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....
A vector database is any database that can natively store and manage vector embeddings and handle the unstructured data they describe, such as documents, images, video, or audio. With the importance ofvector search for generative AI, the tech industry has spawned many specialized, standalone vec...
This is how vector databases work. They align data (memories) for fast mathematical comparison so that generic AI models can find the most likely result. LLMs like ChatGPT, for example, need to compare what logically completes a thought or sentence by quickly and efficiently comparing all the ...
A vector database stores, manages and indexes high-dimensional vector data. Data points are stored as arrays of numbers called “vectors,” which are clustered based on similarity. This design enables low-latency queries, making it ideal for AI applications. Vector databases are growing in popular...
As we currently live amid the AI revolution, it is important to understand that a lot of these new applications rely on vector embedding. So let’s learn more about vector databases and why they are important to LLMs. What is a Vector Database?
Vectorize is a globally distributed vector database for querying data stored in no-egress-fee object storage (R2) or documents stored in Workers Key Value. Combined with the development platform Cloudflare Workers AI, developers can use Cloudflare to quickly start experimenting with their own LLMs....
Back in 1997 Ramon Neco and Mikel Forcada suggested the “encoder-decoder” structure for machine translations, which became popular after 2016. 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 ...
you can use a vector to find themost similaror relevant data based on semantic or contextual meaning. This is how vector-based search delivers fast, meaningful, and highly-relevant results, even over unstructured data. It is yet another reason for developers to build AI s...
In retrieval-augmented generation, LLMs are enhanced with embedding and reranking models, storing knowledge in a vector database for precise query retrieval. The embedding model then compares these numeric values to vectors in a machine-readable index of an available knowledge base. When it finds ...
LlamaIndex provides this data integration by using data from multiple unique sources,embedding that data as vectors, and storing that vectorized data in avector database. Ii then uses that data to perform complex operations likevector searchwith low latency response times. ...