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Commitac317e5

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Revert "GITBOOK-33: No subject"
This reverts commit4ff71b0.
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‎pgml-cms/blog/SUMMARY.md‎

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#Table of contents
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*[Home](README.md)
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*[The 1.0 SDK is Here](the-1.0-sdk-is-here.md)
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*[Using PostgresML with Django and embedding search](using-postgresml-with-django-and-embedding-search.md)
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*[PostgresML is going multicloud](postgresml-is-going-multicloud.md)
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*[Introducing the OpenAI Switch Kit: Move from closed to open-source AI in minutes](introducing-the-openai-switch-kit-move-from-closed-to-open-source-ai-in-minutes.md)
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*[Postgres Full Text Search is Awesome!](postgres-full-text-search-is-awesome.md)
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*[Oxidizing Machine Learning](oxidizing-machine-learning.md)
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*[Data is Living and Relational](data-is-living-and-relational.md)
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*[The 1.0 SDK is Here](the-1.0-sdk-is-here.md)

‎pgml-cms/blog/announcing-gptq-and-ggml-quantized-llm-support-for-huggingface-transformers.md‎

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GPTQ & GGML allow PostgresML to fit larger models in less RAM. These
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algorithms perform inference significantly faster on NVIDIA, Apple and Intel
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hardware.
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featured:false
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tags:[engineering]
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image:".gitbook/assets/image (14).png"
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#Announcing GPTQ & GGML Quantized LLM support for Huggingface Transformers

‎pgml-cms/blog/announcing-support-for-aws-us-east-1-region.md‎

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---
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description:>-
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We added aws us east 1 to our list of support aws regions.
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featured:false
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tags:[product]
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---
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#Announcing Support for AWS us-east-1 Region
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<divalign="left">

‎pgml-cms/blog/data-is-living-and-relational.md‎

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A common problem with data science and machine learning tutorials is the
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published and studied datasets are often nothing like what you’ll find in
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industry.
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featured:false
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tags:[engineering]
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#Data is Living and Relational

‎pgml-cms/blog/generating-llm-embeddings-with-open-source-models-in-postgresml.md‎

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description:>-
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How to use the pgml.embed(...) function to generate embeddings with free and
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open source models in your own database.
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image:".gitbook/assets/blog_image_generating_llm_embeddings.png"
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features:true
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#Generating LLM embeddings with open source models in PostgresML

‎pgml-cms/blog/how-to-improve-search-results-with-machine-learning.md‎

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PostgresML makes it easy to use machine learning on your data and scale
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workloads horizontally in our cloud. One of the most common use cases is to
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improve search results.
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featured:true
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image:".gitbook/assets/image (2) (2).png"
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tags:["Engineering"]
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#How-to Improve Search Results with Machine Learning

‎pgml-cms/blog/introducing-the-openai-switch-kit-move-from-closed-to-open-source-ai-in-minutes.md‎

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featured:true
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tags:[engineering, product]
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image:https://postgresml.org/dashboard/static/images/open_source_ai_social_share.png
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description:>-
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Quickly and easily transition from the confines of the OpenAI APIs to higher
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quality embeddings and unrestricted text generation models.
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image:".gitbook/assets/blog_image_switch_kit.png"
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#Introducing the OpenAI Switch Kit: Move from closed to open-source AI in minutes

‎pgml-cms/blog/postgres-full-text-search-is-awesome.md‎

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description:>-
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If you want to improve your search results, don't rely on expensive O(n*m)
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word frequency statistics. Get new sources of data instead.
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image:".gitbook/assets/image (53).png"
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#Postgres Full Text Search is Awesome!

‎pgml-cms/blog/postgresml-is-going-multicloud.md‎

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#PostgresML is going multicloud
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<figure><imgsrc=".gitbook/assets/lev.jpg"alt="Author"width="100"><figcaption></figcaption></figure>
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</div>
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Lev Kokotov
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Jan 18, 2024
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We started PostgresML two years ago with the goal of making machine learning and AI accessible and easy for everyone. To make this a reality, we needed to deploy PostgresML as closely as possible to our end users. With that goal mind, today we're proud to announce support for a new cloud provider: Azure.
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###How we got here

‎pgml-cms/blog/speeding-up-vector-recall-5x-with-hnsw.md‎

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HNSW indexing is the latest upgrade in vector recall performance. In this post
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we announce our updated SDK that utilizes HNSW indexing to give world class
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performance in vector search.
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tags:[engineering]
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featured:true
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image:".gitbook/assets/blog_image_hnsw.png"
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#Speeding up vector recall 5x with HNSW

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