What Is Milvus Vector Database?
This is the third part of our vector database blog series.
In our previous posts, we discussed the significance of specialized vector databases for handling large amounts of vector embeddings and introduced the concept of unstructured data. Today, we’re diving deep into Milvus, the world’s pioneering open source vector database. We’ll take you through its architecture, benefits and use cases, and provide a handy guide to kickstart your journey with Milvus.
Milvus: The Popular Open Source Vector Database
Milvus is an open source vector database that aims to enhance embedding similarity search and bolster AI applications. It’s a groundbreaking tool that democratizes the search for unstructured data, ensuring a uniform user experience across diverse deployment environments.
Milvus was established in 2019 and made its source code publicly available on GitHub under the Apache 2.0 license during the same year. As of September 2023, Milvus has gained over 22,868 GitHub stars, placing it at the forefront of all vector-search technologies.
It graduated from the Linux Foundation incubator as part of the LF AI & Data Foundation in June 2021, aligning itself with the AI era and strengthening its ties with the open source community.
Milvus Architecture Overview
Milvus is a powerful tool for similarity searches in dense vector datasets containing millions or even billions of vectors. It uses a distributed architecture that separates storage and computing, allowing for horizontal scalability in computing nodes.
The system comprises four layers: the access layer, the coordinator service, the worker nodes and the storage layer.
- Access layer: It comprises a group of stateless proxies and serves as the front layer of the system that users interact with.
- Coordinator service: It assigns tasks to the worker nodes, acting as the system’s brain.
- Worker nodes: They follow instructions from the coordinator service and execute user-triggered DML/DDL commands.
- Storage: It is the bone of the system and is responsible for data persistence. It comprises meta storage, log broker and object storage.
Each layer can be scaled or recovered independently, making the system more robust, scalable and reliable.
Milvus Key Benefits
Milvus is a valuable tool across diverse applications. Here are the top five advantages of harnessing Milvus:
Lightning-Fast Vector Similarity Search
Milvus delivers millisecond-level search performance on vast vector data sets. Its advanced indexing and search algorithms are ideal for image and video retrieval, recommendation systems and natural language-processing applications.
Infinite Scalability and High Availability
Milvus is built to handle enormous data volumes and can seamlessly scale horizontally to accommodate increasing workloads. It ensures high availability and data reliability through built-in replication and failover mechanisms.
Flexible Data Handling
Milvus is a versatile player, supporting various data types, including vectors, scalar and structured data. This flexibility streamlines data management and analysis within a single system.
Milvus provides software development kits (SDKs) and connectors for popular programming languages like Python, Java and Go. This flexibility simplifies integration into existing workflows and frameworks, and is compatible with data processing and analytics tools like TensorFlow, PyTorch and Apache Spark.
Active Community Support
Milvus thrives on its vibrant developer and user community. Regular updates, bug fixes and feature enhancements ensure Milvus stays up to date and aligns with evolving user needs. The community offers resources, tutorials and support to facilitate a smooth Milvus experience.
Milvus for Retrieval Augmented Generation
As generative AI becomes increasingly prevalent, vector databases like Milvus have become integral to the retrieval augmented generation (RAG) stack. This solution is famous for addressing large language model (LLM) challenges, including hallucinations and lack of domain-specific knowledge.
Milvus offers a secure means for developers and enterprises to store up-to-date and confidential private data outside LLMs. When a user poses a question, LLM applications use embedding models to convert the question into vector embeddings. Milvus then performs similarity searches to identify the most relevant topk results for the query. Ultimately, these results are combined with the original question to provide a prompt that offers a comprehensive context for the LLM to generate more accurate answers.
Milvus Popular Use Cases
Milvus is a popular and efficient tool used in various use cases, enabling the development of numerous real-life industry applications.
- Semantic/text similarity search: Search for semantically similar texts within extensive natural language document collections.
- Recommender systems: Recommend similar information or products based on user behaviors and preferences.
- Image similarity search: Find visually similar images from extensive image libraries.
- Audio similarity search: Discover similar audio results from massive amounts of audio data, such as music, sound effects and speeches.
- Question answering system: Build an interactive QA chatbot that automatically answers user questions.
- Molecular similarity search: Search for similar substructures, superstructures and other structures for a specified molecule.
Milvus has also proven beneficial in various scenarios, including DNA sequence classification, data deduplication, fraud detection, drug discovery and copyright protection.
Getting Started with Milvus in Minutes Using Milvus Lite
Milvus offers different deployment options to meet the diverse needs of its users. You can install Milvus Standalone on Kubernetes or with Docker Composer, use Milvus Cluster on Kubernetes or go for Milvus Offline with Helm charts.
While traditional deployment methods offer superior functionality, new users may need more time to set up the full version. To help users try Milvus quicker, Bin Ji, a top contributor to the Milvus community, developed Milvus Lite, a lightweight version of Milvus. It can help you get started with Milvus in minutes, while at the same time offering many benefits:
- Integration into Python applications without adding excess weight.
- Thanks to its compatibility with embedded etcd and local storage, self-containment eliminates external dependencies.
- Functionality as a Python library and a command-line interface-based (CLI) stand-alone server.
- Seamless compatibility with Google Colab and Jupyter Notebook.
- Safe data migration between different Milvus instances without data loss.
For more details on Milvus Lite, see this tutorial.
Note: We do not recommend using Milvus Lite in any production environment or if you require high-performance, strong availability or high scalability. Instead, consider using Milvus clusters or fully managed Milvus on Zilliz Cloud for production.
Milvus is distinguished by its impressive architecture, scalability and versatility, making it a top vector database option. Whether you’re digging into AI or seeking to improve your current data search abilities, Milvus could be the answer to unlocking numerous possibilities.