Aug 26, 2024 · Aug 26, 2024
diff --git a/pgml-cms/blog/SUMMARY.md b/pgml-cms/blog/SUMMARY.md
 # Table of contents

 * [Home](README.md)
 * [Sudowrite + PostgresML](sudowrite-postgresml.md)
 * [Korvus x Firecrawl: Rag in a single query](korvus-firecrawl-rag-in-a-single-query.md)
 * [A Speed Comparison of the Most Popular Retrieval Systems for RAG](a-speed-comparison-of-the-most-popular-retrieval-systems-for-rag.md)
 * [Korvus The All-in-One RAG Pipeline for PostgresML](introducing-korvus-the-all-in-one-rag-pipeline-for-postgresml.md)
diff --git a/pgml-cms/blog/how-to-improve-search-results-with-machine-learning.md b/pgml-cms/blog/how-to-improve-search-results-with-machine-learning.md
  PostgresML makes it easy to use machine learning on your data and scale
  workloads horizontally in our cloud. One of the most common use cases is to
  improve search results.
 featured:true
 featured:false
 image: ".gitbook/assets/image (2) (2).png"
 tags: ["Engineering"]
 ---
diff --git a/...roducing-the-openai-switch-kit-move-from-closed-to-open-source-ai-in-minutes.md b/...roducing-the-openai-switch-kit-move-from-closed-to-open-source-ai-in-minutes.md
 ---
 featured:true
 featured:false
 tags: [engineering, product]
 description: >-
  Quickly and easily transition from the confines of the OpenAI APIs to higher
diff --git a/pgml-cms/blog/korvus-firecrawl-rag-in-a-single-query.md b/pgml-cms/blog/korvus-firecrawl-rag-in-a-single-query.md
 ---
 description: How to perform all-in-one RAG over any website with Firecrawl and Korvus.
 featured:true
 featured:false
 tags: [engineering]
 image: ".gitbook/assets/Blog-Image_Korvus-Firecrawl.jpg"
 ---
diff --git a/pgml-cms/blog/meet-us-at-the-2024-ai-dev-summit-conference.md b/pgml-cms/blog/meet-us-at-the-2024-ai-dev-summit-conference.md
 ---
 featured:true
 featured:false
 description: in South San Francisco May 29-30
 image: ".gitbook/assets/image/ai_dev_summit.png"
 ---

 AI DevSummit is the world’s largest artificial intelligence developer & engineering conference with tracks covering chatbots, machine learning, open source AI libraries, AI for the enterprise, and deep AI / neural networks.

 <divid="ai-dev-summit-tip-container">
 <divclass="hide-admonition-title-container">

 !!! tip

diff --git a/pgml-cms/blog/semantic-search-in-postgres-in-15-minutes.md b/pgml-cms/blog/semantic-search-in-postgres-in-15-minutes.md
 ---
 description: >-
  How to implement semantic search in Postgres with nothing but SQL.
 featured:true
 featured:false
 tags: ["Engineering"]
 image: ".gitbook/assets/Blog-Image_Semantic-Search.jpg"
 ---
diff --git a/pgml-cms/blog/serverless-llms-are-dead-long-live-serverless-llms.md b/pgml-cms/blog/serverless-llms-are-dead-long-live-serverless-llms.md
 ---
 description: >-
  Building LLM infrastructure presents a series of tradeoffs that aren't obvious at the outset, even for seasoned teams. This is our journey to high-performance LLMs at scale.
 featured:true
 featured:false
 tags: [engineering]
 image: ".gitbook/assets/serverless_llms.png"
 ---
diff --git a/pgml-cms/blog/speeding-up-vector-recall-5x-with-hnsw.md b/pgml-cms/blog/speeding-up-vector-recall-5x-with-hnsw.md
  we announce our updated SDK that utilizes HNSW indexing to give world class
  performance in vector search.
 tags: [engineering]
 featured:true
 featured:false
 image: ".gitbook/assets/blog_image_hnsw.png"
 ---

diff --git a/pgml-cms/blog/sudowrite-postgresml.md b/pgml-cms/blog/sudowrite-postgresml.md
 ---
 description: How the best AI-powered app for fiction writers built their winning RAG stack
 featured: true
 tags: []
 image: ".gitbook/assets/sudowrite-pgml_blog-image.png"
 ---

 # Sudowrite + PostgresML

 <div align="left">

 <figure><img src=".gitbook/assets/image.png" alt="Author" width="100"><figcaption></figcaption></figure>

 </div>

 Cassandra Stummer

 August 26, 2024

 ## The challenge

 [Sudowrite](https://www.sudowrite.com/) is an AI-powered writing assistant that helps author's craft compelling stories and overcome writer's block. They wanted to give authors a cool new feature: the ability to chat with an AI editor about their stories.

 James Yu, Sudowrite’s founder and CTO, knew that meant standing up a RAG (retrieval augmented generation) system. RAG is a cutting-edge AI technique, but James was searching for a solution that worked in production and at-scale, not just in the latest prototype trending on Hacker News.

 “I didn’t want to geek out about RAG for days or weeks. Just give me something that approximately works and then I can move on to the next thing.”

 ## Enter PostgresML

 PostgresML is simple – it’s PostgreSQL with GPUs for ML/AI apps. Along with GPUs, the PostgresML Cloud provides a full-featured machine learning platform right in the database; with functionality for search, embeddings, retrieval and more.

 James was sold on the simplicity of doing AI in Postgres, the database his engineers already use and love


 <div class="hide-admonition-title-container">

 !!! tip

 <p class="center">
    "Why add yet another database to your stack if you don't have to? Being able to co-locate your data – to query across the same metadata stack – is a no brainer.”
 </p>

 <p><i>James Yu, Founder @Sudowrite</i></p>

 !!!

 </div>

 ## Quick and easy implementation

 Time to prototype was key for the Sudowrite team when testing out RAG systems. They used the Javascript SDK to get a full proof of concept chatbot fully synced to document changes in three hours flat. Once they decided to use PostgresML, it just took a few function calls with the SDK to start syncing data with production.

 “It was pretty easy,” James said. “I also just like the visibility. As it's indexing I can just refresh my Postgres and I see the chunks, I can inspect it all. It’s immediate validation.” His team knows Postgres, so there was no need to get familiar with a niche vector database service like Pinecone or Qdrant.

 James added: “I tried Pinecone and it felt very opaque - it’s a weird API and the data felt weirdly structured. I’m not going to pay exorbitant fees for a proprietary database where I’m not even sure how they’re performing the queries. I had to go through their UI, whereas for PostgresML I could visually see it in the same way as all my other data.”

 And since PostgresML has ML/AI functionality built-in, they didn’t need to create complex data pipelines to connect to embedding services, data pre-processors, or other ML/AI microservices. The Sudowrite team performs embedding generation and retrieval using SQL queries, right inside their PostgresML database.

 Additionally the Sudowrite team had access to an on-call PostgresML engineer and a private slack channel with same-day responses to ensure implementation was as smooth and fast as possible.

 "The support from the PostgresML team has been top-notch," James adds. "They're always quick to respond when we have questions, and they understand our need for flexibility.”

 ## The results: In-database AI is a win for devs and users

 With PostgresML in place, Sudowrite's new AI chatbot feature is already making waves:

 - Sudowrite's RAG system makes more than 1 million calls per hour
 - The engineering team is loving the streamlined operations
 - A growing percentage of daily active users are chatting it up with the AI editor

 Performance and scalability were initial concerns for Sudowrite, given their large document base. James recalls his pleasant surprise: **"I thought, 'wow it's really fast, it's indexing all these things.' I was skeptical at first because we had a lot of documents, but it indexed quickly and it's really performant."**

 <div class="hide-admonition-title-container">

 !!! tip

 <p class="center">
 “The quality – especially the RAG piece – has been great. In terms of scaling and everything, it’s been great.”
 </p>

 !!!

 </div>

 Additionally, PostgresML's integration has been seamless for Sudowrite's development team,  allowing engineers to focus on enhancing the user experience rather than wrestling with complex infrastructure. “I even have a contractor, and we handed it off to him pretty easily…And for him to be able to get up to speed was relatively painless,” James added.

 This efficiency has given Sudowrite confidence in their ability to scale the chatbot feature to meet growing demand – and the Sudowrite team sees tremendous potential for further adoption: "People want more chat. We have plans to make it more up front and center in the app."

 ## What's next for Sudowrite?

 James and his team are just getting started. They're cooking up plans to:

 - Make the chatbot even more visible in the app
 - Allow authors to import their entire novel and interact with it via RAG
 - Create automated knowledge graphs from author’s stories


 <div class="hide-admonition-title-container">

 !!! tip

 <p class="center">
 “PostgresML has given us a solid foundation for our product. Their RAG extends the capabilities of our LLMs. It’s an essential ingredient for us to create tools that help writers create even more amazing stories."
 </p>

 !!!

 </div>

 ## The bottom line

 By choosing PostgresML, Sudowrite found a powerful, flexible solution that:

 - Integrates seamlessly with their existing systems
 - Scales effortlessly without the need for complex infra management
 - Provides the transparency and flexibility to customize and expand their offering

 James sums it up perfectly: "For me, PostgresML just makes a lot of sense.”
diff --git a/pgml-dashboard/static/css/scss/layout/_containers.scss b/pgml-dashboard/static/css/scss/layout/_containers.scss
    background-color: #{$pink};
 }

 #ai-dev-summit-tip-container {
 .hide-admonition-title-container {
    .admonition-title {
        display: none
        display: none !important;
    }

    .admonition-tip {
Original file line number	Diff line number	Diff line change
		@@ -1,6 +1,7 @@
		# Table of contents

		* [Home](README.md)
		* [Sudowrite + PostgresML](sudowrite-postgresml.md)
		* [Korvus x Firecrawl: Rag in a single query](korvus-firecrawl-rag-in-a-single-query.md)
		* [A Speed Comparison of the Most Popular Retrieval Systems for RAG](a-speed-comparison-of-the-most-popular-retrieval-systems-for-rag.md)
		* [Korvus The All-in-One RAG Pipeline for PostgresML](introducing-korvus-the-all-in-one-rag-pipeline-for-postgresml.md)
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -3,7 +3,7 @@ description: >-
		PostgresML makes it easy to use machine learning on your data and scale
		workloads horizontally in our cloud. One of the most common use cases is to
		improve search results.
		featured:true
		featured:false
		image: ".gitbook/assets/image (2) (2).png"
		tags: ["Engineering"]
		---
Expand Down
Original file line number	Diff line number	Diff line change
		@@ -1,5 +1,5 @@
		---
		featured:true
		featured:false
		tags: [engineering, product]
		description: >-
		Quickly and easily transition from the confines of the OpenAI APIs to higher
Expand Down
Original file line number	Diff line number	Diff line change
		@@ -1,6 +1,6 @@
		---
		description: How to perform all-in-one RAG over any website with Firecrawl and Korvus.
		featured:true
		featured:false
		tags: [engineering]
		image: ".gitbook/assets/Blog-Image_Korvus-Firecrawl.jpg"
		---
Expand Down
Original file line number	Diff line number	Diff line change
		@@ -1,7 +1,7 @@
		---
		description: >-
		How to implement semantic search in Postgres with nothing but SQL.
		featured:true
		featured:false
		tags: ["Engineering"]
		image: ".gitbook/assets/Blog-Image_Semantic-Search.jpg"
		---
Expand Down
Original file line number	Diff line number	Diff line change
		@@ -1,7 +1,7 @@
		---
		description: >-
		Building LLM infrastructure presents a series of tradeoffs that aren't obvious at the outset, even for seasoned teams. This is our journey to high-performance LLMs at scale.
		featured:true
		featured:false
		tags: [engineering]
		image: ".gitbook/assets/serverless_llms.png"
		---
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -4,7 +4,7 @@ description: >-
		we announce our updated SDK that utilizes HNSW indexing to give world class
		performance in vector search.
		tags: [engineering]
		featured:true
		featured:false
		image: ".gitbook/assets/blog_image_hnsw.png"
		---

Expand Down
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1,118 @@
		---
		description: How the best AI-powered app for fiction writers built their winning RAG stack
		featured: true
		tags: []
		image: ".gitbook/assets/sudowrite-pgml_blog-image.png"
		---

		# Sudowrite + PostgresML

		<div align="left">

		<figure><img src=".gitbook/assets/image.png" alt="Author" width="100"><figcaption></figcaption></figure>

		</div>

		Cassandra Stummer

		August 26, 2024

		## The challenge

		[Sudowrite](https://www.sudowrite.com/) is an AI-powered writing assistant that helps author's craft compelling stories and overcome writer's block. They wanted to give authors a cool new feature: the ability to chat with an AI editor about their stories.

		James Yu, Sudowrite’s founder and CTO, knew that meant standing up a RAG (retrieval augmented generation) system. RAG is a cutting-edge AI technique, but James was searching for a solution that worked in production and at-scale, not just in the latest prototype trending on Hacker News.

		“I didn’t want to geek out about RAG for days or weeks. Just give me something that approximately works and then I can move on to the next thing.”

		## Enter PostgresML

		PostgresML is simple – it’s PostgreSQL with GPUs for ML/AI apps. Along with GPUs, the PostgresML Cloud provides a full-featured machine learning platform right in the database; with functionality for search, embeddings, retrieval and more.

		James was sold on the simplicity of doing AI in Postgres, the database his engineers already use and love


		<div class="hide-admonition-title-container">

		!!! tip

		<p class="center">
		"Why add yet another database to your stack if you don't have to? Being able to co-locate your data – to query across the same metadata stack – is a no brainer.”
		</p>

		<p><i>James Yu, Founder @Sudowrite</i></p>

		!!!

		</div>

		## Quick and easy implementation

		Time to prototype was key for the Sudowrite team when testing out RAG systems. They used the Javascript SDK to get a full proof of concept chatbot fully synced to document changes in three hours flat. Once they decided to use PostgresML, it just took a few function calls with the SDK to start syncing data with production.

		“It was pretty easy,” James said. “I also just like the visibility. As it's indexing I can just refresh my Postgres and I see the chunks, I can inspect it all. It’s immediate validation.” His team knows Postgres, so there was no need to get familiar with a niche vector database service like Pinecone or Qdrant.

		James added: “I tried Pinecone and it felt very opaque - it’s a weird API and the data felt weirdly structured. I’m not going to pay exorbitant fees for a proprietary database where I’m not even sure how they’re performing the queries. I had to go through their UI, whereas for PostgresML I could visually see it in the same way as all my other data.”

		And since PostgresML has ML/AI functionality built-in, they didn’t need to create complex data pipelines to connect to embedding services, data pre-processors, or other ML/AI microservices. The Sudowrite team performs embedding generation and retrieval using SQL queries, right inside their PostgresML database.

		Additionally the Sudowrite team had access to an on-call PostgresML engineer and a private slack channel with same-day responses to ensure implementation was as smooth and fast as possible.

		"The support from the PostgresML team has been top-notch," James adds. "They're always quick to respond when we have questions, and they understand our need for flexibility.”

		## The results: In-database AI is a win for devs and users

		With PostgresML in place, Sudowrite's new AI chatbot feature is already making waves:

		- Sudowrite's RAG system makes more than 1 million calls per hour
		- The engineering team is loving the streamlined operations
		- A growing percentage of daily active users are chatting it up with the AI editor

		Performance and scalability were initial concerns for Sudowrite, given their large document base. James recalls his pleasant surprise: "I thought, 'wow it's really fast, it's indexing all these things.' I was skeptical at first because we had a lot of documents, but it indexed quickly and it's really performant."

		<div class="hide-admonition-title-container">

		!!! tip

		<p class="center">
		“The quality – especially the RAG piece – has been great. In terms of scaling and everything, it’s been great.”
		</p>

		!!!

		</div>

		Additionally, PostgresML's integration has been seamless for Sudowrite's development team, allowing engineers to focus on enhancing the user experience rather than wrestling with complex infrastructure. “I even have a contractor, and we handed it off to him pretty easily…And for him to be able to get up to speed was relatively painless,” James added.

		This efficiency has given Sudowrite confidence in their ability to scale the chatbot feature to meet growing demand – and the Sudowrite team sees tremendous potential for further adoption: "People want more chat. We have plans to make it more up front and center in the app."

		## What's next for Sudowrite?

		James and his team are just getting started. They're cooking up plans to:

		- Make the chatbot even more visible in the app
		- Allow authors to import their entire novel and interact with it via RAG
		- Create automated knowledge graphs from author’s stories


		<div class="hide-admonition-title-container">

		!!! tip

		<p class="center">
		“PostgresML has given us a solid foundation for our product. Their RAG extends the capabilities of our LLMs. It’s an essential ingredient for us to create tools that help writers create even more amazing stories."
		</p>

		!!!

		</div>

		## The bottom line

		By choosing PostgresML, Sudowrite found a powerful, flexible solution that:

		- Integrates seamlessly with their existing systems
		- Scales effortlessly without the need for complex infra management
		- Provides the transparency and flexibility to customize and expand their offering

		James sums it up perfectly: "For me, PostgresML just makes a lot of sense.”
Original file line number	Diff line number	Diff line change
Expand Up		@@ -180,9 +180,9 @@
		background-color: #{$pink};
		}

		#ai-dev-summit-tip-container {
		.hide-admonition-title-container {
		.admonition-title {
		display: none
		display: none !important;
		}

		.admonition-tip {
Expand Down