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feat: expand unifiedSearch to accept object input with optional vector (#855) #1184

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feat: expand unifiedSearch to accept object input with optional vector (#855) #1184

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188 changes: 164 additions & 24 deletions graphile/graphile-search/src/__tests__/unified-search.test.ts
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Original file line number Diff line number Diff line change
Expand Up @@ -594,7 +594,7 @@ describe('graphile-search (unified search plugin)', () => {
const allResult = await query<AllDocumentsResult>(`
query {
allDocuments(
where: { unifiedSearch: "machine learning" }
where: { unifiedSearch: { text: "machine learning" } }
orderBy: BODY_BM25_SCORE_ASC
) {
nodes { rowId title bodyBm25Score }
Expand All @@ -609,7 +609,7 @@ describe('graphile-search (unified search plugin)', () => {
const limitResult = await query<AllDocumentsResult>(`
query {
allDocuments(
where: { unifiedSearch: "machine learning" }
where: { unifiedSearch: { text: "machine learning" } }
orderBy: BODY_BM25_SCORE_ASC
first: 1
) {
Expand Down Expand Up @@ -732,38 +732,26 @@ describe('graphile-search (unified search plugin)', () => {
}
});

it('mega query v2: unifiedSearch + searchScore with composite ordering', async () => {
// Mega Query v2 — New-style: uses the unified `unifiedSearch` composite
// filter that fans out to all text-compatible algorithms (tsvector, BM25, trgm)
// with a single string, plus a manual pgvector filter for semantic search.
// Orders by composite searchScore (highest overall relevance first).
it('mega query v2: unifiedSearch { text } + separate vectorEmbedding filter', async () => {
// Mega Query v2 — uses the unified `unifiedSearch` input type with text field
// for text-compatible algorithms, plus a separate pgvector filter.
const result = await query<AllDocumentsResult>(`
query MegaQueryV2_UnifiedSearch {
allDocuments(
where: {
# unifiedSearch: single string fans out to tsvector + BM25 + trgm
# automatically — no need to specify each algorithm separately
unifiedSearch: "machine learning"

# pgvector still needs its own filter (vectors aren't text)
unifiedSearch: { text: "machine learning" }
vectorEmbedding: { vector: [1, 0, 0], metric: COSINE }
}
# Order by composite searchScore (higher = more relevant across all algorithms),
# then by vector distance as tiebreaker (lower = semantically closer)
orderBy: [SEARCH_SCORE_DESC, EMBEDDING_VECTOR_DISTANCE_ASC]
) {
nodes {
rowId
title
body

# Per-adapter scores — populated by unifiedSearch for text algorithms
tsvRank
bodyBm25Score
titleTrgmSimilarity
embeddingVectorDistance

# Composite normalized score — the single number that blends everything
searchScore
}
}
Expand All @@ -776,16 +764,70 @@ describe('graphile-search (unified search plugin)', () => {
expect(nodes!.length).toBeGreaterThan(0);

for (const node of nodes!) {
// searchScore should be populated (composite of active algorithms)
expect(typeof node.searchScore).toBe('number');
expect(node.searchScore).toBeGreaterThanOrEqual(0);
expect(node.searchScore).toBeLessThanOrEqual(1);

// Vector distance should be populated (manual filter)
expect(typeof node.embeddingVectorDistance).toBe('number');
expect(node.embeddingVectorDistance).toBeGreaterThanOrEqual(0);
}
});

it('mega query v3: true hybrid via unifiedSearch { text, vector }', async () => {
// Mega Query v3 — true hybrid: text + vector in a SINGLE unifiedSearch input.
// WHERE clauses are OR-combined (match text OR vector).
// searchScore blends all adapters into a single 0..1 relevance number.
const result = await query<AllDocumentsResult>(`
query MegaQueryV3_HybridUnified {
allDocuments(
where: {
unifiedSearch: {
text: "machine learning"
vector: [1, 0, 0]
metric: COSINE
}
}
orderBy: SEARCH_SCORE_DESC
) {
nodes {
rowId
title
tsvRank
bodyBm25Score
titleTrgmSimilarity
embeddingVectorDistance
searchScore
}
}
}
`);

expect(result.errors).toBeUndefined();
const nodes = result.data?.allDocuments?.nodes;
expect(nodes).toBeDefined();
expect(nodes!.length).toBeGreaterThan(0);

for (const node of nodes!) {
// searchScore blends ALL active signals (text + vector)
expect(typeof node.searchScore).toBe('number');
expect(node.searchScore).toBeGreaterThanOrEqual(0);
expect(node.searchScore).toBeLessThanOrEqual(1);
}

// At least one node should have vector distance populated (from the vector path)
const hasVectorScore = nodes!.some(
(n) => n.embeddingVectorDistance != null && n.embeddingVectorDistance >= 0
);
expect(hasVectorScore).toBe(true);

// At least one node should have a text score populated (from the text path)
const hasTextScore = nodes!.some(
(n) => (n.tsvRank != null && n.tsvRank > 0) ||
(n.bodyBm25Score != null) ||
(n.titleTrgmSimilarity != null && n.titleTrgmSimilarity > 0)
);
expect(hasTextScore).toBe(true);
});
});

// ─── unifiedSearch composite filter ────────────────────────────────────
Expand All @@ -795,7 +837,7 @@ describe('graphile-search (unified search plugin)', () => {
const result = await query<AllDocumentsResult>(`
query {
allDocuments(where: {
unifiedSearch: "learning"
unifiedSearch: { text: "learning" }
}) {
nodes {
title
Expand All @@ -813,7 +855,7 @@ describe('graphile-search (unified search plugin)', () => {
const result = await query<AllDocumentsResult>(`
query {
allDocuments(where: {
unifiedSearch: "machine learning"
unifiedSearch: { text: "machine learning" }
}) {
nodes {
title
Expand Down Expand Up @@ -843,7 +885,7 @@ describe('graphile-search (unified search plugin)', () => {
const result = await query<AllDocumentsResult>(`
query {
allDocuments(where: {
unifiedSearch: "learning"
unifiedSearch: { text: "learning" }
tsvTsv: "machine"
}) {
nodes {
Expand All @@ -864,7 +906,7 @@ describe('graphile-search (unified search plugin)', () => {
const result = await query<AllDocumentsResult>(`
query {
allDocuments(where: {
unifiedSearch: "xyzzy_nonexistent_term_12345"
unifiedSearch: { text: "xyzzy_nonexistent_term_12345" }
}) {
nodes {
title
Expand All @@ -877,6 +919,104 @@ describe('graphile-search (unified search plugin)', () => {
const nodes = result.data?.allDocuments?.nodes ?? [];
expect(nodes.length).toBe(0);
});

it('vector-only via unifiedSearch: { vector } (no text)', async () => {
const result = await query<AllDocumentsResult>(`
query {
allDocuments(where: {
unifiedSearch: {
vector: [1, 0, 0]
metric: COSINE
distance: 1.5
}
}) {
nodes {
rowId
title
embeddingVectorDistance
searchScore
}
}
}
`);

expect(result.errors).toBeUndefined();
const nodes = result.data?.allDocuments?.nodes ?? [];
expect(nodes.length).toBeGreaterThan(0);

for (const node of nodes) {
// Vector distance should be populated
expect(typeof node.embeddingVectorDistance).toBe('number');
expect(node.embeddingVectorDistance).toBeGreaterThanOrEqual(0);
expect(node.embeddingVectorDistance).toBeLessThanOrEqual(1.5);

// searchScore should blend the vector signal
expect(typeof node.searchScore).toBe('number');
expect(node.searchScore).toBeGreaterThanOrEqual(0);
expect(node.searchScore).toBeLessThanOrEqual(1);
}
});

it('hybrid text + vector: returns broader results than either alone', async () => {
// Text-only results
const textResult = await query<AllDocumentsResult>(`
query {
allDocuments(where: {
unifiedSearch: { text: "quantum" }
}) {
nodes { rowId }
}
}
`);

// Vector-only results (close to [0, 0, 1])
const vectorResult = await query<AllDocumentsResult>(`
query {
allDocuments(where: {
unifiedSearch: { vector: [0, 0, 1], metric: COSINE, distance: 0.5 }
}) {
nodes { rowId }
}
}
`);

// Hybrid results (text OR vector)
const hybridResult = await query<AllDocumentsResult>(`
query {
allDocuments(where: {
unifiedSearch: {
text: "quantum"
vector: [0, 0, 1]
metric: COSINE
distance: 0.5
}
}) {
nodes { rowId }
}
}
`);

expect(textResult.errors).toBeUndefined();
expect(vectorResult.errors).toBeUndefined();
expect(hybridResult.errors).toBeUndefined();

const textIds = new Set((textResult.data?.allDocuments?.nodes ?? []).map((n) => n.rowId));
const vectorIds = new Set((vectorResult.data?.allDocuments?.nodes ?? []).map((n) => n.rowId));
const hybridIds = new Set((hybridResult.data?.allDocuments?.nodes ?? []).map((n) => n.rowId));

// Hybrid results should be a superset of both text and vector results
// (since WHERE is OR-combined: match text OR match vector)
for (const id of textIds) {
expect(hybridIds.has(id)).toBe(true);
}
for (const id of vectorIds) {
expect(hybridIds.has(id)).toBe(true);
}

// Hybrid should have at least as many results as either alone
expect(hybridIds.size).toBeGreaterThanOrEqual(textIds.size);
expect(hybridIds.size).toBeGreaterThanOrEqual(vectorIds.size);
});
});

// ─── Pagination ─────────────────────────────────────────────────────────
Expand Down
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