We use cookies to enhance your experience and measure how the site performs. Choose "Essential Only" to disable analytics. Read our Privacy Policy.

    Odeus Docs

    Azure AI Search

    Connect Azure AI Search to Odeus to run semantic vector searches over your indexed documents

    Azure AI Search

    Connect Azure AI Search to Odeus to run semantic vector searches over your indexed documents

    Overview

    Azure AI Search is Microsoft's AI-powered information retrieval platform. Once connected, you can perform semantic vector searches across your indexed documents directly from Odeus conversations.

    Authentication: API Key Category: Vector Database Availability: All workspace plans

    Prerequisites

    Before setting up the integration, make sure you have:

    • An Azure subscription with access to Azure AI Search
    • An Azure AI Search service instance with at least one index
    • An admin API key for your Azure AI Search service
    • Documents uploaded to your index with vector embeddings

    If you are new to Azure AI Search, see Microsoft's Vector Search documentation to set up your first index with vector search support.

    Setup

    In Odeus, go to [Integrations](https://app.odeus.ai/integrations) and find **Azure AI Search** in the integrations list.
    
    
    
    Fill in the required configuration fields (see the [table below](#configuration-parameters)).
    
    
    
    Save the integration — Odeus will validate that your index exists and is accessible.
    
    
    
    Tag the integration with `@` in any agent or add the **Search documents** action to your agent to search your indexed documents.
    

    Configuration Parameters

    Required Fields

    FieldDescriptionExample
    NameA name for this connectionCompany Knowledge Base
    API KeyAzure Portal → Settings → Keys → Generate a primary admin keyYour admin key
    Index NameThe exact name of your Azure AI Search indexodeus-prod-company
    URLYour Azure AI Search service endpointhttps://my-service.search.windows.net
    Search FieldThe vector field name in your index schemacontentVector
    Top KNumber of search results to retrieve5

    Optional Fields

    FieldDescriptionDefault
    Embedding ModelModel used for embeddings (display only)Ada v2
    SelectComma-separated fields to returnAll fields
    FilterOData filter expression to narrow resultsNone

    Where to find your credentials:

    Common Use Cases

    • Enterprise Knowledge Search — Search across internal documentation, policies, and knowledge bases using natural language

    • Research & Analysis — Find relevant research papers, reports, and data from large document collections

    • Customer Support — Quickly retrieve product information, FAQs, and support articles to answer customer queries

    • Content Discovery — Surface relevant content from archives, wikis, or document repositories

    Troubleshooting

    IssueCauseSolution
    Index not foundIndex name mismatch or doesn't existVerify the exact index name in Azure Portal matches your configuration (case-sensitive)
    No search resultsVector field name incorrect or filter preventing outputVerify the vector field name matches your index schema, and check that any OData filter expression is not excluding all results
    Low search scoresEmbedding model mismatchEnsure all documents use the same embedding model (e.g., text-embedding-ada-002)
    Authentication failedInvalid or expired API keyCopy a fresh admin key from Azure Portal → Keys

    Validation checklist

    • Service URL format: https://[service-name].search.windows.net
    • Index name matches exactly (case-sensitive)
    • Search field matches your vector field name (e.g., contentVector)
    • Documents contain vector embeddings