Milvus

Milvus 2.6.15 is an open-source vector database designed for high-performance similarity search, AI applications, and large-scale embedding storage and retrieval. It helps developers store, index, and search vector embeddings generated from text, images, audio, and other unstructured data.

The solution supports common AI and machine learning workflows including semantic search, recommendation systems, retrieval-augmented generation (RAG), image similarity search, anomaly detection, and intelligent data retrieval. It is ideal for developers, data teams, and AI applications that require fast and scalable vector search capabilities.

Features of Milvus 2.6.15:

• Open-source vector database for AI and similarity search workloads.
• Supports high-dimensional vector storage, indexing, and search.
• Suitable for semantic search, RAG, recommendations, and image similarity use cases.
• Provides REST API, SDK-based access, and WebUI support.
• Runs as a standalone service with Docker Compose using Milvus, etcd, and MinIO containers.
• Supports scalable vector indexing and fast nearest-neighbor search.

Usage instructions for Milvus 2.6.15
$ sudo su
$ cd /opt/milvus
$ sudo docker compose up -d
$ sudo docker compose ps
$ sudo docker exec milvus-standalone milvus --version

Version: Milvus 2.6.15

Access Milvus WebUI:
Open your browser and navigate to:
http://your-server-ip:9091/webui/

Milvus API endpoint:
http://your-server-ip:19530

Example:
http://40.114.26.21:9091/webui/

Test Milvus API
$ curl -X POST "http://127.0.0.1:19530/v2/vectordb/collections/list" -H "Content-Type: application/json" -d '{}'

Expected output:
{"code":0,"data":[]}

Credentials: Milvus standalone does not require a default username and password unless authentication is manually enabled.

Disclaimer: Milvus 2.6.15 is provided “as is” under applicable open-source licenses. Users are responsible for proper configuration, security hardening, access control, data protection, and validation before using it in production environments. This solution is best suited for AI applications, vector search, semantic search, and machine learning data retrieval workloads.