Machine Learning Engineer at a consumer goods company with 51-200 employees
Real User
Top 5
2024-05-13T07:22:51Z
May 13, 2024
Initially, I used it with software support and related products. Personally, I installed it locally on Docker containers for testing. I used it for data storage and search queries, mainly for sharing data across different selections. I used Milvus to create datasets, each with different scenarios or purposes.
Data Scientist at a tech services company with 1,001-5,000 employees
Real User
Top 10
2024-04-29T11:52:00Z
Apr 29, 2024
Milvus is primarily used in RAG, which involves retrieving relevant documents or data to augment the generation of new content. Milvus helps convert text and other data into a vector space, and the embeddings of this data are stored in the database. When a query is made, Milvus matches the query against the vector space to retrieve the most relevant vectors. For example, if we ask Milvus to retrieve the four best-matching vectors, it will provide those vectors. These vectors can then be decoded or reconverted into text, which can be further processed or used for generating new content.
PeerSpot users agreed that functionality is of utmost importance to a quality Open Source Database (OSD). The specifications will change depending on the task you are trying to accomplish, but any Open Source Database needs to be solidly functional or there is nothing to work with. On an individual basis, scalability, metrics, and security are important features to look for. Users were clear that the efficiency of the medium which will connect the OSD with the application running it is...
Initially, I used it with software support and related products. Personally, I installed it locally on Docker containers for testing. I used it for data storage and search queries, mainly for sharing data across different selections. I used Milvus to create datasets, each with different scenarios or purposes.
Milvus is primarily used in RAG, which involves retrieving relevant documents or data to augment the generation of new content. Milvus helps convert text and other data into a vector space, and the embeddings of this data are stored in the database. When a query is made, Milvus matches the query against the vector space to retrieve the most relevant vectors. For example, if we ask Milvus to retrieve the four best-matching vectors, it will provide those vectors. These vectors can then be decoded or reconverted into text, which can be further processed or used for generating new content.
We use Milvus mostly for RAG (Retrieval-augmented generation).
I use Milvus mostly for text processing and natural language processing.