A Vector Database is a specialized database system designed for efficiently indexing, querying, and retrieving high-dimensional vector data. Those systems enable advanced data analysis and similarity-search operations that extend well beyond the traditional, structured query approach of conventional database...
A vector database stores, manages and indexes high-dimensional vector data to be stored as arrays of numbers called “vectors,” clustered based on similarity.
A vector database is a data storage system that organises information in the form of vectors, which are mathematical representations. These databases are designed to store, index, and query vector embeddings or numerical representations of unstructured data, including text documents, multimedia content...
This is where a vector database comes in handy: a dataset goes through the model only once (or periodically as it changes), and the model's embeddings of that data are stored in a vector database. This saves a tremendous amount of processing time. It makes building user-facing application...
A vector database is an organized collection of vector embeddings that can be created, read, updated, and deleted at any point in time.
A vector index is a critical piece of the puzzle for implementing RAG in a generative AI application. Avector indexis a data structure that enables fast and accurate search and retrieval of vector embeddings from a large dataset of objects.Datastax Astra DB(built on Apache Cassandra) is a ve...
Vector similarity search uses machine learning to translate the similarity of text, images, or audio into a vector space, making search faster, more accurate, and more scalable.
Needless to say, the complexity of this can become quite daunting to try and comprehend. However, at the core of vector search is the ability to mathematically calculate the distance or similarity between vectors, and this is done with a number of mathematical formulas like cosine similarity or...
Imagine translation is a text-to-text procedure, where you need techniques to first encode the input sentence to vector space, and then decode it to the translated sentence. This is the very simplified logic of encoder-decoder models. Encoder-decoder. Image source Let's discuss the example of...
How to anchor for the omic circos plot using a set of genes? DPLYR not recognizing a column that is in my dataframe Knit to HTML Error Retrieving stored ARIMA model using REACTIVE function -- Error in as.vector: cannot coerce type 'closure' to vector of type 'character Selecting ...