Exploring Sparse and Dense Embeddings: A Guide for Effective Information Retrieval with Milvus

Sparse vectors are very high-dimensional but contain few non-zero values, making them suitable for traditional information retrieval use cases. Typically, the dimensions represent different tokens in one or more languages, with values assigned to each of these indicating their relative importance in that document. Dense vectors, on the other hand, are embeddings from neural networks which, when combined together in an ordered array, captures the semantics of the input data. These vectors are typically generated by text embedding models and are characterized by most or all elements being non-zero.

What you'll learn:
Ins and outs of both sparse and dense vectors
Differences between sparse and dense vectors
When you’d want to use one over the other (or both in conjunction)
Examples of how to use both in Milvus

Resources:
Notebook: https://github.com/milvus-io/bootcamp...
Hugging Face BGE-M3: https://huggingface.co/BAAI/bge-m3#bg...
Slides: https://24054828.fs1.hubspotuserconte...

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