featureform
awesome-vector-search
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featureform | awesome-vector-search | |
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28 | 20 | |
1,667 | 1,228 | |
1.4% | 4.2% | |
9.7 | 6.8 | |
2 days ago | about 1 month ago | |
Jupyter Notebook | ||
Mozilla Public License 2.0 | MIT License |
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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
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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/
- [D] Your 🫵 Preferred Feature Stores?
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How to Build a Recommender System with Embeddinghub
Usually embeddings — dense numerical representations of real-world objects and relationships, expressed as a vector — are stored in database servers such as PostgreSQLEmbedding. However Embeddinghub makes it easier to store your embeddings and load them. You can get started with minimal setup, and it also makes your code look less verbose as compared to, say, building a KNN model using scikit-learn.
- [P] Embeddinghub: A vector database built for ML embeddings
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.
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Find anything fast with Google's vector search technology
If anyone is interested, I maintain a list of open source vector search engine services[1].
Feel free to submit a new issues or merge request if you wish for new library added
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Facebook AI Similarity Search (Faiss)
Here's a list of vector similarity search projects: https://github.com/currentsapi/awesome-vector-search, you can find other alternative method than faiss.
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[P] Embeddinghub: A vector database built for ML embeddings
How's it different from Pinecone, Milvus, Faiss, and others?
What are some alternatives?
feast - Feature Store for Machine Learning
pgvector - Open-source vector similarity search for Postgres
annoy - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
Milvus - A cloud-native vector database, storage for next generation AI applications
feathr - Feathr – A scalable, unified data and AI engineering platform for enterprise
hnswlib - Header-only C++/python library for fast approximate nearest neighbors
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.
vearch - Distributed vector search for AI-native applications
marqo - Unified embedding generation and search engine. Also available on cloud - cloud.marqo.ai