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Star History Monthly Oct 2023 | Open-source AI Extensions for Postgres
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class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #8 - Prompt flow</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/clipboard"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #7 - Clipboard</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/hoppscotch"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #6 - Hoppscotch</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/metisfl"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #5 - MetisFL</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/chatgpt-js"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #4 - chatgpt.js</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/mockoon"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #3 - Mockoon</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/dlta-ai"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #2 - DLTA-AI</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/sniffnet"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #1 - Sniffnet</span></a></li></ul></div></div></div><div class="w-full flex flex-col justify-start items-center"><div class="w-full p-4 md:p-0 mt-6 md:w-5/6 lg:max-w-6xl h-full flex flex-col justify-start items-center self-center"><img class="hidden md:block w-auto max-w-full object-scale-down" src="/assets/blog/ai-for-postgres/banner.webp" alt=""/><div class="w-auto max-w-6xl mt-4 md:mt-12 prose prose-indigo prose-xl md:prose-2xl flex flex-col justify-center items-center"><h1 class="leading-16">Star History Monthly Oct 2023 | Open-source AI Extensions for Postgres</h1></div><div class="w-full mt-8 mb-2 max-w-6xl px-2 flex flex-row items-center justify-center text-sm text-gray-900 font-semibold trackingwide uppercase"><div class="flex space-x-1 text-gray-500"><span class="text-gray-900">Mila</span><span aria-hidden="true"> · </span><time dateTime="2023-11-24T09:50:06.000Z">Nov 24, 2023</time><span aria-hidden="true"> · </span><span> <!-- -->5<!-- --> min read </span></div></div><div class="mt-8 w-full max-w-5xl prose prose-indigo prose-xl md:prose-2xl"><p>Postgres was named the most popular database by the <a href="https://survey.stackoverflow.co/2023/#technology">2023 Stack Overflow Developer Survey</a>, taking over the crown from MySQL. Why, you ask? We believe that a major factor comes down to Postgres <a href="https://www.postgresql.org/docs/current/sql-createextension.html">extensions</a>. Thanks to the extensible architecture and the fact that it's still a community-owned project, the Postgres ecosystem has been nothing but thriving lately.</p> <p>Postgres extensions add extra functionality to your database just like built-in features. Several extensions for AI use cases have appeared over the years. A handful of them came out this year, but surprisingly, there are a few first appeared a few years back and remain to be the frontrunners of the pack.</p> <p>In this post, we'll share five Postgres extensions that just might boost your AI experience.</p> <h2>pgvector</h2> <p><img src="/assets/blog/ai-for-postgres/pgvector.webp" alt="pgvector"></p> <p><a href="https://github.com/pgvector/pgvector">pgvector</a> is an open-source vector similarity search specifically designed for Postgres (supports Postgres 11+). It can also be used for storing embeddings. It's been around since 2021, way before AI became part of our daily routine. The spike in popularity came around 2023.2, when vector similarity search became a hot topic.</p> <p>If you're looking for a vector database, Postgres got you covered, you can continue using your Postgres with benefits including:</p> <ul> <li>store your vectors with the rest of your data</li> <li>exact and approximate nearest neighbor search</li> <li>L2 distance, inner product, and cosine distance</li> <li>any language with a Postgres client</li> </ul> <p>If you use plain Postgres, simply compile and install the extension; if you are using Postgres services or apps, some of them already have pgvector included, e.g. Aiven, Neon, Supabase, Postgres.app, etc.</p> <h2>PostgresML</h2> <p><img src="/assets/blog/ai-for-postgres/postgresml.webp" alt="postgresml"></p> <p><a href="https://github.com/postgresml/postgresml">PostgresML</a> is an extension that allows developers to integrate machine learning (ML) models into Postgres that scored a <a href="https://postgresml.org/blog/postgresml-raises-4.7M-to-launch-serverless-ai-application-databases-based-on-postgres">$4.7M seed funding round</a> this May. It enables training and inference on text and data using SQL queries, greatly reducing the complexity of application development.</p> <p>If you don't have fancy ML workloads (because not all of us are AI giants) in your company, but would like to build your own machine learning model that can learn from fresh data, this would be a good way to get started.</p> <p><img src="/assets/blog/ai-for-postgres/postgresml-textgen.webp" alt="postgresml-textgen"></p> <p><em>Side note #1: They got another open-source project under the organization: <a href="https://github.com/postgresml/pgcat">PgCat</a>, a Postgres pooler, which was first released back in February, 2022. Interesting to see them pivot to the more sexy topic of AI.</em></p> <p><em>Side note #2: PostgresML was actually</em> <a href="https://star-history.com/blog/postgresml"><em>featured on the starlet issue</em></a> <em>a while back, and that kinda gave me the inspo for this issue.</em></p> <h2>MADlib</h2> <p><img src="/assets/blog/ai-for-postgres/madlib.webp" alt="madlib"></p> <p>Apache <a href="https://github.com/apache/madlib">MADlib</a> is a tool for big data Machine Learning in SQL. It started out way back before ChatGPT brought the heat to AI, the repo was released on GitHub somewhere in 2016, but the initial release was as early as in 2011 (and continues to evolve).</p> <p><img src="/assets/blog/ai-for-postgres/madlib-alpha.webp" alt="madlib-alpha"></p> <p>The perception that SQL doesn't work well enough for inferential, predictive or causal analysis on larger or unstructured data sets is no longer true. MADlib basically an in-database machine learning library, where you get to perform advanced machine learning within your database where your data resides. Once installed, you can use SQL to perform various data analysis tasks, including regression and classification easily.</p> <p>Interesting fact: MAD in MADlib actually stands for Magnetic, Agile, and Deep.</p> <p><img src="/assets/blog/ai-for-postgres/madlib-mad.webp" alt="madlib-mad"></p> <h2>pg_embedding</h2> <p><img src="/assets/blog/ai-for-postgres/pg-embedding.webp" alt="pg-embedding"></p> <p>Now we are onto a few wildcards, they are riveting projects for sure, but use them at your own risk.</p> <p><a href="https://github.com/neondatabase/pg_embedding">pg_embedding</a> is an offering by Neon that was released this July.</p> <p>It uses Hierarchical Navigable Small Worlds (HNSW) index to for high-dimensional similarity search, which was before pgvector added support for HNSW (Hard to understand why it was <a href="https://neon.tech/blog/pg-embedding-extension-for-vector-search">20x faster than pgvector</a> when it first came out).</p> <p><img src="/assets/blog/ai-for-postgres/pg-embedding-faster.webp" alt="pg-embedding-faster"></p> <p>But a few months later in September, they added <a href="https://github.com/neondatabase/pg_embedding#pg_embedding">a note</a> mentioning that they are no longer committing to pg_embedding. Looks like there really is no Buy or Build dilemma: if there's a decent service out there, it's the obvious choice.</p> <h2>pg_gpt</h2> <p><img src="/assets/blog/ai-for-postgres/pg-gpt.webp" alt="pg-gpt"></p> <p><a href="https://github.com/cloudquery/pg_gpt">pg_gpt</a> is an experimental Postgres extension uses the OpenAI GPT API inside Postgres, so you can ask your database questions using natural language. And it's actually built by CloudQuery, an open-source ELT platform.</p> <p>This plugin works by sending parts of your database schema (although without the data) to OpenAI GPT API, so would not recommended to use it on production databases, but if you'd like to know something about a public schema, it's a fun tool to use. For example, if you were to look for top submissions on Hacker News this past month that mentions Sam Altman, this is a way to go (or, you know, <a href="https://hn.algolia.com/?dateRange=all&page=0&prefix=false&query=&sort=byPopularity&type=story">Search Hacker News</a> also works).</p> <h2>Conclusion</h2> <p>We've seen a lot this year: the tech world shifting towards AI and looking for a way to navigate their way to find their standing ground. Good thing that Postgres is open to support extensions, and we are blessed with these extensions to add AI-capabilities to our good-ol Postgres, without having to turn to a new database.</p> <hr> <h2>Star History Monthly Previous Editions</h2> <ul> <li><a href="/blog/coding-ai">Coding AI</a></li> <li><a href="/blog/cli-tool-for-llm">CLI Tools for Working with LLMs</a></li> <li><a href="/blog/llama2">Llama 2 and Ecosystem</a></li> <li><a href="/blog/star-history-monthly-pick-202303">ChatGPT Special</a></li> </ul> </div></div><div class="mt-12"><iframe src="https://embeds.beehiiv.com/2803dbaa-d8dd-4486-8880-4b843f3a7da6?slim=true" data-test-id="beehiiv-embed" height="52" frameBorder="0" scrolling="no" style="margin:0;border-radius:0px !important;background-color:transparent"></iframe></div></div><div class="w-full hidden lg:block"></div></div><footer class="relative w-full shrink-0 h-auto mt-6 flex flex-col justify-end items-center"><div class="w-full py-2 px-3 md:w-5/6 lg:max-w-7xl flex 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type="application/json" crossorigin="">{"props":{"pageProps":{"blog":{"title":"Star History Monthly Oct 2023 | Open-source AI Extensions for Postgres","slug":"ai-for-postgres","author":"Mila","featured":true,"featureImage":"/assets/blog/ai-for-postgres/banner.webp","publishedDate":"2023-11-24T09:50:06.000Z","excerpt":"Postgres extensions can add extra functionality to your database, and we're sharing five Postgres extensions that just might boost your AI experience.","readingTime":5},"parsedBlogHTML":"\u003cp\u003ePostgres was named the most popular database by the \u003ca href=\"https://survey.stackoverflow.co/2023/#technology\"\u003e2023 Stack Overflow Developer Survey\u003c/a\u003e, taking over the crown from MySQL. Why, you ask? We believe that a major factor comes down to Postgres \u003ca href=\"https://www.postgresql.org/docs/current/sql-createextension.html\"\u003eextensions\u003c/a\u003e. Thanks to the extensible architecture and the fact that it\u0026#39;s still a community-owned project, the Postgres ecosystem has been nothing but thriving lately.\u003c/p\u003e\n\u003cp\u003ePostgres extensions add extra functionality to your database just like built-in features. Several extensions for AI use cases have appeared over the years. A handful of them came out this year, but surprisingly, there are a few first appeared a few years back and remain to be the frontrunners of the pack.\u003c/p\u003e\n\u003cp\u003eIn this post, we\u0026#39;ll share five Postgres extensions that just might boost your AI experience.\u003c/p\u003e\n\u003ch2\u003epgvector\u003c/h2\u003e\n\u003cp\u003e\u003cimg src=\"/assets/blog/ai-for-postgres/pgvector.webp\" alt=\"pgvector\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/pgvector/pgvector\"\u003epgvector\u003c/a\u003e is an open-source vector similarity search specifically designed for Postgres (supports Postgres 11+). It can also be used for storing embeddings. It\u0026#39;s been around since 2021, way before AI became part of our daily routine. The spike in popularity came around 2023.2, when vector similarity search became a hot topic.\u003c/p\u003e\n\u003cp\u003eIf you\u0026#39;re looking for a vector database, Postgres got you covered, you can continue using your Postgres with benefits including:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003estore your vectors with the rest of your data\u003c/li\u003e\n\u003cli\u003eexact and approximate nearest neighbor search\u003c/li\u003e\n\u003cli\u003eL2 distance, inner product, and cosine distance\u003c/li\u003e\n\u003cli\u003eany language with a Postgres client\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf you use plain Postgres, simply compile and install the extension; if you are using Postgres services or apps, some of them already have pgvector included, e.g. Aiven, Neon, Supabase, Postgres.app, etc.\u003c/p\u003e\n\u003ch2\u003ePostgresML\u003c/h2\u003e\n\u003cp\u003e\u003cimg src=\"/assets/blog/ai-for-postgres/postgresml.webp\" alt=\"postgresml\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/postgresml/postgresml\"\u003ePostgresML\u003c/a\u003e is an extension that allows developers to integrate machine learning (ML) models into Postgres that scored a \u003ca href=\"https://postgresml.org/blog/postgresml-raises-4.7M-to-launch-serverless-ai-application-databases-based-on-postgres\"\u003e$4.7M seed funding round\u003c/a\u003e this May. It enables training and inference on text and data using SQL queries, greatly reducing the complexity of application development.\u003c/p\u003e\n\u003cp\u003eIf you don\u0026#39;t have fancy ML workloads (because not all of us are AI giants) in your company, but would like to build your own machine learning model that can learn from fresh data, this would be a good way to get started.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"/assets/blog/ai-for-postgres/postgresml-textgen.webp\" alt=\"postgresml-textgen\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSide note #1: They got another open-source project under the organization: \u003ca href=\"https://github.com/postgresml/pgcat\"\u003ePgCat\u003c/a\u003e, a Postgres pooler, which was first released back in February, 2022. Interesting to see them pivot to the more sexy topic of AI.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSide note #2: PostgresML was actually\u003c/em\u003e \u003ca href=\"https://star-history.com/blog/postgresml\"\u003e\u003cem\u003efeatured on the starlet issue\u003c/em\u003e\u003c/a\u003e \u003cem\u003ea while back, and that kinda gave me the inspo for this issue.\u003c/em\u003e\u003c/p\u003e\n\u003ch2\u003eMADlib\u003c/h2\u003e\n\u003cp\u003e\u003cimg src=\"/assets/blog/ai-for-postgres/madlib.webp\" alt=\"madlib\"\u003e\u003c/p\u003e\n\u003cp\u003eApache \u003ca href=\"https://github.com/apache/madlib\"\u003eMADlib\u003c/a\u003e is a tool for big data Machine Learning in SQL. It started out way back before ChatGPT brought the heat to AI, the repo was released on GitHub somewhere in 2016, but the initial release was as early as in 2011 (and continues to evolve).\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"/assets/blog/ai-for-postgres/madlib-alpha.webp\" alt=\"madlib-alpha\"\u003e\u003c/p\u003e\n\u003cp\u003eThe perception that SQL doesn\u0026#39;t work well enough for inferential, predictive or causal analysis on larger or unstructured data sets is no longer true. MADlib basically an in-database machine learning library, where you get to perform advanced machine learning within your database where your data resides. Once installed, you can use SQL to perform various data analysis tasks, including regression and classification easily.\u003c/p\u003e\n\u003cp\u003eInteresting fact: MAD in MADlib actually stands for Magnetic, Agile, and Deep.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"/assets/blog/ai-for-postgres/madlib-mad.webp\" alt=\"madlib-mad\"\u003e\u003c/p\u003e\n\u003ch2\u003epg_embedding\u003c/h2\u003e\n\u003cp\u003e\u003cimg src=\"/assets/blog/ai-for-postgres/pg-embedding.webp\" alt=\"pg-embedding\"\u003e\u003c/p\u003e\n\u003cp\u003eNow we are onto a few wildcards, they are riveting projects for sure, but use them at your own risk.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/neondatabase/pg_embedding\"\u003epg_embedding\u003c/a\u003e is an offering by Neon that was released this July.\u003c/p\u003e\n\u003cp\u003eIt uses Hierarchical Navigable Small Worlds (HNSW) index to for high-dimensional similarity search, which was before pgvector added support for HNSW (Hard to understand why it was \u003ca href=\"https://neon.tech/blog/pg-embedding-extension-for-vector-search\"\u003e20x faster than pgvector\u003c/a\u003e when it first came out).\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"/assets/blog/ai-for-postgres/pg-embedding-faster.webp\" alt=\"pg-embedding-faster\"\u003e\u003c/p\u003e\n\u003cp\u003eBut a few months later in September, they added \u003ca href=\"https://github.com/neondatabase/pg_embedding#pg_embedding\"\u003ea note\u003c/a\u003e mentioning that they are no longer committing to pg_embedding. Looks like there really is no Buy or Build dilemma: if there\u0026#39;s a decent service out there, it\u0026#39;s the obvious choice.\u003c/p\u003e\n\u003ch2\u003epg_gpt\u003c/h2\u003e\n\u003cp\u003e\u003cimg src=\"/assets/blog/ai-for-postgres/pg-gpt.webp\" alt=\"pg-gpt\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/cloudquery/pg_gpt\"\u003epg_gpt\u003c/a\u003e is an experimental Postgres extension uses the OpenAI GPT API inside Postgres, so you can ask your database questions using natural language. And it\u0026#39;s actually built by CloudQuery, an open-source ELT platform.\u003c/p\u003e\n\u003cp\u003eThis plugin works by sending parts of your database schema (although without the data) to OpenAI GPT API, so would not recommended to use it on production databases, but if you\u0026#39;d like to know something about a public schema, it\u0026#39;s a fun tool to use. For example, if you were to look for top submissions on Hacker News this past month that mentions Sam Altman, this is a way to go (or, you know, \u003ca href=\"https://hn.algolia.com/?dateRange=all\u0026page=0\u0026prefix=false\u0026query=\u0026sort=byPopularity\u0026type=story\"\u003eSearch Hacker News\u003c/a\u003e also works).\u003c/p\u003e\n\u003ch2\u003eConclusion\u003c/h2\u003e\n\u003cp\u003eWe\u0026#39;ve seen a lot this year: the tech world shifting towards AI and looking for a way to navigate their way to find their standing ground. Good thing that Postgres is open to support extensions, and we are blessed with these extensions to add AI-capabilities to our good-ol Postgres, without having to turn to a new database.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003eStar History Monthly Previous Editions\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"/blog/coding-ai\"\u003eCoding AI\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/blog/cli-tool-for-llm\"\u003eCLI Tools for Working with LLMs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/blog/llama2\"\u003eLlama 2 and Ecosystem\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/blog/star-history-monthly-pick-202303\"\u003eChatGPT Special\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n"},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"ai-for-postgres"},"buildId":"xKX4ZiOi_N7h3OBOEsSZu","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>