# azure-search-documents-py

Teaches your agent to write Azure AI Search code in Python: vector and hybrid search, semantic ranking, index setup.

- skill · `type=skill` · Claude Code, GitHub Copilot · 2 installs · ★ 3148
- Author: Microsoft
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-python/skills/azure-search-documents-py/SKILL.md
- Updated: 2026-10-23
- HTML: https://affpapa.org/en/nevibecode/skills/azure-search-documents-py

## What it does

The skill hands the agent working snippets for the `azure-search-documents` library: building an index with a vector field, uploading documents, keyword search, vector search, hybrid search, semantic ranking. Three clients are covered — `SearchClient` for search and documents, `SearchIndexClient` for index management and synonym maps, `SearchIndexerClient` for indexers, data sources and skillsets.

Authentication gets its own section. The advice is to prefer `DefaultAzureCredential`: it works locally through Azure CLI, VS Code or Developer CLI and in Azure through managed identity, with no code change. In production you set `AZURE_TOKEN_CREDENTIALS=prod` to constrain the credential chain. `AzureKeyCredential` stays only for existing keyed deployments that have not moved to Entra ID yet.

Two variables are required before anything runs: `AZURE_SEARCH_ENDPOINT` and `AZURE_SEARCH_INDEX_NAME`. `AZURE_SEARCH_API_KEY` matters only on the legacy key path. Every client should sit inside a context manager, so HTTP transports, sockets and token caches are released deterministically — skip that and long-running processes hold connections open.

## Requirements
- Works with: Azure

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add microsoft/skills --skill azure-search-documents-py -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/azure-search-documents-py/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/azure-search-documents-py/

### Codex

**Manually: put the files in place**
- File: `.agents/skills/azure-search-documents-py/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

**Manually: put the files in place**
- File: `.cursor/skills/azure-search-documents-py/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install microsoft/skills azure-search-documents-py
```

2. **Manually: put the files in place**
- File: `.github/skills/azure-search-documents-py/SKILL.md`

### Gemini CLI

**Platform CLI**
```bash
gemini skills install https://github.com/microsoft/skills --path .github/plugins/azure-sdk-python/skills/azure-search-documents-py
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/azure-search-documents-py/SKILL.md` — The legacy .windsurf/skills/ path still works

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-python/skills/azure-search-documents-py/SKILL.md

## When to use it
- create an Azure AI Search index with a vector field and HNSW profile
- upload documents with embeddings via `upload_documents`
- build a hybrid query from text plus a vector
- turn on semantic ranking and read captions from results
- migrate code from an API key to `DefaultAzureCredential`

## Example prompts
- show me how to create an Azure AI Search index with a 1536-dimension content_vector field
- write a hybrid search: text "azure search" plus a vector, top 10
- rewrite this client to use `DefaultAzureCredential` and a context manager

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 6a0fcc1cb0c2471c.

BEGIN THIRD-PARTY FILE 6a0fcc1cb0c2471c
````text
---
name: azure-search-documents-py
description: |
  Azure AI Search SDK for Python. Use for vector search, hybrid search, semantic ranking, indexing, and skillsets.
  Triggers: "azure-search-documents", "SearchClient", "SearchIndexClient", "vector search", "hybrid search", "semantic search".
license: MIT
metadata:
  author: Microsoft
  version: "1.0.0"
  package: azure-search-documents
---

# Azure AI Search SDK for Python

Full-text, vector, and hybrid search with AI enrichment capabilities.

## Installation

```bash
pip install azure-search-documents
```

## Environment Variables

```bash
AZURE_SEARCH_ENDPOINT=https://<service-name>.search.windows.net  # Required for all auth methods
AZURE_SEARCH_INDEX_NAME=<your-index-name>  # Required for all auth methods
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production
AZURE_SEARCH_API_KEY=<your-api-key>  # Only required for the legacy API-key auth path below
```

## Authentication & Lifecycle

> **🔑 Two rules apply to every code sample below:**
>
> 1. **Prefer `DefaultAzureCredential`.** It works locally (Azure CLI / VS Code / Developer CLI) and in Azure (managed identity, workload identity) with no code change. Avoid connection strings, account/API keys — they bypass Entra audit and rotation.
>    - Local dev: `DefaultAzureCredential` works as-is.
>    - Production: set `AZURE_TOKEN_CREDENTIALS=prod` (or `AZURE_TOKEN_CREDENTIALS=<specific_credential>`) to constrain the credential chain to production-safe credentials.
> 2. **Wrap every client in a context manager** so HTTP transports, sockets, and token caches are released deterministically:
>    - Sync: `with <Client>(...) as client:`
>    - Async: `async with <Client>(...) as client:` **and** `async with DefaultAzureCredential() as credential:` (from `azure.identity.aio`)
>
> Snippets may abbreviate this setup, but production code should always follow both rules.

```python
import os
from azure.identity import DefaultAzureCredential, ManagedIdentityCredential
from azure.search.documents import SearchClient

# Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
credential = DefaultAzureCredential(require_envvar=True)
# Or use a specific credential directly in production:
# See https://learn.microsoft.com/python/api/overview/azure/identity-readme?view=azure-python#credential-classes
# credential = ManagedIdentityCredential()

with SearchClient(
    endpoint=os.environ["AZURE_SEARCH_ENDPOINT"],
    index_name=os.environ["AZURE_SEARCH_INDEX_NAME"],
    credential=credential,
) as client:
    results = list(client.search(search_text="*", top=5))
```

### Legacy: API Key (existing keyed deployments)
````
END THIRD-PARTY FILE 6a0fcc1cb0c2471c

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---
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