What's New in Milvus version 2.2.5

What's New in Milvus version 2.2.5
We are proud to announce the release of Milvus 2.2.5 on behalf of the Milvus community. The 2.2.5 release contains a few new features and many improvements. This blog post will highlight some of the more prominent features. For a complete list of changes, check the release notes.
- 📦 PyPI: https://pypi.org/project/milvus/
- 📚 Docs: https://milvus.io/docs
- 🛠️ Release Notes: https://milvus.io/docs/release_notes.md#225
- 🐳 Docker Image: docker pull
- 🚀 Release: https://github.com/milvus-io/milvus/releases/tag/v2.2.5
One of the highlights of this release is a applying a security fix for MinIO (MinIO CVE-2023-28432) by updating to the latest MinIO release (RELEASE.2023-03-20T20-16-18Z). In addition, there were several enhancements added including the following feature:
- First/Random replica selection policy — This First/Random replica selection policy selects replicas in a round-robin fashion. If the first replica chosen fails, the policy will randomly select another replica for the operation, improving the throughput by reducing the time it takes to complete.
Please note that there are several bug fixes and performance enhancements in the Milvus 2.2.5 release, so check out the release notes for more details.
Summary
In addition to all of the features listed above, Milvus 2.2.5 includes several bug fixes and improvements. To learn more:
See the release notes for version 2.2.5 for the complete list of changes Download Milvus and get started
Start Free, Scale Easily
Try the fully-managed vector database built for your GenAI applications.
Try Zilliz Cloud for FreeKeep Reading

Balancing Precision and Performance: How Zilliz Cloud's New Parameters Help You Optimize Vector Search
Optimize vector search with Zilliz Cloud’s level and recall features to tune accuracy, balance performance, and power AI applications.

ColPali + Milvus: Redefining Document Retrieval with Vision-Language Models
When combined with Milvus's powerful vector search capabilities, ColPali becomes a practical solution for real-world document retrieval challenges.

RocketQA: Optimized Dense Passage Retrieval for Open-Domain Question Answering
RocketQA is a highly optimized dense passage retrieval framework designed to enhance open-domain question-answering (QA) systems.