ann-benchmarks VS pgvector

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

ann-benchmarks

Benchmarks of approximate nearest neighbor libraries in Python (by erikbern)
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ann-benchmarks pgvector
51 84
4,757 10,633
- 7.4%
7.5 9.8
about 1 month ago 9 days ago
Python C
MIT License GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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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.

pgvector

Posts with mentions or reviews of pgvector. 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
    With the rise of LLMs(Large Language Models) and RAG(Retrieval-Augmented Generation), vector databases, like Milvus and Pinecone, are getting a lot of attention. Traditional databases are also catching up in vector search support via third-party extensions, such as pgvector for PostgreSQL and sqlite-vss for SQLite.
  • Ask HN: What RAG setup gets me 95% of the way there?
    3 projects | news.ycombinator.com | 5 Jul 2024
    If I did it I would try this first: https://github.com/pgvector/pgvector
  • SpringAI, llama3 and pgvector: bRAGging rights!
    8 projects | dev.to | 15 Jun 2024
    To support the exploration, I've developed a simple Retrieval Augmented Generation (RAG) workflow that works completely locally on the laptop for free. If you're interested, you can find the code itself here. Basically, I've used Testcontainers to create a Postgres database container with the pgvector extension to store text embeddings and an open source LLM with which I send requests to: Meta's llama3 through ollama.
  • Open-source vector similarity search for Postgres
    1 project | news.ycombinator.com | 5 Jun 2024
  • beginner guide to fully local RAG on entry-level machines
    5 projects | dev.to | 2 Jun 2024
    # postgres image with `pgvector` enabled FROM postgres:16.3 RUN apt-get update \ && apt-get install -y postgresql-server-dev-all build-essential \ && apt-get install -y git \ && git clone https://github.com/pgvector/pgvector.git \ && cd pgvector \ && make \ && make install \ && apt-get remove -y git build-essential \ && apt-get autoremove -y \ && rm -rf /var/lib/apt/lists/* EXPOSE 5432
  • Show HN: Smart website search powered by open models
    1 project | news.ycombinator.com | 15 May 2024
    I helped work on the RAG part of this :-)

    We used https://github.com/pgvector/pgvector under the hood and found it extremely easy to integrate with our database schema - being able to just specify the structure of a table and have metadata fields alongside the embeddings made the code very easy to reason about.

  • Integrate txtai with Postgres
    2 projects | dev.to | 25 Apr 2024
    # Install Postgres and pgvector !apt-get update && apt install postgresql postgresql-server-dev-14 !git clone --branch v0.6.2 https://github.com/pgvector/pgvector.git !cd pgvector && make && make install # Start database !service postgresql start !sudo -u postgres psql -U postgres -c "ALTER USER postgres PASSWORD 'pass';"
  • Vector Database solutions on AWS
    1 project | dev.to | 28 Mar 2024
    When talking about Vector Databases, in the market we can find the specialized ones and multi-model, most of the major database providers like Oracle, PostgreSQL or MongoDB, for mention some of them, have integrated a specific solution to retrieve vector data.
  • Using pgvector To Locate Similarities In Enterprise Data
    2 projects | dev.to | 21 Mar 2024
    For this example, I wanted to focus on how pgvector  – an open-source vector similarity search for Postgres – can be used to identify data similarities that exist in enterprise data.
  • pgvector vs. pgvecto.rs in 2024: A Comprehensive Comparison for Vector Search in PostgreSQL
    1 project | dev.to | 19 Mar 2024
    pgvector supports dense vector search well, but it does not have plan to support sparse vector.

What are some alternatives?

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

faiss - A library for efficient similarity search and clustering of dense vectors.

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

tlsh

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​.

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

Elasticsearch - Free and Open, Distributed, RESTful Search Engine

vald - Vald. A Highly Scalable Distributed Vector Search Engine

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

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

pinecone - Peer-to-peer overlay routing for the Matrix ecosystem

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