Cognitive Search - Python package

Create Indexes, indexers, suggesters, analyzers, scoring profiles, custom and predefined skills via Python.
Upload documents or manage data sources for Azure Search.
Create a data pipeline from source, through cognitive skills (either predefined or custom), into Azure Search

For this to work you need the following environment variables set:

AZURE_SEARCH_API_KEY={a regular search api key}
AZURE_SEARCH_ADMIN_API_KEY={a search admin api key}
AZURE_SEARCH_URL=https://{your search service name}.search.windows.net

In addition, a Cognitive Search resource is needed in case you use Azure Cognitive Search. For more info: https://docs.microsoft.com/en-us/azure/search/cognitive-search-attach-cognitive-services

Features:

1. Define fields and indexes through Python
2. Deine skills and skillsets: Predefined Cognitive Search skills and custom skills (WebAPI skills)
3. Define analyzers (custom analyzers and predefined analyzers)
4. Define scoring profiles, suggesters
5. Upload documents to Azure Search
6. Manage data sources

originally forked from https://github.com/python-azure-search/python-azure-search

Example usage (WIP):

    #create datasource. json holds the datasource params (name, connection string etc.)
    datasource = DataSource.load(name="datasource",connection_string="xxx",container_name="cont")
    datasource.delete_if_exists()
    datasource.create()
    # define fields and index
    field1 = StringField("id",key=True)
    field2 = CollectionField("keyPhrases")
    field3 = StringField("content")
    index = Index("my-index",fields = [field1,field2,field3])
    index.delete_if_exists()
    index.create()
    # Define skills
    keyph_skill = KeyPhraseExtractionSkill()
    skillset = Skillset(skills=[keyph_skill],
                        name="my-skillset",
                        description="skillset with one skill",
                        cognitive_services_key="YOUR_COG_SERVICES_KEY")
    skillset.delete_if_exists()
    skillset.create()
    ## Define Indexer
    indexer = Indexer(name="my-indexer", data_source_name=datasource.name,
                      target_index_name=index.name, skillset_name=skillset.name)
    indexer.delete_if_exists()
    indexer.create()
    indexer_status = ""
    last_run_status = None
    while indexer_status != "error" and (last_run_status is None or last_run_status == "inProgress"):
        status = indexer.get_status()
        indexer_status = status.get("status")
        last_run_status = status.get("lastResult")
        if last_run_status is not None:
            last_run_status = last_run_status.get("status")
            print("last run status: " + last_run_status)
        print("indexer status is: " + indexer_status)
        time.sleep(3)  # wait for 3 seconds until rechecking
    assert indexer_status == "running"
    assert last_run_status == "success"
    indexer.verify()
    ## Search something
    res = index.search("Microsoft")
    print(res)
    ## Delete all
    datasource.delete()
    index.delete()
    skillset.delete()
    indexer.delete()