This typically involves converting

shafi654@gmail.

These vectors not only contain the semantic information of words, but also capture subtle changes in words in specific contexts. For example, for the polysemous word "bank," the system can distinguish based on context whether the user is asking about a financial institution or a geographical river bank. The client system then uses these vectors to build a representation of the user's query.  the entire sentence or conversation history entered by the user into a fixed-length vector, a process known as sentence embedding. Sentence


embeddings enable client systems to understand the user's Afghanistan WhatsApp Number intent as a whole query, not just individual words. This is especially important when dealing with complex queries, as the user’s intent often needs to be understood within the context of the entire conversation. In order to generate appropriate replies, the intelligent customer system needs a powerful response module, which can map the user's query vector to an appropriate reply vector. During this process, the system will also consider a variety of factors, including the user's emotional state, historical





interaction records, and possible reply options. In this way, the system is able to generate responses that are both accurate and human.The principle of data minimization : only collect the data necessary to achieve your goals and avoid collecting unnecessary personal information.