pgsql-http
pgvector
pgsql-http | pgvector | |
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17 | 78 | |
1,164 | 9,473 | |
- | 8.2% | |
5.8 | 9.9 | |
25 days ago | 5 days ago | |
C | C | |
MIT License | GNU General Public License v3.0 or later |
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pgsql-http
- PostgreSQL Is Enough
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becauseBackendIsJustASocialConstructRight
I don’t understand the question https://github.com/pramsey/pgsql-http
- What are my options to send a notification everytime a new row is inserted into my PostgreSQL RDS database/Aurora database?
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How to perform authenticated http requests with the http REST client extension?
I am trying to use the supabase http rest client extension to fetch data from an external API. Following the supabase docs and the GitHub repo readme, I have not been able to successfully make a request that requires auth, specifically an API key in the request header with key x-api-key.
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Sketch of a Post-ORM
- Hasura Remote Schema (https://hasura.io/blog/tagged/remote-schemas/)
If you want more control over the web API and you were going to fetch the data within your Python back-end and process it there, for some use-cases (not all, but some), there are options:
- pg_http (https://github.com/pramsey/pgsql-http)
Life is about trade-offs. Doing the work in SQL is not without its drawbacks, but it's also not without its benefits, and that's true for doing the work in a general-purpose language as well. Whatever the drawbacks of doing it in SQL, one of the benefits has got to be eliminating the impedance mismatch (for people who regard that mismatch as a problem, and the OP seems to be one such person). What I claim is that doing the work directly in the database shouldn't be ruled out in general (the specifics of a given use-case may rule it out in particular) any more the the other common patterns (API hand-written in Python, for instance) shouldn't be ruled out in general.
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Watching for changes to DB by another app
You could e.g. use the trigger to call http api using e.g. https://github.com/pramsey/pgsql-http
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How to best fetch JSON data from external API and write to supabase every hour?
I do this all the time just with Postgres functions. Just turn on the following extensions: http (https://github.com/pramsey/pgsql-http) pg_cron (https://github.com/citusdata/pg\_cron)
- What's Postgres Got to Do with AI?
- Edge Functions or Database Functions?
- Pgsql-HTTP: HTTP client for PostgreSQL
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?
Multicorn - Data Access Library
Milvus - A cloud-native vector database, storage for next generation AI applications
supabase-mailer - Send and track email from Supabase / PostgreSQL using a Transactional Email Provider
faiss - A library for efficient similarity search and clustering of dense vectors.
pg_net - A PostgreSQL extension that enables asynchronous (non-blocking) HTTP/HTTPS requests with SQL
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.
graphile-engine - Monorepo home of graphile-build, graphile-build-pg, graphile-utils, postgraphile-core and graphql-parse-resolve-info. Build a high-performance easily-extensible GraphQL schema by combining plugins!
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
amforeas - A RESTful Interface to your database
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
Hasura - Blazing fast, instant realtime GraphQL APIs on your DB with fine grained access control, also trigger webhooks on database events.
ann-benchmarks - Benchmarks of approximate nearest neighbor libraries in Python