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, suc
AI. Vectorize is a globally distributed vector database for querying data stored in no-egress-fee object storage (R2) or documents stored inWorkers Key Value. Combined with the development platformCloudflare Workers AI, developers can use Cloudflare to quickly start experimenting with their own LLM...
Large language models (LLMs) currently have the AI world in a chokehold. It is essential to understand why vector databases are important to LLMs.
A vector database allows vast quantities of data to be stored in a vectorized format. This includes complex, sensitive datasets such as hospital data and financial information. Thevector database stores data as its vector representations, which facilitates data management and retrieval. Fine-Tuning T...
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...
For instance, OpenAI GPT’s Byte-Pair Encoding (BPE) is a widely used tokenizer for text processing. Conversely, Vision Transformer (ViT) and BERT for Image Transformers (BEiT), are popular tokenizers for visual processing.Understanding Types of Embeddings in LLM If tokens are vector ...
A vector database stores, manages and indexes high-dimensional vector data to be stored as arrays of numbers called “vectors,” clustered based on similarity.
similarity search. When data is indexed with a vector store index, it can be leveraged locally for smaller datasets and by a single application or for larger datasets and/or to be used across multiple different LLMs/applications it can be stored in ahigh-performance vector database like Astra...
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 database. For example, ...
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. ...