Optimize Azure AI Search with Embedding Vector
Enhance Azure AI Search with Vector Embedding for Sharper, Relevant Results
Michael Megel has integrated his SharePoint documents into Azure AI Search and shared his experience in enhancing search quality using vector similarity search. Although Azure OpenAI initially offered acceptable results using precise keywords, it struggled with content-rich documents and multilingual queries. Megel identifies the need for an improvement in Azure AI Search's capabilities.
He points out that PDFs and PowerPoint documents create different challenges for Azure AI Search due to their content structure. PDFs often contain much more textual content, while PowerPoints have less structured information. Embedding vector or vector search could be the solution—it involves locating similar items within a dataset using their vector representations. Azure AI Search supports vector similarity search to improve search results.
To enable vector search, Megel first generates "Embeddings" for his documents by deploying a new model in Azure AI Studio. He uses REST API specifications provided by Microsoft to make an HTTP request, captures the output, and then moves to incorporate these vectors into his Azure AI Search index. An additional field for embedding information is created within the search index, aligning with model dimensions and cosine similarity computations.
Megel extends his search setup by adding skills to the indexer. These skills consist of an Embedding skill from Azure AI and a Text Split skill that chunks large documents into smaller parts. By combining these skills, he organizes content into pages and generates embeddings for each page, enhancing Azure AI Search's ability to process and retrieve relevant documents.
The integration of vector search in Azure AI Search significantly improves the chat completion results. Megel demonstrates this advancement by comparing results from queries both before and after the implementation. His tests reveal that the chatbot now provides richer responses with correct citations, even in different languages.
In summary, the embedding vector feature in Azure AI Search streamlines the incorporation of document data into Azure AI, boosting search quality. Megel added a vector field and vectorizer profile to his search index, along with predefined Azure AI Search Indexer skills. Reconfiguring Azure OpenAI Chat Completion with the new setup yielded successful results, a testament to the power of vector similarity search in enhancing Azure OpenAI's functionalities. Microsoft's embedding vector feature, though still in preview, is already making a significant impact.
General overview on Azure OpenAI and Vector Similarity Search
The integration of vector similarity search in Azure AI Search has resolved many challenges associated with content retrieval. This technology uses vectors to represent items and find others that are similar in a multi-dimensional space. It has proven to be a game changer for Azure AI Search, which greatly improves the user experience by providing more accurate and relevant search results. Users can leverage this feature for documents stored in SharePoint, significantly streamlining the process of extracting and utilizing information within an organization. With embedding vectors in place, Azure OpenAI's capabilities grow, reflecting the ever-advancing landscape of AI search solutions and enriching the user interface with quality outcomes.
People also ask about Open AI
What are embeddings in Azure OpenAI?
Embeddings in Azure OpenAI refer to numerical representations of text that capture semantic meaning and can be used to improve the performance of machine learning models, particularly in natural language processing (NLP) tasks. These representations allow for the comparison of textual similarity and are leveraged in various applications, such as semantic search, classification, and content recommendation.Does Azure Cognitive Search use vector database?
Azure Cognitive Search incorporates vector search capabilities, enabling it to perform similarity searches based on embeddings that represent complex data like text, images, or other non-scalar attributes. While not a vector database per se, Azure Cognitive Search can store and index vectors, allowing for efficient retrieval of items based on the embedded vector representations.Does Azure have a vector database?
As of my knowledge cutoff date in 2023, Azure itself is not specifically branded as offering a vector database. However, Azure services such as Azure Cognitive Search support functionalities typical of vector databases, like storing and querying data by vector embeddings, making it possible to conduct similarity searches and other operations often associated with vector databases.What is vector search in Azure?
Vector search in Azure refers to the capability of Azure Cognitive Search to execute searches based on vector embeddings. It enables users to perform queries that return results based on the similarity of the embeddings, which is useful for finding similar items in a dataset, such as similar text documents, images, or other complex objects that can be represented as vectors. This approach goes beyond traditional keyword search to understand the semantic context of the query and the content.Keywords
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