Vectors! what the heck and why the heck!
What's our vector Victor :)
Sorry, I can’t help it, the classic comedy movie “Airplane” has a lot to answer for!
Just in case you are not familiar with the movie, here is a bit relevant to today’s post:
Anyway, on to more serious subjects of what vectors are in the AI/database and coding worlds.
Firstly, what are vectors in the context of text embeddings? text embeddings you shout?
Understanding Text Embeddings
Vector embeddings are a popular technique in machine learning and NLP to represent information in a format that can be easily processed by algorithms.
Text embeddings allow us to represent words in a way that computers can understand by converting them into arrays of numbers.
Each numeric representation captures the semantic meaning of the word.
Words with similar meanings have embeddings that are closer to each other in the vector space.
Text embeddings can be used to find words with similar meanings in large bodies of text.
Different machine learning models can generate text embeddings, such as OpenAI's Create, HuertaVEC, and GloVe.
"Text embeddings allow computers to understand the meaning of words and find similar words in large bodies of text."
Applications of Vector Embeddings
Vector embeddings can be applied to various types of data, not just text.
We can create embeddings for sentences, documents, notes, graphs, images, and even faces.
Word embeddings, like Word2Vec and GloVe, convert words into dense vectors where similar words are closer in the vector space.
Document and sentence embeddings, like Doc2Vec and SBERT, can represent entire documents or sentences as vectors.
These embeddings enable us to compare and analyze similarity between words, sentences, and documents.
Vector embeddings have wide applications in natural language understanding, recommendation systems, information retrieval, and more.
"Vector embeddings can be used to represent and compare various types of data, including words, sentences, documents, and images."
Rise of Vector Databases
There is a growing popularity of vector databases in the tech industry. A vector database is a storage system specifically designed for arrays of numbers called vectors, which can represent complex objects such as words, sentences, images, or audio files in a continuous high-dimensional space known as an embedding.
Embeddings are used to map the semantic meaning of words or similar features in various data types. These embeddings find applications in recommendation systems, search engines, and even text generation, like the popular chatbot ChatGPT.
Vector databases store and query these embeddings efficiently. Unlike traditional relational or document databases, vector databases cluster vectors based on their similarity and allow for ultra-low latency querying. Examples of vector databases mentioned include open-source options like Weaviate and Milvus, as well as popular alternatives like Pinecone and Chroma.
One significant advantage of vector databases is their ability to extend long-term memory models like GPT-4 or LLaMDA. By combining a general-purpose model with user-specific data stored in a vector database, the model can provide customized responses and retrieve historical data, creating a form of long-term memory.
Vector databases and long-term memory models are being leveraged in cutting-edge projects aimed at creating artificial general intelligence. This includes notable repositories on GitHub, such as Microsoft's Jarvis, Auto GPT, and baby AGI.
So, that is a wrap for now. Exciting? scary? well who knows, like most tools, it is the application that matters IMHO.