Milvus
nmslib
Our great sponsors
| Milvus | nmslib | |
|---|---|---|
| 104 | 4 | |
| 26,645 | 3,278 | |
| 3.6% | 1.2% | |
| 10.0 | 0.0 | |
| 1 day ago | about 1 month ago | |
| Go | C++ | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Milvus
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Ask HN: Who is hiring? (April 2024)
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 database that shines in 1B+ vector use cases. Examples include autonomous driving, e-commerce, and drug discovery. (and, of course, RAG)
We are also hiring for other roles that I am not personally involved in the hiring process for such as product managers, software engineers, and recruiters.
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Unlock Advanced Search Capabilities with Milvus and Read about RAG
Get started with Milvus on GitHub.
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Milvus VS pgvecto.rs - a user suggested alternative
2 projects | 13 Mar 2024
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How to choose the right type of database
Milvus: An open-source vector database designed for AI and ML applications. It excels in handling large-scale vector similarity searches, making it suitable for recommendation systems, image and video retrieval, and natural language processing tasks.
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Simplifying the Milvus Selection Process
Selecting the right version of open-source Milvus is important to the success of any project leveraging vector search technology. With Milvus offering different versions of its vector database tailored to varying requirements, understanding the significance of selecting the correct version is key for achieving desired outcomes.
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7 Vector Databases Every Developer Should Know!
Milvus is an open-source vector database designed to handle large-scale similarity search and vector indexing. It supports multiple index types and offers highly efficient search capabilities, making it suitable for a wide range of AI and ML applications, including image and video recognition, natural language processing, and recommendation systems.
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Ask HN: Who is hiring? (February 2024)
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), the most widely adopted vector database. Vector databases are a crucial piece of any technology stack looking to take advantage of unstructured data. Most recently and notably, Retrieval Augmented Generation (RAG). For RAG, vector databases like Milvus are used as the tool to inject customized data. In other words, vector databases make things like customized chat bots, personalized product recommendations, and more possible.
We are hiring for Developer Advocates, Senior+ Level Engineers and Product people, and Talent Acquisition. Check out all the roles here: https://zilliz.com/careers
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Qdrant, the Vector Search Database, raised $28M in a Series A round
Good on them, I know the crustaceans are out here happy about this raise for a Rust based Vector DB!
(now I'm gonna plug what I work on)
If you're interested in a more scalable vector database written in Go, check out Milvus (https://github.com/milvus-io/milvus)
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Open Source Advent Fun Wraps Up!
But before we do, I do want to say that 🤩 all these lovely Open-Source projects would love a little 🎉💕 love by getting a GitHub star ⭐ for their efforts. Including Open Source Milvus 🥰
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First 15 Open Source Advent projects
1. Milvus by Zilliz | Github
nmslib
- Vector search just got up to 10x faster and vertically scalable
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The Missing ”WHERE” Clause for Vector Search
Amazon's Opensearch (fork of Elasticsearch) natively supports vector-based approximate KNN (using https://github.com/nmslib/nmslib/) which is integrated with Opensearch's native filtering functionality. Elasticsearch also has similar functionality, but I don't know if their KNN code scales quite as well.
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Vector Search Indexes
nmslib (https://github.com/nmslib/nmslib) supports sparse vectors for some of its spaces. It has fewer indexing methods than faiss, though.
https://github.com/nmslib/nmslib/blob/master/manual/spaces.m...
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Are there more practical tools for KNN searches and storing documents/embeddings?
I also needed to build a similar system and I used nmslib, maybe check it out - https://github.com/nmslib/nmslib
What are some alternatives?
pgvector - Open-source vector similarity search for Postgres
faiss - A library for efficient similarity search and clustering of dense vectors.
TorchPQ - Approximate nearest neighbor search with product quantization on GPU in pytorch and cuda
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
Weaviate - Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database.
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
vald - Vald. A Highly Scalable Distributed Vector Search Engine
Face Recognition - The world's simplest facial recognition api for Python and the command line
knowhere - Knowhere is an open-source vector search engine, integrating FAISS, HNSW, etc.