pgvector
pg_search
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pgvector | pg_search | |
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77 | 7 | |
8,697 | 1,223 | |
10.9% | 2.5% | |
9.7 | 6.8 | |
about 13 hours ago | 3 months ago | |
C | Ruby | |
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
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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.
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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
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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)
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Use pgvector for searching images on Azure Cosmos DB for PostgreSQL
Official GitHub repository of the pgvector extension
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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:
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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
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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.
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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.
pg_search
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The Ultimate Search for Rails - Episode 1
On the backend, we'll need a few tools. Apart from the classics (ActiveRecord scopes and the pg_search gem), you’ll see how the (yet officially unreleased but production-tested) all_futures gem, built by SR authors, will act as an ideal ephemeral object to temporarily store our filter params and host our search logic. Finally, we’ll use pagy for pagination duties.
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Application Search Feature more that ActiveRecord;
You can take a look at pg_search if you’re using Postgres
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Instant search with Rails 6 and Hotwire
Cleaner, more performant database queries: Definitely don't just leave your query sitting in the controller! For production use cases, you'd want to consider an option like pg_search
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Postgres Full-Text Search: A Search Engine in a Database
If you are using Rails with Postgres you can use pg_search gem to build the named scopes to take advantage of full text search.
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Tips for optimizing pg_search?
Hey guys. Looking to release an app for mobile that will be using a rails API. The app will heavily rely on search. I know the go-to is to use elasticsearch but wanted to see if there was enough user demand for the MVP before shelling out $50/mo for the heroku add on. In the mean time I've been using pg_search. From the eye test it's performing okay but will be adding a table that houses over 350K records. With this in mind I was wondering if you all had any tips for increasing the overall speed for search from the model and controller level. Also should note that I'm open to any other free search gems if they deem bette fit.
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Rails Search Bar
There are two basic search configurations with pg_search, a Single Model search scope or a multi Model configuration. In my case I am only using the Single Model configuration, but you can read more about multi-search in the documentation.
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.
ransack - Object-based searching.
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/
Elasticsearch Rails - Elasticsearch integrations for ActiveModel/Record and Ruby on Rails
ann-benchmarks - Benchmarks of approximate nearest neighbor libraries in Python
textacular - Textacular exposes full text search capabilities from PostgreSQL, and allows you to declare full text indexes. Textacular will extend ActiveRecord with named_scope methods making searching easy and fun!
elasticsearch-ruby - Ruby integrations for Elasticsearch
Searchkick - Intelligent search made easy
pinecone - Peer-to-peer overlay routing for the Matrix ecosystem