ann-benchmarks VS hnswlib

Compare ann-benchmarks vs hnswlib and see what are their differences.

ann-benchmarks

Benchmarks of approximate nearest neighbor libraries in Python (by erikbern)

hnswlib

Header-only C++/python library for fast approximate nearest neighbors (by nmslib)
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ann-benchmarks hnswlib
51 13
4,757 4,165
- 2.6%
7.5 5.1
about 1 month ago 11 days ago
Python C++
MIT License Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

ann-benchmarks

Posts with mentions or reviews of ann-benchmarks. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-10-30.
  • Using Your Vector Database as a JSON (Or Relational) Datastore
    1 project | news.ycombinator.com | 23 Apr 2024
    On top of my head, pgvector only supports 2 indexes, those are running in memory only. They don't support GPU indexing, nor Disk based indexing, they also don't have separation of query and insertions.

    Also with different people I've talked to, they struggle with scale past 100K-1M vector.

    You can also have a look yourself from a performance perspective: https://ann-benchmarks.com/

  • ANN Benchmarks
    1 project | news.ycombinator.com | 25 Jan 2024
  • Approximate Nearest Neighbors Oh Yeah
    5 projects | news.ycombinator.com | 30 Oct 2023
    https://ann-benchmarks.com/ is a good resource covering those libraries and much more.
  • pgvector vs Pinecone: cost and performance
    1 project | dev.to | 23 Oct 2023
    We utilized the ANN Benchmarks methodology, a standard for benchmarking vector databases. Our tests used the dbpedia dataset of 1,000,000 OpenAI embeddings (1536 dimensions) and inner product distance metric for both Pinecone and pgvector.
  • Vector database is not a separate database category
    3 projects | news.ycombinator.com | 2 Oct 2023
    Data warehouses are columnar stores. They are very different from row-oriented databases - like Postgres, MySQL. Operations on columns - e.g., aggregations (mean of a column) are very efficient.

    Most vector databases use one of a few different vector indexing libraries - FAISS, hnswlib, and scann (google only) are popular. The newer vector dbs, like weaviate, have introduced their own indexes, but i haven't seen any performance difference -

    Reference: https://ann-benchmarks.com/

  • How We Made PostgreSQL a Better Vector Database
    2 projects | news.ycombinator.com | 25 Sep 2023
    (Blog author here). Thanks for the question. In this case the index for both DiskANN and pgvector HNSW is small enough to fit in memory on the machine (8GB RAM), so there's no need to touch the SSD. We plan to test on a config where the index size is larger than memory (we couldn't this time due to limitations in ANN benchmarks [0], the tool we use).

    To your question about RAM usage, we provide a graph of index size. When enabling PQ, our new index is 10x smaller than pgvector HNSW. We don't have numbers for HNSWPQ in FAISS yet.

    [0]: https://github.com/erikbern/ann-benchmarks/

  • Do we think about vector dbs wrong?
    7 projects | news.ycombinator.com | 5 Sep 2023
  • Vector Search with OpenAI Embeddings: Lucene Is All You Need
    2 projects | news.ycombinator.com | 3 Sep 2023
    In terms of "All You Need" for Vector Search, ANN Benchmarks (https://ann-benchmarks.com/) is a good site to review when deciding what you need. As with anything complex, there often isn't a universal solution.

    txtai (https://github.com/neuml/txtai) can build indexes with Faiss, Hnswlib and Annoy. All 3 libraries have been around at least 4 years and are mature. txtai also supports storing metadata in SQLite, DuckDB and the next release will support any JSON-capable database supported by SQLAlchemy (Postgres, MariaDB/MySQL, etc).

  • Vector databases: analyzing the trade-offs
    5 projects | news.ycombinator.com | 20 Aug 2023
    pg_vector doesn't perform well compared to other methods, at least according to ANN-Benchmarks (https://ann-benchmarks.com/).

    txtai is more than just a vector database. It also has a built-in graph component for topic modeling that utilizes the vector index to autogenerate relationships. It can store metadata in SQLite/DuckDB with support for other databases coming. It has support for running LLM prompts right with the data, similar to a stored procedure, through workflows. And it has built-in support for vectorizing data into vectors.

    For vector databases that simply store vectors, I agree that it's nothing more than just a different index type.

  • Vector Dataset benchmark with 1536/768 dim data
    3 projects | news.ycombinator.com | 14 Aug 2023
    The reason https://ann-benchmarks.com is so good, is that we can see a plot of recall vs latency. I can see you have some latency numbers in the leaderboard at the bottom, but it's very difficult to make a decision.

    As a practitioner that works with vector databases every day, just latency is meaningless to me, because I need to know if it's fast AND accurate, and what the tradeoff is! You can't have it both ways. So it would be helpful if you showed plots showing this tradeoff, similar to ann-benchmarks.

hnswlib

Posts with mentions or reviews of hnswlib. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-07-18.
  • Introducing vectorlite: A Fast and Tunable Vector Search Extension for SQLite
    7 projects | dev.to | 18 Jul 2024
    import vectorlite_py import apsw import numpy as np """ Quick start of using vectorlite extension. """ conn = apsw.Connection(':memory:') conn.enable_load_extension(True) # enable extension loading conn.load_extension(vectorlite_py.vectorlite_path()) # load vectorlite cursor = conn.cursor() # check if vectorlite is loaded print(cursor.execute('select vectorlite_info()').fetchall()) # Vector distance calculation for distance_type in ['l2', 'cosine', 'ip']: v1 = "[1, 2, 3]" v2 = "[4, 5, 6]" # Note vector_from_json can be used to convert a JSON string to a vector distance = cursor.execute(f'select vector_distance(vector_from_json(?), vector_from_json(?), "{distance_type}")', (v1, v2)).fetchone() print(f'{distance_type} distance between {v1} and {v2} is {distance[0]}') # generate some test data DIM = 32 # dimension of the vectors NUM_ELEMENTS = 10000 # number of vectors data = np.float32(np.random.random((NUM_ELEMENTS, DIM))) # Only float32 vectors are supported by vectorlite for now # Create a virtual table using vectorlite using l2 distance (default distance type) and default HNSW parameters cursor.execute(f'create virtual table my_table using vectorlite(my_embedding float32[{DIM}], hnsw(max_elements={NUM_ELEMENTS}))') # Vector distance type can be explicitly set to cosine using: # cursor.execute(f'create virtual table my_table using vectorlite(my_embedding float32[{DIM}] cosine, hnsw(max_elements={NUM_ELEMENTS}))') # Insert the test data into the virtual table. Note that the rowid MUST be explicitly set when inserting vectors and cannot be auto-generated. # The rowid is used to uniquely identify a vector and serve as a "foreign key" to relate to the vector's metadata. # Vectorlite takes vectors in raw bytes, so a numpy vector need to be converted to bytes before inserting into the table. cursor.executemany('insert into my_table(rowid, my_embedding) values (?, ?)', [(i, data[i].tobytes()) for i in range(NUM_ELEMENTS)]) # Query the virtual table to get the vector at rowid 12345. Note the vector needs to be converted back to json using vector_to_json() to be human-readable. result = cursor.execute('select vector_to_json(my_embedding) from my_table where rowid = 1234').fetchone() print(f'vector at rowid 1234: {result[0]}') # Find 10 approximate nearest neighbors of data[0] and there distances from data[0]. # knn_search() is used to tell vectorlite to do a vector search. # knn_param(V, K, ef) is used to pass the query vector V, the number of nearest neighbors K to find and an optional ef parameter to tune the performance of the search. # If ef is not specified, ef defaults to 10. For more info on ef, please check https://github.com/nmslib/hnswlib/blob/v0.8.0/ALGO_PARAMS.md result = cursor.execute('select rowid, distance from my_table where knn_search(my_embedding, knn_param(?, 10))', [data[0].tobytes()]).fetchall() print(f'10 nearest neighbors of row 0 is {result}') # Find 10 approximate nearest neighbors of the first embedding in vectors with rowid within [1000, 2000) using metadata(rowid) filtering. rowids = ','.join([str(rowid) for rowid in range(1000, 2000)]) result = cursor.execute(f'select rowid, distance from my_table where knn_search(my_embedding, knn_param(?, 10)) and rowid in ({rowids})', [data[0].tobytes()]).fetchall() print(f'10 nearest neighbors of row 0 in vectors with rowid within [1000, 2000) is {result}') conn.close()
  • Show HN: A fast HNSW implementation in Rust
    6 projects | news.ycombinator.com | 14 Mar 2024
    How does this compare to hsnwlib - is it faster? https://github.com/nmslib/hnswlib
  • Show HN: Moodflix – a movie recommendation engine based on your mood
    1 project | news.ycombinator.com | 9 Nov 2023
    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.

  • Hierarchical Navigable Small Worlds
    2 projects | news.ycombinator.com | 10 Jul 2023
    Actually the "ef" is not epsilon. It is a parameter of the HNSW index: https://github.com/nmslib/hnswlib/blob/master/ALGO_PARAMS.md...
  • Vector Databases 101
    3 projects | /r/datascience | 25 Jun 2023
    If you want to go larger you could still use some simple setup in conjunction with faiss, annoy or hnsw.
  • [P] Compose a vector database
    2 projects | /r/MachineLearning | 13 May 2023
    Many vector databases are using Hnswlib and that is a supported vector index alongside Faiss and Annoy.
  • Faiss: A library for efficient similarity search
    14 projects | news.ycombinator.com | 30 Mar 2023
    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

  • Storing OpenAI embeddings in Postgres with pgvector
    9 projects | news.ycombinator.com | 6 Feb 2023
    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. ;-)

  • Seeking advice on improving NLP search results
    4 projects | /r/LanguageTechnology | 22 Jan 2023
    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.
  • How to Build a Semantic Search Engine in Rust
    3 projects | news.ycombinator.com | 9 Nov 2022
    hnswlib is in cpp and has python bindings (you should be able to make your own for other languages).

    https://github.com/nmslib/hnswlib

What are some alternatives?

When comparing ann-benchmarks and hnswlib you can also consider the following projects:

pgvector - Open-source vector similarity search for Postgres

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

Milvus - A cloud-native vector database, storage for next generation AI applications

qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/

tlsh

awesome-vector-search - Collections of vector search related libraries, service and research papers

pgANN - Fast Approximate Nearest Neighbor (ANN) searches with a PostgreSQL database.

semantic-search-through-wikipedia-with-weaviate - Semantic search through a vectorized Wikipedia (SentenceBERT) with the Weaviate vector search engine

vald - Vald. A Highly Scalable Distributed Vector Search Engine

txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows

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