hora
faiss
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hora | faiss | |
---|---|---|
9 | 69 | |
2,545 | 27,545 | |
0.9% | 3.7% | |
0.0 | 9.4 | |
about 2 months ago | 6 days ago | |
Rust | C++ | |
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.
hora
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Building a Vector Database with Rust to Make Use of Vector Embeddings
We have been playing around with Hora as a replacement for the Rust-CV implementation as we want PQ as well. I'll check out instanct-distance, looks very interesting!
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Faiss: A library for efficient similarity search
Maybe https://github.com/hora-search/hora but I've never used it
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Hora, an blazingly fast AI Similarity search algorithm library (IOS Version)
$ rustup target add aarch64-apple-ios aarch64-apple-ios $ cargo install cargo-lipo $ git clone https://github.com/hora-search/hora-ios $ cd hora-ios/hora $ cargo lipo --release
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Announcing Hora 0.1.0, an approximate nearest neighbor search algorithm library in rust
github: https://github.com/hora-search/hora
faiss
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You Shouldn't Invest in Vector Databases?
You can try txtai (https://github.com/neuml/txtai) with a Faiss backend.
This Faiss wiki article might help (https://github.com/facebookresearch/faiss/wiki/Indexing-1G-v...).
For example, a partial Faiss configuration with 4-bit PQ quantization and only using 5% of the data to train an IVF index is shown below.
faiss={"components": "IVF,PQ384x4fs", "sample": 0.05}
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Approximate Nearest Neighbors Oh Yeah
If you want to experiment with vector stores, you can do that locally with something like faiss which has good platform support: https://github.com/facebookresearch/faiss
Doing full retrieval-augmented generation (RAG) and getting LLMs to interpret the results has more steps but you get a lot of flexibility, and there's no standard best-practice. When you use a vector DB you get the most similar texts back (or an index integer in the case of faiss), you then feed those to an LLM like a normal prompt.
The codifer for the RAG workflow is LangChain, but their demo is substantially more complex and harder-to-use than even a homegrown implementation: https://news.ycombinator.com/item?id=36725982
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Can someone please help me with this problem?
According to this documentation page, faiss-gpu is only supported on Linux, not on Windows.
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Code Search with Vector Embeddings: A Transformer's Approach
As the size of the codebase grows, storing and searching through embeddings in memory becomes inefficient. This is where vector databases come into play. Tools like Milvus, Faiss, and others are designed to handle large-scale vector data and provide efficient similarity search capabilities. I've wrtten about how to also use sqlite to store vector embeddings. By integrating a vector database, you can scale your code search tool to handle much larger codebases without compromising on search speed.
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Unum: Vector Search engine in a single file
But FAISS has their own version ("FastScan") https://github.com/facebookresearch/faiss/wiki/Fast-accumula...
- Introduction to Vector Similarity Search
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Vector Databases 101
If you want to go larger you could still use some simple setup in conjunction with faiss, annoy or hnsw.
- I'm an undergraduate data science intern and trying to run kmodes clustering. Did this elbow method to figure out how many clusters to use, but I don't really see an "elbow". Tips on number of clusters?
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Disrupting the AI Scene with Open Source and Open Innovation
Facebook research for having open source licensed an amazing vector based indexing library
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Meilisearch across the Semantic Verse
I just used huggingface sentence transformer to compute the embeddings and FAISS to get the nearest entries. This is very close to this tutorial on huggingface.
What are some alternatives?
annoy - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
Milvus - A cloud-native vector database, storage for next generation AI applications
hnswlib - Header-only C++/python library for fast approximate nearest neighbors
pgvector - Open-source vector similarity search for Postgres
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​.
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
hdbscan - A high performance implementation of HDBSCAN clustering.
ann-benchmarks - Benchmarks of approximate nearest neighbor libraries in Python
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Top2Vec - Top2Vec learns jointly embedded topic, document and word vectors.
txtai.go - Go client for txtai
KeyBERT - Minimal keyword extraction with BERT