Image Matching with Embeddings
A clean pattern for “find the best matching image from a library given a text query”.
Flow
(question / keywords)
↓
text embedding
↓
vector similarity search
↓
best matching existing image
Why this works
Same idea as RAG retrieval — you pre-compute embeddings for each image (or its caption / tags), store them in a vector store, then at query time embed the user’s question and run a nearest-neighbour search. The library doesn’t need to be regenerated; you’re matching against existing assets.
Open questions
- Is the embedding done over the image directly (CLIP-style) or over text descriptions of the image? Note suggests the latter, but worth confirming for any concrete project.
See next
- Vertex-AI-RAG-Tools — embedding + retrieval over text follows the same pattern