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Commitb2639e7

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Fix merge (#1315)
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‎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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---
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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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---
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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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---
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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

‎pgml-cms/blog/using-postgresml-with-django-and-embedding-search.md

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description:>-
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An example application using PostgresML and Django to build embedding based search.
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tags:[engineering]
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---
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#Using PostgresML with Django and embedding search
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<divalign="left">
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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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Feb 15, 2024
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Building web apps on top of PostgresML allows anyone to integrate advanced machine learning and AI features into their products without much work or needing to understand how it really works. In this blog post, we'll talk about building a classic to-do Django app, with the spicy addition of semantic search powered by embedding models running inside your PostgreSQL database.
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###Getting the code
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Djago Rest Framework provides the bulk of the implementation. We just added a`ModelViewSet` for the`TodoItem` model, with just one addition: a search endpoint. The search endpoint required us to write a bit of SQL to embed the search query and accept a few filters, but the core of it can be summarized in a single annotation on the query set:
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<preclass="language-python"><codeclass="lang-python"><strong>results = TodoItem.objects.annotate(
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</strong> similarity=RawSQL(
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```python
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results= TodoItem.objects.annotate(
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similarity=RawSQL(
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"pgml.embed('intfloat/e5-small',%s)::vector(384) &#x3C;=> embedding",
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[query],
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)
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).order_by("similarity")
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</code></pre>
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```
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This single line of SQL does quite a bit:
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