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A customer's review photo shows the food looks great. The review text alone reads uncertain, and my RAG search only sees the text.
What you'll have at the end
The review carrying a photo climbs out of the bottom of the results and back near the top on the exact question it used to lose, once its photo's caption joins the index beside its own text.
You need
You already have a working RAG pipeline: reviews chunked and embedded into a vector store with a text embedding model, plus a batch of customer photos with file paths sitting outside that index entirely.
Not covered
Embedding the raw photo itself into a shared image-text vector space, or letting the model see the picture at answer time; this only makes a photo's content reachable through the ordinary text queries your index already handles.
Leans on
Turn a photo of a damaged package into the right function call
when a photo needs to trigger an action right away, like flagging a damaged order, wire it straight into a function call instead of folding it into a search index.
Cut a vision call's token cost by resizing images first
when the photo backlog is large enough that the captioning calls themselves are the expensive part, resize each photo down before sending it off to be described.
Checked 15 Aug 2026
Part of the Multimodal AI cookbook