postgres-elasticsearch-fdw
pgvector
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postgres-elasticsearch-fdw | pgvector | |
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3 | 78 | |
106 | 9,211 | |
- | 10.4% | |
4.2 | 9.9 | |
29 days ago | 3 days ago | |
Python | C | |
MIT License | GNU General Public License v3.0 or later |
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postgres-elasticsearch-fdw
- Full-text search engine with PostgreSQL (part 2): Postgres vs. Elasticsearch
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Postgres Full Text Search vs. the Rest
My experience with Postgres FTS (did a comparison with Elastic a couple years back), is that filtering works fine and is speedy enough, but ranking crumbles when the resulting set is large.
If you have a large-ish data set with lots of similar data (4M addresses and location names was the test case), Postgres FTS just doesn't perform.
There is no index that helps scoring results. You would have to install an extension like RUM index (https://github.com/postgrespro/rum) to improve this, which may or may not be an option (often not if you use managed databases).
If you want a best of both worlds, one could investigate this extensions (again, often not an option for managed databases): https://github.com/matthewfranglen/postgres-elasticsearch-fd...
Either way, writing something that indexes your postgres database into elastic/opensearch is a one time investment that usually pays off in the long run.
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Lesser Known PostgreSQL Features
I used a foreign data wrapper to query elasticsearch indexes from within postgres.[0]
It pushed alot of complexity down away from higher-level app developers not familiar with ES patterns.
[0]: https://github.com/matthewfranglen/postgres-elasticsearch-fd...
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?
rum - RUM access method - inverted index with additional information in posting lists
Milvus - A cloud-native vector database, storage for next generation AI applications
tbls - tbls is a CI-Friendly tool for document a database, written in Go.
faiss - A library for efficient similarity search and clustering of dense vectors.
pg-ulid - ULID Functions for PostgreSQL
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.
postgres-elasticsearch-fd
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
js-id - ID generation for JavaScript & TypeScript Applications
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
quickwit - Cloud-native search engine for observability. An open-source alternative to Datadog, Elasticsearch, Loki, and Tempo.
ann-benchmarks - Benchmarks of approximate nearest neighbor libraries in Python