dhi.io/milvus
Milvus
Milvus is a high-performance, cloud-native vector database built for scalable vector similarity search, powering RAG, semantic search and recommendation systems.
pushed 1 day ago
linux/amd64, linux/arm64
356.23 MB
for linux/amd64
1312
for linux/amd64
Active support
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Build Level 3
Tamper-evident proof of how and from what sources this image was built. Build Level 3 is the highest the SLSA build track defines.
CycloneDX SBOM + SPDX SBOM
A software bill of materials in both CycloneDX and SPDX formats so it drops straight into your existing tooling. Audit exactly what you're shipping.
A Vulnerability Exploitability exchange document, Docker's assessment of which CVEs actually affect this image and which don't apply, so you can focus on real risk instead of chasing false positives.
Available
A link to everything used to build the image, package source code, Git repos, and build files, so you can audit or reproduce the build and stay compliant with open source licenses.
Every known vulnerability in this image, shown in full rather than hidden. The VEX data flags which ones actually apply, so you can tell real exposure from noise before you ship.
Exactly what changed in this build, down to the package bumps and fixes behind the version you're pulling.
Verifies no keys, tokens, or credentials were accidentally baked into the image.
The image layers were scanned for known malware signatures before publishing.
About Milvus
Milvus is a high-performance, cloud-native vector database built for large-scale approximate nearest neighbor (ANN) search over embedding vectors. It stores, indexes, and searches billions of vectors alongside their metadata, and is a common building block for retrieval-augmented generation (RAG), semantic search, and recommendation systems.
Milvus can run as a single self-contained standalone process — with an embedded etcd, local object storage, and an
embedded message queue — for development and small production workloads, or scale out into a distributed cluster backed
by an external etcd, S3-compatible object storage, and a Pulsar or Kafka message queue for larger deployments.
About Docker Hardened Images
Docker Hardened Images are built to meet the highest security and compliance standards. They provide a trusted foundation for containerized workloads by incorporating security best practices from the start.
Why use Docker Hardened Images?
These images are published with near-zero known CVEs, include signed provenance, and come with a complete Software Bill of Materials (SBOM) and VEX metadata. They're designed to secure your software supply chain while fitting seamlessly into existing Docker workflows.
Trademarks
Milvus is a graduated project of the LF AI & Data Foundation. Milvus® is a registered trademark of LF Projects, LLC. All rights in the mark are reserved to LF Projects, LLC. Any use by Docker is for referential purposes only and does not indicate sponsorship, endorsement, or affiliation.