You need to provide more information.

Is your data organized or is just a dump of unrelated content?

- If you have a bag of files without any metadata the best option is to create something like a RAG, with a pre OCR step for image files (or even some multimodal model call).

- If the content is well organized with a logic structure an agent could extract information with a little look around.

Is static or varies day by day?

- If is static you could index all at once, if not, an agent that pick what to reindex would be a better call.

I'm not aware of a solution like this, but seems doable as an MCP server. But the cost will scale quiclky.

local as in on your filesystem?

Have you tried asking this question at an LLM? :p

Seems that you're looking for a RAG System, and you may have a try RAGFlow

What you described would be a great solution for plenty of tasks, but maybe solving some fallacies one at a time would also be great. For example, when we're sure by placing a properly named file at a directory location, we will find it by recalling the folder name or the name of the file itself while in reality we're often surprised that after months or years this won't work, the expected path to the file either not exists or doesn't contain what we're looking for. The same fallacy is true also for different hierarchical notes organizers.

In this case LLMs with their ability to find semantic equivalence might be a great help. And with the current state of affairs I even think that an LLM with a sufficiently large context window might absorb some kind of the file system dump with directory paths and file names and answer a question about some obscure file from the past.

Multimodal Qwen is pretty good at OCR although it's pretty slow without a GPU.

For pure search you're almost certainly better off building an index of CLIP embeddings and then doing cosine similarity with a query embedding to find things. I have gigabytes of reaction images and memes I've been thinking about doing this with.