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
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Qdrant Articles
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Data Privacy with Qdrant: Implementing Role-Based Access Control (RBAC)
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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
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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.
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Qdrant 1.8.0 - Major Performance Enhancements
Much faster sparse vectors, optimized indexation of text fields and optional CPU resource management configuration.
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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
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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
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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
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Discovery Search: A New Approach to Vector Space
Explore the next frontier in search technology with Discovery Search. Learn how this innovative API provides precise and tailored results.
Luis Cossío
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What is a Vector Database?
An overview of vector databases, detailing their functionalities, architecture, and diverse use cases in modern data processing.
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
Vector Search as a dedicated service
Why vector search requires a dedicated service.
Andrey Vasnetsov
·November 30, 2023
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