tool g
The method that represents meaningal relations of words, phrases or objects as numerical vectors.
Recessed (gömme vector), word, sentence, visual or any object’s meaningal properties are often represented in a continuous numerical vector space of hundreds or thousands of sizes. These vectors are not randomly assigned; a model learns to be close to each other in this multi-dimensional space, meaningless or contextically similar items. A classic example is the "men" vector from the "cral" vector in the word embedding spaces and close to the "craliçe" vector of the result when the result is added "wolf"; this indicates that vectors really capture the meaningal relations.
Recesses are the basis of many critical applications of today’s AI systems. In the mean search engines, queries and documents are converted into embedding vectors, and there are most relevant results by calculating the cosinus analog among them; user and products are represented in a common embedding space in the recommendation systems; while in RAG (access-supported production) systems, documents are converted to embedding in a vector database, the user question is connected to the context of the model with the nearest embeddings when it comes to. In large language models, each token is converted into its own embedding vector before processing, which is the first step to represent the meaning in the language of the model.
Recesses are the basis of many critical applications of today’s AI systems. In the mean search engines, queries and documents are converted into embedding vectors, and there are most relevant results by calculating the cosinus analog among them; user and products are represented in a common embedding space in the recommendation systems; while in RAG (access-supported production) systems, documents are converted to embedding in a vector database, the user question is connected to the context of the model with the nearest embeddings when it comes to. In large language models, each token is converted into its own embedding vector before processing, which is the first step to represent the meaning in the language of the model.