A vector database is an organized collection of vector embeddings that can be created, read, updated, and deleted at any point in time.
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...
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...
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 ...
Embedding Model: The query is then passed to an embedding model. This model converts the query into a vector, which is a numerical representation that can be understood and processed by the system. Vector Database (DB) Retrieval: The query vector is used to search through a vector database...
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 ...
Using the example of a chatbot, once a user inputs a prompt, RAG summarizes that prompt usingvector embeddings-- which arecommonly managedin vector databases -- keywords or semantic data. The converted data is sent to a search platform to retrieve the requested data, which is then sorted bas...
What is a Vector Index? A vector index is a data structure used in computer science and information retrieval to efficiently store and retrievehigh-dimensional vector data, enablingfast similarity searchesandnearest neighbor queries. The use of generative AI andlarge language models (LLMs)is rapidly...
The magic begins with input embeddings, where the input text undergoes tokenization, breaking down into individual words and sub-words. These tokens are transformed into continuous vector representations, capturing the semantic and syntactic nuances of the input. This foundational step is crucial f...
Generative AI adds anotherlayer of ethical complexity. These tools can produce highly realistic and convincing text, images and audio -- a useful capability for many legitimate applications, but also a potential vector of misinformation and harmful content such asdeepfakes. ...