How to implement a knowledge base for LLM if the data volume exceeds the context window? What is a context window?

A context window is the maximum amount of text (number of tokens) that a large language model (LLM) can process in a single request. For example, if the window is 4,000 tokens, the model cannot consider more text at once.

If the data volume for the knowledge base exceeds the context window, the data needs to be split into parts and methods should be used that allow efficient searching and substitution of relevant fragments into the model's query. For example:

• Indexing and vector representation of documents (embedding).
• Searching by vector representations to select the most relevant parts.
• Passing only the selected parts to the model to avoid exceeding the context limit.

Thus, the knowledge base is built as a system of search and filtering information that prepares relevant context for the LLM without overloading its limit.