Embeddings are a way to represent words, phrases, or even other types of data, like images or audio, in a numerical form. It's like turning words or data into numbers so that computers can understand and work with them better.
Imagine you have a bunch of words, like "dog," "cat," and "bird." These words have meaning, right? Well, embeddings assign each word a unique set of numbers (vectors) that capture their meaning and relationships to other words.
For example, the word "dog" might be represented as [0.5, 0.2, -0.7], "cat" as [0.8, -0.3, 0.1], and "bird" as [0.3, 0.9, 0.4]. The numbers in the vectors carry information about the characteristics of each word, like whether they are related to animals or how similar they are to each other.
The amazing thing is that embeddings can be learned from large amounts of data, so they can figure out similarities and differences between words automatically. These numerical representations help AI algorithms understand language and make sense of complex patterns, which is crucial in various applications like language translation, sentiment analysis, and recommendation systems. They also make it easier and faster for AI models to process information and provide more accurate results!
The following databases are currently supported.
Just link the property Database of the TsgcAIOpenAIEmbeddings to any of the databases supported.