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 index 3ffa41d73..f28cd75f8 100644 --- 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 @@ -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 diff --git a/pgml-cms/blog/personalize-embedding-results-with-application-data-in-your-database.md b/pgml-cms/blog/personalize-embedding-results-with-application-data-in-your-database.md index 734371233..d28a2222c 100644 --- a/pgml-cms/blog/personalize-embedding-results-with-application-data-in-your-database.md +++ b/pgml-cms/blog/personalize-embedding-results-with-application-data-in-your-database.md @@ -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)

Embeddings can be combined into personalized perspectives when stored as vectors in the database.

diff --git a/pgml-cms/blog/tuning-vector-recall-while-generating-query-embeddings-in-the-database.md b/pgml-cms/blog/tuning-vector-recall-while-generating-query-embeddings-in-the-database.md index 4724740df..b26ae0365 100644 --- a/pgml-cms/blog/tuning-vector-recall-while-generating-query-embeddings-in-the-database.md +++ b/pgml-cms/blog/tuning-vector-recall-while-generating-query-embeddings-in-the-database.md @@ -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. pFad - Phonifier reborn

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