hnswlib
txtai.go
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| hnswlib | txtai.go | |
|---|---|---|
| 12 | 10 | |
| 3,984 | 63 | |
| 3.1% | - | |
| 6.6 | 5.0 | |
| 27 days ago | about 2 months ago | |
| C++ | Go | |
| Apache License 2.0 | Apache License 2.0 |
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.
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.
txtai.go
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# Run txtai in native code
txtai currently has two main methods of execution: Python or via a HTTP API. There are API bindings for JavaScript, Java, Rust and Go.
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Build semantic search applications with txtai
In addition to running in Python, txtai can run as an API service and has bindings for JavaScript, Java, Go and Rust.
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Transform tabular data with composable workflows
Next we'll execute the workflow. txtai has API bindings for JavaScript, Java, Rust and Golang. But to keep things simple, we'll just run the commands via cURL.
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txtai 3.4 released - Build AI-powered semantic search applications in Go
txtai has a full API service that supports Go - https://github.com/neuml/txtai.go
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txtai - Semantic search backed by machine-learning powered workflows
🔗 API bindings for JavaScript, Java, Rust and Go
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txtai 3.0 released - Machine-learning workflows, similarity search and Go support via API
Example in Go - https://github.com/neuml/txtai.go/tree/master/examples
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txtai: AI-powered search engine for Go
Labeling with zero-shot classification
What are some alternatives?
faiss - A library for efficient similarity search and clustering of dense vectors.
annoy - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
blast - Blast is a full text search and indexing server, written in Go, built on top of Bleve.
awesome-vector-search - Collections of vector search related libraries, service and research papers
elasticsql - convert sql to elasticsearch DSL in golang(go)
semantic-search-through-wikipedia-with-weaviate - Semantic search through a vectorized Wikipedia (SentenceBERT) with the Weaviate vector search engine
txtai.rs - Rust client for txtai