What is a Vector Database? A vector database is an organized collection of vector embeddings that can be created, read, updated, and deleted at any point in time. Vector embeddings represent chunks of data, such as text or images, as numerical values....
In 2013, an algorithm known asword2vecbecame the LLM's most recent ancestor. Word2vec is a natural language processing (NLP) algorithm used to take one word and convert it into an array of numbers known as a vector. This may seem basic on the surface, but what was amazing about word...
LLMs are trained on huge sets of data— hence the name "large." LLMs are built on machine learning: specifically, a type of neural network called a transformer model. In simpler terms, an LLM is a computer program that has been fed enough examples to be able to recognize and interpret...
This is done through a combination of embedding techniques. Embeddings are the representations of tokens, such as sentences, paragraphs, or documents, in a high-dimensional vector space, where each dimension corresponds to a learned feature or attribute of the language. The embedding process takes ...
etc in rows and columns. When querying from traditional databases, we are querying for rows that match our query. However, vector databases work with vectors rather than strings, etc. Vector databases also apply a similarity metric which is used to help find a vector most similar to the query...
A vector database stores, manages and indexes high-dimensional vector data to be stored as arrays of numbers called “vectors,” clustered based on similarity.
These tokens are transformed into continuous vector representations, capturing the semantic and syntactic nuances of the input. This foundational step is crucial for the model to understand the language it will be processing. Positional encoding: Keeping track of words in context This component ma...
Retrieval-Augmented Generation (RAG) is a new way to build language models. RAG integrates information retrieval directly into the generation process.
generate their outputs. They’re using vector databases that contain up-to-date enterprise information. This architectural approach, called retrieval-augmented generation, lets an LLM that was trained on vast amounts of generalized data enhance its response by using private data found in a vector ...
In the softmax layer, the higher scores are elevated, and the lower scores get depressed. The attention scores [Q*K] are multiplied with the value vector [V] to produce an output vector for each word. If the resultant vector is large, it is retained. If the vector is tending towards ...