How to design a storage and search system for 10,000 Telegram channels with 300,000 posts per day with latency less than 2 seconds?
Machine Learning / AI
If the worker is synchronous, will it have batching?
Can you consider embeddings synchronously directly in the API endpoint? What will happen if you process embeddings for a large stream of documents in a FastAPI endpoint?
How to design a system for automatic content generation for Telegram channels in the style of the channel's author?
When to choose LoRA and when to retrieve by similar posts? How much data is needed for each approach?
How did you validate LoRA fine-tuning?
Have you used neural networks as a quality assessment metric?
What rank did you set for LoRA and what does it depend on?
Tell us about your experience with RAG systems and what you did in your last project.
Did you do all this yourself (RAG, LoRA, working with neural networks) or did you help someone?
In which layers did you insert LoRA and what is q_proj?
Have you deployed neural networks on your servers or in the cloud?
What metrics did you use to evaluate system quality and how did you achieve a 40% improvement?
Why can LoRA overfit even with a small number of parameters? Does the choice of rank affect overfitting?
Why is a vector database needed in this architecture?