Which method to choose for a chat bot that answers questions about a specific service?
Let’s imagine that there is Bank X. I want to create a chatbot that should answer user questions about this bank. How to get a loan, what are the conditions, why is my card blocked, and so on.What’s the best approach?Prepare a dataset in advance with detailed information about this bank x. Split documents, tokenize each part and store it in a vector database. After a request from the user, I use semantic search to find the most suitable document, then extract and generate a response from the document using language models (for example Mistral or Llama)From a pre-prepared dataset, generate a new dataset that will consist of a question and an answer. For example, for document 1, generate 5 questions and answers that refer to this document and create a dataset in the format context(document) : question : answerThen take Mistral and train this model based on this dataset and create a model that will subsequently generate answers to user questionsPlease give me advice on which option is the most optimal in terms of costs, accuracy, response speed, scalability, and so on