A library for efficient similarity search and clustering of dense vectors. (by facebookresearch)

Faiss Alternatives

Similar projects and alternatives to faiss

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a better faiss alternative or higher similarity.

faiss reviews and mentions

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 2024-04-28.
  • 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


    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...
  • Introduction to Vector Similarity Search
    4 projects | news.ycombinator.com | 11 Jul 2023
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    www.saashub.com | 26 May 2024
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Basic faiss repo stats
1 day ago

facebookresearch/faiss is an open source project licensed under MIT License which is an OSI approved license.

The primary programming language of faiss is C++.

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