Milvus is a highly flexible, reliable, and blazing-fast cloud-native, open-source vector database. It powers embedding similarity search and AI applications and strives to make vector databases accessible to every organization. Milvus can store, index, and manage a billion+ embedding vectors generated by deep neural networks and other machine learning (ML) models. This level of scale is vital to handling the volumes of unstructured data generated to help organizations to analyze and act on it to provide better service, reduce fraud, avoid downtime, and make decisions faster.
Milvus is a graduated-stage project of the LF AI & Data Foundation.
High Performance
Milvus is designed to manage and process large-scale vector data extremely fast, making it suitable for handling real-time processing of massive datasets.
Scalability
Milvus supports horizontal scaling, ensuring that as the data grows, the system can scale out by adding more nodes to maintain performance.
Flexible Deployment
Milvus can be deployed on-premises, on cloud services, or in hybrid environments, providing flexibility for different infrastructure needs.
Community and Support
As an open-source project, Milvus has a strong community and support network, including comprehensive documentation and active community forums.
Rich Ecosystem
Milvus integrates well with various machine learning and data processing tools, such as TensorFlow, PyTorch, and other AI frameworks, facilitating seamless workflows.
Built-in Indexing
Milvus provides built-in indexing capabilities like IVF, HNSW, and ANNOY, which enhance the speed and efficiency of similarity searches on vector data.
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Milvus is generally regarded as a good option, especially for businesses and developers working in the field of AI and data science. Its open-source nature allows for flexibility and community support, and it is backed by a solid architecture designed for scalability and efficiency.
We have collected here some useful links to help you find out if Milvus is good.
Check the traffic stats of Milvus on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of Milvus on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of Milvus's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of Milvus on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
The latest comments about Milvus on Reddit. This can help you find out how popualr the product is and what people think about it.
More engines. The engine abstraction is clean, adding a new one means implementing four methods (initialize, upsert, search, count). Weaviate, Chroma, and Milvus are very interesting candidates. I should evaluate if they fit the ecosystem and what they offer as peculiarity. Maybe a "plugin system" would be a good implementation to let folks implement their preferred semantic engine. - Source: dev.to / 3 months ago
Weaviate and Milvus: Additional open-source options. - Source: dev.to / 12 months ago
If you like this tutorial, show your support by giving our Milvus GitHub repo a star โญโit means the world to us and inspires us to keep creating! ๐. - Source: dev.to / over 1 year ago
Overview: Milvus is an open-source vector database designed for handling massive-scale vector data. It supports both NNS and ANNS and integrates well with various ML frameworks. - Source: dev.to / almost 2 years ago
If you enjoyed this blog post, consider giving us a star on Github and joining our Discord to share your experiences with the community. - Source: dev.to / about 2 years ago
You can access the code on Github, feel free to ask questions on our Discord, and give us a star on Github. - Source: dev.to / about 2 years ago
Zilliz (zilliz.com) | Hybrid/ONSITE (SF, NYC) | Full-time I am part of the hiring team for DevRel NYC - https://boards.greenhouse.io/zilliz/jobs/4307910005 SF - https://boards.greenhouse.io/zilliz/jobs/4317590005 Zilliz is the company behind Milvus (https://github.com/milvus-io/milvus), the most starred vector database on GitHub. Milvus is a distributed vector... - Source: Hacker News / over 2 years ago
Zilliz is hiring! We're looking for REMOTE and/or HYBRID roles in SF Zilliz is the company behind Milvus (https://github.com/milvus-io/milvus. - Source: Hacker News / over 2 years ago
Congrats to Qdrant's team, $28M for a Series is really nice. There are a lot of OSS vector search databases out there, we could probably list the main ones: - Qdrant https://github.com/qdrant/qdrant - Milvus https://github.com/milvus-io/milvus What else? - Source: Hacker News / over 2 years ago
But before we do, I do want to say that ๐คฉ all these lovely Open-Source projects would love a little ๐๐ love by getting a GitHub star โญ for their efforts. Including Open Source Milvus ๐ฅฐ. - Source: dev.to / over 2 years ago
We are celebrating 25 different open source projects during the Open Source Advent this month! You can earn points all month long for a chance to win an exclusive swag pack from Zilliz and the participating projects! Itโs a great chance to learn new skills and have some winter fun. Today is the first day and we are featuring Milvus! You can join us in our Discord channel or check us out on GitHub! We'd love a โญ... - Source: dev.to / over 2 years ago
Faiss is a library that supports indexing, not a fully-fledged vector database on its own. Milvus uses Faiss with a few other libraries to build a full vector database (https://github.com/milvus-io/milvus#acknowledgments). - Source: Hacker News / over 2 years ago
If you're just starting out, I'd use sentence-transformers for calculating embeddings. You'll want a bi-encoder model since they produce embeddings. As the author of the blog, I'm partial towards Milvus (https://github.com/milvus-io/milvus) due to its enterprise and scale, but FAISS is a great option too if you're just looking for something more local and... - Source: Hacker News / about 3 years ago
Milvus (16.6k โญ) โ An open-source vector database that can manage trillions of vector datasets and supports multiple vector search indexes and built-in filtering. - Source: dev.to / about 3 years ago
Milvus is completely open source (https://github.com/milvus-io/milvus) and support various consistency levels, scalar/metadata filtering, and time travel. We started working on Milvus back in 2018, with 2.0 being released in January 2022 (https://github.com/milvus-io/milvus/releases/tag/v2.0.0. For those who don't want to be burdened with installing and maintaining a local database, there's a managed service... - Source: Hacker News / over 3 years ago
Solid work OpenAI, though I'd definitely like to see some more benchmarks on a wider variety of datasets in addition to the ones listed in the post. Regardless, it's good to see embeddings becoming more and more mainstream and easier to leverage out-of-box. We tried image embeddings many moons ago (2015) with AlexNet trained across a custom dataset, but we still had to add quite a few custom roles post-inference.... - Source: Hacker News / over 3 years ago
Thanks for the shout-out! For folks interested in playing around with vector and/or hybrid search: Milvus is open-source (https://github.com/milvus-io/milvus). - Source: Hacker News / over 3 years ago
And of course could do some sort of vector search engine like Milvus with nearest neighbors on embeddings. Source: over 3 years ago
Hashes are great, but to say that "vectors are over" is just plain nonsense. We continue to see vectors as a core part of production systems for entity representation and recommendation (example: https://slack.engineering/recommend-api) and within models themselves (example: multimodal and diffusion models). For folks into metrics, we're building a vector database specifically for storing, indexing, and searching... - Source: Hacker News / almost 4 years ago
Usually this is done in three steps. The first step is using a neural network to create a bounding box around the object, then generating vector embeddings of the object, and then using similarity search on vector embeddings. The first step is accomplished by training a detection model to generate the bounding box around your object, this can usually be done by finetuning an already trained detection model. For... - Source: Hacker News / almost 4 years ago
You can look into Milvus or fastdup. I am using fastdup and its really good. Have created a pipeline using Milvus too. If you need that DM me. Https://github.com/milvus-io/milvus Https://github.com/visualdatabase/fastdup. Source: almost 4 years ago
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Is Milvus good? This is an informative page that will help you find out. Moreover, you can review and discuss Milvus here. The primary details have not been verified within the last quarter, and they might be outdated. If you think we are missing something, please use the means on this page to comment or suggest changes. All reviews and comments are highly encouranged and appreciated as they help everyone in the community to make an informed choice. Please always be kind and objective when evaluating a product and sharing your opinion.
It's the next great step towards looking for similarity and objects classification with machine learning models.