffs in a
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// reasonable round-trip budget.
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const CURATION_THRESHOLD = 30000;
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const DIFF_SHARD_SIZE = 4500;
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const CALL_TIMEOUT_MS = 120_000;
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const REPO_ROOT = "/home/profit/lakehouse";
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export async function runInferenceCheck(claims: Claim[], diff: string): Promise<Finding[]> {
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export interface InferenceContext {
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pr_number: number;
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head_sha: string;
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}
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const AUDIT_FACTS_JSONL = "/home/profit/lakehouse/data/_kb/audit_facts.jsonl";
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export async function runInferenceCheck(
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claims: Claim[],
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diff: string,
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ctx?: InferenceContext,
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): Promise<Finding[]> {
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if (claims.length === 0) {
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return [{
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check: "inference",
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@ -51,9 +77,26 @@ export async function runInferenceCheck(claims: Claim[], diff: string): Promise<
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}];
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}
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const truncated = diff.length > MAX_DIFF_CHARS
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? diff.slice(0, MAX_DIFF_CHARS) + `\n...[${diff.length - MAX_DIFF_CHARS} more chars truncated]`
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: diff;
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// Diff source for the cloud prompt — either the raw diff (small
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// enough to fit), or a tree-split scratchpad (curation layer). We
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// prefer curation to truncation: truncation silently drops files
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// past the window; curation summarizes them so the cloud still sees
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// what changed, just densified.
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let diffForPrompt: string;
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let curationNote = "";
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if (diff.length > CURATION_THRESHOLD) {
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const ts = await treeSplitDiff(diff, verifiable);
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diffForPrompt = ts.scratchpad;
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curationNote = ` (curated: ${diff.length} chars → ${ts.shards} shards → scratchpad ${ts.scratchpad.length} chars)`;
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} else {
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diffForPrompt = diff;
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}
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// Belt-and-suspenders truncation — even a tree-split scratchpad
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// shouldn't exceed MAX_DIFF_CHARS in practice, but guard anyway so
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// pathological inputs can't burst the prompt.
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const truncated = diffForPrompt.length > MAX_DIFF_CHARS
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? diffForPrompt.slice(0, MAX_DIFF_CHARS) + `\n...[${diffForPrompt.length - MAX_DIFF_CHARS} more chars truncated]`
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: diffForPrompt;
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// Build the reviewer prompt in the same shape as run_codereview's
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// review stage (llm_team_ui.py:10950), adapted for claim verification:
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@ -61,6 +104,30 @@ export async function runInferenceCheck(claims: Claim[], diff: string): Promise<
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// "Code: ..."
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// "Review: bugs/security/perf/style/edge. Provide corrected code."
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// We add: claim list upfront + ask for structured JSON verdict.
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//
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// When the diff was curated (tree-split scratchpad), we add an
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// explicit anti-false-positive instruction: the scratchpad is a
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// distillation, not the full source, so absence-from-scratchpad is
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// NOT evidence of absence-from-diff. Mirrors the fix we made in
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// scrum_master's review prompt for the same class of error.
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const isCurated = curationNote.length > 0;
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const curationGuard = isCurated
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? [
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"",
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"CRITICAL: the 'Diff' below is a curated multi-shard scratchpad,",
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"NOT the full raw diff. The scratchpad distills each shard down",
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"to facts useful for claim verification and drops the rest.",
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"DO NOT flag a function/field/feature as 'missing' or 'not",
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"implemented' based solely on its absence from the scratchpad —",
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"absence in a distillation is NOT evidence of absence in the",
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"actual diff. Only judge a claim NOT BACKED when the scratchpad",
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"DIRECTLY contradicts it (e.g. scratchpad shows the function was",
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"added empty, or shows the claimed code path is a stub).",
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"Skip the unflagged_gaps section entirely when operating on a",
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"curated scratchpad — you can't reliably detect gaps from a",
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"distillation, and false positives there are worse than misses.",
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].join("\n")
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: "";
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const systemMsg = [
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"You review pull-request diffs against the author's own ship-claims.",
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"For each claim, decide: is it backed by actual code in the diff, or is",
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@ -74,6 +141,7 @@ export async function runInferenceCheck(claims: Claim[], diff: string): Promise<
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" - the claim claims integration but the integration point is a stub",
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" - the diff contains unimplemented!() / todo!() / TODO comments",
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" - the claim says 'works end-to-end' but the diff has no end-to-end test",
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curationGuard,
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"",
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"Respond with strict JSON only. No prose before or after. Shape:",
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"{",
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@ -174,7 +242,7 @@ export async function runInferenceCheck(claims: Claim[], diff: string): Promise<
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findings.push({
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check: "inference",
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severity: "info",
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summary: `cloud review completed (model=${MODEL}, tokens=${usage.total_tokens ?? "?"})`,
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summary: `cloud review completed (model=${MODEL}, tokens=${usage.total_tokens ?? "?"})${curationNote}`,
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evidence: [
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`claim_verdicts: ${parsed.claim_verdicts?.length ?? 0}, unflagged_gaps: ${parsed.unflagged_gaps?.length ?? 0}`,
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],
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@ -204,7 +272,29 @@ export async function runInferenceCheck(claims: Claim[], diff: string): Promise<
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}
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}
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for (const g of parsed.unflagged_gaps ?? []) {
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// Route the curated scratchpad through llm_team's extract-facts
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// pipeline when we have (a) a curated scratchpad (best signal about
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// what the PR actually changed) and (b) PR context to scope facts.
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// AWAITED (not fire-and-forget) so CLI callers like audit_one.ts
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// don't exit before extraction lands; the systemd poller has plenty
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// of headroom (90s cycle vs ~15s extraction). A failure inside
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// extractAndPersistFacts is caught + logged but never throws.
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if (isCurated && ctx && process.env.LH_AUDITOR_SKIP_EXTRACT !== "1") {
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try {
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await extractAndPersistFacts(diffForPrompt, ctx);
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} catch (e) {
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console.error(`[inference] fact extraction failed: ${(e as Error).message}`);
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}
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}
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// Belt-and-suspenders: when operating on a curated scratchpad, drop
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// the unflagged_gaps section entirely. The distillation can't
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// reliably ground gap-detection, and false positives are worse than
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// misses for this signal class. The systemMsg already asks the
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// cloud to skip this section when curated — but the model may still
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// emit it, so we filter here too.
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const gapsToEmit = isCurated ? [] : (parsed.unflagged_gaps ?? []);
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for (const g of gapsToEmit) {
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const summary = String(g?.summary ?? "?");
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const location = String(g?.location ?? "?");
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// False-positive guard — when the cloud says "X not defined in this
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@ -248,6 +338,122 @@ export async function runInferenceCheck(claims: Claim[], diff: string): Promise<
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return findings;
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}
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// Extract structured knowledge from the curated scratchpad and append
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// to data/_kb/audit_facts.jsonl — one row per extract run, keyed by
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// PR number + head SHA for scope tracking. kb_query tails this next
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// audit to surface recurring entities/relationships across PRs.
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async function extractAndPersistFacts(scratchpad: string, ctx: InferenceContext): Promise<void> {
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const ex = await extractFacts(scratchpad);
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if (ex.error && ex.entities.length === 0 && ex.facts.length === 0) {
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// Full failure — log but don't write an empty row.
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console.error(`[inference] extractFacts skipped row: ${ex.error}`);
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return;
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}
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const row = {
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pr_number: ctx.pr_number,
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head_sha: ctx.head_sha,
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extracted_at: ex.extracted_at,
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extractor: ex.extractor_model,
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verifier: ex.verifier_model,
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llm_team_run_id: ex.llm_team_run_id ?? null,
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facts: ex.facts,
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entities: ex.entities,
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relationships: ex.relationships,
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verification_preview: ex.verification.slice(0, 400),
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};
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await mkdir("/home/profit/lakehouse/data/_kb", { recursive: true });
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await appendFile(AUDIT_FACTS_JSONL, JSON.stringify(row) + "\n");
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}
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// Curation via tree-split — ports the scrum_master pattern into the
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// inference check. Shards the raw diff into DIFF_SHARD_SIZE chunks,
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// summarizes each shard *against the claim-verification task* so the
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// summary preserves exactly what the cloud needs to judge claims
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// (function signatures, struct fields, deletions, new files), drops
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// everything else. Merges into a compact scratchpad.
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//
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// Cost: N cloud calls for the shard summaries + 1 cloud call for the
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// final verification = N+1 calls instead of 1. Mitigation: shards run
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// serially (not parallel) to keep gateway load bounded; summary calls
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// use max_tokens=400 so they're fast (~2s each on gpt-oss:120b).
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//
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// Determinism: each shard summary call uses temp=0 + think=true (same
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// as the top-level inference call), so identical input yields
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// identical scratchpad. The final verification call then sees a
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// stable scratchpad, giving stable verdicts.
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async function treeSplitDiff(
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fullDiff: string,
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claims: Claim[],
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): Promise<{ scratchpad: string; shards: number }> {
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const shards: Array<{ from: number; to: number; text: string }> = [];
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for (let i = 0; i < fullDiff.length; i += DIFF_SHARD_SIZE) {
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const end = Math.min(i + DIFF_SHARD_SIZE, fullDiff.length);
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shards.push({ from: i, to: end, text: fullDiff.slice(i, end) });
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}
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// Curate the claim list into a short form the summary prompt can
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// use to bias extraction toward relevant facts.
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const claimDigest = claims.map((c, i) =>
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`${i}. [${c.strength}] "${c.text.slice(0, 100)}"`
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).join("\n");
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let scratchpad = "";
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for (const [si, shard] of shards.entries()) {
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const prompt = [
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`You are summarizing shard ${si + 1}/${shards.length} (chars ${shard.from}..${shard.to}) of a PR diff.`,
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`The downstream task will verify these ship-claims against the full-PR summary. Extract ONLY facts that could confirm or refute these claims:`,
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"",
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claimDigest,
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"",
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"Extract: new function/method signatures, struct fields, deletions, new files, wiring (function X calls Y), absence-of-implementation markers, TODO comments on added lines.",
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"Skip: comment-only edits, whitespace, import reordering, unrelated cosmetic changes.",
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"",
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"─────── shard diff ───────",
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shard.text,
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"─────── end shard ───────",
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"",
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"Output: up to 180 words of facts in bullet form. No prose preamble, no claim verdicts (that's for the downstream step).",
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].join("\n");
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const r = await callCloud(prompt, 400);
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if (r.content) {
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scratchpad += `\n--- shard ${si + 1} (chars ${shard.from}..${shard.to}) ---\n${r.content.trim()}\n`;
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}
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}
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return { scratchpad: scratchpad.trim(), shards: shards.length };
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}
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// Minimal cloud caller used only by treeSplitDiff — same gateway +
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// model as the top-level call, but think=false. Shards are small
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// (≤DIFF_SHARD_SIZE ~4500 chars) and the task is pure fact
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// extraction, not reasoning. think=true on the shards introduced
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// variance in reasoning traces that compounded across 23 calls into
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// a non-deterministic scratchpad (observed during curation
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// validation: same-SHA runs produced 5/7/8 final findings).
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// think=false on small prompts is stable — only breaks at the main
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// call's 10K+ prompt size, which keeps think=true.
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async function callCloud(prompt: string, maxTokens: number): Promise<{ content: string }> {
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try {
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const r = await fetch(`${GATEWAY}/v1/chat`, {
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method: "POST",
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headers: { "content-type": "application/json" },
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body: JSON.stringify({
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provider: "ollama_cloud",
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model: MODEL,
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messages: [{ role: "user", content: prompt }],
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max_tokens: maxTokens,
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temperature: 0,
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think: false,
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}),
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signal: AbortSignal.timeout(CALL_TIMEOUT_MS),
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});
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if (!r.ok) return { content: "" };
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const j: any = await r.json();
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return { content: j?.choices?.[0]?.message?.content ?? "" };
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} catch {
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return { content: "" };
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}
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}
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// Pull out plausible code-symbol names from a summary string.
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// Matches:
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// - identifier with backticks: `foo_bar`
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@ -25,6 +25,7 @@ const OBSERVER_OPS = "/home/profit/lakehouse/data/_observer/ops.jsonl";
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const BOT_CYCLES_DIR = "/home/profit/lakehouse/data/_bot/cycles";
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const SCRUM_REVIEWS_JSONL = "/home/profit/lakehouse/data/_kb/scrum_reviews.jsonl";
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const AUDIT_LESSONS_JSONL = "/home/profit/lakehouse/data/_kb/audit_lessons.jsonl";
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const AUDIT_FACTS_JSONL = "/home/profit/lakehouse/data/_kb/audit_facts.jsonl";
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const TAIL_LINES = 500;
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const MAX_BOT_CYCLE_FILES = 30;
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@ -61,6 +62,14 @@ export async function runKbCheck(claims: Claim[], prFiles: string[] = []): Promi
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findings.push(...scrumFindings);
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}
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// 6b. Audit-facts (llm_team extract pipeline output) — surface
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// entities that recur across multiple PRs. These are the
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// "core system entities" accumulating in the knowledge base;
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// showing them as info on future audits gives reviewers
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// architectural context the raw diff doesn't convey.
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const factFindings = await checkAuditFacts();
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findings.push(...factFindings);
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// 6. Audit-lessons feedback loop — summarize the top recurring
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// patterns from prior audits' block/warn findings. If the same
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// pattern signature has fired 3+ times across prior audits,
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@ -207,6 +216,99 @@ function observerBySource(ops: any[]): string {
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return Object.entries(c).sort((a, b) => b[1] - a[1]).map(([k, v]) => `${k}=${v}`).join(", ") || "empty";
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}
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// Audit-facts — reads data/_kb/audit_facts.jsonl (populated by every
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// curated inference run via llm_team's extract pipeline). Each row
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// has arrays: facts, entities, relationships. We explode entities and
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// aggregate them across PRs using kb_index. An entity seen in 3+ PRs
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// is a "core system entity" — we surface the top N as info context.
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//
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// Filters out short names (<3 chars, likely qwen2.5 truncation
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// artifacts) and generic types ("string", "number") that would
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// otherwise dominate the ranking.
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const ENTITY_NAME_MIN_LEN = 3;
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const GENERIC_ENTITY_NAMES = new Set([
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"string", "number", "boolean", "any", "void", "unknown", "never",
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"object", "array", "function", "const", "let", "var", "true", "false",
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"null", "undefined", "promise", "map", "set", "record",
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]);
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async function checkAuditFacts(): Promise<Finding[]> {
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// Read raw rows — each row has multiple entities, so we can't just
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// use aggregate() directly (it's one-signature-per-row). Explode
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// entities into (row, entity) pairs, then aggregate by entity name.
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let raw: string;
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try { raw = await (await import("node:fs/promises")).readFile(AUDIT_FACTS_JSONL, "utf8"); }
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catch { return []; }
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const lines = raw.split("\n").filter(l => l.length > 0);
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if (lines.length === 0) return [];
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interface EntityRow { entity_key: string; pr_number: number; type: string; name: string; description: string }
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const entityRows: EntityRow[] = [];
|
||||
for (const line of lines.slice(-TAIL_LINES * 2)) {
|
||||
let row: any;
|
||||
try { row = JSON.parse(line); } catch { continue; }
|
||||
const prNum = Number(row?.pr_number);
|
||||
if (!Number.isFinite(prNum)) continue;
|
||||
for (const e of Array.isArray(row?.entities) ? row.entities : []) {
|
||||
const name = String(e?.name ?? "").trim();
|
||||
if (name.length < ENTITY_NAME_MIN_LEN) continue;
|
||||
if (GENERIC_ENTITY_NAMES.has(name.toLowerCase())) continue;
|
||||
entityRows.push({
|
||||
entity_key: name.toLowerCase(),
|
||||
pr_number: prNum,
|
||||
type: String(e?.type ?? "?"),
|
||||
name,
|
||||
description: String(e?.description ?? "").slice(0, 160),
|
||||
});
|
||||
}
|
||||
}
|
||||
if (entityRows.length === 0) return [];
|
||||
|
||||
// Aggregate manually — one key per entity name, distinct_scopes by PR.
|
||||
type Agg = { count: number; scopes: Set<number>; types: Set<string>; last_name: string; last_desc: string };
|
||||
const byEntity = new Map<string, Agg>();
|
||||
for (const r of entityRows) {
|
||||
const a = byEntity.get(r.entity_key) ?? {
|
||||
count: 0, scopes: new Set<number>(), types: new Set<string>(), last_name: "", last_desc: "",
|
||||
};
|
||||
a.count += 1;
|
||||
a.scopes.add(r.pr_number);
|
||||
a.types.add(r.type);
|
||||
a.last_name = r.name;
|
||||
a.last_desc = r.description;
|
||||
byEntity.set(r.entity_key, a);
|
||||
}
|
||||
|
||||
// Rank: require 2+ distinct PRs (same-PR entity-repeats don't count
|
||||
// as "cross-cutting"). Take the top 5 to avoid flooding the verdict.
|
||||
const ranked = Array.from(byEntity.entries())
|
||||
.filter(([_, a]) => a.scopes.size >= 2)
|
||||
.sort((a, b) => b[1].scopes.size - a[1].scopes.size || b[1].count - a[1].count)
|
||||
.slice(0, 5);
|
||||
|
||||
if (ranked.length === 0) {
|
||||
// Useful to know the KB is being populated — emit a single
|
||||
// summary so operators see fact extraction is alive.
|
||||
return [{
|
||||
check: "kb_query",
|
||||
severity: "info",
|
||||
summary: `audit_facts KB has ${entityRows.length} entity-observations across ${new Set(entityRows.map(r => r.pr_number)).size} PRs (no cross-PR recurrences yet)`,
|
||||
evidence: [`source: ${AUDIT_FACTS_JSONL}`],
|
||||
}];
|
||||
}
|
||||
|
||||
return ranked.map(([_, a]) => ({
|
||||
check: "kb_query" as const,
|
||||
severity: "info" as const,
|
||||
summary: `core entity \`${a.last_name}\` recurs in ${a.scopes.size} PRs (types: ${Array.from(a.types).join(",")})`,
|
||||
evidence: [
|
||||
`count=${a.count} distinct_PRs=${a.scopes.size}`,
|
||||
`description: ${a.last_desc.slice(0, 200)}`,
|
||||
`PRs: ${Array.from(a.scopes).sort((x, y) => x - y).join(",")}`,
|
||||
],
|
||||
}));
|
||||
}
|
||||
|
||||
// Audit-lessons — reads data/_kb/audit_lessons.jsonl (populated by
|
||||
// every audit's appendAuditLessons). Uses the shared kb_index
|
||||
// aggregator: groups by `signature`, distinct-scopes keyed by PR
|
||||
|
||||
183
auditor/fact_extractor.ts
Normal file
183
auditor/fact_extractor.ts
Normal file
@ -0,0 +1,183 @@
|
||||
// fact_extractor — routes curated TEXT through llm_team_ui's
|
||||
// "knowledge extract facts" mode (mode=extract at /api/run).
|
||||
//
|
||||
// What it gives us: structured {facts, entities, relationships} from
|
||||
// whatever curated blob we send. Auditor sends the tree-split
|
||||
// inference scratchpad (the best distillation of what a PR changed).
|
||||
// Scrum_master will later send its accepted review bodies.
|
||||
//
|
||||
// Why route through llm_team and not just extract directly from our
|
||||
// own checks: llm_team's extract uses a local EXTRACTOR model
|
||||
// (qwen2.5) + a separate VERIFIER (gemma2). This cross-check is the
|
||||
// discipline J wants for knowledge going into the playbook — facts
|
||||
// go in only after a second model has rated them CORRECT /
|
||||
// UNVERIFIABLE. Fast (local models, ~10-20s), free, and matches the
|
||||
// codereview pattern J already trusts.
|
||||
//
|
||||
// SSE parsing: llm_team streams SSE events. We're only interested in
|
||||
// the final "response" event with role="final" + the extraction
|
||||
// response (role="extraction N"). Parse the JSON from the extractor's
|
||||
// response text.
|
||||
|
||||
const LLM_TEAM = process.env.LH_LLM_TEAM_URL ?? "http://localhost:5000";
|
||||
const EXTRACTOR = process.env.LH_FACT_EXTRACTOR ?? "qwen2.5:latest";
|
||||
const VERIFIER = process.env.LH_FACT_VERIFIER ?? "gemma2:latest";
|
||||
const EXTRACT_TIMEOUT_MS = 120_000;
|
||||
|
||||
export interface Entity {
|
||||
name: string;
|
||||
type: string;
|
||||
description?: string;
|
||||
}
|
||||
|
||||
export interface Relationship {
|
||||
from: string;
|
||||
to: string;
|
||||
type: string;
|
||||
}
|
||||
|
||||
export interface ExtractedFacts {
|
||||
facts: string[];
|
||||
entities: Entity[];
|
||||
relationships: Relationship[];
|
||||
verification: string;
|
||||
extractor_model: string;
|
||||
verifier_model: string;
|
||||
source_preview: string;
|
||||
// Populated when the extract run completed server-side (llm_team
|
||||
// persists to its own team_runs; this is for our own cross-ref).
|
||||
llm_team_run_id?: number;
|
||||
extracted_at: string;
|
||||
error?: string;
|
||||
}
|
||||
|
||||
/**
|
||||
* Run the llm_team extract pipeline on `source` text. Returns
|
||||
* structured {facts, entities, relationships}.
|
||||
*
|
||||
* Returns an object with `error` set if the pipeline failed — never
|
||||
* throws, because fact extraction is best-effort enrichment (the
|
||||
* primary audit must not break if llm_team is down).
|
||||
*/
|
||||
export async function extractFacts(source: string): Promise<ExtractedFacts> {
|
||||
const base: ExtractedFacts = {
|
||||
facts: [],
|
||||
entities: [],
|
||||
relationships: [],
|
||||
verification: "",
|
||||
extractor_model: EXTRACTOR,
|
||||
verifier_model: VERIFIER,
|
||||
source_preview: source.slice(0, 240),
|
||||
extracted_at: new Date().toISOString(),
|
||||
};
|
||||
|
||||
let resp: Response;
|
||||
try {
|
||||
resp = await fetch(`${LLM_TEAM}/api/run`, {
|
||||
method: "POST",
|
||||
headers: { "content-type": "application/json" },
|
||||
body: JSON.stringify({
|
||||
mode: "extract",
|
||||
prompt: source,
|
||||
extractor: EXTRACTOR,
|
||||
verifier: VERIFIER,
|
||||
source: "prompt",
|
||||
skip_cache: true, // cache by prompt would dedup identical
|
||||
// scratchpads, but we want fresh extraction
|
||||
// for per-audit facts; cheap since local.
|
||||
}),
|
||||
signal: AbortSignal.timeout(EXTRACT_TIMEOUT_MS),
|
||||
});
|
||||
} catch (e) {
|
||||
return { ...base, error: `fetch failed: ${(e as Error).message}` };
|
||||
}
|
||||
|
||||
if (!resp.ok) {
|
||||
const body = await resp.text().catch(() => "");
|
||||
return { ...base, error: `llm_team /api/run ${resp.status}: ${body.slice(0, 200)}` };
|
||||
}
|
||||
|
||||
// Stream SSE lines; collect the one extraction response + the run_saved event
|
||||
// so we can capture the team-runs ID for cross-ref.
|
||||
const decoder = new TextDecoder();
|
||||
const reader = resp.body?.getReader();
|
||||
if (!reader) return { ...base, error: "no response body" };
|
||||
|
||||
let buffer = "";
|
||||
let extractionText = "";
|
||||
let verifierText = "";
|
||||
let runId: number | undefined = undefined;
|
||||
|
||||
try {
|
||||
while (true) {
|
||||
const { done, value } = await reader.read();
|
||||
if (done) break;
|
||||
buffer += decoder.decode(value, { stream: true });
|
||||
let nl: number;
|
||||
while ((nl = buffer.indexOf("\n\n")) >= 0) {
|
||||
const chunk = buffer.slice(0, nl);
|
||||
buffer = buffer.slice(nl + 2);
|
||||
const dataLine = chunk.split("\n").find(l => l.startsWith("data: "));
|
||||
if (!dataLine) continue;
|
||||
try {
|
||||
const ev = JSON.parse(dataLine.slice(6));
|
||||
if (ev.type === "response") {
|
||||
const role = String(ev.role ?? "");
|
||||
if (role.startsWith("extraction")) extractionText = String(ev.text ?? "");
|
||||
else if (role === "verifier") verifierText = String(ev.text ?? "");
|
||||
} else if (ev.type === "run_saved") {
|
||||
const id = Number(ev.run_id);
|
||||
if (Number.isFinite(id)) runId = id;
|
||||
}
|
||||
} catch { /* skip malformed SSE */ }
|
||||
}
|
||||
}
|
||||
} catch (e) {
|
||||
return { ...base, error: `SSE read failed: ${(e as Error).message}` };
|
||||
}
|
||||
|
||||
// Pull the JSON object out of extractionText (may be wrapped in ```json fences).
|
||||
const parsed = extractFirstJsonObject(extractionText);
|
||||
if (!parsed) {
|
||||
return { ...base, error: "extractor returned no parseable JSON", verification: verifierText };
|
||||
}
|
||||
|
||||
return {
|
||||
...base,
|
||||
facts: Array.isArray(parsed.facts) ? parsed.facts.slice(0, 50).map(String) : [],
|
||||
entities: Array.isArray(parsed.entities)
|
||||
? parsed.entities.slice(0, 30).map((e: any) => ({
|
||||
name: String(e?.name ?? ""),
|
||||
type: String(e?.type ?? ""),
|
||||
description: typeof e?.description === "string" ? e.description.slice(0, 240) : undefined,
|
||||
})).filter(e => e.name.length > 0)
|
||||
: [],
|
||||
relationships: Array.isArray(parsed.relationships)
|
||||
? parsed.relationships.slice(0, 30).map((r: any) => ({
|
||||
from: String(r?.from ?? ""),
|
||||
to: String(r?.to ?? ""),
|
||||
type: String(r?.type ?? ""),
|
||||
})).filter(r => r.from.length > 0 && r.to.length > 0)
|
||||
: [],
|
||||
verification: verifierText.slice(0, 1500),
|
||||
llm_team_run_id: runId,
|
||||
};
|
||||
}
|
||||
|
||||
// Lift the first balanced JSON object out of (possibly fenced) text.
|
||||
// Same discipline as inference.ts::extractJson.
|
||||
function extractFirstJsonObject(text: string): any | null {
|
||||
const cleaned = text.replace(/^```(?:json)?\s*/im, "").replace(/```\s*$/im, "");
|
||||
let depth = 0, start = -1;
|
||||
for (let i = 0; i < cleaned.length; i++) {
|
||||
const c = cleaned[i];
|
||||
if (c === "{") { if (depth === 0) start = i; depth++; }
|
||||
else if (c === "}") {
|
||||
depth--;
|
||||
if (depth === 0 && start >= 0) {
|
||||
try { return JSON.parse(cleaned.slice(start, i + 1)); } catch { start = -1; }
|
||||
}
|
||||
}
|
||||
}
|
||||
return null;
|
||||
}
|
||||
Loading…
x
Reference in New Issue
Block a user