pg_search
pgvector
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pg_search | pgvector | |
---|---|---|
7 | 78 | |
1,229 | 9,211 | |
1.9% | 10.4% | |
6.8 | 9.9 | |
4 months ago | 1 day ago | |
Ruby | C | |
MIT License | GNU General Public License v3.0 or later |
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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.
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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How to build a search engine with Ruby on Rails
This was a really good read, thanks. I've got into the habit of jumping straight to PgSearch but could definitely apply this approach to some existing projects.
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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.
https://github.com/Casecommons/pg_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.
pgvector
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Integrate txtai with Postgres
# 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';"
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Vector Database solutions on AWS
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.
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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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pgvector vs. pgvecto.rs in 2024: A Comprehensive Comparison for Vector Search in PostgreSQL
pgvector supports dense vector search well, but it does not have plan to support sparse vector.
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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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pgvector 0.6.0: 30x faster with parallel index builds
pgvector 0.6.0 was just released and will be available on Supabase projects soon. Again, a special shout out to Andrew Kane and everyone else who worked on parallel index builds.
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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:
What are some alternatives?
ransack - Object-based searching.
Milvus - A cloud-native vector database, storage for next generation AI applications
Elasticsearch Rails - Elasticsearch integrations for ActiveModel/Record and Ruby on Rails
faiss - A library for efficient similarity search and clustering of dense vectors.
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!
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-ruby - Ruby integrations for Elasticsearch
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
Searchkick - Intelligent search made easy
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
MeiliSearch - A lightning-fast search API that fits effortlessly into your apps, websites, and workflow
ann-benchmarks - Benchmarks of approximate nearest neighbor libraries in Python