Azure AI Search
Connect Azure AI Search to ajan 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 ajan conversations.
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
Setup
Navigate to Integrations
In ajan, go to Integrations and find Azure AI Search in the integrations list.
Enter your credentials
Fill in the required configuration fields (see the table below).
Test the connection
Save the integration — ajan will validate that your index exists and is accessible.
Start searching
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
| Field | Description | Example |
|---|---|---|
| Name | A name for this connection | Company Knowledge Base |
| API Key | Azure Portal → Settings → Keys → Generate a primary admin key | Your admin key |
| Index Name | The exact name of your Azure AI Search index | ajan-prod-company |
| URL | Your Azure AI Search service endpoint | https://my-service.search.windows.net |
| Search Field | The vector field name in your index schema | contentVector |
| Top K | Number of search results to retrieve | 5 |
Optional Fields
| Field | Description | Default |
|---|---|---|
| Embedding Model | Model used for embeddings (display only) | Ada v2 |
| Select | Comma-separated fields to return | All fields |
| Filter | OData filter expression to narrow results | None |
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
| Issue | Cause | Solution |
|---|---|---|
| Index not found | Index name mismatch or doesn't exist | Verify the exact index name in Azure Portal matches your configuration (case-sensitive) |
| No search results | Vector field name incorrect or filter preventing output | Verify the vector field name matches your index schema, and check that any OData filter expression is not excluding all results |
| Low search scores | Embedding model mismatch | Ensure all documents use the same embedding model (e.g., text-embedding-ada-002) |
| Authentication failed | Invalid or expired API key | Copy a fresh admin key from Azure Portal → Keys |