Open Source Vector Database & AI Infrastructure Solutions
Zilliz engineers are the original creators of many renowned open-source projects for AI-powered applications, offering comprehensive solutions for building and enhancing your artificial intelligence infrastructure. The Milvus vector database and various accompanying AI tools are specifically designed to simplify data management and optimize the performance of your AI applications.
We are proud to be the maintainers of these popular open source AI infrastructure projects
Explore the following AI tools to acquire essential insights for deploying and utilizing Zilliz's advanced vector search technology, unlocking the potential of unstructured data for maximized AI capabilities.
Milvus is an open-source vector database that can store, index, and search billion-scale unstructured data through high-dimensionaal vector embeddings. It is perfect for building modern AI applications such as retrieval augmented generation (RAG), semantic search, multimodal search, and recommendation systems.
PyMilvus is the Python SDK for Milvus. It enables Python developers to build indexes, perform vector searches, and handle many other data operations efficiently. It seamlessly integrates with popular embedding models, making it easier to transform your data into searchable vectors.
VTS (Vector Transport Service) is an open-source data migration tool designed to handle the complex task of moving vectors and unstructured data between different systems, particularly vector databases like Milvus and Zilliz Cloud. Built on Apache SeaTunnel, VTS supports both real-time data synchronization and batch processing.
Milvus Backup is a tool for backing up and restoring data in Milvus, accessible through the command line or an API server. It creates snapshots of collections with minimal performance impact, ensuring data integrity while allowing the Milvus cluster to remain fully functional during backup and restoration. This makes it ideal for safeguarding data and supporting migrations.
The Milvus Sizing Tool helps users configure their Milvus deployment by selecting optimal index types (e.g., HNSW, IVF_FLAT) and segment sizes for better performance. It balances memory, disk space, accuracy, and speed, guiding decisions on deployment setups like data streaming.
Milvus CLI (Command Line Interface) is an open-source command-line tool that supports database connections, data operations, and data imports and exports. Based on the PyMilvus, it allows the execution of commands through a terminal using interactive command-line prompts.
VectorDBBench is an open-source benchmarking tool designed to evaluate and compare the performance of mainstream vector databases such as Milvus and Zilliz Cloud using their own datasets. It also helps developers choose the most suitable vector database for their use cases.
GPTCache is an open-source library designed to optimize the performance of GenAI apps by caching responses from large language models (LLMs). It reduces costs and latency by storing frequently used query results, enabling faster retrieval and improving the efficiency of repeated queries in applications like chatbots, content generation, and more.
Towhee is an open-source machine learning pipeline designed to transform unstructured data into vector embeddings for AI applications, streamlining the process of building neural data processing pipelines with a wide range of AI models, algorithms, and transformations.
Attu is an open-source graphical user interface (GUI) designed to manage and interact with Milvus, a vector database for AI applications. With just a few clicks, you can visualize your cluster status, manage metadata, perform data queries, and much more.
Feder is an open-source JavaScript tool by Zilliz for visualizing embedding vectors and understanding ANN (Approximate Nearest Neighbor) index files such as Faiss and HNSWlib. It helps users explore vector embeddings and index structures interactively and offers JavaScript and Python libraries for use in various environments.
DeepSearcher is an open-source project that combines the power of LLMs (DeepSeek, OpenAI, etc.) and Vector Databases (Milvus, etc.) to perform search, evaluation, and reasoning based on private data, providing highly accurate answers and comprehensive reports. This project is ideal for enterprise knowledge management, intelligent Q&A systems, and information retrieval.