AmazonTextractPDFParser#
- class langchain_community.document_loaders.parsers.pdf.AmazonTextractPDFParser(textract_features: Sequence[int] | None = None, client: Any | None = None, *, linearization_config: TextLinearizationConfig | None = None)[source]#
Send PDF files to Amazon Textract and parse them.
For parsing multi-page PDFs, they have to reside on S3.
The AmazonTextractPDFLoader calls the [Amazon Textract Service](https://aws.amazon.com/textract/) to convert PDFs into a Document structure. Single and multi-page documents are supported with up to 3000 pages and 512 MB of size.
For the call to be successful an AWS account is required, similar to the [AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html) requirements.
Besides the AWS configuration, it is very similar to the other PDF loaders, while also supporting JPEG, PNG and TIFF and non-native PDF formats.
`python from langchain_community.document_loaders import AmazonTextractPDFLoader loader=AmazonTextractPDFLoader("example_data/alejandro_rosalez_sample-small.jpeg") documents = loader.load() `One feature is the linearization of the output. When using the features LAYOUT, FORMS or TABLES together with Textract
```python from langchain_community.document_loaders import AmazonTextractPDFLoader # you can mix and match each of the features loader=AmazonTextractPDFLoader(
“example_data/alejandro_rosalez_sample-small.jpeg”, textract_features=[“TABLES”, “LAYOUT”])
it will generate output that formats the text in reading order and try to output the information in a tabular structure or output the key/value pairs with a colon (key: value). This helps most LLMs to achieve better accuracy when processing these texts.
Initializes the parser.
- Parameters:
textract_features (Optional[Sequence[int]]) – Features to be used for extraction, each feature should be passed as an int that conforms to the enum Textract_Features, see amazon-textract-caller pkg
client (Optional[Any]) – boto3 textract client
linearization_config (Optional[TextLinearizationConfig]) – Config to be used for linearization of the output should be an instance of TextLinearizationConfig from the textractor pkg
Methods
__init__([textract_features, client, ...])Initializes the parser.
lazy_parse(blob)Iterates over the Blob pages and returns an Iterator with a Document for each page, like the other parsers If multi-page document, blob.path has to be set to the S3 URI and for single page docs the blob.data is taken
parse(blob)Eagerly parse the blob into a document or documents.
- __init__(textract_features: Sequence[int] | None = None, client: Any | None = None, *, linearization_config: TextLinearizationConfig | None = None) None[source]#
Initializes the parser.
- Parameters:
textract_features (Optional[Sequence[int]]) – Features to be used for extraction, each feature should be passed as an int that conforms to the enum Textract_Features, see amazon-textract-caller pkg
client (Optional[Any]) – boto3 textract client
linearization_config (Optional[TextLinearizationConfig]) – Config to be used for linearization of the output should be an instance of TextLinearizationConfig from the textractor pkg
- Return type:
None
- lazy_parse(blob: Blob) Iterator[Document][source]#
Iterates over the Blob pages and returns an Iterator with a Document for each page, like the other parsers If multi-page document, blob.path has to be set to the S3 URI and for single page docs the blob.data is taken