postgres_lsp
pgvector
postgres_lsp | pgvector | |
---|---|---|
6 | 78 | |
3,134 | 9,473 | |
0.6% | 7.0% | |
9.2 | 9.9 | |
11 days ago | about 17 hours ago | |
Rust | C | |
MIT License | GNU General Public License v3.0 or later |
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.
postgres_lsp
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We built our customer data warehouse all on Postgres
Thank you for turning me on top Cornucopia, it looks awesome. I've used the very similar aiosql in Python, but I hadn't realized there was a Rust analog.
To tell the truth I've been waiting for postgres_lsp to mature before trying it out, but based on this example [1] I think it does support multiple queries.
Since it uses a parser extracted from Postgres, the nonstandard syntax would probably trip it up, but there's probably a way to fix that.
[1] https://github.com/supabase/postgres_lsp/blob/main/example/f...
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compile-time SQL validations and type generation in TypeScript & Node
Cool. How does this compare to SafeQL, PgTyped, and Postgres language server ?
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Supabase Local Dev: migrations, branching, and observability
While code editors have great support for most programming languages, SQL support is underwhelming. We want to make Postgres as simple as Python. Our recently announced Postgres Language Server takes us a step in that direction - eventually it will provide first-class support for Postgres in your favorite code editor including Linting, Syntax Highlighting, Migrations Parsing, SQL Auto-complete, and Intellisense.
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Hugging Face is now supported in Supabase
Postgres Language Server
- Show HN: Postgres Language Server
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?
pspg - Unix pager (with very rich functionality) designed for work with tables. Designed for PostgreSQL, but MySQL is supported too. Works well with pgcli too. Can be used as CSV or TSV viewer too. It supports searching, selecting rows, columns, or block and export selected area to clipboard.
Milvus - A cloud-native vector database, storage for next generation AI applications
edge-runtime - A server based on Deno runtime, capable of running JavaScript, TypeScript, and WASM services.
faiss - A library for efficient similarity search and clustering of dense vectors.
vecs - Postgres/pgvector Python Client
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.
basejump - Teams, personal accounts, permissions and billing for your Supabase app
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
declarative-schemas
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
supabase-test-helpers - Test helpers for pgTAP and Supabase
ann-benchmarks - Benchmarks of approximate nearest neighbor libraries in Python