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BigSimmo merged 3 commits into from
Jul 3, 2026
Merged

test(eval): force-embedding flag + 10 vector-exercising golden cases #249

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4f03aef
test(eval): add force-embedding flag + 10 vector-exercising golden cases
BigSimmo Jul 3, 2026
b62f516
test(eval): address Codex review — isolate vector, key cache, propaga…
BigSimmo Jul 3, 2026
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Merge branch 'main' into claude/vector-golden-cases
BigSimmo Jul 3, 2026
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1 change: 1 addition & 0 deletions scripts/eval-quality.ts
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Original file line number Diff line number Diff line change
Expand Up @@ -753,6 +753,7 @@ async function runRetrievalQualityCases(args: {
topK: testCase.topK,
minSimilarity: 0.12,
skipCache: true,
forceEmbedding: testCase.forceEmbedding,

@chatgpt-codex-connector chatgpt-codex-connector Bot Jul 3, 2026

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P2 Badge Gate release eval on forced misses

Forwarding forceEmbedding here makes eval:quality:release run the new vector probes, but that command still exits based only on the aggregate 0.8 retrieval thresholds in buildEvalQualityReport; with 34 cases, six forced vector misses can still leave hit/recall at 28/34 = 0.823 and pass release. If these cases are meant to protect re-index quality, forced-case failures (or any golden failed_cases) need to be blocking rather than only reported.

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}),
);
const latencyMs =
Expand Down
11 changes: 11 additions & 0 deletions scripts/eval-retrieval.ts
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Expand Up @@ -19,6 +19,10 @@ const goldenCaseSchema = z.object({
expectedContentTerms: z.array(contentExpectationSchema).default([]),
topK: z.number().int().positive().default(8),
expectTableEvidence: z.boolean().default(false),
// Bypass the lexical text-fast-path so this case always exercises the embedding/vector
// index. Use for "vector-*" probes that would otherwise be answered by a lexical shortcut,
// so a re-index's effect on vector retrieval is actually measured.
forceEmbedding: z.boolean().optional(),
});

const goldenCasesSchema = z.array(goldenCaseSchema);
Expand All @@ -37,6 +41,7 @@ type EvalArgs = {
caseTimeoutMs: number;
p90BudgetMs: number;
p50BudgetMs: number;
forceEmbedding: boolean;
};

export type GoldenRetrievalResult = {
Expand Down Expand Up @@ -116,6 +121,7 @@ function parseArgs(argv: string[]): EvalArgs {
caseTimeoutMs: inferredMode === "latency" ? 25_000 : 0,
p90BudgetMs: 20_000,
p50BudgetMs: 8_000,
forceEmbedding: false,
};

for (let index = 0; index < argv.length; index += 1) {
Expand All @@ -138,6 +144,10 @@ function parseArgs(argv: string[]): EvalArgs {
if (args.caseTimeoutMs <= 0) args.caseTimeoutMs = 25_000;
continue;
}
if (token === "--force-embedding") {
args.forceEmbedding = true;
continue;
}

const value = argv[index + 1];
if (!value || value.startsWith("--")) throw new Error(`Missing value for ${token}`);
Expand Down Expand Up @@ -743,6 +753,7 @@ async function main() {
topK: testCase.topK,
minSimilarity: 0.12,
skipCache: args.mode !== "latency",
forceEmbedding: testCase.forceEmbedding || args.forceEmbedding,
}),
);
const searchOutcome = await withCaseTimeout(searchPromise, args.caseTimeoutMs);
Expand Down
103 changes: 103 additions & 0 deletions scripts/fixtures/rag-retrieval-golden.json
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Expand Up @@ -224,5 +224,108 @@
],
"topK": 12,
"expectTableEvidence": true
},
{
"id": "vector-ptsd",
"query": "How is post-traumatic stress disorder managed in a person with intrusive flashbacks, nightmares, hyperarousal and avoidance after a traumatic event?",
"expectedQueryClass": "broad_summary",
"expectedDocumentSubstrings": ["Traumatic Stress"],
"expectedContentTerms": [["trauma", "traumatic", "ptsd", "flashback"]],
"topK": 8,
"expectTableEvidence": false,
"forceEmbedding": true
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},
{
"id": "vector-ocd",
"query": "How is obsessive-compulsive disorder managed in a person with distressing intrusive obsessions and repetitive compulsive rituals?",
"expectedQueryClass": "broad_summary",
"expectedDocumentSubstrings": ["Obsessive"],
"expectedContentTerms": [["obsess", "compuls", "intrusive", "ocd"]],
"topK": 8,
"expectTableEvidence": false,
"forceEmbedding": true
},
{
"id": "vector-panic",
"query": "How is panic disorder managed in a person with recurrent unexpected panic attacks, palpitations and anticipatory anxiety?",
"expectedQueryClass": "broad_summary",
"expectedDocumentSubstrings": ["Panic"],
"expectedContentTerms": [["panic", "attack", "anxiety"]],
"topK": 8,
"expectTableEvidence": false,
"forceEmbedding": true
},
{
"id": "vector-anorexia",
"query": "How is anorexia nervosa managed in a person with severe dietary restriction, intense fear of weight gain and body-image disturbance?",
"expectedQueryClass": "broad_summary",
"expectedDocumentSubstrings": ["Anorexia"],
"expectedContentTerms": [["anorexia", "eating", "weight"]],
"topK": 8,
"expectTableEvidence": false,
"forceEmbedding": true
},
{
"id": "vector-gad-worry",
"query": "How is a patient managed who has persistent, excessive worry about many everyday things that they find very hard to control, along with restlessness and muscle tension?",
"expectedQueryClass": "broad_summary",
"expectedDocumentSubstrings": ["Generalised Anxiety"],
"expectedContentTerms": [
["worry", "anxiety", "generalised"],
["cbt", "ssri", "snri", "antidepressant", "psychotherapy"]
],
"topK": 8,
"expectTableEvidence": false,
"forceEmbedding": true
},
{
"id": "vector-tourette",
"query": "How is Tourette syndrome managed in a child with chronic motor tics and vocal tics?",
"expectedQueryClass": "broad_summary",
"expectedDocumentSubstrings": ["Tourette"],
"expectedContentTerms": [["tic", "tics", "tourette"]],
"topK": 8,
"expectTableEvidence": false,
"forceEmbedding": true
},
{
"id": "vector-postnatal",
"query": "How is postnatal (postpartum) depression managed in a new mother who develops low mood and poor bonding with her infant in the first weeks postpartum?",
"expectedQueryClass": "broad_summary",
"expectedDocumentSubstrings": ["Postnatal"],
"expectedContentTerms": [["postnatal", "postpartum", "perinatal", "depression"]],
"topK": 8,
"expectTableEvidence": false,
"forceEmbedding": true
},
{
"id": "vector-bipolar",
"query": "How is bipolar disorder managed in an adult with recurrent episodes of mania and depression?",
"expectedQueryClass": "broad_summary",
"expectedDocumentSubstrings": ["Bipolar"],
"expectedContentTerms": [["bipolar", "mania", "manic", "mood"]],
"topK": 8,
"expectTableEvidence": false,
"forceEmbedding": true
},
{
"id": "vector-adhd",
"query": "How is attention deficit hyperactivity disorder managed in an adult with inattention, distractibility and impulsivity?",
"expectedQueryClass": "broad_summary",
"expectedDocumentSubstrings": ["Attention Deficit Hyperactivity"],
"expectedContentTerms": [["attention", "adhd", "hyperactiv", "impuls"]],
"topK": 8,
"expectTableEvidence": false,
"forceEmbedding": true
},
{
"id": "vector-opioid",
"query": "How is opioid use disorder managed in a person dependent on heroin?",
"expectedQueryClass": "broad_summary",
"expectedDocumentSubstrings": ["Opioid Use Disorder"],
"expectedContentTerms": [["opioid", "heroin", "opiate", "methadone", "buprenorphine"]],
"topK": 8,
"expectTableEvidence": false,
"forceEmbedding": true
}
]
24 changes: 18 additions & 6 deletions src/lib/rag.ts
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Expand Up @@ -307,6 +307,10 @@ export type SearchChunksArgs = {
// Internal: set when this call is a re-run on a trigram-corrected query, to prevent the
// unsupported-short-circuit typo-correction path from recursing more than once.
typoCorrected?: boolean;
// Diagnostic/eval-only: bypass every lexical text-fast-path so retrieval always exercises
// the embedding/vector stage. Lets the golden eval measure the vector index directly for a
// re-index, instead of being masked by lexical shortcuts. Never set on production paths.
forceEmbedding?: boolean;
};

export type AnswerProgressEvent = {
Expand Down Expand Up @@ -1429,7 +1433,7 @@ function stableHash(value: string) {
export function retrievalPlanCacheQuery(
args: Pick<
SearchChunksArgs,
"query" | "documentId" | "documentIds" | "ownerId" | "queryMode" | "topK" | "minSimilarity"
"query" | "documentId" | "documentIds" | "ownerId" | "queryMode" | "topK" | "minSimilarity" | "forceEmbedding"
>,
queryClass?: RagQueryClass,
queryVariants: string[] = [],
Expand All @@ -1445,6 +1449,7 @@ export function retrievalPlanCacheQuery(
`topK:${args.topK ?? 8}`,
`min:${args.minSimilarity ?? 0.15}`,
`rag:${ragDeepMemoryVersion}`,
`force:${args.forceEmbedding ? 1 : 0}`,
].join("|");
return queryCacheKeyForStorage(cacheKey);
}
Expand Down Expand Up @@ -5516,7 +5521,7 @@ export async function searchChunksWithTelemetry(args: SearchChunksArgs) {
});

const baseTextFastPath = decideTextFastPath(args.query, baseTextResults, queryClassification.queryClass);
if (shouldReturnBeforeMemory(queryClassification.queryClass, baseTextFastPath)) {
if (!args.forceEmbedding && shouldReturnBeforeMemory(queryClassification.queryClass, baseTextFastPath)) {
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textFastResults = await attachPageVisualEvidence(supabase, baseTextResults);
textFastResults = applySecondStageRerankIfNeeded({
queryClass: queryClassification.queryClass,
Expand Down Expand Up @@ -5567,7 +5572,7 @@ export async function searchChunksWithTelemetry(args: SearchChunksArgs) {
telemetry.rerank_latency_ms += Date.now() - rerankStartedAt;

const boostedTextFastPath = decideTextFastPath(args.query, textFastResults, queryClassification.queryClass);
if (boostedTextFastPath.returnFastPath) {
if (!args.forceEmbedding && boostedTextFastPath.returnFastPath) {
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markEmbeddingSkippedByTextFastPath(telemetry, boostedTextFastPath.reason);
telemetry.retrieval_strategy = "text_fast_path";
recordSearchScoreTelemetry(telemetry, textFastResults);
Expand Down Expand Up @@ -5673,7 +5678,7 @@ export async function searchChunksWithTelemetry(args: SearchChunksArgs) {
documentLookupResults,
queryClassification.queryClass,
);
if (documentLookupFastPath.returnFastPath) {
if (!args.forceEmbedding && documentLookupFastPath.returnFastPath) {
markEmbeddingSkippedByTextFastPath(
telemetry,
documentLookupFastPath.reason ? `document_lookup_fast_path:${documentLookupFastPath.reason}` : null,
Expand All @@ -5700,7 +5705,7 @@ export async function searchChunksWithTelemetry(args: SearchChunksArgs) {
});
const coverageGate = evaluateEvidenceCoverageGate(args.query, coverageGateResults, queryClassification.queryClass);
applyCoverageGateTelemetry(telemetry, coverageGate, coverageGate.accepted);
if (coverageGate.accepted) {
if (!args.forceEmbedding && coverageGate.accepted) {
telemetry.retrieval_strategy = coverageGate.strategy;
recordSearchScoreTelemetry(telemetry, coverageGateResults);
setCachedSearch(args, coverageGateResults, telemetry, queryVariants);
Expand Down Expand Up @@ -5744,6 +5749,13 @@ export async function searchChunksWithTelemetry(args: SearchChunksArgs) {
latencyMs: telemetry.embedding_latency_ms,
});

if (args.forceEmbedding) {

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P2 Badge Fail forced evals when embeddings fail

Because the lexical candidates are cleared only after embedTextWithTelemetry succeeds, the default RAG_PROVIDER_MODE=auto failure path above this block still returns the pre-embedding lexical textFastResults on quota/rate-limit/transient embedding errors. In that scenario a forced vector-* eval can pass without exercising any vector retrieval, which defeats the new guard; when forceEmbedding is set, the embedding failure should fail the search or at least not fall back to lexical results.

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// Force-embedding eval isolation: drop the lexical / memory-card / table candidates gathered
// before embedding so the returned results reflect the embedding-driven retrieval layers only
// (otherwise a broken vector index could still be masked by the lexical text candidate path).
textFastResults = [];
}

// A1: the embedding-field, index-unit, and chunk-hybrid RPCs each depend only on the
// already-computed query embedding and have no data dependency on one another, so run
// them concurrently instead of as three sequential Supabase round-trips. The two helper
Expand Down Expand Up @@ -5780,7 +5792,7 @@ export async function searchChunksWithTelemetry(args: SearchChunksArgs) {
const startedAt = Date.now();
const { data, error } = await supabase.rpc("match_document_chunks_hybrid", {
query_embedding: embedding as unknown as string,
query_text: textSearchQuery,
query_text: args.forceEmbedding ? "" : textSearchQuery,

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P2 Badge Make all forced RPCs vector-only

Fresh evidence after the prior isolation fix: this only blanks query_text for match_document_chunks_hybrid, while the same forced path still calls searchEmbeddingFieldCandidates and searchIndexUnitCandidates with buildClinicalTextSearchQuery(args.query). Those hybrid RPCs select/boost lexical text hits, so a vector-* golden case can still pass through embedding-field or index-unit text branches even if the chunk vector index/regeneration is broken; propagate the forced mode to those helpers or assert vector-only provenance before treating these cases as vector probes.

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match_count: candidateCount,
min_similarity: minSimilarity,
document_filters: documentFilterList ?? undefined,
Expand Down
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