Search results
Results from the WOW.Com Content Network
A vector database, vector store or vector search engine is a database that can store vectors (fixed-length lists of numbers) along with other data items. Vector databases typically implement one or more Approximate Nearest Neighbor algorithms, [1] [2] [3] so that one can search the database with a query vector to retrieve the closest matching database records.
In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis.Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [1]
An alternative direction is to aggregate word embeddings, such as those returned by Word2vec, into sentence embeddings. The most straightforward approach is to simply compute the average of word vectors, known as continuous bag-of-words (CBOW). [9] However, more elaborate solutions based on word vector quantization have also been proposed.
The word with embeddings most similar to the topic vector might be assigned as the topic's title, whereas far away word embeddings may be considered unrelated. As opposed to other topic models such as LDA, top2vec provides canonical ‘distance’ metrics between two topics, or between a topic and another embeddings (word, document, or ...
Typically, the data to be referenced is converted into LLM embeddings, numerical representations in the form of a large vector space. RAG can be used on unstructured (usually text), semi-structured, or structured data (for example knowledge graphs). [1] These embeddings are then stored in a vector database to allow for document retrieval.
Working with non-numerical data can be tough, even for experienced data scientists. A typical machine learning model expects its features to be numbers, not words, emails, website pages, lists ...
Other typical requirements are: any extremal monomorphism is an embedding and embeddings are stable under pullbacks. Ideally the class of all embedded subobjects of a given object, up to isomorphism, should also be small, and thus an ordered set. In this case, the category is said to be well powered with respect to the class of embeddings.
In addition to our best in class vector database, we are taking a distinct approach offering customers an efficient way to create, store and search vector embeddings beyond those provided by point ...