Scaling embeddings for 50k+ documents a day
The practical problem is how to run work at large scale while improving . The scale is at least 50,000 documents per day, with each document roughly 10 to 20 pages long. This matters in RAG systems because documents must be turned into searchable vectors before an AI agent can retrieve only the parts it needs.
The concrete need is for proven operating practices and the current state of the art for making this process faster and more efficient. The item raises the scaling question, but it does not provide specific results or a finished playbook.
Key points
- The assumed scale is at least 50,000 documents per day.
- Each document is roughly 10 to 20 pages long.
- The focus is improving for .
- The request asks for real operating lessons and the current state of the art.
- The topic connects to RAG, AI agent , and control.