hnswlib
awesome-vector-search
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hnswlib | awesome-vector-search | |
---|---|---|
12 | 20 | |
3,984 | 1,257 | |
3.1% | 4.1% | |
6.6 | 6.1 | |
26 days ago | 7 days ago | |
C++ | ||
Apache 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.
hnswlib
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Show HN: A fast HNSW implementation in Rust
How does this compare to hsnwlib - is it faster? https://github.com/nmslib/hnswlib
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Show HN: Moodflix – a movie recommendation engine based on your mood
Last week I released Moodflix (https://moodflix.streamlit.app), a movie recommendation engine based to find movies based on your mood.
Moodflix was created on top of a movie dataset of 10k movies from The Movie Database. I vectorised the films using Hugging Face's T5 model (https://huggingface.co/docs/transformers/model_doc/t5) using the film's plot synopsis, genres and languages. Then I indexed the vectors using hnswlib (https://github.com/nmslib/hnswlib). LLMs can understand a movie's plot pretty well and distill the similarities between a user's query (mood) to the movie's plot and genres.
I have got feedback from close friends around linking movies to other review sites like IMDB or Rotten Tomatoes, linking movies to sites to stream the movie and adding movie posters. I would also love to hear from the community what things you like, what you want to see and what things you consider can be improved.
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Hierarchical Navigable Small Worlds
Actually the "ef" is not epsilon. It is a parameter of the HNSW index: https://github.com/nmslib/hnswlib/blob/master/ALGO_PARAMS.md...
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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.
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[P] Compose a vector database
Many vector databases are using Hnswlib and that is a supported vector index alongside Faiss and Annoy.
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Faiss: A library for efficient similarity search
hnswlib (https://github.com/nmslib/hnswlib) is a strong alternative to faiss that I have enjoyed using for multiple projects. It is simple and has great performance on CPU.
After working through several projects that utilized local hnswlib and different databases for text and vector persistence, I integrated hnswlib with sqlite to create an embedded vector search engine that can easily scale up to millions of embeddings. For self-hosted situations of under 10M embeddings and less than insane throughput I think this combo is hard to beat.
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Storing OpenAI embeddings in Postgres with pgvector
https://github.com/nmslib/hnswlib
Used it to index 40M text snippets in the legal domain. Allows incremental adding.
I love how it just works. You know, doesn’t ANNOY me or makes a FAISS. ;-)
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Seeking advice on improving NLP search results
3000 texts doesn't sound like to many, so may be a brute force cos calculation to find the most similar vector would work. If that's taking too much time, may be look at KNN or ANN modules to speed up finding the most similar vector. I use hsnwlib in knn mode for this. SOrt through about 350,000 vectors in about 30-50 msec.
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How to Build a Semantic Search Engine in Rust
hnswlib is in cpp and has python bindings (you should be able to make your own for other languages).
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Anatomy of a txtai index
embeddings - The embeddings index file. This is an Approximate Nearest Neighbor (ANN) index with either Faiss (default), Hnswlib or Annoy, depending on the settings.
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?
faiss - A library for efficient similarity search and clustering of dense vectors.
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/
semantic-search-through-wikipedia-with-weaviate - Semantic search through a vectorized Wikipedia (SentenceBERT) with the Weaviate vector search engine
Milvus - A cloud-native vector database, storage for next generation AI applications
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
featureform - The Virtual Feature Store. Turn your existing data infrastructure into a feature store.
ann-benchmarks - Benchmarks of approximate nearest neighbor libraries in Python
vearch - Distributed vector search for AI-native applications