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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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 / 2 months 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 / 3 months 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 / 3 months 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 / 6 months 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 / 8 months 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 / 9 months ago
But before we do, I do want to say that π€© all these lovely Open-Source projects would love a little ππ love by getting a GitHub star β for their efforts. Including Open Source Milvus π₯°. - Source: dev.to / 9 months 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! Itβs 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 β... - Source: dev.to / 10 months 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 / 11 months 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 / over 1 year ago
Milvus (16.6k β) β 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 / over 1 year 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 1 year 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 / almost 2 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 / almost 2 years ago
And of course could do some sort of vector search engine like Milvus with nearest neighbors on embeddings. Source: almost 2 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 / about 2 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 / about 2 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: about 2 years ago
Nooo! This is how I keep up with new trends in Open Source! Without this, I would never found https://github.com/milvus-io/milvus, for example. - Source: Hacker News / about 2 years ago
Cool to see a vector search application that leverages traditional image features. I'm curious to know which of these methods performed best on cropped images, which remain somewhat of a challenge for traditional classification models (contrastive models trained specifically with image crops tend to work much better). Also for the vector database itself, have you considered spinning up your own open-source... - Source: Hacker News / about 2 years ago
Great article. I've been working in and around this space since 2014, and I think similarity learning, vector search, and embedding management will be a core part of future applications that leverage ML. I recently built a similarity search application that recommends new Pinterest users channels to follow based on liked images using Milvus (https://github.com/milvus-io/milvus) as a backend. Similarity learning is... - Source: Hacker News / about 2 years ago
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It's the next great step towards looking for similarity and objects classification with machine learning models.