# Build Azure AI Search Solutions with Python

Azure AI Search implementations require careful SDK configuration across indexes, vectors, authentication, and query modes. This skill provides focused Python patterns for building and reviewing those workflows.

## Install

```bash
npx skillstore add sickn33/azure-search-documents-py
```

## Metadata

- Status: approved
- Slug: sickn33-azure-search-documents-py
- Skillstore revision: r2
- Version status: missing
- Tree hash: 543e5f97f62d7a8ea3ea8ed988cf7cc9a44ca8f9a39878b72746fa694a5e01b5
- Author: sickn33
- GitHub username: sickn33
- License: MIT
- Repository: https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/azure-search-documents-py
- Ref: f9e2c34b4f19c7f3e6b0a1e93227b5f77cc12526
- Supported tools: Claude, Codex, Claude Code
- Audit status: complete
- Agent install advisory: allowed
- Manual install advisory: allowed
- Artifact signature: available
- Audit attestation: unavailable
- Human verification: not\_verified
- Risk factors: scripts, external\_commands, network, env\_access
- Quality score: 77
- Quality tier: bronze
- Public page: https://skillstore.pages.dev/skills/sickn33-azure-search-documents-py
- Manifest: https://skillstore.pages.dev/api/skills/sickn33-azure-search-documents-py/manifest

## Capabilities

- Configure Azure Search clients with API keys or DefaultAzureCredential.
- Define text and vector indexes with HNSW profiles and semantic configurations.
- Upload, merge, upsert, and delete documents through standard or buffered clients.
- Implement keyword, vector, hybrid, semantic, filtered, faceted, autocomplete, and suggestion queries.
- Configure data sources, skillsets, and indexers for AI enrichment workflows.
- Apply asynchronous clients and Azure SDK exception handling patterns.

## Use Cases

- Add Search to a Python Service: Create authenticated clients, upload documents, and implement keyword or filtered search for an application.
- Design Vector and Hybrid Retrieval: Define vector fields, HNSW profiles, semantic settings, and combined query patterns for relevant retrieval.
- Build Enriched Indexing Pipelines: Configure data sources, skillsets, indexers, and batch document operations for managed ingestion.

## Prompt Templates

### Create a Basic Search Client

```
Create a basic Azure AI Search Python example using DefaultAzureCredential. Include SearchClient setup, one keyword query, selected fields, and brief setup notes.
```

### Design a Vector Index

```
Design an index for documents with id, title, content, and a 1536-dimension vector. Add HNSW configuration and explain each field choice.
```

### Implement Hybrid Semantic Search

```
Draft a hybrid search workflow using keyword text, a vector query, semantic ranking, filters, and selected fields. Include result handling and validation steps.
```

### Review a Production Architecture

```
Review my Azure AI Search architecture for authentication, indexing, vector dimensions, batching, async usage, and error handling. Identify risks and propose production-ready improvements.
```

## Limitations

- The skill provides guidance and examples but does not execute or verify Azure resources.
- Examples contain placeholders and may require additional imports, variables, permissions, and service configuration.
- Embedding dimensions, models, fields, and semantic settings must match the target Azure environment.
- Referenced supporting files and setup scripts are not included in this package.

## Best Practices

- Prefer DefaultAzureCredential for production and grant the minimum required Azure roles.
- Match vector dimensions and profiles to the embedding model before creating the index.
- Validate batch failures, close clients, and test queries against the target service configuration.

## Anti Patterns

- Do not place real API keys in prompts, source files, examples, or logs.
- Do not reuse sample dimensions or field names without checking the embedding model and index schema.
- Do not assume successful batch submission means every document was indexed.

## Security Audit

- Audited at: 2026-07-23T22:39:35.438\+00:00
- Summary: All static findings are false positives caused by Markdown formatting, static Python imports, Azure placeholders, documented environment access, and search terminology. No command execution, embedded secret, exfiltration, prompt injection, or malicious intent was found.

## Stats

- Views: 124
- Downloads: 11
- Favorites: 0
- Popularity score: 0