pgvector
zombodb
pgvector | zombodb | |
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78 | 23 | |
9,349 | 4,611 | |
7.0% | - | |
9.9 | 8.3 | |
7 days ago | 28 days ago | |
C | PLpgSQL | |
GNU General Public License v3.0 or later | 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.
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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:
zombodb
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Introducing pgzx: create PostgreSQL extensions using Zig
And lots of interesting extensions use it, like
https://github.com/tembo-io/pgmq
https://github.com/zombodb/zombodb
https://github.com/supabase/pg_jsonschema
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Create a search engine with PostgreSQL: Postgres vs Elasticsearch
Point 2 is generally solvable via engineering effort and careful dedicated code. From the existing tools, PGSync is an open source project that aims to specifically solve this problem. ZomboDB is an interesting Postgres extension that tackles point 2 (and I think partially point 3), by controlling and querying Elasticsearch through Postgres. I haven't yet tried either of these two projects, so I can't comment on their trade-offs, but I wanted to mention them.
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Creating an advanced search engine with PostgreSQL
Curious, did you try zombodb? [https://www.zombodb.com/]
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💃🏼 Quickwit 0.6 released!🕺🏼: Elasticsearch API compatibility, Grafana plugin, and more....
What about zombodb, do you think that quickwit has all the necessary APIs?
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Write Postgres functions in Rust
No. Haha. Was just the right name for https://github.com/zombodb/zombodb at the time. Software where the only limit is yourself!
- Integrate PostgreSQL and Elasticsearch – ZomboDB
- Postgres Full Text Search vs. the Rest
- ZomboDB: Making Postgres and Elasticsearch work together like it's 2022
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Postgres Full-Text Search: A Search Engine in a Database
> The hardest part of building any search engine is keeping the index up-to-date with changes made to the underlying data store.
This deserves mention, as it solves that problem: https://github.com/zombodb/zombodb
From the README:
> ZomboDB brings powerful text-search and analytics features to Postgres by using Elasticsearch as an index type. Its comprehensive query language and SQL functions enable new and creative ways to query your relational data.
> From a technical perspective, ZomboDB is a 100% native Postgres extension that implements Postgres' Index Access Method API. As a native Postgres index type, ZomboDB allows you to CREATE INDEX ... USING zombodb on your existing Postgres tables. At that point, ZomboDB takes over and fully manages the remote Elasticsearch index and guarantees transactionally-correct text-search query results.
I find other things also hard in search engines: dealing with the plethora of human languages and all the requirements we may have to processing them. A mature solution like ES therefor is almost a must in the more demanding cases.
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State of the art for serde-compatible CBOR encoding/decoding?
You can read more about it on our GitHub repo, but basically it brings most of the power of elasticsearch’s searching and analytics abilities straight into Postgres.
What are some alternatives?
Milvus - A cloud-native vector database, storage for next generation AI applications
pg_search - pg_search builds ActiveRecord named scopes that take advantage of PostgreSQL’s full text search
faiss - A library for efficient similarity search and clustering of dense vectors.
Typesense - Open Source alternative to Algolia + Pinecone and an Easier-to-Use alternative to ElasticSearch ⚡ 🔍 ✨ Fast, typo tolerant, in-memory fuzzy Search Engine for building delightful search experiences
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.
noria - Fast web applications through dynamic, partially-stateful dataflow
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
squawk - 🐘 linter for PostgreSQL, focused on migrations
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
stolon - PostgreSQL cloud native High Availability and more.
ann-benchmarks - Benchmarks of approximate nearest neighbor libraries in Python
helium-etl-queries - A collection of SQL views used to enrich data produced by a Helium blockchain-etl