ann-benchmarks VS faiss

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

ann-benchmarks

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

faiss

A library for efficient similarity search and clustering of dense vectors. (by facebookresearch)
Scout Monitoring - Free Django app performance insights with Scout Monitoring
Get Scout setup in minutes, and let us sweat the small stuff. A couple lines in settings.py is all you need to start monitoring your apps. Sign up for our free tier today.
www.scoutapm.com
featured
InfluxDB - Power Real-Time Data Analytics at Scale
Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
www.influxdata.com
featured
ann-benchmarks faiss
51 72
4,748 29,475
- 2.8%
7.5 9.5
27 days ago 1 day ago
Python C++
MIT License MIT License
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.

faiss

Posts with mentions or reviews of faiss. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-06-19.
  • OpenAI api RAG system with Qdrant
    3 projects | dev.to | 19 Jun 2024
    You can swap out any of the components in this project with something else. You could use Faiss instead of qdrant, you could use OpenAI models for everything(embeddings/chat completion) or you could use open models.
  • Haystack DB – 10x faster than FAISS with binary embeddings by default
    3 projects | news.ycombinator.com | 28 Apr 2024
    There are also FAISS binary indexes[0], so it'd be great to compare binary index vs binary index. Otherwise it seems a little misleading to say it is a FAISS vs not FAISS comparison, since really it would be a binary index vs not binary index comparison. I'm not too familiar with binary indexes, so if there's a significant difference between the types of binary index then it'd be great to explain what that is too.

    [0] https://github.com/facebookresearch/faiss/wiki/Binary-indexe...

  • Show HN: Chromem-go – Embeddable vector database for Go
    4 projects | news.ycombinator.com | 5 Apr 2024
    Or just use FAISS https://github.com/facebookresearch/faiss
  • OpenAI: New embedding models and API updates
    1 project | news.ycombinator.com | 25 Jan 2024
  • You Shouldn't Invest in Vector Databases?
    4 projects | news.ycombinator.com | 25 Nov 2023
    You can try txtai (https://github.com/neuml/txtai) with a Faiss backend.

    This Faiss wiki article might help (https://github.com/facebookresearch/faiss/wiki/Indexing-1G-v...).

    For example, a partial Faiss configuration with 4-bit PQ quantization and only using 5% of the data to train an IVF index is shown below.

    faiss={"components": "IVF,PQ384x4fs", "sample": 0.05}

  • Approximate Nearest Neighbors Oh Yeah
    5 projects | news.ycombinator.com | 30 Oct 2023
    If you want to experiment with vector stores, you can do that locally with something like faiss which has good platform support: https://github.com/facebookresearch/faiss

    Doing full retrieval-augmented generation (RAG) and getting LLMs to interpret the results has more steps but you get a lot of flexibility, and there's no standard best-practice. When you use a vector DB you get the most similar texts back (or an index integer in the case of faiss), you then feed those to an LLM like a normal prompt.

    The codifer for the RAG workflow is LangChain, but their demo is substantially more complex and harder-to-use than even a homegrown implementation: https://news.ycombinator.com/item?id=36725982

  • Can someone please help me with this problem?
    2 projects | /r/learnprogramming | 24 Sep 2023
    According to this documentation page, faiss-gpu is only supported on Linux, not on Windows.
  • Ask HN: Are there any unsolved problems with vector databases
    1 project | news.ycombinator.com | 16 Sep 2023
    Indexes for vector databases in high dimensions are nowhere near are effective as the 2-d indexes used in GIS or the 1-d B-tree indexes that are commonly used in databases.

    Back around 2005 I was interested in similarity search and read a lot of conference proceedings on the top and was basically depressed at the state of vector database indexes and felt that at least for the systems I was prototyping I was OK with a full scan and later in 2013 I had the assignment of getting a search engine for patents using vector embeddings in front of customers and we got performance we found acceptable with full scan.

    My impression today is that the scene is not too different than it was in 2005 but I can't say I haven't missed anything. That is, you have tradeoffs between faster algorithms that miss some results and slower algorithms that are more correct.

    I think it's already a competitive business. You have Pinecone which had the good fortune of starting before the gold rush. Many established databases are adding vector extension. I know so many engineering managers who love postgresql and they're just going to load a vector extension and go. My RSS reader YOShInOn uses SBERT embeddings to cluster and classify text and certainly More Like This and semantic search are on the agenda, I'd expect it to take about an hour to get

    https://github.com/facebookresearch/faiss

    up and working, I could spend more time stuck on some "little" front end problem like getting something to look right in Bootstrap than it would take to get working.

    I can totally believe somebody could make a better vector db than what's out there but will it be better enough? A startup going through YC now could spend 2-3 to get a really good product and find customers and that is forever in a world where everybody wants to build AI applications right now.

  • Code Search with Vector Embeddings: A Transformer's Approach
    3 projects | dev.to | 27 Aug 2023
    As the size of the codebase grows, storing and searching through embeddings in memory becomes inefficient. This is where vector databases come into play. Tools like Milvus, Faiss, and others are designed to handle large-scale vector data and provide efficient similarity search capabilities. I've wrtten about how to also use sqlite to store vector embeddings. By integrating a vector database, you can scale your code search tool to handle much larger codebases without compromising on search speed.
  • Unum: Vector Search engine in a single file
    8 projects | news.ycombinator.com | 31 Jul 2023
    But FAISS has their own version ("FastScan") https://github.com/facebookresearch/faiss/wiki/Fast-accumula...

What are some alternatives?

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

pgvector - Open-source vector similarity search for Postgres

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

tlsh

hnswlib - Header-only C++/python library for fast approximate nearest neighbors

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

vald - Vald. A Highly Scalable Distributed Vector Search Engine

Weaviate - Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database​.

vald-client-python - A Python gRPC client library for Vald

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

Scout Monitoring - Free Django app performance insights with Scout Monitoring
Get Scout setup in minutes, and let us sweat the small stuff. A couple lines in settings.py is all you need to start monitoring your apps. Sign up for our free tier today.
www.scoutapm.com
featured
InfluxDB - Power Real-Time Data Analytics at Scale
Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
www.influxdata.com
featured

Did you konow that Python is
the 1st most popular programming language
based on number of metions?