Skills encode deep Qdrant knowledge so coding agents can make the engineering decisions that determine whether vector search works well: quantization, sharding, tenant isolation, hybrid search, model ...

A proof of concept that pairs Meta's V-JEPA 2, a self-supervised video "world model," with Qdrant, using the vector search engine to search, evaluate, and compress the model's output. Measured on ...

Semantic caching can use any vector store. If you’re already using a vector store such as Qdrant, you can use it to speed up semantically similar requests and reduce token usage without adding another ...

🚀 I just published a new project! I’m excited to share my latest video 🔥 📊 A complete Data Analysis Dashboard project using Python and Streamlit, built from scratch. In this project, I show how to ...