pgvector
supabase
pgvector | supabase | |
---|---|---|
78 | 769 | |
9,349 | 66,465 | |
7.0% | 2.4% | |
9.9 | 10.0 | |
7 days ago | about 22 hours ago | |
C | TypeScript | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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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.
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:
supabase
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Wasp x Supabase: Smokin’ Hot Full-Stack Combo 🌶️ 🔥
It was a great experience using Supabase’s rock-solid PostgreSQL database for this app. The DX around that product is phenomenal: viewing and managing the DB data was a lifesaver when you don’t want to craft your own admin panel from scratch.
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How I migrated from Firebase to Supabase
I didn't really give much thought as to which backend I would use. I already had 2 projects in Supabase (BOXCUT & MineWork), but also a few projects in Firebase too. I was more concerned at the time at actually building the product.
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How to get free Postgres
Sign up for SupaBase: Head over to SupaBase and sign up. Create a new workspace and project with your preferred names.
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Creating a Pokémon guessing game using Supabase, Drizzle, and Next.js in just 2 hours!
Setting up Supabase Create a new Supabase project, and get the connection string for the database from settings > database.
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How To Make An Insanely Fast AI App (Supabase, LLAMA 3 and Groq)
Supabase (start for free)
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Building a self-creating website with Supabase and AI
Built with Supabase, Astro, Unreal Speech, Stable Diffusion, Replicate, Metropolitan Museum of Art
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How I built a Markdown Rendered Blog using Supabase and Chakra UI
Supabase will be used for storing article data in the database and the cover image of the article in storage. Chakra UI will be used to provide style to the elements. By using both, we can build the blog with ease.
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I got #1 Product of the Day on Product Hunt without Spending a Dollar
For AutoRepurpose, I opted for Supabase as the backbone of the backend. It has reliably supported Penelope AI, which garnered over 15k users in 2022 without any issues.
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AI Inference now available in Supabase Edge Functions
Semantic search demo
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Creating an OG image using React and Netlify Edge Functions
1. Create a new Supabase project: Visit Supabase and create a new project.
What are some alternatives?
Milvus - A cloud-native vector database, storage for next generation AI applications
Appwrite - Your backend, minus the hassle.
faiss - A library for efficient similarity search and clustering of dense vectors.
pocketbase - Open Source realtime backend in 1 file
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.
nhost - The Open Source Firebase Alternative with GraphQL.
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
neon - Neon: Serverless Postgres. We separated storage and compute to offer autoscaling, branching, and bottomless storage.
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
next-auth - Authentication for the Web.
ann-benchmarks - Benchmarks of approximate nearest neighbor libraries in Python
Hasura - Blazing fast, instant realtime GraphQL APIs on your DB with fine grained access control, also trigger webhooks on database events.