hnswlib
hnsqlite
hnswlib | hnsqlite | |
---|---|---|
12 | 6 | |
4,015 | 143 | |
1.5% | 1.4% | |
6.2 | 5.5 | |
19 days ago | 10 months ago | |
C++ | Python | |
Apache License 2.0 | Apache License 2.0 |
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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.
hnsqlite
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LangChain: The Missing Manual
For anyone thinking about applications of langchain and pinecone but who are looking for something more turn-key check out https://jiggy.ai
The core is actually open source as well, allowing you to take your data back out via sqlite and hnswlib (https://github.com/jiggy-ai/hnsqlite)
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I built an open source website that lets you upload large files, such as in-depth novels or academic papers, and ask ChatGPT questions based on your specific knowledge base. So far, I've tested it with long books like the Odyssey and random research papers that I like, and it works shockingly well.
We are built on open core https://github.com/jiggy-ai. Our open source hnsqlite is light weight, easy to use. And best of all, we make it easy for you to get your data out of JiggyBase. You can download a sqlite file that contains your document text data, metadata, embedding vectors, and embedding index. This can be used directly in the open source hnsqlite package.
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What Is a Vector Database
After working through several projects that utilized local hnswlib and different databases for text and vector persistence, I integrated open source 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
- Show HN: Hnsqlite: hnswlib and SQLite integrated for text embedding search
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Faiss: A library for efficient similarity search
Thanks Leobg!
For anyone else: you pass it directly in metadata see https://github.com/jiggy-ai/hnsqlite/blob/main/test/test_col...
What are some alternatives?
faiss - A library for efficient similarity search and clustering of dense vectors.
langchainrb - Build LLM-powered applications in Ruby
annoy - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
NeMo-Guardrails - NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
awesome-vector-search - Collections of vector search related libraries, service and research papers
semantic-search-through-wikipedia-with-weaviate - Semantic search through a vectorized Wikipedia (SentenceBERT) with the Weaviate vector search engine
GPT4Memory
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
raft - RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.