Enable natural language search with vectors using Azure AI Search and Azure OpenAI Service.

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Enable Natural Language Search with Vectors using Azure AI Search and Azure OpenAI Service

Enabling natural language search with vectors is a key innovation that improves the performance and accuracy of search functionalities within databases. This is made possible by leveraging Azure AI Search as your managed search index alongside Azure OpenAI Service. The combination of these two powerful tools allows you to generate vector embeddings based on defined fields in your database, which are then used to build and maintain an efficient index.

When a user inputs a prompt, it is vectorized in real time. The query engine uses this vectorized data, utilizing a mechanism similar to GPS coordinates, to find the closest similarities between the input prompt and the data stored in the database. This process significantly enhances the ability to find relevant and accurate results.

Key Components:

  1. Azure AI Search: Acts as the managed search index.
  2. Azure OpenAI Service: Utilized to generate vector embeddings.
  3. Vector Embeddings: Created based on defined database fields.
  4. Real-Time Vectorization: User prompts are vectorized as they are inputted.
  5. Query Engine: Uses vectorized data to find similarities.

Keywords

  • Natural Language Search
  • Vectors
  • Azure AI Search
  • Azure OpenAI Service
  • Vector Embeddings
  • Search Index
  • Real-Time Vectorization
  • Query Engine

FAQ

Q: What is Azure AI Search? A: Azure AI Search is a cloud-based search service by Microsoft that provides powerful and sophisticated search capabilities, acting as a managed search index for your data.

Q: How does Azure OpenAI Service contribute to natural language search? A: Azure OpenAI Service generates vector embeddings from the database fields and user input prompts, enabling nuanced and accurate search results by measuring similarities between vectors.

Q: What are vector embeddings? A: Vector embeddings are numerical representations of text data that capture semantic meaning and context, allowing for more precise and relevant search results.

Q: How does the query engine work in this setup? A: The query engine uses vectorized data to compare and find the closest similarities between the user's prompt and the indexed data, akin to navigating using GPS coordinates.

Q: What benefits does real-time vectorization offer? A: Real-time vectorization ensures that user input is immediately processed and compared to the database, providing prompt and accurate search results almost instantaneously.