Milvus
tantivy
Our great sponsors
Milvus | tantivy | |
---|---|---|
103 | 48 | |
26,132 | 9,682 | |
3.5% | 3.7% | |
10.0 | 9.1 | |
7 days ago | 6 days ago | |
Go | Rust | |
Apache License 2.0 | MIT License |
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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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
Github Repository
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
tantivy
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SeekStorm VS tantivy - a user suggested alternative
2 projects | 22 Mar 2024
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What is Hybrid Search?
Tantivy - a full-text indexing library written in Rust. Has a great performance and featureset.
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RAG Using Unstructured Data and Role of Knowledge Graphs
By this I presume you mean build a search index that can retrieve results based on keywords? I know certain databases use Lucene to build a keyword-based index on top of unstructured blobs of data. Another alternative is to use Tantivy (https://github.com/quickwit-oss/tantivy), a Rust version of Lucene, if building search indices via Java isn't your cup of tea :)
Both libraries offer multilingual support for keywords, I believe, so that's a benefit to vector search where multilingual embedding models are rather expensive.
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Show HN: Quickwit – OSS Alternative to Elasticsearch, Splunk, Datadog
We also implemented our schemaless columnar storage optimized for object storage.
The inverted index and columnar storage are part of tantivy [0], which is the fastest search library out there. We maintain it and we decided to build the distributed engine on top of it.
[0] tantivy github repo: https://github.com/quickwit-oss/tantivy
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Pg_bm25: Elastic-Quality Full Text Search Inside Postgres
The issue for geo search is here: https://github.com/quickwit-oss/tantivy/issues/44
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Grimoire - A recipe management application.
Search index : Custom-built using tantivy.
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A Compressed Indexable Bitset
The roaring bitmap variant is used only for the optional index (1 docid => 0 or 1 value) in the columnar storage (DocValues), not for the inverted index. Since this is used for aggregation, some queries may be a full scan.
The inverted index in tantivy uses bitpacked values of 128 elements with a skip index on top.
> I didn't follow the rest of your comment, select is what EF is good at, every other data structure needs a lot more scanning once you land on the right chunk. With BMI2 you can also use the PDEP instruction to accelerate the final select on a 64-bit block
The select for the sparse codec is a [simple array index access](https://github.com/quickwit-oss/tantivy/blob/main/columnar/s...), that is hard to beat. Compression is not good near the 5k threshold though.
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Job: Rust + Retrieval Systems at Etsy
Hi /r/rust, I’m a SWE on Etsy’s Retrieval Systems team where we’re building a platform based on rust and tantivy (https://github.com/quickwit-oss/tantivy). We’re looking to bring two new engineers onto the team.
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Announcing Velo - Your Rust-Powered Brainstorming and Note-Taking Tool
Quick Search: Easily find specific notes with Velo's fuzzy-search feature, powered by tantivy. tantivy might have been a little overkill, but it was really easy to integrate.
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Quickwit 0.6.0 - Search and analytics on billions of logs with minimal hardware
Two years after, we are finally reaching a version that can deliver our promise. Two years is both very long for a startup and very short when building a distributed engine. And we decided to do it the hard way: we implemented our own OSS gossip library, our own {S3,JSON}-friendly columnar format for schemaless analytics, and of course, we maintain our own search library, tantivy. This is a lot of engineering investment and obviously, it takes some time to finally reach the end users.
What are some alternatives?
pgvector - Open-source vector similarity search for Postgres
faiss - A library for efficient similarity search and clustering of dense vectors.
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
Face Recognition - The world's simplest facial recognition api for Python and the command line
vald - Vald. A Highly Scalable Distributed Vector Search Engine
nmslib - Non-Metric Space Library (NMSLIB): An efficient similarity search library and a toolkit for evaluation of k-NN methods for generic non-metric spaces.
annoy - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
CompreFace - Leading free and open-source face recognition system
towhee - Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.
sonic - 🦔 Fast, lightweight & schema-less search backend. An alternative to Elasticsearch that runs on a few MBs of RAM.