Dynamic numeric data usage in LLM - Any better methods otherthan RAG or Agents?

Google AI Studio
1

I am developing a Q&A application , that needs to answer the prompts based on the following types of data

  1. Static documents ( weblinks, pdf, video’s )
  • we can use the fine-tuning or RAG for this type of data.
  1. Dynamically updating documents - New data will be appended to the existing documents at regular intervals
  • We can use RAG to include this type of data. Any other options can we use for efficiency ?
  1. Dynamically generated text and numeric data –
  • Is there any best way to generate the embeddings for numeric data ? (categorization of the numeric data is one option, but it is not suitable for all types of data ) ?
  • Is there any agents available to fetch the data from data sources like Elasticdb and MangoDB
  • How efficient are the LLM’s for forecasting and outlier detection, based on numeric embeddings generated through different methods?

Are there any better approaches to implement the Q&A using the above type of data, otherthan using RAG And Agents ? If, so , can you please mention about the same and at which stage ?