Building proper search requires selecting the right embedding model for your specific use case. This guide helps you navigate the selection process based on performance, cost, and other practical considerations.| qdrant.tech
A Qdrant Star shares her hard-won lessons from her extensive open-source building| qdrant.tech
We discovered something interesting. Standard dense embedding models can perform surprisingly well in late interaction scenarios.| qdrant.tech
Explore how RAG enables LLMs to retrieve and utilize relevant external data when generating responses, rather than being limited to their original training data alone.| qdrant.tech
Sparse vectors, Discovery API, user-defined sharding, and snapshot-based shard transfer. That's what you can find in the latest Qdrant 1.7.0 release!| qdrant.tech