There are already solutions to this kind of problem. Using embeddings to store semantic meaning -> query the vector database with a question -> use extractive q/a models to get relevant context -> using a Reader model to generate answers based on the context from the document.
just checkout Haystack tutorials. I started looking into it after getting introduced to the concept by articles mentioning OpenAI embeddings and GPT 3 api, but it can be done using open source models.
just checkout Haystack tutorials. I started looking into it after getting introduced to the concept by articles mentioning OpenAI embeddings and GPT 3 api, but it can be done using open source models.