langchain_community.document_loaders.pdf.AmazonTextractPDFLoader

class langchain_community.document_loaders.pdf.AmazonTextractPDFLoader(file_path: str, textract_features: Optional[Sequence[str]] = None, client: Optional[Any] = None, credentials_profile_name: Optional[str] = None, region_name: Optional[str] = None, endpoint_url: Optional[str] = None, headers: Optional[Dict] = None, *, linearization_config: Optional[TextLinearizationConfig] = None)[source]

Load PDF files from a local file system, HTTP or S3.

To authenticate, the AWS client uses the following methods to automatically load credentials: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html

If a specific credential profile should be used, you must pass the name of the profile from the ~/.aws/credentials file that is to be used.

Make sure the credentials / roles used have the required policies to access the Amazon Textract service.

Example

Initialize the loader.

Parameters
  • file_path (str) – A file, url or s3 path for input file

  • textract_features (Optional[Sequence[str]]) – Features to be used for extraction, each feature should be passed as a str that conforms to the enum Textract_Features, see amazon-textract-caller pkg

  • client (Optional[Any]) – boto3 textract client (Optional)

  • credentials_profile_name (Optional[str]) – AWS profile name, if not default (Optional)

  • region_name (Optional[str]) – AWS region, eg us-east-1 (Optional)

  • endpoint_url (Optional[str]) – endpoint url for the textract service (Optional)

  • linearization_config (Optional[TextLinearizationConfig]) – Config to be used for linearization of the output should be an instance of TextLinearizationConfig from the textractor pkg

  • headers (Optional[Dict]) –

Attributes

source

Methods

__init__(file_path[, textract_features, ...])

Initialize the loader.

alazy_load()

A lazy loader for Documents.

aload()

Load data into Document objects.

lazy_load()

Lazy load documents

load()

Load given path as pages.

load_and_split([text_splitter])

Load Documents and split into chunks.

__init__(file_path: str, textract_features: Optional[Sequence[str]] = None, client: Optional[Any] = None, credentials_profile_name: Optional[str] = None, region_name: Optional[str] = None, endpoint_url: Optional[str] = None, headers: Optional[Dict] = None, *, linearization_config: Optional[TextLinearizationConfig] = None) None[source]

Initialize the loader.

Parameters
  • file_path (str) – A file, url or s3 path for input file

  • textract_features (Optional[Sequence[str]]) – Features to be used for extraction, each feature should be passed as a str that conforms to the enum Textract_Features, see amazon-textract-caller pkg

  • client (Optional[Any]) – boto3 textract client (Optional)

  • credentials_profile_name (Optional[str]) – AWS profile name, if not default (Optional)

  • region_name (Optional[str]) – AWS region, eg us-east-1 (Optional)

  • endpoint_url (Optional[str]) – endpoint url for the textract service (Optional)

  • linearization_config (Optional[TextLinearizationConfig]) – Config to be used for linearization of the output should be an instance of TextLinearizationConfig from the textractor pkg

  • headers (Optional[Dict]) –

Return type

None

async alazy_load() AsyncIterator[Document]

A lazy loader for Documents.

Return type

AsyncIterator[Document]

async aload() List[Document]

Load data into Document objects.

Return type

List[Document]

lazy_load() Iterator[Document][source]

Lazy load documents

Return type

Iterator[Document]

load() List[Document][source]

Load given path as pages.

Return type

List[Document]

load_and_split(text_splitter: Optional[TextSplitter] = None) List[Document]

Load Documents and split into chunks. Chunks are returned as Documents.

Do not override this method. It should be considered to be deprecated!

Parameters

text_splitter (Optional[TextSplitter]) – TextSplitter instance to use for splitting documents. Defaults to RecursiveCharacterTextSplitter.

Returns

List of Documents.

Return type

List[Document]

Examples using AmazonTextractPDFLoader