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When a KB has mode = 'multimodal', ingestion extracts images alongside
text and embeds both into a shared 512-d vector space via BGE-VL-base.
Image hits become first-class search results — useful for slides,
reports, and diagrams where text-only retrieval loses ~30-50% of the
content.
Backend
- embeddings.py: new encode_images(image_bytes_list) — opens bytes via
PIL and routes to the SentenceTransformer (BGE-VL accepts PIL images
in the same encode call as text).
- ingestion.py: _subprocess_worker gains document_id arg and a new
_stream_image_chunks() helper. For multimodal KBs the standard text
chunking runs first, then images are saved to
rag_uploads_root() / 'images' / <document_id> / img-NNNN.<ext> and
embedded; for each image with an adjacent caption, both an
'image'-kind chunk (vector = encoded image) and a 'caption'-kind
chunk (vector = encoded caption text) are streamed back with a
shared pair_group field.
- ingestion.py parent: _insert_chunks_and_collect_for_bm25 now reads
kind / image_path / pair_group from the subprocess message,
populates the new rag_chunks columns, and runs a second pass that
sets linked_chunk_id for each image ↔ caption pair. BM25 indexes
text + caption chunks only — image chunks have no tokenisable body.
- retrieval.py: Hit gains a `kind` field plumbed through bm25, dense,
RRF, and rerank paths.
- reranker.py: image-kind hits skip CrossEncoder rerank (text-only
model) but are appended back in their original relative position
rather than dropped.
- routes/rag.py: new GET /api/rag/images/{document_id}/{filename}
static-file route with realpath containment check. SearchHit gains
`kind` and `image_url` fields so the chat UI can render image
thumbnails alongside text hits. KB-doc upload threads kind/mode
through to ingestion.
Frontend
- rag-api.ts: SearchHit gains optional `kind` and `image_url`.
- kb-create-dialog.tsx: new Mode select (Text / Multimodal) alongside
the existing Chunking strategy select. The forbidden
(multimodal + late) combo is enforced in the UI — each side
disables the conflicting option on the other side with a tooltip
explaining why. Embedding-model placeholder cycles through the
three valid defaults (bge-small / nomic / BGE-VL).
- kb-list.tsx + chat-settings-sheet.tsx: 🖼️ MM badge alongside the
⚡ Late one so multimodal KBs are obvious at a glance.
Tests
- test_rag_multimodal.py: parser returns images when want_images=True
and skips them when False; _validate_mode_combo rejects the
forbidden (multimodal, late) pair with 400; RAG_EMBEDDER_MATRIX
contains the three valid combos and excludes the forbidden one;
image URL construction shape is verified. A server-marked test
loads BGE-VL-base end-to-end and confirms image + text vectors
share the same dimension.
Phase 3 of the plan is now feature-complete on the backend; the
remaining items (re-ingest UX for changing strategy on existing KBs)
are tracked under "Backfill UX" and can land separately.
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| .npmrc | ||
| biome.json | ||
| components.json | ||
| data-designer.openapi (1).yaml | ||
| eslint.config.js | ||
| index.html | ||
| package-lock.json | ||
| package.json | ||
| tsconfig.app.json | ||
| tsconfig.json | ||
| tsconfig.node.json | ||
| vite.config.ts | ||