hnswlib
txtai
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hnswlib | txtai | |
---|---|---|
12 | 354 | |
4,000 | 6,953 | |
3.5% | 6.3% | |
6.6 | 9.3 | |
13 days ago | 7 days ago | |
C++ | Python | |
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.
https://github.com/jiggy-ai/hnsqlite
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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).
https://github.com/nmslib/hnswlib
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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
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Build knowledge graphs with LLM-driven entity extraction
txtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows.
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Bootstrap or VC?
Bootstrapping only works if you have the runway to do it and you don't feel the need to grow fast.
With NeuML (https://neuml.com), I've went the bootstrapping route. I've been able to build a fairly successful open source project (txtai 6K stars https://github.com/neuml/txtai) and a revenue positive company. It's a "live within your means" strategy.
VC funding can have a snowball effect where you need more and more. Then you're in the loop of needing funding rounds to survive. The hope is someday you're acquired or start turning a profit.
I would say both have their pros and cons. Not all ideas have the luxury of time.
- txtai: An embeddings database for semantic search, graph networks and RAG
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Ask HN: What happened to startups, why is everything so polished?
I agree that in many cases people are puffing their feathers to try to be something they're not (at least not yet). Some believe in the fake it until you make it mentality.
With NeuML (https://neuml.com), the website is a simple HTML page. On social media, I'm honest about what NeuML is, that I'm in my 40s with a family and not striving to be the next Steve Jobs. I've been able to build a fairly successful open source project (txtai 6K stars https://github.com/neuml/txtai) and a revenue positive company. For me, authenticity and being genuine is most important. I would say that being genuine has been way more of an asset than liability.
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Are we at peak vector database?
I'll add txtai (https://github.com/neuml/txtai) to the list.
There is still plenty of room for innovation in this space. Just need to focus on the right projects that are innovating and not the ones (re)working on problems solved in 2020/2021.
- Txtai: An all-in-one embeddings database for semantic search and LLM workflows
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Generate knowledge with Semantic Graphs and RAG
txtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows.
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Show HN: Open-source Rule-based PDF parser for RAG
Nice project! I've long used Tika for document parsing given it's maturity and wide number of formats supported. The XHTML output helps with chunking documents for RAG.
Here's a couple examples:
- https://neuml.hashnode.dev/build-rag-pipelines-with-txtai
- https://neuml.hashnode.dev/extract-text-from-documents
Disclaimer: I'm the primary author of txtai (https://github.com/neuml/txtai).
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RAG Using Unstructured Data and Role of Knowledge Graphs
If you're interested in graphs + RAG and want an alternate approach, txtai has a semantic graph component.
https://neuml.hashnode.dev/introducing-the-semantic-graph
https://github.com/neuml/txtai
Disclaimer: I'm the primary author of txtai
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Ten Noteworthy AI Research Papers of 2023
fwiw this link looks interesting, everyone
https://github.com/neuml/txtai
What are some alternatives?
faiss - A library for efficient similarity search and clustering of dense vectors.
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
annoy - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
tika-python - Tika-Python is a Python binding to the Apache Tikaâ„¢ REST services allowing Tika to be called natively in the Python community.
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
awesome-vector-search - Collections of vector search related libraries, service and research papers
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
semantic-search-through-wikipedia-with-weaviate - Semantic search through a vectorized Wikipedia (SentenceBERT) with the Weaviate vector search engine
CLIP - CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
ann-benchmarks - Benchmarks of approximate nearest neighbor libraries in Python
paperai - 📄 🤖 Semantic search and workflows for medical/scientific papers