Hybrid Search Revamped
Qdrant 1.10 introduces a new Query API to build a search system that combines different search methods to improve the search quality.
Kacper Łukawski
·July 25, 2024
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Qdrant Articles
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BM42: New Baseline for Hybrid Search
Introducing BM42 - a new sparse embedding approach, which combines the benefits of exact keyword search with the intelligence of transformers.
Andrey Vasnetsov
·July 01, 2024
Data Privacy with Qdrant: Implementing Role-Based Access Control (RBAC)
Discover how Qdrant's Role-Based Access Control (RBAC) ensures data privacy and compliance for your AI applications. Build secure and scalable systems with ease. Read more now!
Qdrant Team
·June 18, 2024
Optimizing RAG Through an Evaluation-Based Methodology
Learn how Qdrant-powered RAG applications can be tested and iteratively improved using LLM evaluation tools like Quotient.
Atita Arora
·June 12, 2024
What is RAG: Understanding Retrieval-Augmented Generation
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.
Sabrina Aquino
·March 19, 2024
Qdrant 1.8.0: Enhanced Search Capabilities for Better Results
Explore the latest in search technology with Qdrant 1.8.0! Discover faster performance, smarter indexing, and enhanced search capabilities.
David Myriel, Mike Jang
·March 06, 2024
Is RAG Dead? The Role of Vector Databases in Vector Search | Qdrant
Uncover the necessity of vector databases for RAG and learn how Qdrant's vector database empowers enterprise AI with unmatched accuracy and cost-effectiveness.
David Myriel
·February 27, 2024
Optimizing OpenAI Embeddings: Enhance Efficiency with Qdrant's Binary Quantization
Explore how Qdrant's Binary Quantization can significantly improve the efficiency and performance of OpenAI's Ada-003 embeddings. Learn best practices for real-time search applications.
Nirant Kasliwal
·February 21, 2024
What are Vector Embeddings? - Revolutionize Your Search Experience
Discover the power of vector embeddings. Learn how to harness the potential of numerical machine learning representations to create a personalized Neural Search Service with FastEmbed.
Sabrina Aquino
·February 06, 2024
How to Implement Multitenancy and Custom Sharding in Qdrant
Discover how multitenancy and custom sharding in Qdrant can streamline your machine-learning operations. Learn how to scale efficiently and manage data securely.
David Myriel
·February 06, 2024
Discovery needs context
Discovery Search, an innovative way to constrain the vector space in which a search is performed, relying only on vectors.
Luis Cossío
·January 31, 2024
What is a Vector Database?
Discover what a vector database is, its core functionalities, and real-world applications. Unlock advanced data management with our comprehensive guide.
Sabrina Aquino
·January 25, 2024
Qdrant 1.7.0 has just landed!
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!
Kacper Łukawski
·December 10, 2023
What is a Sparse Vector? How to Achieve Vector-based Hybrid Search
Learn what sparse vectors are, how they work, and their importance in modern data processing. Explore methods like SPLADE for creating and leveraging sparse vectors efficiently.
Nirant Kasliwal
·December 09, 2023
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