By integrating with the Ollama framework, XRAG enables a breakthrough solution for localized retrieval inference. 4-bit quantization provided by Ollama reduces model memory requirements by 75%, enabling large models such as LLaMA, Mistral, etc. to run on consumer-grade hardware. This deployment ensures that sensitive data does not need to be outbound, and full-link data closure is achieved through local vector databases such as ChromaDB. Tests have shown that the XRAG-Ollama combination in offline environments can still maintain an online performance of more than 90%, making it particularly suitable for strong compliance scenarios such as healthcare and finance. The solution eliminates API call latency and network dependency, showing significant advantages in weak network conditions such as industrial sites.
This answer comes from the articleXRAG: A Visual Evaluation Tool for Optimizing Retrieval Enhancement Generation SystemsThe
