Mar 22, 2024 · Mar 22, 2024
diff --git a/pgml-cms/blog/generating-llm-embeddings-with-open-source-models-in-postgresml.md b/pgml-cms/blog/generating-llm-embeddings-with-open-source-models-in-postgresml.md

 This article is the first in a multipart series that will show you how to build a post-modern semantic search and recommendation engine, including personalization, using open source models.

 1. Generating LLM Embeddings with HuggingFace models
 2. Tuning vector recall with pgvector
 3. Personalizing embedding results with application data
 4. Optimizing semantic results with an XGBoost ranking model - coming soon!
 1.[Generating LLM Embeddings with HuggingFace models](generating-llm-embeddings-with-open-source-models-in-postgresml.md)
 2.[Tuning vector recall with pgvector](tuning-vector-recall-while-generating-query-embeddings-in-the-database.md)
 3.[Personalizing embedding results with application data](personalize-embedding-results-with-application-data-in-your-database.md)
 4.[Optimizing semantic results with an XGBoost ranking model](/docs/use-cases/improve-search-results-with-machine-learning)

 ## Introduction

diff --git a/...ms/blog/personalize-embedding-results-with-application-data-in-your-database.md b/...ms/blog/personalize-embedding-results-with-application-data-in-your-database.md

 This article is the third in a multipart series that will show you how to build a post-modern semantic search and recommendation engine, including personalization, using open source models. You may want to start with the previous articles in the series if you aren't familiar with PostgresML's capabilities.

 1. Generating LLM Embeddings with HuggingFace models
 2. Tuning vector recall with pgvector
 3. Personalizing embedding results with application data
 4. Optimizing semantic results with an XGBoost ranking model - coming soon!
 1.[Generating LLM Embeddings with HuggingFace models](generating-llm-embeddings-with-open-source-models-in-postgresml.md)
 2.[Tuning vector recall with pgvector](tuning-vector-recall-while-generating-query-embeddings-in-the-database.md)
 3.[Personalizing embedding results with application data](personalize-embedding-results-with-application-data-in-your-database.md)
 4.[Optimizing semantic results with an XGBoost ranking model](/docs/use-cases/improve-search-results-with-machine-learning)

 <figure><img src=".gitbook/assets/image (24).png" alt=""><figcaption><p>Embeddings can be combined into personalized perspectives when stored as vectors in the database.</p></figcaption></figure>

diff --git a/.../blog/tuning-vector-recall-while-generating-query-embeddings-in-the-database.md b/.../blog/tuning-vector-recall-while-generating-query-embeddings-in-the-database.md

 This article is the second in a multipart series that will show you how to build a post-modern semantic search and recommendation engine, including personalization, using open source models.

 1. Generating LLM Embeddings with HuggingFace models
 2. Tuning vector recall with pgvector
 3. Personalizing embedding results with application data
 4. Optimizing semantic results with an XGBoost ranking model - coming soon!
 1.[Generating LLM Embeddings with HuggingFace models](generating-llm-embeddings-with-open-source-models-in-postgresml.md)
 2.[Tuning vector recall with pgvector](tuning-vector-recall-while-generating-query-embeddings-in-the-database.md)
 3.[Personalizing embedding results with application data](personalize-embedding-results-with-application-data-in-your-database.md)
 4.[Optimizing semantic results with an XGBoost ranking model](/docs/use-cases/improve-search-results-with-machine-learning)

 The previous article discussed how to generate embeddings that perform better than OpenAI's `text-embedding-ada-002` and save them in a table with a vector index. In this article, we'll show you how to query those embeddings effectively.
Original file line number	Diff line number	Diff line change
Expand Up		@@ -22,10 +22,10 @@ PostgresML makes it easy to generate embeddings from text in your database using

		This article is the first in a multipart series that will show you how to build a post-modern semantic search and recommendation engine, including personalization, using open source models.

		1. Generating LLM Embeddings with HuggingFace models
		2. Tuning vector recall with pgvector
		3. Personalizing embedding results with application data
		4. Optimizing semantic results with an XGBoost ranking model - coming soon!
		1.[Generating LLM Embeddings with HuggingFace models](generating-llm-embeddings-with-open-source-models-in-postgresml.md)
		2.[Tuning vector recall with pgvector](tuning-vector-recall-while-generating-query-embeddings-in-the-database.md)
		3.[Personalizing embedding results with application data](personalize-embedding-results-with-application-data-in-your-database.md)
		4.[Optimizing semantic results with an XGBoost ranking model](/docs/use-cases/improve-search-results-with-machine-learning)

		## Introduction

Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -22,10 +22,10 @@ PostgresML makes it easy to generate embeddings using open source models from Hu

		This article is the third in a multipart series that will show you how to build a post-modern semantic search and recommendation engine, including personalization, using open source models. You may want to start with the previous articles in the series if you aren't familiar with PostgresML's capabilities.

		1. Generating LLM Embeddings with HuggingFace models
		2. Tuning vector recall with pgvector
		3. Personalizing embedding results with application data
		4. Optimizing semantic results with an XGBoost ranking model - coming soon!
		1.[Generating LLM Embeddings with HuggingFace models](generating-llm-embeddings-with-open-source-models-in-postgresml.md)
		2.[Tuning vector recall with pgvector](tuning-vector-recall-while-generating-query-embeddings-in-the-database.md)
		3.[Personalizing embedding results with application data](personalize-embedding-results-with-application-data-in-your-database.md)
		4.[Optimizing semantic results with an XGBoost ranking model](/docs/use-cases/improve-search-results-with-machine-learning)

		<figure><img src=".gitbook/assets/image (24).png" alt=""><figcaption><p>Embeddings can be combined into personalized perspectives when stored as vectors in the database.</p></figcaption></figure>

Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -22,10 +22,10 @@ PostgresML makes it easy to generate embeddings using open source models and per

		This article is the second in a multipart series that will show you how to build a post-modern semantic search and recommendation engine, including personalization, using open source models.

		1. Generating LLM Embeddings with HuggingFace models
		2. Tuning vector recall with pgvector
		3. Personalizing embedding results with application data
		4. Optimizing semantic results with an XGBoost ranking model - coming soon!
		1.[Generating LLM Embeddings with HuggingFace models](generating-llm-embeddings-with-open-source-models-in-postgresml.md)
		2.[Tuning vector recall with pgvector](tuning-vector-recall-while-generating-query-embeddings-in-the-database.md)
		3.[Personalizing embedding results with application data](personalize-embedding-results-with-application-data-in-your-database.md)
		4.[Optimizing semantic results with an XGBoost ranking model](/docs/use-cases/improve-search-results-with-machine-learning)

		The previous article discussed how to generate embeddings that perform better than OpenAI's `text-embedding-ada-002` and save them in a table with a vector index. In this article, we'll show you how to query those embeddings effectively.

Expand Down