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Loading...Introduction to Vector Databases
When I first started working with vector databases, I was surprised by the complexity of optimizing their performance. Our team had been using Milvus 2.2, but we were curious about how it compared to other popular options like Faiss 1.7 and Annoy 1.8. In this article, I'll share our findings from a comprehensive benchmarking analysis, including the challenges we faced and the optimizations that made the biggest impact.
The Problem of Similarity Search
Similarity search is a critical component of many AI-driven applications, from recommender systems to natural language processing. However, as the size of your dataset grows, so does the complexity of finding similar vectors. This is where vector databases come in - they're designed to efficiently store and query large datasets of dense vectors.
Benchmarking Methodology
To compare the performance of Milvus 2.2, Faiss 1.7, and Annoy 1.8, we used a combination of synthetic and real-world datasets.
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