Supercharge Your Apps with Semantic Search

Table of Contents

1. Introduction
2. State of the Art Search Capabilities in Custom Applications
   1. Semantic Search for Ranking Top Search Results
   2. Machine Reading and Comprehension for Surface Answers
3. Azure Cognitive Search: An Overview
4. Integration of Cognitive Search into Applications
   1. Customer-Facing Mobile Apps
   2. Ecommerce Sites
   3. Line of Business Apps
5. The Collaboration Between Azure and Bing Teams
6. Data Ingestion in Cognitive Search
   1. Ingesting Data from Azure Data Sources
   2. Pushing Data to the Search Index
7. Enriching and Extracting Information from Different Formats
   1. Machine Learning Techniques for Information Extraction
   2. Custom Skills and Models
8. Cognitive Search with Cognitive Services
   1. Understanding the Content that is Indexed
   2. Enhancing Search with Cognitive Services
9. Example of Intelligent Search with Azure Cognitive Search
   1. Extracting Insights from John F Kennedy Files
   2. Uncovering Unseen Information with Azure Cognitive Search
10. Introducing the New Semantic Search Capabilities
    1. Semantic Relevance
    2. Captions
    3. Answers
11. Enabling Semantic Search in Applications
    1. Demo Application with "msmarco" Dataset
    2. Improving Search Results with Semantic Search
    3. Adding Caption and Answer Features
12. Ingesting and Enriching Data in Cognitive Search
    1. Creating a Cognitive Search Service
    2. Importing Data from Azure Stores
    3. Enabling Cognitive Services and Custom Models for Data Enrichment
    4. Customizing the Index Definition
    5. Setting Up Indexer Options
13. Running Searches with Cognitive Search
    1. Using Client Libraries or HTTP API
    2. Performing Keyword Search
    3. Exploring Semantic Search Results
    4. Enabling Captions and Answers
14. The Complexity Behind Azure Cognitive Search
    1. Data Science and Custom Ranking Models
    2. Efficient Infrastructure for Running Models
15. Improvements in Recall and Precision for Search Results
    1. Spelling Correction for Document Recall
    2. Scoring Approach for Retrieving Top Candidates
    3. Focus on Relevant Parts of Documents Using Transformer Models
16. Leveraging Natural Language Processing for Semantic Search
    1. Mapping Words to High Dimensional Vector Space
    2. Understanding Sentences and Paragraphs with Transformers
    3. Specialized Models for Summarization and Comprehension
17. Optimizing Model Performance for Fast Search Results
    1. Reducing Parameter Count for Faster Execution
    2. Deploying Models on GPUs in Azure
    3. Parallelizing Scoring Operations to Speed Up Ranking
18. Conclusion
19. Resources for Learning More

State of the Art Search Capabilities in Custom Applications

In this article, we will explore how state-of-the-art search capabilities can be integrated into custom applications and content management systems. We will discuss the latest enhancements in semantic search for ranking top search results and how machine reading and comprehension can be utilized to surface answers to search queries.

Introduction

During this special edition of Microsoft Mechanics, Distinguished Engineer Pablo Castro joins us to showcase the capabilities of bringing advanced search functionalities to custom applications. We will Delve into the world of Azure Cognitive Search, a platform that enables the integration of sophisticated search capabilities into various applications. With this platform, businesses like Howden, a large industrial manufacturer, can leverage the power of search to quickly delve into the details of customer equipment requests and provide accurate bids. Azure Cognitive Search allows for seamless ingestion, enrichment, and exploration of both structured and unstructured data. This article will guide You through the process and advancements in semantic search, as well as the collaborative efforts between Azure and Bing teams.

Azure Cognitive Search: An Overview

Azure Cognitive Search is a Platform-as-a-Service (PaaS) solution available on Azure that empowers developers to incorporate robust search capabilities into their applications. It brings together the best work from Microsoft in the realms of search and artificial intelligence, making these capabilities accessible to a broader audience. With the collaboration between Azure and Bing teams, Azure Cognitive Search has steadily evolved and integrated numerous advancements over the years. This article will demonstrate how Azure Cognitive Search can be utilized to enhance search capabilities in custom applications.

Integration of Cognitive Search into Applications

Azure Cognitive Search can be seamlessly integrated into various applications, including customer-facing mobile apps, ecommerce sites, and line of business apps. Through its flexible and adaptable architecture, developers can easily leverage the functionalities of Azure Cognitive Search to provide superior search experiences for their users.

The collaboration between Azure and Bing teams has resulted in significant advancements in delivering state-of-the-art search capabilities. By combining the learnings from Microsoft Research and extensive partnership with the Bing Team, Azure Cognitive Search has evolved to offer enhanced search functionalities. These advancements enable developers to Create powerful, customized search experiences in their applications, bringing the benefits of intelligent search to their users.

Data Ingestion in Cognitive Search

Data ingestion is the initial step in the search process, allowing you to bring in data from various sources for indexing and search. Azure Cognitive Search offers multiple options for data ingestion, including automatic data pulling from Azure data sources and data pushing via the push API. By supporting diverse ingestion methods, developers can seamlessly integrate their data pipelines with Azure Cognitive Search to ensure a continuous flow of updated information.

Enriching and Extracting Information from Different Formats

Real-world data comes in various formats, ranging from structured records to lengthy text documents and even images. To make the most of this varied data, Azure Cognitive Search employs machine learning techniques to extract and index information from different formats. These techniques enable the search engine to uncover the latent structure within the data, extracting key phrases, tagging images, detecting language, locations, and organization names.

Additionally, Azure Cognitive Search allows developers to bring in their custom skills and models, further enhancing the search capabilities to suit their specific requirements. This combination of cognitive search with cognitive services makes the search engine smart enough to understand the Context and content it is indexing.

Cognitive Search with Cognitive Services

The integration of Azure Cognitive Search with cognitive services opens up a realm of possibilities for developers. By combining the capabilities of cognitive search with cognitive services, developers can leverage additional functionalities like natural language processing, sentiment analysis, image recognition, and more. This integration enables the search engine to better understand the content it is indexing, leading to more relevant and contextual search results.

Moreover, developers can integrate their custom-built cognitive services and models into Azure Cognitive Search, tailoring the search capabilities to their specific needs. This flexibility empowers developers to create unique search experiences that go beyond conventional search engines, delivering more personalized and nuanced results.

Example of Intelligent Search with Azure Cognitive Search

One notable example of intelligent search powered by Azure Cognitive Search is the exploration of John F. Kennedy files. These files, comprising of handwritten notes, photos, and Typed documents, were processed using Azure Cognitive Search to uncover valuable insights. By ingesting the data and applying semantic search capabilities, the search engine was able to identify Patterns, connections, and insights that were previously Hidden. This demonstrates the potential of Azure Cognitive Search to unlock valuable knowledge and information from diverse content sources.

Introducing the New Semantic Search Capabilities

The latest announcement in Azure Cognitive Search introduces the new Semantic Search capabilities, including semantic relevance, captions, and answers. These enhancements aim to improve search precision and user experience by enabling the search engine to understand the contextual meaning of search queries.

Semantic relevance allows the search engine to go beyond exact matching of keywords. Instead, it focuses on understanding the context and meaning of words, ensuring that search results are relevant and aligned with the user's intent. Captions provide Meaningful contextual descriptions of search results, enhancing the user's understanding of the search outcomes. Answers, generated through machine reading and comprehension, provide direct answers to user queries without the need for navigating to external pages.

Enabling Semantic Search in Applications

Enabling Semantic Search in applications is straightforward and requires minimal effort. Using a demo application powered by the "msmarco" dataset for evaluation purposes, developers can directly experience the capabilities of Semantic Search. By searching for queries like "What is the capital of France," developers can witness the difference between keyword search and semantic search in terms of relevance and precision. With the introduction of semantic captions and answers, search results become more informative and concise, enhancing the overall user experience.

Adding Semantic Search capabilities to applications involves simple API calls or utilizing client libraries and HTTP APIs. Since this option does not require re-indexing, existing applications can easily leverage the power of Semantic Search without significant modifications. This seamless integration allows developers to enhance their applications with intelligent search features, bringing the benefits of web search engines to their own datasets.

Ingesting and Enriching Data in Cognitive Search

To begin utilizing Azure Cognitive Search, developers first need to create a Cognitive Search service. This service allows for the importing and ingestion of data from various Azure stores, such as blob storage accounts. Additionally, developers can enable cognitive services like optical character recognition (OCR), entity extraction, and computer vision to enrich the ingested data. This enrichment process enhances the search capabilities by extracting valuable information from the ingested data.

Developers also have the flexibility to customize the index definition and set up indexer options according to their specific requirements. This allows for fine-tuning the search engine and tailoring it to the unique characteristics of the data being indexed. Once the ingestion process is set up, the search engine automatically detects changes, enriches the data, and pushes it to the index for seamless indexing and search capabilities.

Running Searches with Cognitive Search

Running searches with Azure Cognitive Search can be accomplished through client libraries or the HTTP API. These methods provide developers with the flexibility to perform keyword search, semantic search, and retrieve relevant search results. By sending HTTP requests and utilizing the Search API, developers can explore the vast capabilities of Azure Cognitive Search. Developers can observe the differences in search results between keyword search and semantic search, witnessing the improved relevance and precision offered by semantic search.

The Complexity Behind Azure Cognitive Search

Behind the scenes, Azure Cognitive Search handles the complexity of data science and infrastructure management. The platform provides ready-to-use ranking models without the need to build custom models from scratch. By leveraging the extensive computing power and infrastructure of Azure, Azure Cognitive Search ensures fast and efficient execution of ranking and machine learning models. This helps deliver high-quality search results while managing the resource-intensive nature of these models.

Improvements in Recall and Precision for Search Results

Traditional keyword search relies on an inverted index to retrieve documents Based on the presence of specific words or search terms. While effective, this approach has limitations in terms of exact matching and ranking based on word frequencies. To address these limitations, Azure Cognitive Search introduces improvements in recall and precision.

By incorporating spelling correction services, document recall is enhanced, ensuring that relevant documents are not missed due to minor spelling errors. Additionally, a sophisticated scoring approach is used to retrieve the top candidates from the search index, maintaining efficient performance even when dealing with millions of potential candidates. Furthermore, machine learning models, such as transformers, enable the search engine to understand the semantic similarity of words, sentences, and paragraphs. This understanding improves the precision and relevance of search results, providing a more nuanced understanding of language and context.

Leveraging Natural Language Processing for Semantic Search

To achieve semantic search capabilities, Azure Cognitive Search harnesses the advancements in natural language processing (NLP). NLP techniques enable the search engine to map words to vector representations in high-dimensional vector spaces. These representations capture the relationships and conceptual similarities between words, even if they do not share lexical or spelling similarities. This mapping facilitates understanding the contextual meaning and nuance of words, enhancing the search capabilities.

Transformers, a Novel neural network architecture, are utilized to understand the semantic similarity of not only words but also sentences and paragraphs. Transformers employ Attention mechanisms to capture long-range dependencies within text, enabling a deeper understanding of the contextual relationships between words. Microsoft's Turing family of models serves as the foundation for Azure Cognitive Search, which is then specialized using domain-specific training data from Bing. This combination of techniques empowers the search engine to contextualize search queries and provide more accurate and relevant results.

Optimizing Model Performance for Fast Search Results

Running large-Scale models for ranking and machine reading can be computationally and memory-intensive, resulting in slow search results. To optimize performance, Azure Cognitive Search employs model distillation and retuning techniques. These techniques reduce the model's parameter count, enabling faster execution without compromising the quality of search results.

Models are deployed on GPUs in Azure to leverage their Parallel processing capabilities. By parallelizing scoring operations across multiple GPUs, the search engine achieves fast and efficient execution of ranking models. Azure Cognitive Search optimizes the infrastructure and resource allocation to deliver high-performance search capabilities, ensuring that search results are delivered quickly without sacrificing quality.

Conclusion

Azure Cognitive Search brings powerful search capabilities to custom applications and content management systems. With its state-of-the-art features, such as semantic search, captions, and answers, developers can enhance the search experience and provide users with precise and relevant results. The integration of cognitive services further extends the search capabilities, enabling developers to leverage natural language processing, sentiment analysis, image recognition, and more. By utilizing Azure Cognitive Search, developers can unlock the potential of their data and deliver intelligent search experiences to their users.

Resources for Learning More

To learn more about Azure Cognitive Search and its capabilities, you can sign up for the public preview of Semantic Search here. Additionally, you can access more guidance on how to get started with Azure Cognitive Search here.

Highlights

• Azure Cognitive Search empowers developers to integrate advanced search capabilities into custom applications.
• The collaboration between Azure and Bing teams brings together the best work in search and artificial intelligence for Azure Cognitive Search.
• Data ingestion in Azure Cognitive Search allows for the integration of various data sources to enable comprehensive search capabilities.
• Cognitive Search with Cognitive Services enhances search results by incorporating natural language processing and cognitive functionalities.
• Semantic Search capabilities in Azure Cognitive Search improve search precision and relevance by understanding the contextual meaning of search queries.
• Developers can easily enable Semantic Search in their applications using simple API calls.
• Azure Cognitive Search optimizes model performance to deliver fast search results without compromising quality.

FAQ

Q: What is Azure Cognitive Search?

Azure Cognitive Search is a Platform-as-a-Service solution available on Azure that allows developers to integrate advanced search capabilities into their applications and content management systems.

Q: How does Semantic Search work in Azure Cognitive Search?

Semantic Search in Azure Cognitive Search uses state-of-the-art machine learning models to understand the context and meaning of search queries, enabling more relevant and precise search results.

Q: Can I integrate custom cognitive services and models with Azure Cognitive Search?

Yes, Azure Cognitive Search allows developers to bring in their custom-built cognitive services and models, enhancing the search capabilities to suit their specific requirements.

Q: Is Azure Cognitive Search scalable?

Yes, Azure Cognitive Search is designed to scale seamlessly to handle large volumes of data and deliver fast search results.

Q: What are some examples of applications that can benefit from Azure Cognitive Search?

Azure Cognitive Search can be integrated into various applications such as customer-facing mobile apps, ecommerce sites, and line of business apps to enhance search capabilities and provide a more personalized user experience.