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azure-search-python-samples

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Azure Search Python sample code

파일 탐색기

최종 버전 다운로드 (.zip)

- extensions.json
- launch.json
- settings.json
- tasks.json
- agent-example.ipynb
- README.md
- requirements.txt
- sample.env
- - __init__.py
- .funcignore
- .gitignore
- function_app.py
- host.json
- local.settings.json.rename
- lookup.py
- lookup.sample.dat
- readme.md
- requirements.txt
- search.py
- search.sample.dat
- suggest.py
- suggest.sample.dat
- bulk-insert.py
- good-books-index.json
- readme.md
- requirements.txt
- cmk-example.ipynb
- - purview_sharepoint_acl_indexer.ipynb
  - requirements.txt
  - sample.env
- - purview_sharepoint_labels.ipynb
  - requirements.txt
  - sample.env
- image_serving.ipynb
- requirements.txt
- sample.env
- .gitignore
- quickstart-agentic-retrieval.ipynb
- README.md
- requirements.txt
- sample.env
- document-permissions-pull-api.ipynb
- requirements.txt
- sample.env
- document-permissions-push-api.ipynb
- requirements.txt
- sample.env
- azure-search-quickstart.ipynb
- README.md
- README.md
- requirements.txt
- sample.env
- semantic-ranking-quickstart.ipynb
- README.md
- requirements.txt
- sample.env
- vector-search-quickstart.ipynb
.gitignore
CONTRIBUTING.md
LICENSE.md
README.md

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/Azure-Samples/azure-search-python-samples

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd azure-search-python-samples

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install -r Quickstart-Agentic-Retrieval/requirements.txt

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

jupyter notebook

브라우저에서 노트북(.ipynb) 파일들을 열람하고 실행할 수 있는 Jupyter 화면을 켭니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

// repository documentation

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# Python samples for Azure AI Search

This repository contains Python code samples used in Azure AI Search documentation. Unless noted otherwise, all samples run on the shared (free) pricing tier of a [search service](https://learn.microsoft.com/azure/search/search-create-service-portal).

If your configuration uses a search service managed identity for indexer connections, your search service must be on the Basic tier or higher.

## Day-one quickstarts

| Sample | Description |
|--------|-------------|
| [Quickstart-Agentic-Retrieval](Quickstart-Agentic-Retrieval/quickstart-agentic-retrieval.ipynb) | Create a knowledge base in Azure AI Search to integrate LLM reasoning into query planning. |
| [Quickstart-Document-Permissions-Pull-API](Quickstart-Document-Permissions-Pull-API/document-permissions-pull-api.ipynb) | Using an indexer "pull API" approach, flow access control lists from a data source to search results and apply permission filters that restrict access to authorized content. Indexer support is limited to Azure Data Lake Storage (ADLS) Gen2 permission metadata. |
| [Quickstart-Document-Permissions-Push-API](Quickstart-Document-Permissions-Push-API/document-permissions-push-api.ipynb) | Using the push APIs for indexing a JSON payload, flow embedded permission metadata to indexed documents and search results that are filtered based on user access to authorized content. |
| [Quickstart-Keyword-Search](Quickstart-Keyword-Search/README.md) | Introduces the fundamental tasks of working with a classic search index: create, load, and query. The index is modeled on a subset of the hotels dataset, which is widely used in Azure AI Search samples but reduced in this sample for readability and comprehension. |
| [Quickstart-Semantic-Ranking](Quickstart-Semantic-Ranking/semantic-ranking-quickstart.ipynb) | Extends the quickstart through modifications that invoke semantic ranking. This notebook adds a semantic configuration to the index and semantic query options that formulate the query and response. |
| [Quickstart-Vector-Search](Quickstart-Vector-Search/quickstart-vector-search.ipynb) | Introduces vector search in Azure AI Search. This notebook demonstrates how to create, load, and query a vector index. |

## Deeper dive tutorials and examples

| Sample | Description |
|--------|-------------|
| [agentic-retrieval-pipeline-example](agentic-retrieval-pipeline-example/agent-example.ipynb) | Extends the quickstart by integrating Foundry Agent Service. Add an AI agent and MCP tool to your Azure AI Search agentic retrieval pipeline for an end-to-end conversational search experience. |
| [azure-function-search](azure-function-search/readme.md) | An Azure Function that sends query requests to an Azure AI Search service. You can substitute this code to replace the contents of the `api` folder in the C# sample [azure-search-static-web-app](https://github.com/Azure-Samples/azure-search-static-web-app). |
| [bulk-insert](bulk-insert/readme.md) | Create and load an index using the push APIs and sample data. You can substitute this code to replace the contents of the `bulk-insert` folder in the C# sample [azure-search-static-web-app](https://github.com/Azure-Samples/azure-search-static-web-app) |
| [cmk-encryption](cmk-example/cmk-example.ipynb) | Encrypt content using customer-managed keys. |
| [image-serving-example](image-serving-example/image_serving.ipynb) | Runs managed ingestion, compares retrieval with image serving disabled and enabled, and independently queries and downloads an indexed image asset from Azure Blob Storage. |

## Archived samples

+ **azureml-custom-skill**: See the **Archive** branch of this repository.
+ **image-processing**: See [azure-search-sample-archive/tree/main/image-processing](https://github.com/Azure-Samples/azure-search-sample-archive/tree/main/image-processing).