What is the Milvus Vector database?

Milvus: a popular open source vector database

Milvus was created in 2019 and in the same year, it posted its source code to the open source on GitHub under the Apache 2.0 license. As of September 2023, Milvus has accumulated over 22,868+ stars on GitHub, putting it in the lead among all vector search technologies.

Milvus Architecture Overview

Milvus is a powerful tool for similarity search in dense vector data sets containing millions or even billions of vectors. It uses a distributed architecture that separates storage and computation, allowing horizontal scaling of computational nodes.

The system consists of four tiers: access tier, coordinator service, worker nodes, and storage tier.

• Access Layer: It includes a group of proxy servers with no static parameters and serves as the front layer of the system with which users interact.
• Facilitator Services:
• Worker nodes: They follow the instructions of the coordinator service and execute DML/DDL commands initiated by the user.
• Storage: This is the backbone of the system responsible for data storage and includes metastore, log broker and object storage.

Each layer can be scaled or rebuilt independently, making the system more reliable, scalable and robust.

Key benefits of Milvus

Milvus is a valuable tool for a wide variety of applications. Here are five key benefits of using Milvus:

Lightning fast vector similarity search

Milvus delivers millisecond-level search performance on huge vector datasets. Its advanced indexing and search algorithms are ideal for image and video retrieval, recommender systems, and natural language processing applications.

Infinite scalability and high availability

Milvus is designed to handle massive amounts of data and can seamlessly scale horizontally to accommodate growing workloads. It provides high availability and data reliability with built-in replication and failover mechanisms.

Flexible data processing

Milvus is a versatile player that supports a variety of data types, including vectors, scalar and structured data. This flexibility simplifies data management and analysis within a single system.

Seamless integration

Milvus provides software development kits (SDKs) and connectors for popular programming languages such as Python, Java and Go. This flexibility simplifies integration into existing workflows and frameworks, and is compatible with data processing and analytics tools such as TensorFlow, PyTorch, and Apache Spark.

Active community support

Milvus thrives on an active community of developers and users. Regular updates, bug fixes, and feature enhancements keep Milvus relevant and responsive to changing user needs. The community offers resources, tutorials, and support to make working with Milvus easier.

Milvus for augmented generation search

As generative AI is becoming more prevalent, vector databases such as Milvus have become an integral part of the advanced search (RAG) stack. This solution is known for solving large language model (LLM) problems, including hallucinations and lack of domain-specific knowledge.

Milvus offers developers and enterprises a secure means to store relevant and sensitive private data outside of the LLM. When a user asks a question, LLM applications use embedding models to transform the question into vector embeddings. Milvus then performs similarity searches to identify the most relevant topk results for the query. Eventually, these results are combined with the original question to produce a hint that offers full context for LLM to generate more accurate answers.

Popular Milvus use cases

Milvus is a popular and efficient tool used in a variety of fields, enabling the development of many real-world industrial applications.

• Semantic/textual similarity search: Search for semantically similar texts in large collections of natural language documents.
• Recommender systems: Recommend similar information or products based on user behavior and preferences.
• Image Similarity Search: Find visually similar images from extensive image libraries.
• Audio Similarity Search: Detect similar audio results from a huge amount of audio data such as music, sound effects and speech.
• Question Answering System: Create an interactive QA chatbot that automatically answers user questions.
• Molecular similarity search: Search for similar substructures, superstructures and other structures for a given molecule.

Milvus has also proven its effectiveness in a variety of scenarios including DNA sequence classification, data deduplication, fraud detection, drug discovery and copyright protection.

Get started with Milvus in minutes with Milvus Lite

Milvus offers different deployment options to meet a variety of user needs. You can deploy Milvus Standalone on Kubernetes or with Docker Composer, use Milvus Cluster on Kubernetes, or go Milvus Offline with Helm charts.

While traditional deployment methods offer more functionality, new users may need more time to set up the full version. To help users learn Milvus faster, Bin Ji, one of the leading contributors to the Milvus community, has developed Milvus Lite, a lightweight version of Milvus. It will help you get started with Milvus in minutes, while offering many benefits:

• Integration into Python applications without the extra weight.
• Due to compatibility with built-in etcd and local storage, self-sufficiency eliminates external dependencies.
• Functionality in the form of a Python library and a standalone server based on a command line interface (CLI).
• Seamless compatibility with Google Colab and Jupyter Notebook.
• Securely migrate data between different Milvus instances without data loss.

Note: We do not recommend using Milvus Lite in a production environment or if you require high performance, high availability, or high scalability. Instead, consider using Milvus clusters or fully managed Milvus in Zilliz Cloud for production.