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How to Build Live Image Search With Vision Model and Query With Natural Language
- The article discusses building a live image search with a vision model and querying it with natural language.
- It showcases using a multimodal embedding model to understand and embed images, creating a vector index for efficient retrieval.
- Tools like CocoIndex, CLIP ViT-L/14, Qdrant, and FastAPI are utilized for this project.
- The flow design involves reading image files, using CLIP to understand images, storing embeddings in a vector database, and querying the index.
- Custom functions are used to embed images and queries and a FastAPI endpoint is defined for semantic image search.
- The application setup includes using FastAPI with CORS middleware, serving static images, and initializing the Qdrant client.
- A frontend component is kept simple to focus on image search functionality.
- Instructions for setup, including creating a collection in Qdrant, setting up the indexing flow, running the backend and frontend, and testing the search functionality, are detailed.
- Real-time indexing updates and monitoring options are also mentioned for continuous improvements.
- Readers are encouraged to contribute by giving a star on GitHub if they find the article helpful.
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