> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langdock.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Azure AI Search

> Connect Azure AI Search to Langdock 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 Langdock conversations.

<Info>
  **Authentication:** API Key
  **Category:** Vector Database
  **Availability:** All workspace plans
</Info>

## 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

<Tip>
  If you are new to Azure AI Search, see Microsoft's [Vector Search documentation](https://learn.microsoft.com/en-us/azure/search/vector-search-overview) to set up your first index with vector search support.
</Tip>

## Setup

<Steps>
  <Step title="Navigate to Integrations">
    In Langdock, go to [Integrations](https://app.langdock.com/integrations) and find **Azure AI Search** in the integrations list.
  </Step>

  <Step title="Enter your credentials">
    Fill in the required configuration fields (see the [table below](#configuration-parameters)).
  </Step>

  <Step title="Test the connection">
    Save the integration — Langdock will validate that your index exists and is accessible.
  </Step>

  <Step title="Start searching">
    Tag the integration with `@` in any agent or add the **Search documents** action to your agent to search your indexed documents.
  </Step>
</Steps>

### 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                  | `langdock-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       |

<Info>
  **Where to find your credentials:**

  * **Service URL:** See [Manage your Azure AI Search service](https://learn.microsoft.com/en-us/azure/search/search-manage) — your endpoint is listed on the Overview page
  * **API Key:** See [Connect using API keys](https://learn.microsoft.com/en-us/azure/search/search-security-api-keys) — Azure Portal → Settings → Keys → generate a primary admin key
</Info>

## Common Use Cases

<CardGroup cols={2}>
  <Card title="Enterprise Knowledge Search" icon="magnifying-glass">
    Search across internal documentation, policies, and knowledge bases using natural language
  </Card>

  <Card title="Research & Analysis" icon="flask">
    Find relevant research papers, reports, and data from large document collections
  </Card>

  <Card title="Customer Support" icon="headset">
    Quickly retrieve product information, FAQs, and support articles to answer customer queries
  </Card>

  <Card title="Content Discovery" icon="lightbulb">
    Surface relevant content from archives, wikis, or document repositories
  </Card>
</CardGroup>

## 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                                                                                 |

<Tip>
  **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
</Tip>