Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersBeautiful SoupGoogle Cloud Document AIDoctran: extract propertiesDoctran: interrogate documentsDoctran: language translationHTML to textNucliaOpenAI metadata taggerText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument transformersGoogle Cloud Document AIGoogle Cloud Document AIDocument AI is a document understanding platform from Google Cloud to transform unstructured data from documents into structured data, making it easier to understand, analyze, and consume.Learn more:Document AI overviewDocument AI videos and labsTry it!The module contains a PDF parser based on DocAI from Google Cloud.You need to install two libraries to use this parser:First, you need to set up a Google Cloud Storage (GCS) bucket and create your own Optical Character Recognition (OCR) processor as described here: https://cloud.google.com/document-ai/docs/create-processorThe GCS_OUTPUT_PATH should be a path to a folder on GCS (starting with gs://) and a PROCESSOR_NAME should look like projects/PROJECT_NUMBER/locations/LOCATION/processors/PROCESSOR_ID or projects/PROJECT_NUMBER/locations/LOCATION/processors/PROCESSOR_ID/processorVersions/PROCESSOR_VERSION_ID. You can get it either programmatically or copy from the Prediction endpoint section of the Processor details tab in the Google Cloud Console.GCS_OUTPUT_PATH = "gs://BUCKET_NAME/FOLDER_PATH"PROCESSOR_NAME = "projects/PROJECT_NUMBER/locations/LOCATION/processors/PROCESSOR_ID"from langchain.document_loaders.blob_loaders import Blobfrom langchain.document_loaders.parsers import DocAIParserNow, create a DocAIParser.parser = DocAIParser(    location="us", processor_name=PROCESSOR_NAME, gcs_output_path=GCS_OUTPUT_PATH)For this example, you can use an Alphabet
Document AI is a document understanding platform from Google Cloud to transform unstructured data from documents into structured data, making it easier to understand, analyze, and consume.
Document AI is a document understanding platform from Google Cloud to transform unstructured data from documents into structured data, making it easier to understand, analyze, and consume. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersBeautiful SoupGoogle Cloud Document AIDoctran: extract propertiesDoctran: interrogate documentsDoctran: language translationHTML to textNucliaOpenAI metadata taggerText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument transformersGoogle Cloud Document AIGoogle Cloud Document AIDocument AI is a document understanding platform from Google Cloud to transform unstructured data from documents into structured data, making it easier to understand, analyze, and consume.Learn more:Document AI overviewDocument AI videos and labsTry it!The module contains a PDF parser based on DocAI from Google Cloud.You need to install two libraries to use this parser:First, you need to set up a Google Cloud Storage (GCS) bucket and create your own Optical Character Recognition (OCR) processor as described here: https://cloud.google.com/document-ai/docs/create-processorThe GCS_OUTPUT_PATH should be a path to a folder on GCS (starting with gs://) and a PROCESSOR_NAME should look like projects/PROJECT_NUMBER/locations/LOCATION/processors/PROCESSOR_ID or projects/PROJECT_NUMBER/locations/LOCATION/processors/PROCESSOR_ID/processorVersions/PROCESSOR_VERSION_ID. You can get it either programmatically or copy from the Prediction endpoint section of the Processor details tab in the Google Cloud Console.GCS_OUTPUT_PATH = "gs://BUCKET_NAME/FOLDER_PATH"PROCESSOR_NAME = "projects/PROJECT_NUMBER/locations/LOCATION/processors/PROCESSOR_ID"from langchain.document_loaders.blob_loaders import Blobfrom langchain.document_loaders.parsers import DocAIParserNow, create a DocAIParser.parser = DocAIParser(    location="us", processor_name=PROCESSOR_NAME, gcs_output_path=GCS_OUTPUT_PATH)For this example, you can use an Alphabet