pgvector
ann-benchmarks
Our great sponsors
pgvector | ann-benchmarks | |
---|---|---|
77 | 50 | |
8,586 | 4,484 | |
9.7% | - | |
9.7 | 8.1 | |
7 days ago | 2 days ago | |
C | Python | |
GNU General Public License v3.0 or later | MIT License |
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.
pgvector
-
Using pgvector To Locate Similarities In Enterprise Data
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.
-
Pg_vectorize: The simplest way to do vector search and RAG on Postgres
There's an issue in the pgvector repo about someone having several ~10-20million row tables and getting acceptable performance with the right hardware and some performance tuning: https://github.com/pgvector/pgvector/issues/455
I'm in the early stages of evaluating pgvector myself. but having used pinecone I currently am liking pgvector better because of it being open source. The indexing algorithm is clear, one can understand and modify the parameters. Furthermore the database is postgresql, not a proprietary document store. When the other data in the problem is stored relationally, it is very convenient to have the vectors stored like this as well. And postgresql has good observability and metrics. I think when it comes to flexibility for specialized applications, pgvector seems like the clear winner. But I can definitely see pinecone's appeal if vector search is not a core component of the problem/business, as it is very easy to use and scales very easily
- FLaNK 04 March 2024
-
Vector Database and Spring IA
The Spring AI project aims to streamline the development of applications that incorporate artificial intelligence functionality without unnecessary complexity. On this example we use features like: Embedding, Prompts, ETL and save all embedding on PGvector(Postgres Vector database)
-
Use pgvector for searching images on Azure Cosmos DB for PostgreSQL
Official GitHub repository of the pgvector extension
-
Store embeddings in Azure Cosmos DB for PostgreSQL with pgvector
The pgvector extension adds vector similarity search capabilities to your PostgreSQL database. To use the extension, you have to first create it in your database. You can install the extension, by connecting to your database and running the CREATE EXTENSION command from the psql command prompt:
-
Are we at peak vector database?
It’s about to get a lot better too. Pgvector now supports multi-threaded build
https://github.com/pgvector/pgvector/issues/409#issuecomment...
pgvector has you covered: https://github.com/pgvector/pgvector
-
Vector Databases: A Technical Primer [pdf]
You don't need a dedicated vector db, you can use pgvector.
You could maybe use Cube for euclidean space search, but you're better off using optimized algorithms for embedding space search.
-
How Modern SQL Databases Are Changing Web Development - #4 Into the AI Era
PostgreSQL's pgvector extension is probably the most widely used SQL solution for storing and searching vector data today. The extension introduces a "vector" type specialized for storing high-dimensional vector data. It allows you to create vector indices (in "IVFFlat" or "HNSW" format for different indexing/searching performance tradeoffs) and leverage them to do various types of similarity searches.
ann-benchmarks
-
Approximate Nearest Neighbors Oh Yeah
https://ann-benchmarks.com/ is a good resource covering those libraries and much more.
-
Vector database is not a separate database category
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
(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.
- Do we think about vector dbs wrong?
-
Vector Search with OpenAI Embeddings: Lucene Is All You Need
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
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
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.
-
Do we need a specialized vector database?
The article makes the argument that it is easier to query vector spaces when your database already supports them: why use an external vector db when you can use pgvector in postgreSQL ?
That is a fine argument if you don't mind that pgvector is second-to-worst amongst all open-source vector search implementations, and two orders of magnitude slower than the state of the art [1].
The author also makes the argument that traditional DBs are better because they are battle-tested, and then goes and rewrites the pgvector plugin from C to rust.
- Unum: Vector Search engine in a single file
- Comparison of Vector Databases
What are some alternatives?
Milvus - A cloud-native vector database, storage for next generation AI applications
faiss - A library for efficient similarity search and clustering of dense vectors.
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.
Elasticsearch - Free and Open, Distributed, RESTful 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/
pinecone - Peer-to-peer overlay routing for the Matrix ecosystem
smlar - PostgreSQL extension for an effective similarity search || mirror of git://sigaev.ru/smlar.git || see https://www.pgcon.org/2012/schedule/track/Hacking/443.en.html
awesome-vector-search - Collections of vector search related libraries, service and research papers
vespa - AI + Data, online. https://vespa.ai
patroni - A template for PostgreSQL High Availability with Etcd, Consul, ZooKeeper, or Kubernetes