cross modal search

Summary

gbrain has a working multimodal embedding pipeline (Voyage multimodal-3, embedding_image column, 11K image chunks indexed) but search is siloed: text queries only search text embeddings, image queries don't exist. This proposal adds cross-modal query routing so text queries can surface images and image queries can surface text, using Voyage multimodal-3's shared embedding space.

Problem

What the user sees: You can't search "photos from the hackathon" and get actual images. You can't upload a photo and ask "what do we know about this person?" Text search returns text. Image embeddings sit unused except via explicit embeddingColumn: 'embedding_image' override, which no user-facing path triggers.

What the system does:

Text queries embed through the configured text model (OpenAI/ZE) and search the text column
The embedding_image column exists (Voyage multimodal-3, 1024d) with 11,204 embedded chunks and a valid 83 MB HNSW index
postgres-engine.ts:searchVector() supports embeddingColumn: 'embedding_image' but the query vector must come from a compatible model (Voyage multimodal, 1024d)
Currently, embedQuery() always uses the text embedding model, producing a 1536d or 2560d vector that can't query the 1024d image column

What it should do:

Detect cross-modal intent in a search query ("show me photos of...", "find images from...", or explicit image search flag)
Embed the text query through Voyage multimodal-3 (same model used for image embeddings)
Search the embedding_image column with the multimodal query vector
Return image results alongside or instead of text results
Support image-as-query: accept an image input, embed it through Voyage multimodal-3, search text embeddings (if a shared multimodal column exists) or the image column

Evidence

Image embeddings exist and are indexed

-- Production state (May 2026)
SELECT COUNT(*) FROM content_chunks WHERE embedding_image IS NOT NULL;
-- 11,204

SELECT indexrelid::regclass, indisvalid, pg_size_pretty(pg_relation_size(indexrelid))
FROM pg_index WHERE indrelid = 'content_chunks'::regclass
AND indexrelid::regclass::text LIKE '%image%';
-- idx_chunks_embedding_image | true | 83 MB

Modality metadata is broken

SELECT COUNT(*) FROM content_chunks WHERE modality = 'image';
-- 10 (should be ~11,204)

Most image chunks have embedding_image IS NOT NULL but modality is not set to 'image'. This is a backfill gap from the v0.27.1 migration.

From Voyage docs: voyage-multimodal-3 encodes text, images, and interleaved text+image into the same 1024-dimensional vector space. A text query embedded through this model can find relevant images, and vice versa. gbrain already uses it for the image column but never for query embedding.

Search routing is text-only

hybrid.ts line ~414:

const embeddings = await Promise.all(queries.map(q => embedQuery(q)));

embedQuery() always uses the global text model. No path exists to embed a text query through the multimodal model for cross-modal search.

Proposed Fix

Phase 1: Text → Image Search

1. Cross-modal intent detection (new file: src/core/search/cross-modal.ts)

Add a lightweight intent classifier that detects when a query is looking for images:

function detectCrossModalIntent(query: string): 'text' | 'image' | 'both' {
  // Explicit image patterns
  const imagePatterns = [
    /\b(show|find|get)\s+(me\s+)?(photos?|images?|pictures?|screenshots?)/i,
    /\bwhat\s+does\s+.+\s+look\s+like/i,
    /\b(whiteboard|diagram|slide|screenshot)\b/i,
    /\bphoto(s)?\s+(of|from|at|with)\b/i,
  ];
  if (imagePatterns.some(p => p.test(query))) return 'image';
  return 'text'; // Default: text-only
}

2. Multimodal query embedding (extend embedding.ts)

Add embedQueryMultimodal(text: string): Promise<Float32Array> that routes through the configured multimodal model (Voyage multimodal-3) instead of the text model.

export async function embedQueryMultimodal(text: string): Promise<Float32Array> {
  // Use the multimodal provider, not the text provider
  return gatewayEmbedQuery(text, { provider: cfg.embedding_multimodal_model });
}

3. Hybrid search routing (extend hybrid.ts)

When cross-modal intent is detected:

Embed query through multimodal model (Voyage multimodal-3, 1024d)
Search embedding_image column
Return results with a modality: 'image' tag
If intent is 'both': run text search AND image search, merge with RRF

4. SearchOpts extension (extend types.ts)

interface SearchOpts {
  // ... existing fields
  crossModal?: 'text' | 'image' | 'both' | 'auto';  // Default: 'auto' (intent detection)
}

Phase 2: Image → Text Search (future)

Accept an image buffer/URL as search input. Embed through Voyage multimodal-3. Search text embeddings. This requires a new search entry point (searchByImage) and MCP tool exposure. Defer to a follow-up PR.

Phase 3: Unified Multimodal Column (future)

Embed ALL content (text + images) through Voyage multimodal-3 into a single column. This creates a truly unified search space but doubles embedding costs and requires re-embedding all text. Evaluate after Phase 1 results.

Backfill: Fix modality metadata

Before cross-modal search is useful, fix the modality column:

-- Chunks with image embeddings but wrong modality
UPDATE content_chunks
SET modality = 'image'
WHERE embedding_image IS NOT NULL AND (modality IS NULL OR modality != 'image');

This is a prerequisite for Phase 1 since result display needs to know which chunks are images.

Test Guidance

Red tests (should fail before fix, pass after)

Intent detection: detectCrossModalIntent("show me photos from the hackathon") returns 'image'.
Intent detection negative: detectCrossModalIntent("what is founder mode?") returns 'text'.
Multimodal embed routing: embedQueryMultimodal("hackathon") returns a 1024d vector (Voyage multimodal dims), not 1536d or 2560d.
Cross-modal search: hybridSearch("show me hackathon photos", { crossModal: 'image' }) returns results from the embedding_image column.
Default behavior unchanged: hybridSearch("what is founder mode?") returns text results as before (no cross-modal unless detected).
Explicit override: hybridSearch("anything", { crossModal: 'image' }) forces image search regardless of intent detection.

Edge cases

Query matches image intent but no image embeddings exist for the topic: return empty image results, fall back to text.
Multimodal model not configured: skip cross-modal, log warning, return text results.
Mixed results ('both' mode): text and image results merged, each tagged with modality for display.

PR #1106 adds dynamic text embedding column selection (prerequisite: the embedding_columns registry and provider routing from that PR make this easier to implement)
v0.27.1 introduced the dual-column schema (embedding + embedding_image)
importImageFile in postgres-engine.ts handles image ingestion and multimodal embedding
Voyage multimodal-3 is already configured as embedding_multimodal_model in gbrain config
The image OCR pipeline (embedding_image_ocr: true) extracts text from images before embedding, so image chunks have both visual and text representation

Phasing

Phase	Scope	Effort	Value
1 (this PR)	Text → Image search with intent detection	Medium	High — unlocks "find photos" queries
2	Image → Text search (upload photo, find related text)	Medium	Medium — cool but niche use case
3	Unified multimodal column (everything in one space)	Large	High — but expensive and requires re-embedding
Prereq	Fix modality column backfill	Small	Required for Phase 1

Cross-Modal Search: Text↔Image Retrieval