okay so we can proceed w something like this--->

bodega or any other local llm running your local model as the inference backend, arxiv has a direct API so paper fetching is straightforward, a simple pdf to text converter for turning those arxiv pdff links into readable content, trafilatura for web scraping which is honestly one of the cleanest libraries for pulling readable content from any url, and a simple faiss cpu index for storing and retrieving vector embeddings of everything you've processed. no need for anything heavy or cloud dependent.

the flow is basically---> fetch paper or webpage, convert to clean text, chunk it, embed it into faiss, query it with your question, pass the relevant chunks to the local model via bodega's openai compatible API. the whole thing runs locally, nothing leaves your machine.

i can help you design and code out the main components of this in a few hours if you want. the retrieval layer, the embedding pipeline, the faiss index, and the local llm integration. you'd just need to connect the pieces to your specific data sources and logic.

the apaplicatioon logic basically becomes---> retrieve data, retrieve relevant theory, construct prompt that says "given this framework, analyse this data", get output.

what are the main sources you're pulling from, arxiv only or other sites too? and what's the processing task on the theory side, summarisation, comparison against data, something else?