What are the main use cases for Milvus?

Milvus's core capability, fast similarity search over large vector collections, applies across a range of applications built around finding "things like this."

1. Semantic search - finding documents or passages related to a query by meaning, not just keyword overlap.
2. Retrieval-augmented generation (RAG) - retrieving relevant context to feed into a large language model's prompt.
3. Recommendation systems - finding items similar to what a user has previously engaged with.
4. Image and video search - finding visually similar content using image embeddings.
5. Anomaly and fraud detection - identifying data points that are unusually distant from normal patterns in embedding space.
6. Multimodal search - combining embeddings from different data types (text, image) in one search system.

What unifies these use cases is that they all reduce to the same underlying operation: given a query vector, efficiently find the most similar stored vectors, with the specific application determining what those vectors represent and what "similar" is ultimately being used to accomplish.