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SilasMarvin merged 13 commits into from
Oct 7, 2024
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Readme updates #1627

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A start
SilasMarvin Oct 1, 2024
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SilasMarvin Oct 1, 2024
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SilasMarvin Oct 2, 2024
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Fix language
SilasMarvin Oct 2, 2024
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Update README.md
SilasMarvin Oct 2, 2024
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SilasMarvin Oct 2, 2024
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Update README.md (#1628)
cassandrastumer Oct 2, 2024
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Update README.md
cassandrastumer Oct 2, 2024
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Update README.md
SilasMarvin Oct 7, 2024
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Added architecture diagram
SilasMarvin Oct 7, 2024
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SilasMarvin Oct 7, 2024
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SilasMarvin Oct 7, 2024
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cassandrastumer Oct 7, 2024
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@SilasMarvin
SilasMarvin committed Oct 7, 2024
commit 3fcb869086f32f3e85a5e36a241425d9eab6b2f4
22 changes: 16 additions & 6 deletions README.md
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Expand Up @@ -34,6 +34,8 @@ Data for ML & AI systems is inherently larger and more dynamic than the models.
- [Transform](#transform)
- [Machine Learning](#machine-learning)

## Architecture

<b>PostgresML is a powerful Postgres extension that seamlessly combines data storage and machine learning inference within your database</b>. By integrating these functionalities, PostgresML eliminates the need for separate systems and data transfers, enabling you to perform ML operations directly on your data where it resides.

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Expand All @@ -44,6 +46,20 @@ Data for ML & AI systems is inherently larger and more dynamic than the models.
</picture>
</div>

## Features at a Glance

- **In-Database ML/AI**: Run machine learning and AI operations directly within PostgreSQL.
- **GPU Acceleration**: Leverage GPU power for faster computations and model inference.
- **Large Language Models**: Integrate and use state-of-the-art LLMs from Hugging Face.
- **RAG Pipeline**: Built-in functions for chunking, embedding, ranking, and transforming text.
- **Vector Search**: Efficient similarity search using pgvector integration.
- **Diverse ML Algorithms**: 47+ classification and regression algorithms available.
- **High Performance**: 8-40X faster inference compared to HTTP-based model serving.
- **Scalability**: Support for millions of transactions per second and horizontal scaling.
- **NLP Tasks**: Wide range of natural language processing capabilities.
- **Security**: Enhanced data privacy by keeping models and data together.
- **Seamless Integration**: Works with existing PostgreSQL tools and client libraries.

# Getting Started

The only prerequisites for using PostgresML is a Postgres database with our open-source `pgml` extension installed.
Expand Down Expand Up @@ -85,12 +101,6 @@ We have a number of other tools and libraries that are specifically designed to

PostgresML brings models directly to your data, eliminating the need for costly and time-consuming data transfers. This approach significantly enhances performance, security, and scalability for AI-driven applications.

<b>TODO: SOME MORE LANGUAGE ON THIS</b>

# Large Language Models

PostgresML brings models directly to your data, eliminating the need for costly and time-consuming data transfers. This approach significantly enhances performance, security, and scalability for AI-driven applications.

By running models within the database, PostgresML enables:

- Reduced latency and improved query performance
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