How to build real-time image search and query with natural language
🔍 Build Real-Time Image Search with CocoIndex, CLIP, and Qdrant
#OpenSource
CocoIndex is an ultra-performant real-time data transformation framework, built for scale and live updates. We just published a new tutorial blog https://cocoindex.io/blogs/live-image-search.
In this walkthrough, we’ll show how to build a semantic image search engine powered by multimodal AI – searchable with natural language in real time, with data insight to understand what’s going on step by step.
We combine:
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CocoIndex for live data processing and transformation.
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CLIP (ViT-L/14) is a vision-language model that can understand both images and texts. It's trained to align visual and textual representations in a shared embedding space.
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Qdrant is a high performance vector database. We use it to store and query the embeddings.
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FastAPI to build web API for image search.
You can search for “a cute animal” or “a red car”, and the system returns visually relevant results — no manual tagging needed.
👩‍💻 We’ll code this together step by step, and show how CocoIndex detects new files, computes embeddings, and streams them into the vector DB.
Let’s build a semantic search engine with:
- Continuously monitor folder updates.
- Process delta and keep index in sync with the source.
- Natural language search mapped into the same latent space.
đź”—Repo: https://github.com/cocoindex-io/cocoindex
We’d appreciate a star in our repo if it is helpful :)
đź”—Read the tutorial: https://cocoindex.io/blogs/live-image-search
