Similarity Search Application utilizing Vector types on vCore-based Azure Cosmos DB for MongoDB
summary
A working similarity-search app uses vCore-based Azure Cosmos DB for MongoDB, Azure AI Vision to generate 1024-dimensional embeddings, and Azure Blob storage for 2767 images; embeddings are stored in documents and queried via a Vector IVF index with cosine similarity. A Streamlit UI issues text-to-image and image-to-image queries, the demo shows exact matches and sensible nearest neighbors, and the pipeline (embedding → upload CSV → DB search → blob retrieval) completes successfully.
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chapters (7)
- 0:00Introduction to Azure Cosmos DB
- 1:07Understanding vector search
- 2:13Demo architecture and setup
- 3:56Data structure and indexing
- 5:30Text-to-image search demo
- 8:51Image-to-image search demo
- 10:31Conclusion and resources
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