featureform
awesome-vector-search
featureform | awesome-vector-search | |
---|---|---|
28 | 20 | |
1,705 | 1,284 | |
2.2% | 3.2% | |
9.6 | 5.7 | |
2 days ago | 29 days ago | |
Jupyter Notebook | ||
Mozilla Public 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.
featureform
- Still look familiar?
- Featureform: A Python Framework for the Entire Feature Lifecycle. Define, Version, Orchestrate, & Deploy ML Features with OSS Featureform!
- Does this look familiar?
- Does this look familiar? Define, Manage, & Serve your ML Features with OSS Featureform!
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What’s your process for deploying a data pipeline from a notebook, running it, and managing it in production?
Feature store: new hot one: https://www.featureform.com/
- featureform / featureform :
- Featureform: An Open-Source Feature Store for your ML Features
awesome-vector-search
- Show HN: SimSIMD vs. SciPy: How AVX-512 and SVE make SIMD cleaner and ML faster
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Reality check on good embedding model (and this idea in general)
Probably. But there are a number of free open source ones. For example, I've got a document that I'm doing embedding-keys for that has about 8000 sentences. Here's a list of some [ https://github.com/currentslab/awesome-vector-search ]
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Rye, meet GPT3 ... and vice versa :)
note: search for vector databases not written in Go but with Go clients, in case there is anything more local/lightweight: https://github.com/currentslab/awesome-vector-search
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Vector database built for scalable similarity search
https://github.com/currentslab/awesome-vector-search
I was surprised to see Elastic actually has ok support for some of this stuff, though it appears slower for most of the tasks.
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[P] My co-founder and I quit our engineering jobs at AWS to build “Tensor Search”. Here is why.
Supporting sequence of vectors does seems like a fresh air to the vector search service. I have added marqo to the list of awesome vector search (disclosure: I am the maintainer of the list) to increase your exposure.
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What are vector search engines?
If you want a proper curated list of various libraries and standalone services of vector search engines, refer to this awesome GitHub repository by Currents API.
- List of vector search libraries
- List of curated vector search libraries
- A GitHub repository that collects awesome vector search framework/engine, library, cloud service, and research papers
- Find anything fast with Google's vector search technology
What are some alternatives?
feast - The Open Source Feature Store for Machine Learning
pgvector - Open-source vector similarity search for Postgres
Milvus - A cloud-native vector database, storage for next generation AI applications
annoy - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
feathr - Feathr – A scalable, unified data and AI engineering platform for enterprise
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
hopsworks - Hopsworks - Data-Intensive AI platform with a Feature Store
OpenMLDB - OpenMLDB is an open-source machine learning database that provides a feature platform computing consistent features for training and inference.
hnswlib - Header-only C++/python library for fast approximate nearest neighbors
vald - Vald. A Highly Scalable Distributed Vector Search Engine
vearch - Distributed vector search for AI-native applications