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A Comprehensive Guide to Integrate Azure AI Search to Azure OpenAI

BankTanapat
Employee
Employee

yesterday

The Retrieval-Augmented Generation (RAG) pipeline has gained significant traction in recent years. Large Language Models (LLMs) leverage domain-specific knowledge through the RAG mechanism to generate insightful and contextually relevant responses. Implementing a RAG pipeline requires a well-architected infrastructure, including vector databases and a data ingestion pipeline to efficiently transfer information from source systems to the database.

Azure offers Azure AI Search, a fully managed RAG solution that simplifies implementation and reduces operational complexity. As an enterprise-grade information retrieval system, Azure AI Search processes heterogeneous content, indexes data for optimized retrieval, and delivers relevant information through queries and applications, and it is the recommended retrieval system for developing RAG-based applications on Azure. It features native LLM integrations with Azure OpenAI Service and Azure Machine Learning, supports custom model integration mechanisms, and offers multiple relevance-tuning strategies to enhance search effectiveness.

To further streamline RAG implementation, SnapLogic facilitates seamless integration between Azure AI Search and Azure OpenAI, enabling organizations to build intelligent and efficient AI-powered applications. The following guideline outlines the steps required to achieve this integration.

Basic of Using Azure AI Search

The following steps provide a structured approach to setting up and utilizing Azure AI Search for indexing and querying data.

Step 1: Set Up Azure AI Search

  1. Subscribe to Azure AI Search via the Azure portal.
  2. Import Data:
    • Click "Import Data" and select the data source you want to integrate.
      BankTanapat_0-1741186491872.png

       

  3. Create an Index:
    • Define an index on the data source.
    • Ensure the field you want Azure AI Search to process has the searchable attribute enabled.
      BankTanapat_1-1741186491867.png
  4. Configure the Indexer:
    • Complete the setup wizard to create an indexer.
    • Once the indexer is created, your Azure AI Search instance is ready to use.

 

Step 2: Configure the Azure Data Source AI Snap

BankTanapat_6-1741186632594.png

To enable seamless integration between Azure AI Search and Azure OpenAI, follow these steps to configure the Azure Data Source AI Snap in Snaplogic Designer:

BankTanapat_7-1741186632593.png

  1. Configure the Snap Account
    • Set up the Azure Data Source AI Snap by configuring the associated account.

  2. Provide the AI Search Endpoint
    • Enter the Azure AI Search endpoint to the snap settings.

  3. Specify the Index Name
    • Define the index name that will be used for searching.

  4. Field Mapping (Optional)
    • An array of values.
    • A comma-separated string.
    • Users can optionally provide a field mapping configuration to define relationships between different fields.
    • Specify which field represents the title and which field contains the content.
    • The content field can be either:
    • The title field must be a string.

  5. Set the Query Type (Optional)
    • The default query type is set to "simple".
    • Users can modify this setting as needed or retain the default value.

  6. Connect to Azure Chat Completion
    • Once configured, connect the Data Source AI Search Snap to Azure ChatCompletion to enable intelligent query responses using Azure OpenAI.

Step 3: Configure Azure OpenAI Chat Completion

To complete the integration and enable AI-powered responses, follow these steps to configure Azure OpenAI Chat Completion:

BankTanapat_8-1741187596160.png

  1. Configure the Data Source Field
    • Use the output of the Azure Data Source AI Search Snap as the input for Azure OpenAI Chat Completion.
  2. Provide the Prompt
    • Define the prompt that you want to use for querying the AI model.
    • The prompt should be designed to leverage the retrieved data effectively.
  3. Execute the Pipeline
    BankTanapat_9-1741187596206.png
    • Run the pipeline to process the query.
    • The ChatCompletion Snap will generate responses based on Azure AI Search results.
    • The output will include a "citations" field, indicating the source of the retrieved information.

Using Vector Queries in Azure AI Search

Step 1: Set Up Azure AI Search

  1. Subscribe to Azure AI Search
    • Access the Azure Portal and create an Azure AI Search service.
  2. Import and Vectorize Data
    BankTanapat_10-1741187737977.png
    • Click "Import Data and Vectorize Data" and select the data source to be integrated.
  3. Embed Data into Vectors (if applicable)
    BankTanapat_11-1741187737967.png
    • To enable vector search, data must be converted into vector embeddings using an embedding model.
    • If your dataset already contains vectorized data, you can integrate it directly without re-vectorizing.
  4. Verify Index Creation
    BankTanapat_12-1741187737999.png
    • After completing the index setup, the vector field will be visible in the index schema.

Step 2: Configure the Azure Data Source AI Snap

BankTanapat_13-1741187737653.png

To enable seamless integration between Azure AI Search and Azure OpenAI, configure the Azure Data Source AI Snap by following these steps:

BankTanapat_14-1741187737986.png

 

  1. Configure the Snap Account
    • Set up the Azure Data Source AI Snap by configuring the associated SnapLogic account.
  2. Provide the Azure AI Search Endpoint
    • Enter the Azure AI Search endpoint to establish the connection.
  3. Specify the Index Name
    • Define the index name that will be used for vector-based searching.
  4. Configure Field Mapping (Optional)
    • Users can define field mappings to specify relationships between different fields.
    • Assign a title field and a content field:
      • The content field can be:
        • A list (array) of values.
        • A comma-separated string.
      • The title field must be a string.
    • For vector-based queries, specify the vector field to inform Azure AI Search which field to use for vector comparisons.
      • The vector field can be:
        • A string.
        • A list of strings.
        • A comma-separated string.
  5. Set the Query Type
    • Specify the query type as "vector" to enable vector-based searches.
  6. Connect to Azure Chat Completion
    • Once configured, connect the Azure Data Source AI Search Snap to Azure ChatCompletion to enable AI-powered responses using Azure OpenAI.

Step 3: Configure Azure OpenAI Chat Completion

To complete the integration and enable AI-powered responses, follow these steps to configure Azure OpenAI Chat Completion:

BankTanapat_16-1741187938277.png

  1. Configure the Data Source Field
    • Use the output of the Azure Data Source AI Search Snap as the input for Azure OpenAI Chat Completion.
  2. Set Up the Embedding Model Dependency
    • Ensure that the same embedding model used to vectorize the data is referenced in Azure OpenAI.
    • This step is crucial for accurate vector similarity comparisons and retrieval performance.
  3. Provide the Prompt
    • Define the prompt that will be used for querying the AI model.
    • Ensure the prompt is structured to effectively leverage retrieved vector-based data for optimal AI responses.

Using Semantic Queries in Azure AI Search

Step 1: Set Up Azure AI Search

BankTanapat_17-1741187996490.png

 

  1. Access Azure AI Search
    • Navigate to the Azure AI Search service in the Azure Portal.
  2. Select the Index
    • Choose the index you want to use for semantic search.
  3. Create a Semantic Configuration
    • Define a new semantic configuration for the selected index.
  4. Configure Semantic Fields
    • Specify the required fields:
      • Title Field – Represents the document title.
      • Content Field – Contains the main body of the document.
      • Keywords Field – Includes key terms for enhanced semantic matching.
  5. Save the Configuration
    • Once all fields are assigned, save the configuration.
    • Your index is now ready for semantic search.

Step 2: Configure the Azure Data Source AI Search Snap

BankTanapat_18-1741187996488.png

  1. Change the Query Type
    • Set the query type to "semantic" to enable semantic search capabilities.
  2. Specify the Semantic Configuration
    • Enter the semantic configuration name created in Azure AI Search.
  3. Connect to Azure OpenAI Chat Completion
    • Link the Azure Data Source AI Search Snap to Azure OpenAI ChatCompletion.
    • This integration allows semantic search to enhance the accuracy and relevance of AI-generated responses.

Customizing Search Results in Azure AI Search

To further refine and enhance search accuracy and relevance, Azure AI Search allows users to customize their search queries with hybrid query types and filters.

Hybrid Query Types

Azure AI Search supports hybrid search, which allows combining different query types to improve search results:

  1. Hybrid of Vector and Simple Queries
    • This combines vector-based similarity with traditional keyword-based search, ensuring both semantic relevance and text-based keyword matching.
  2. Hybrid of Vector and Semantic Queries
    • This approach enhances vector similarity search with semantic ranking, enabling context-aware results with better relevance scoring.

To enable hybrid search:

  • Set the query type to either:
    • "vector_simple_hybrid"
    • "vector_semantic_hybrid"
  • This ensures search results are a blend of the two selected query types.

Applying Search Filters

Filters help narrow down search results to match specific conditions or constraints.

Steps to Apply Filters:

  1. Define a Filter Condition

BankTanapat_19-1741187996461.png

  1. Ensure Index Fields are Filterable
    • Filters only work if the index fields have the filterable attribute enabled.
    • Before applying filters, verify that the selected index supports filtering.
      BankTanapat_20-1741187996462.png

       

  2. Integrate the Filter in Your Query
    • Apply custom filters to refine search results based on your requirements.

Conclusion

Integrating Azure AI Search with Azure OpenAI unlocks powerful capabilities for retrieval-augmented generation (RAG), enabling organizations to build intelligent, AI-powered applications with enhanced search functionality. By leveraging vector, semantic, and hybrid search queries, businesses can optimize information retrieval and improve the relevance of AI-generated responses.

This guide has outlined the key steps to:

  • Set up Azure AI Search, including configuring vector and semantic search.
  • Integrate Azure Data Source AI Search Snap, enabling seamless data retrieval.
  • Configure Azure OpenAI Chat Completion, ensuring AI-generated responses are contextually aware and accurate.
  • Customize search results using hybrid search queries and filtering mechanisms to refine and enhance query outcomes.

By following these steps, organizations can maximize the effectiveness of Azure AI Search and OpenAI, improving search relevance, accuracy, and AI-driven insights for a wide range of applications. With scalability, flexibility, and advanced AI integration, this solution is ideal for businesses looking to deploy cutting-edge enterprise search and AI-driven automation.

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