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Haven't looked at the code, but it's unclear from your ReadMe how you turn search queries into embeddings for the semantic search itself. In my experience, this is the problem to solve for local semantic search, since your stored embeddings need to be aligned with the queries (the more expressive your stored embeddings, the more expensive each query will be; your question generation pipeline does not address this). C…
Im not deep into the space but surely zero LLM cost would be default for search???
In traditional RAG (Retrieval-Augmented Generation), you use an LLM at runtime to read those retrieved documents and synthesize a direct, conversational answer for the user. That generative step is what incurs an API cost and adds latency per query. RAGless gives you that direct Q&A experience (the synthesized answer) but removes the runtime generation by pre-computing all the answers offline.