\n
\n
Are you looking to enhance your MySQL applications with natural language understanding, summarization, and GenAI capabilities? This post walks you through how to create intelligent solutions with semantic search and generative AI responses using Azure Open AI and Azure Database for MySQL with Azure AI Search.
","body":"Searching for content on a website uses keyword-based search, a method with its limitations. For example, searching for a ‘rain jacket for women’ on an e-commerce website returns all jackets for both men and women, as the search focuses on just the keyword ‘jacket’. The search simply isn’t contextual enough to provide the desired results.
\n\n
Semantic search is a technique that allows users to search for information using natural language queries rather than specific keywords. With semantic search, the meaning and intent of users’ queries are inferred, and personalized relevant results are returned. Generative AI, on the other hand, is a type of artificial intelligence that can generate new content from existing data, such as text, images, or audio. You can use generative AI to produce summaries, captions, recommendations, or responses based on a user's input and preferences.
\n\n
This blog post discusses how to build intelligent MySQL applications with semantic search and generative AI response using Azure Open AI and Azure Database for MySQL with Azure AI search. As an example, we’ll use a Magento ecommerce app designed to sell jackets, and then build a “Product Recommender CoPilot” chat application that:
\n\n
The simplest way to include the rich capabilities of semantic search and generative AI in your applications is to build a solution using the Retrieval Augmented Generation (RAG) architecture with Azure AI Search and Azure Open AI services.
\n\n
\n
Retrieval Augmented Generation, or RAG, is an architecture that augments the natural language understanding and generation capabilities of LLMs like ChatGPT by adding an information retrieval system like Azure AI Search which works with your data stored in data sources like Azure Database for MySQL. In a typical RAG pattern:
\n\n
\n
A sample RAG architecture for the AI solution we’ll show you how to build in this blog post appears in the following graphic:
\n\n
In this post, we’ll walk you through how to set up the backend data sources, indexers, and models required to build this solution. It is a detailed guide to the sample Python code hosted in our GitHub repository in a Jupyter Notebook: azure-mysql/Azure_MySQL_AI_Search_Sample at master Azure/azure-mysql.
\n\n
For a short demo of the entire process using a Magento ecommerce application, watch the following video!
\n\n\n
\n
\n
Before getting started, you need to ensure that the following prerequisites are in place.
\n\n
\n
Supporting semantic search in an e-commerce application that leverages Azure AI search and Azure Open AI Services in the backend requires completing the following:
\n\n
I. Set up data source connection in Azure AI Search.
\nII. Set up automatic chunking, vectorization and indexing.
\nIII. Use vector search from a sample application.
\nIV. Generate a GPT response to the user.
\nV. Test the solution in the Azure OpenAI Studio playground.
\n\n
Azure AI search pulls the contents and reviews data from a backend MySQL flexible server by using an indexer that runs periodically. The reviewed data is further chunked and vectorized using Azure OpenAI’s text embedding model. In Azure AI search, the vectorized data then persists in a vector search index.
\n\n
\n
The data source definition specifies the data to index, credentials, and policies for identifying changes in the data. The data source is defined as an independent resource so that it can be used by multiple indexers. In this example, we’ll use a custom table with product details and review data which is stored in a database in Azure Database for MySQL.
\n\n
In the Azure AI service, before creating a search Index, we’ll need to create a connection to your data source. We’ll import Azure AI classes like ‘SearchClient’, ‘SearchIndexerClient’, ‘SearchIndexerDataSourceConnection’ and their functions like “create_or_update_data_source_connection()” to setup the data source connection in Azure AI Search. We’ll also import several other models - the comprehensive list is shared in the following code sample.
\n\n
Code: “1. Set up data source connection in Azure AI Search” in the Jupyter Notebook azure-mysql/Azure_MySQL_AI_Search_Sample at master Azure/azure-mysql
\n\n
\n
We are now ready to create an Azure Database for MySQL indexer that periodically pulls the product details and reviews data from the database in Azure Database for MySQL, chunks and vectorizes the data and persists it in a vector search index.
\n\n
To do this, we’ll first create an index which takes the product details and reviews data for each product, splits the combined text into chunks and embeds each chunk as a vector. For any incoming user query, we’ll search for the most relevant chunk using vector search and semantic search.
\n\n
\n
In Azure AI Search, a search index available to the search engine for indexing, full text search, vector search, hybrid search, and filtered queries. An index is defined by a schema and is saved to the search service.
\nCode: “II. Set up automatic chunking, vectorization and indexing” -> “Create index” in the Jupyter Notebook azure-mysql/Azure_MySQL_AI_Search_Sample at master Azure/azure-mysql
\n\n
\n
We’ll create a skillset using two pre-built “skills”:
\nWe’ll then apply an Index Projector to make it so that our final index has one item for every chunk of text, rather than one item for every original row in the database.
\n\n
Code: “II. Set up automatic chunking, vectorization and indexing” -> “Create skillset” in the Jupyter Notebook azure-mysql/Azure_MySQL_AI_Search_Sample at master Azure/azure-mysql
\n\n
\n
After you define the data source and create the index, you're ready to create the indexer. The configuration of the Indexer requires the inputs, parameters, and properties that control run time behaviors.
\n\n
To create an indexer, we’ll provide the name of the data source, index and skillset that we created in the previous steps. We’ll then run the indexer at periodic intervals.
\n\n
Code: “II. Set up automatic chunking, vectorization and indexing” -> “Create indexer” in the Jupyter Notebook azure-mysql/Azure_MySQL_AI_Search_Sample at master Azure/azure-mysql
\n\n
After the indexer is created, you can use the Azure portal to view the indexer under the Indexers blade in Azure AI Search. You can also run the indexer from the Azure portal or using Azure CLI. The runs can be configured to be ad hoc or scheduled:
\n\n
\n
With the Azure AI Search indexer ready and running, you can now use the vector search and semantic search capabilities from your application. To call the search function, provide the user query text, required query parameters like the number of nearest neighbors to return as top hits, and the columns or fields in the index to be considered. Also select query type “Semantic” to include both vector search and semantic search and provide the name of semantic search configuration object that you created in section II.
\n\n
Code: “III. Use vector search from a sample application” in the Jupyter Notebook azure-mysql/Azure_MySQL_AI_Search_Sample at master Azure/azure-mysql
\n\n
For a user query like “suggest me some rain jackets for women“, this single API call performs vector search and semantic re-ranking, and returns the top ranked results as shown in the following screenshot. You’ll also see the vector search score, semantic re-ranker score, and all the details of the recommended product.
\n\n
\n
\n
\n
To generate custom responses to the users, we need to simply make a call to the Azure OpenAI chat completion service. To trigger the completion, we first input some text as a “prompt”. The LLM in the service then generates the completion and attempts to match our context or pattern. An example prompt for this scenario could be: “You are an AI assistant that recommends products to people based on the product reviews data matching their query. Your answer should summarize the review text, include the product ID, include the parent id as review id, and mention the overall sentiment of the review.”
\n\n
To generate the GPT response, we’ll use the Azure OpenAI’s chat.completions.create() API call and supply it with the user query, the Open AI model to be used, the Azure AI Search index as data source, and the prompt.
\n\n
Code: “IV. Generate GPT Response to the user” in the Jupyter Notebook azure-mysql/Azure_MySQL_AI_Search_Sample at master Azure/azure-mysql
\n\n
\n
Finally, it’s important to test the solution in Azure Open AI Studio, a user-friendly platform that allows developers to explore cutting-edge APIs and models and to build, test, deploy, and manage AI solutions for use in websites, applications, and other production environments.
\n\n
For an end-to-end test of the AI solution in the Azure OpenAI Studio playground, perform the following steps:
\n\n
\n
\n
Now, you’re all set to deploy this Product Recommender CoPilot AI solution to production!
\n\n
\n
If you’re running applications, such as content management systems (CMS), e-commerce applications, or gaming sites, with data hosted in Azure Database for MySQL, you can enhance your user experience by building generative AI search and chat applications using LLMs available in Azure OpenAI and vector storage and indexing provided by Azure AI Search. Unleash the power of your data hosted on MySQL with the simple and seamless AI integrations on Azure!
\n\n
If you have any queries or suggestions for more AI-related content, please let us know by contacting us at AskAzureDBforMySQL@service.microsoft.com. We’re also open to collaborating with you on technical collateral! Check out our Contributors initiative at aka.ms/mysql-contributors to learn more.
","body@stringLength":"22459","rawBody":"Searching for content on a website uses keyword-based search, a method with its limitations. For example, searching for a ‘rain jacket for women’ on an e-commerce website returns all jackets for both men and women, as the search focuses on just the keyword ‘jacket’. The search simply isn’t contextual enough to provide the desired results.
\n\n
Semantic search is a technique that allows users to search for information using natural language queries rather than specific keywords. With semantic search, the meaning and intent of users’ queries are inferred, and personalized relevant results are returned. Generative AI, on the other hand, is a type of artificial intelligence that can generate new content from existing data, such as text, images, or audio. You can use generative AI to produce summaries, captions, recommendations, or responses based on a user's input and preferences.
\n\n
This blog post discusses how to build intelligent MySQL applications with semantic search and generative AI response using Azure Open AI and Azure Database for MySQL with Azure AI search. As an example, we’ll use a Magento ecommerce app designed to sell jackets, and then build a “Product Recommender CoPilot” chat application that:
\n\n
The simplest way to include the rich capabilities of semantic search and generative AI in your applications is to build a solution using the Retrieval Augmented Generation (RAG) architecture with Azure AI Search and Azure Open AI services.
\n\n
\n
Retrieval Augmented Generation, or RAG, is an architecture that augments the natural language understanding and generation capabilities of LLMs like ChatGPT by adding an information retrieval system like Azure AI Search which works with your data stored in data sources like Azure Database for MySQL. In a typical RAG pattern:
\n\n
\n
A sample RAG architecture for the AI solution we’ll show you how to build in this blog post appears in the following graphic:
\nIn this post, we’ll walk you through how to set up the backend data sources, indexers, and models required to build this solution. It is a detailed guide to the sample Python code hosted in our GitHub repository in a Jupyter Notebook: azure-mysql/Azure_MySQL_AI_Search_Sample at master Azure/azure-mysql.
\n\n
For a short demo of the entire process using a Magento ecommerce application, watch the following video!
\n\n
\n
\n
Before getting started, you need to ensure that the following prerequisites are in place.
\n\n
\n
Supporting semantic search in an e-commerce application that leverages Azure AI search and Azure Open AI Services in the backend requires completing the following:
\n\n
I. Set up data source connection in Azure AI Search.
\nII. Set up automatic chunking, vectorization and indexing.
\nIII. Use vector search from a sample application.
\nIV. Generate a GPT response to the user.
\nV. Test the solution in the Azure OpenAI Studio playground.
\n\n
Azure AI search pulls the contents and reviews data from a backend MySQL flexible server by using an indexer that runs periodically. The reviewed data is further chunked and vectorized using Azure OpenAI’s text embedding model. In Azure AI search, the vectorized data then persists in a vector search index.
\n\n
\n
The data source definition specifies the data to index, credentials, and policies for identifying changes in the data. The data source is defined as an independent resource so that it can be used by multiple indexers. In this example, we’ll use a custom table with product details and review data which is stored in a database in Azure Database for MySQL.
\n\n
In the Azure AI service, before creating a search Index, we’ll need to create a connection to your data source. We’ll import Azure AI classes like ‘SearchClient’, ‘SearchIndexerClient’, ‘SearchIndexerDataSourceConnection’ and their functions like “create_or_update_data_source_connection()” to setup the data source connection in Azure AI Search. We’ll also import several other models - the comprehensive list is shared in the following code sample.
\n\n
Code: “1. Set up data source connection in Azure AI Search” in the Jupyter Notebook azure-mysql/Azure_MySQL_AI_Search_Sample at master Azure/azure-mysql
\n\n
\n
We are now ready to create an Azure Database for MySQL indexer that periodically pulls the product details and reviews data from the database in Azure Database for MySQL, chunks and vectorizes the data and persists it in a vector search index.
\n\n
To do this, we’ll first create an index which takes the product details and reviews data for each product, splits the combined text into chunks and embeds each chunk as a vector. For any incoming user query, we’ll search for the most relevant chunk using vector search and semantic search.
\n\n
\n
In Azure AI Search, a search index available to the search engine for indexing, full text search, vector search, hybrid search, and filtered queries. An index is defined by a schema and is saved to the search service.
\nCode: “II. Set up automatic chunking, vectorization and indexing” -> “Create index” in the Jupyter Notebook azure-mysql/Azure_MySQL_AI_Search_Sample at master Azure/azure-mysql
\n\n
\n
We’ll create a skillset using two pre-built “skills”:
\nWe’ll then apply an Index Projector to make it so that our final index has one item for every chunk of text, rather than one item for every original row in the database.
\n\n
Code: “II. Set up automatic chunking, vectorization and indexing” -> “Create skillset” in the Jupyter Notebook azure-mysql/Azure_MySQL_AI_Search_Sample at master Azure/azure-mysql
\n\n
\n
After you define the data source and create the index, you're ready to create the indexer. The configuration of the Indexer requires the inputs, parameters, and properties that control run time behaviors.
\n\n
To create an indexer, we’ll provide the name of the data source, index and skillset that we created in the previous steps. We’ll then run the indexer at periodic intervals.
\n\n
Code: “II. Set up automatic chunking, vectorization and indexing” -> “Create indexer” in the Jupyter Notebook azure-mysql/Azure_MySQL_AI_Search_Sample at master Azure/azure-mysql
\n\n
After the indexer is created, you can use the Azure portal to view the indexer under the Indexers blade in Azure AI Search. You can also run the indexer from the Azure portal or using Azure CLI. The runs can be configured to be ad hoc or scheduled:
\n\n
\n
With the Azure AI Search indexer ready and running, you can now use the vector search and semantic search capabilities from your application. To call the search function, provide the user query text, required query parameters like the number of nearest neighbors to return as top hits, and the columns or fields in the index to be considered. Also select query type “Semantic” to include both vector search and semantic search and provide the name of semantic search configuration object that you created in section II.
\n\n
Code: “III. Use vector search from a sample application” in the Jupyter Notebook azure-mysql/Azure_MySQL_AI_Search_Sample at master Azure/azure-mysql
\n\n
For a user query like “suggest me some rain jackets for women“, this single API call performs vector search and semantic re-ranking, and returns the top ranked results as shown in the following screenshot. You’ll also see the vector search score, semantic re-ranker score, and all the details of the recommended product.
\n\n
\n
\n
To generate custom responses to the users, we need to simply make a call to the Azure OpenAI chat completion service. To trigger the completion, we first input some text as a “prompt”. The LLM in the service then generates the completion and attempts to match our context or pattern. An example prompt for this scenario could be: “You are an AI assistant that recommends products to people based on the product reviews data matching their query. Your answer should summarize the review text, include the product ID, include the parent id as review id, and mention the overall sentiment of the review.”
\n\n
To generate the GPT response, we’ll use the Azure OpenAI’s chat.completions.create() API call and supply it with the user query, the Open AI model to be used, the Azure AI Search index as data source, and the prompt.
\n\n
Code: “IV. Generate GPT Response to the user” in the Jupyter Notebook azure-mysql/Azure_MySQL_AI_Search_Sample at master Azure/azure-mysql
\n\n
\n
Finally, it’s important to test the solution in Azure Open AI Studio, a user-friendly platform that allows developers to explore cutting-edge APIs and models and to build, test, deploy, and manage AI solutions for use in websites, applications, and other production environments.
\n\n
For an end-to-end test of the AI solution in the Azure OpenAI Studio playground, perform the following steps:
\n\n
\n
Now, you’re all set to deploy this Product Recommender CoPilot AI solution to production!
\n\n
\n
If you’re running applications, such as content management systems (CMS), e-commerce applications, or gaming sites, with data hosted in Azure Database for MySQL, you can enhance your user experience by building generative AI search and chat applications using LLMs available in Azure OpenAI and vector storage and indexing provided by Azure AI Search. Unleash the power of your data hosted on MySQL with the simple and seamless AI integrations on Azure!
\n\n
If you have any queries or suggestions for more AI-related content, please let us know by contacting us at AskAzureDBforMySQL@service.microsoft.com. We’re also open to collaborating with you on technical collateral! Check out our Contributors initiative at aka.ms/mysql-contributors to learn more.
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Are you looking to enhance your MySQL applications with natural language understanding, summarization, and GenAI capabilities? This post walks you through how to create intelligent solutions with semantic search and generative AI responses using Azure Open AI and Azure Database for MySQL with Azure AI Search.
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