Azure Semantic Search: A Comprehensive Guide to Implementation

Azure Semantic Search is a powerful tool that enables you to search and retrieve specific data from your Azure Storage account.

Azure Semantic Search is built on top of Azure Search, which means you can leverage the scalability and reliability of Azure Search to power your semantic search capabilities.

To get started with Azure Semantic Search, you'll need to create an Azure Search service in the Azure portal. This service will act as the foundation for your semantic search implementation.

With Azure Semantic Search, you can store and retrieve data in a variety of formats, including JSON, CSV, and XML.

Azure semantic search is a powerful tool that leverages advanced techniques to enhance the retrieval of information by understanding the context and intent behind queries.

It utilizes embeddings, which represent concepts as vectors in a high-dimensional space, allowing for a more nuanced understanding of relationships between terms.

This approach enables Azure semantic search to provide more accurate and relevant results, making it a valuable asset for businesses and organizations.

By leveraging embeddings, Azure semantic search can understand the subtleties of language and provide results that are more tailored to the user's needs.

Semantic search in Azure is designed to improve the overall search experience, making it easier to find what you're looking for.

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Azure semantic search is a powerful tool that leverages advanced techniques to enhance information retrieval by understanding the context and intent behind queries.

Embeddings are crucial for creating a semantic memory that mimics human cognitive processes, representing words and phrases as vectors to discern similarities and differences based on their spatial relationships.

Contextual understanding is key to semantic search, as it interprets the meaning behind queries, enabling it to return more relevant results. For instance, a search for "Microsoft document types" would yield results related to both Word and Excel.

Azure's semantic search utilizes embeddings, which represent concepts as vectors in a high-dimensional space, allowing for a more nuanced understanding of relationships between terms.

Here's a brief overview of the key concepts:

By understanding these key concepts, you can unlock the full potential of Azure semantic search and improve your information retrieval capabilities.

When choosing an implementation strategy for Azure Semantic Search, you have two main options: pre-trained models and custom models. Pre-trained models are ready to use and have been trained on extensive datasets, making them suitable for general applications.

You can choose between pre-trained models and custom models based on the complexity of your application. If you're dealing with specialized vocabularies, custom models can be trained to better understand domain-specific language, enhancing the accuracy of search results.

Here's a brief overview of the two options:

By choosing the right implementation strategy, you can effectively leverage Azure Semantic Search to improve the accuracy and relevance of your search results.

Developers have two main options when it comes to implementing semantic functions: pre-trained models and custom models. Pre-trained models are ready to use and have been trained on extensive datasets, making them suitable for general applications.

Pre-trained models are a great choice for most use cases, but if you're dealing with specialized vocabularies, you might want to consider custom models. Custom models can be trained to better understand domain-specific language, enhancing the accuracy of search results.

Custom models require more effort and resources to develop, but they can provide better results when dealing with unique language patterns. For example, if you're building a search engine for a specific industry, a custom model can be trained to understand the industry's terminology and jargon.

Here's a brief overview of the two options:

Ultimately, the choice between pre-trained models and custom models depends on your specific use case and requirements. If you're looking for a quick and easy solution, pre-trained models might be the way to go. But if you need more precise results, custom models are worth considering.

To implement the OpenAI Embeddings, you'll need to import the required libraries. OpenAIEmbeddings is assumed, but if you're using Azure OpenAI, import AzureOpenAIEmbeddings instead.

The choice of library depends on your specific setup and needs. Azure OpenAI Embeddings can be used in place of OpenAI Embeddings.

Curious to learn more? Check out: Azure Search Openai Demo

Creating embeddings and storing them effectively is a crucial step in implementing semantic functions. You can choose between using pre-trained embedding models or training custom models tailored to specific domains.

To create embeddings, you'll need to create instances of the OpenAIEmbeddings and AzureSearch classes. This will give you an empty search index on your Azure AI Search resource, with a default schema provided by the integration module.

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You'll also need to create a vector store instance, which is done by creating an instance of the AzureSearch class using the embeddings from above.

Here's a summary of the steps involved:

By following these steps, you'll be able to create embeddings and store them in a vector store instance, which is a fundamental part of implementing semantic functions.

Performing tasks with Azure Cognitive Search involves several key steps. To execute hybrid search, you can use the search_type or hybrid_search() method, which queries vector and nonvector text fields in parallel and returns the top matches of the unified result set.

You can also create a vector store instance, which is a crucial step in implementing semantic functions with Azure's Semantic Kernel. This is done by creating an instance of the AzureSearch class using the embeddings from above.

In addition to these steps, you can perform a variety of other tasks, such as querying your Cognitive Search service by setting up a SearchClient and defining your query using a SearchOptions object.

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To get the most out of Azure Cognitive Search, it's essential to understand its capabilities and how to leverage them to fulfill user requests, known as ASKs. By doing so, you can unlock the full potential of the service and create customized alert and data analysis systems that provide valuable insights into your business.

Custom schemas allow you to define the structure of your data, which is helpful in hybrid search scenarios. This can be done by replacing the default schema with a custom schema.

You can create a new index with a scoring profile, which is used for relevance tuning of nonvector content. For example, you can create a custom schema that includes a scoring profile definition, which is helpful in hybrid search scenarios.

Custom schemas can be used to define the structure of your data, including the fields and their data types. You can use a custom schema to replace the default schema, which can be done by executing a cell.

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To execute a cell, you should get an empty index on Azure AI Search. This is because there's no data for this schema in this step.

You can add data to the vector store based on the custom schema. For example, you can load text into the title and source fields, where the source field is filterable.

Custom schemas can be used to define the structure of your data, including the fields and their data types. You can use a custom schema to replace the default schema, which can be done by executing a cell.

You can query your Cognitive Search service using a custom schema. For example, you can create a SearchClient that will allow you to make queries against your indexes.

Custom schemas allow you to define the structure of your data, which is helpful in hybrid search scenarios. This can be done by replacing the default schema with a custom schema.

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Provisioning with Bicep is a great way to get started with Azure Cognitive Search. You can provision a Cognitive Search service using the Microsoft.Search/searchServices namespace, which is what we'll be doing in this tutorial.

The free tier of Cognitive Search is a good place to start, as it allows you to explore some of the functionality without breaking the bank. There are limitations to what you can do with the free tier, but it's perfect for dev/test purposes.

To provision a Cognitive Search service, you'll need to deploy a storage account with a blob container called hotel-rooms. This will be used to store the data that we'll be uploading to Cognitive Search.

There are two models for loading data into a search index: Push Model and Pull Model. The Push Model provides the most flexible approach for pushing data into Cognitive Search, giving you full control over connectivity and secure retrieval of documents.

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Here are the two models compared:

To deploy the Cognitive Search service and storage account, run the following commands in your terminal: