​Vector Space Event

​The Qdrant team, in cooperation with Jina AI, invites you to our next Vector Space event, where we will discuss the exciting new features introduced in the latest Qdrant version and related topics.

​Attention is on you, presentation, and also pizza 🍕 and beer 🍺 are on us.

​Agenda:
- 6pm - Doors open
- 7:15pm - "Qdrant v1.10 Release Presentation"
- 8pm - "Understanding Retrieval Models: Embeddings, Rerankers, and ColBERT" by Jina AI
- 8:30 - "Hybrid Search in Haystack by deepset with Qdrant"
- 8:30 - Socializing

​Details:

​Qdrant v1.10 Release Presentation

​Andrey Vasnetsov, CTO @ Qdrant
Description: Dive into new features and approaches introduced in the latest Qdrant engine version 1.10 including Multivector Search, Built-in Hybrid Search with reranking, BM42, and more. Official announcement: https://qdrant.tech/blog/qdrant-1.10.x/

​Understanding Retrieval Models: Embeddings, Rerankers, and ColBERT

​Isabelle Mohr, AI Engineer @ Jina AI
Description: In this talk, we explore the fundamentals of different retrieval models, focusing on embedding models, rerankers, and the ColBERT architecture. We discuss how embedding models transform raw data into dense vector representations, the function of rerankers in refining search results, and the unique contributions of ColBERT in improving retrieval accuracy. Additionally, we examine the pros and cons of each approach, providing a balanced view of their strengths and limitations. By the end, you will gain a comprehensive understanding of these model architectures, how they are trained and employed during inference, and their practical applications in information retrieval.

​Hybrid Search in Haystack by deepset with Qdrant

​Vladimir Blagojevic, Sr. Software Engineer @deepset
Showcase of using Hybrid Search in Haystack framework by deepset.ai using the new Qdrant Universal Query API.