索引器实现中的 DocumentExtractionSkill 问题

Question

我有一个包含 pdf 文件的 blob 存储，我想在其中实施知识挖掘解决方案。我创建了数据源、索引、技能组和索引器。但是，当我运行所有内容时，我收到一条警告“无法执行技能，因为一个或多个技能输入无效。”。

技能组代码：

def create_skillset(search_service_endpoint, search_service_api_key, skillset_name):
    credential = AzureKeyCredential(search_service_api_key)
    indexer_client = SearchIndexerClient(endpoint=search_service_endpoint, credential=credential)

    # Define skills
    doc_extraction_skill = DocumentExtractionSkill(
        name="documentExtractionSkill",
        description="Extract text from documents",
        context="/document",
        configuration={"imageAction": "generateNormalizedImagePerPage"},
        inputs=[InputFieldMappingEntry(name="file_data", source="/document/file_data")],
        outputs=[OutputFieldMappingEntry(name="content", target_name="/documents/content")]
    )

    # Create skillset
    skillset = SearchIndexerSkillset(
        name=skillset_name,
        skills=[doc_extraction_skill]
    )

    # Create skillset in Azure Cognitive Search
    indexer_client.create_skillset(skillset)
    print(f"Skillset '{skillset_name}' created successfully.")

索引器：

# Function to create an indexer
def create_indexer(search_service_endpoint, search_service_api_key, indexer_name, data_source_name, index_name, skillset_name):
    credential = AzureKeyCredential(search_service_api_key)
    indexer_client = SearchIndexerClient(endpoint=search_service_endpoint, credential=credential)
    field_mappings = [
        FieldMapping(source_field_name="metadata_storage_path", target_field_name="metadata_storage_path"),
        FieldMapping(source_field_name="metadata_storage_name", target_field_name="metadata_storage_name"),
        FieldMapping(source_field_name="metadata_storage_last_modified", target_field_name="metadata_storage_last_modified"),
        FieldMapping(source_field_name="metadata_content_type", target_field_name="metadata_content_type"),
    ]

    output_field_mappings = [
        FieldMapping(source_field_name="/document/content", target_field_name= "content"),
    ]
    # Define indexing parameters
    indexing_parameters = IndexingParameters(
        configuration={
            "indexStorageMetadataOnlyForOversizedDocuments": True,
            "failOnUnsupportedContentType": False,
            "indexedFileNameExtensions": ".pdf,.docx,.txt,.json",
            "parseJson": True,
            "parsingMode": "default",
            "allowSkillsetToReadFileData": False
        }
    )

    indexer = SearchIndexer(
        name=indexer_name,
        data_source_name=data_source_name,
        target_index_name=index_name,
        skillset_name=skillset_name,
        field_mappings=field_mappings,
        output_field_mappings=output_field_mappings,
        schedule=IndexingSchedule(interval="PT15M"),
        parameters=indexing_parameters
    )

    indexer_client.create_indexer(indexer)
    print(f"Indexer '{indexer_name}' created.")

Answer 1

您尝试过为索引器设置

"allowSkillsetToReadFileData": True

吗？