I want to build a search engine that can find images based on text descriptions. I've read that is perfect for this, but I'm unsure how to store both the text and image embeddings in the same collection. Do I need to create separate indexes, or can the framework handle multiple vector types for a single document ID?
3 answers
Implementing multimodal search with is very straightforward if you use a unified embedding space like OpenAI's CLIP. Since CLIP maps both images and text into the same high-dimensional vector space, you can store image embeddings in a collection and then query them using a text embedding. The database treats these simply as vectors of numbers, so it doesn't "know" if the input was an image or text—it just finds the closest matches. You should store the image file path or a base64 string in the metadata so that when the query returns the top IDs, your frontend can immediately display the corresponding visual results.
Does using high-dimensional vectors like CLIP's 512-dim significantly slow down the search?
The integration with the 'OpenCLIP' library makes the embedding process very smooth.
Spot on! Having that flexibility within the ecosystem allows you to build truly "intelligent" search tools without needing a massive engineering team.
Not really, Jason. While 512 dimensions is larger than a standard word embedding, the HNSW indexing in the library is extremely efficient at navigating these spaces. You might see a slight increase in index construction time, but the query latency remains in the millisecond range for most datasets under 100k items. The trade-off for the semantic accuracy you get with multimodal models is definitely worth the small performance hit.