haystack/docs/v1.2.0/_src/api/api/file_converter.md

Module base

BaseConverter

class BaseConverter(BaseComponent)

Base class for implementing file converts to transform input documents to text format for ingestion in DocumentStore.

__init__

def __init__(remove_numeric_tables: bool = False, valid_languages: Optional[List[str]] = None)

Arguments:

• remove_numeric_tables: This option uses heuristics to remove numeric rows from the tables. The tabular structures in documents might be noise for the reader model if it does not have table parsing capability for finding answers. However, tables may also have long strings that could possible candidate for searching answers. The rows containing strings are thus retained in this option.
• valid_languages: validate languages from a list of languages specified in the ISO 639-1 (https://en.wikipedia.org/wiki/ISO_639-1) format. This option can be used to add test for encoding errors. If the extracted text is not one of the valid languages, then it might likely be encoding error resulting in garbled text.

convert

@abstractmethod
def convert(file_path: Path, meta: Optional[Dict[str, str]], remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = "utf-8") -> List[Dict[str, Any]]

Convert a file to a dictionary containing the text and any associated meta data.

File converters may extract file meta like name or size. In addition to it, user supplied meta data like author, url, external IDs can be supplied as a dictionary.

Arguments:

• file_path: path of the file to convert
• meta: dictionary of meta data key-value pairs to append in the returned document.
• remove_numeric_tables: This option uses heuristics to remove numeric rows from the tables. The tabular structures in documents might be noise for the reader model if it does not have table parsing capability for finding answers. However, tables may also have long strings that could possible candidate for searching answers. The rows containing strings are thus retained in this option.
• valid_languages: validate languages from a list of languages specified in the ISO 639-1 (https://en.wikipedia.org/wiki/ISO_639-1) format. This option can be used to add test for encoding errors. If the extracted text is not one of the valid languages, then it might likely be encoding error resulting in garbled text.
• encoding: Select the file encoding (default is utf-8)

validate_language

def validate_language(text: str, valid_languages: Optional[List[str]] = None) -> bool

Validate if the language of the text is one of valid languages.

Module docx

DocxToTextConverter

class DocxToTextConverter(BaseConverter)

convert

def convert(file_path: Path, meta: Optional[Dict[str, str]] = None, remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = None) -> List[Dict[str, Any]]

Extract text from a .docx file.

Note: As docx doesn't contain "page" information, we actually extract and return a list of paragraphs here. For compliance with other converters we nevertheless opted for keeping the methods name.

Arguments:

• file_path: Path to the .docx file you want to convert
• meta: dictionary of meta data key-value pairs to append in the returned document.
• remove_numeric_tables: This option uses heuristics to remove numeric rows from the tables. The tabular structures in documents might be noise for the reader model if it does not have table parsing capability for finding answers. However, tables may also have long strings that could possible candidate for searching answers. The rows containing strings are thus retained in this option.
• valid_languages: validate languages from a list of languages specified in the ISO 639-1 (https://en.wikipedia.org/wiki/ISO_639-1) format. This option can be used to add test for encoding errors. If the extracted text is not one of the valid languages, then it might likely be encoding error resulting in garbled text.
• encoding: Not applicable

Module image

ImageToTextConverter

class ImageToTextConverter(BaseConverter)

__init__

def __init__(remove_numeric_tables: bool = False, valid_languages: Optional[List[str]] = ["eng"])

Arguments:

• remove_numeric_tables: This option uses heuristics to remove numeric rows from the tables. The tabular structures in documents might be noise for the reader model if it does not have table parsing capability for finding answers. However, tables may also have long strings that could possible candidate for searching answers. The rows containing strings are thus retained in this option.
• valid_languages: validate languages from a list of languages specified here (https://tesseract-ocr.github.io/tessdoc/Data-Files-in-different-versions.html) This option can be used to add test for encoding errors. If the extracted text is not one of the valid languages, then it might likely be encoding error resulting in garbled text. Run the following line of code to check available language packs:

List of available languages

print(pytesseract.get_languages(config=''))

convert

def convert(file_path: Union[Path, str], meta: Optional[Dict[str, str]] = None, remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = "utf-8") -> List[Dict[str, Any]]

Extract text from image file using the pytesseract library (https://github.com/madmaze/pytesseract)

Arguments:

• file_path: path to image file
• meta: Optional dictionary with metadata that shall be attached to all resulting documents. Can be any custom keys and values.
• remove_numeric_tables: This option uses heuristics to remove numeric rows from the tables. The tabular structures in documents might be noise for the reader model if it does not have table parsing capability for finding answers. However, tables may also have long strings that could possible candidate for searching answers. The rows containing strings are thus retained in this option.
• valid_languages: validate languages from a list of languages supported by tessarect (https://tesseract-ocr.github.io/tessdoc/Data-Files-in-different-versions.html). This option can be used to add test for encoding errors. If the extracted text is not one of the valid languages, then it might likely be encoding error resulting in garbled text.

Module markdown

MarkdownConverter

class MarkdownConverter(BaseConverter)

convert

def convert(file_path: Path, meta: Optional[Dict[str, str]] = None, remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = "utf-8") -> List[Dict[str, Any]]

Reads text from a txt file and executes optional preprocessing steps.

Arguments:

• file_path: path of the file to convert
• meta: dictionary of meta data key-value pairs to append in the returned document.
• encoding: Select the file encoding (default is utf-8)
• remove_numeric_tables: Not applicable
• valid_languages: Not applicable

Returns:

Dict of format {"text": "The text from file", "meta": meta}}

markdown_to_text

@staticmethod
def markdown_to_text(markdown_string: str) -> str

Converts a markdown string to plaintext

Arguments:

• markdown_string: String in markdown format

Module pdf

PDFToTextConverter

class PDFToTextConverter(BaseConverter)

__init__

def __init__(remove_numeric_tables: bool = False, valid_languages: Optional[List[str]] = None)

Arguments:

• remove_numeric_tables: This option uses heuristics to remove numeric rows from the tables. The tabular structures in documents might be noise for the reader model if it does not have table parsing capability for finding answers. However, tables may also have long strings that could possible candidate for searching answers. The rows containing strings are thus retained in this option.
• valid_languages: validate languages from a list of languages specified in the ISO 639-1 (https://en.wikipedia.org/wiki/ISO_639-1) format. This option can be used to add test for encoding errors. If the extracted text is not one of the valid languages, then it might likely be encoding error resulting in garbled text.

convert

def convert(file_path: Path, meta: Optional[Dict[str, str]] = None, remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = "Latin1") -> List[Dict[str, Any]]

Extract text from a .pdf file using the pdftotext library (https://www.xpdfreader.com/pdftotext-man.html)

Arguments:

• file_path: Path to the .pdf file you want to convert
• meta: Optional dictionary with metadata that shall be attached to all resulting documents. Can be any custom keys and values.
• remove_numeric_tables: This option uses heuristics to remove numeric rows from the tables. The tabular structures in documents might be noise for the reader model if it does not have table parsing capability for finding answers. However, tables may also have long strings that could possible candidate for searching answers. The rows containing strings are thus retained in this option.
• valid_languages: validate languages from a list of languages specified in the ISO 639-1 (https://en.wikipedia.org/wiki/ISO_639-1) format. This option can be used to add test for encoding errors. If the extracted text is not one of the valid languages, then it might likely be encoding error resulting in garbled text.
• encoding: Encoding that will be passed as -enc parameter to pdftotext. "Latin 1" is the default encoding of pdftotext. While this works well on many PDFs, it might be needed to switch to "UTF-8" or others if your doc contains special characters (e.g. German Umlauts, Cyrillic characters ...). Note: With "UTF-8" we experienced cases, where a simple "fi" gets wrongly parsed as "xef\xac\x81c" (see test cases). That's why we keep "Latin 1" as default here. (See list of available encodings by running pdftotext -listenc in the terminal)

PDFToTextOCRConverter

class PDFToTextOCRConverter(BaseConverter)

__init__

def __init__(remove_numeric_tables: bool = False, valid_languages: Optional[List[str]] = ["eng"])

Extract text from image file using the pytesseract library (https://github.com/madmaze/pytesseract)

Arguments:

• remove_numeric_tables: This option uses heuristics to remove numeric rows from the tables. The tabular structures in documents might be noise for the reader model if it does not have table parsing capability for finding answers. However, tables may also have long strings that could possible candidate for searching answers. The rows containing strings are thus retained in this option.
• valid_languages: validate languages from a list of languages supported by tessarect (https://tesseract-ocr.github.io/tessdoc/Data-Files-in-different-versions.html). This option can be used to add test for encoding errors. If the extracted text is not one of the valid languages, then it might likely be encoding error resulting in garbled text.

convert

def convert(file_path: Path, meta: Optional[Dict[str, str]] = None, remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = "utf-8") -> List[Dict[str, Any]]

Convert a file to a dictionary containing the text and any associated meta data.

File converters may extract file meta like name or size. In addition to it, user supplied meta data like author, url, external IDs can be supplied as a dictionary.

Arguments:

• file_path: path of the file to convert
• meta: dictionary of meta data key-value pairs to append in the returned document.
• remove_numeric_tables: This option uses heuristics to remove numeric rows from the tables. The tabular structures in documents might be noise for the reader model if it does not have table parsing capability for finding answers. However, tables may also have long strings that could possible candidate for searching answers. The rows containing strings are thus retained in this option.
• valid_languages: validate languages from a list of languages specified in the ISO 639-1 (https://en.wikipedia.org/wiki/ISO_639-1) format. This option can be used to add test for encoding errors. If the extracted text is not one of the valid languages, then it might likely be encoding error resulting in garbled text.
• encoding: Select the file encoding (default is utf-8)

Module tika

TikaConverter

class TikaConverter(BaseConverter)

__init__

def __init__(tika_url: str = "http://localhost:9998/tika", remove_numeric_tables: bool = False, valid_languages: Optional[List[str]] = None)

Arguments:

• tika_url: URL of the Tika server
• remove_numeric_tables: This option uses heuristics to remove numeric rows from the tables. The tabular structures in documents might be noise for the reader model if it does not have table parsing capability for finding answers. However, tables may also have long strings that could possible candidate for searching answers. The rows containing strings are thus retained in this option.
• valid_languages: validate languages from a list of languages specified in the ISO 639-1 (https://en.wikipedia.org/wiki/ISO_639-1) format. This option can be used to add test for encoding errors. If the extracted text is not one of the valid languages, then it might likely be encoding error resulting in garbled text.

convert

def convert(file_path: Path, meta: Optional[Dict[str, str]] = None, remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = None) -> List[Dict[str, Any]]

Arguments:

• file_path: path of the file to convert
• meta: dictionary of meta data key-value pairs to append in the returned document.
• remove_numeric_tables: This option uses heuristics to remove numeric rows from the tables. The tabular structures in documents might be noise for the reader model if it does not have table parsing capability for finding answers. However, tables may also have long strings that could possible candidate for searching answers. The rows containing strings are thus retained in this option.
• valid_languages: validate languages from a list of languages specified in the ISO 639-1 (https://en.wikipedia.org/wiki/ISO_639-1) format. This option can be used to add test for encoding errors. If the extracted text is not one of the valid languages, then it might likely be encoding error resulting in garbled text.
• encoding: Not applicable

Returns:

a list of pages and the extracted meta data of the file.

Module txt

TextConverter

class TextConverter(BaseConverter)

convert

def convert(file_path: Path, meta: Optional[Dict[str, str]] = None, remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None, encoding: Optional[str] = "utf-8") -> List[Dict[str, Any]]

Reads text from a txt file and executes optional preprocessing steps.

Arguments:

• file_path: path of the file to convert
• meta: dictionary of meta data key-value pairs to append in the returned document.
• remove_numeric_tables: This option uses heuristics to remove numeric rows from the tables. The tabular structures in documents might be noise for the reader model if it does not have table parsing capability for finding answers. However, tables may also have long strings that could possible candidate for searching answers. The rows containing strings are thus retained in this option.
• valid_languages: validate languages from a list of languages specified in the ISO 639-1 (https://en.wikipedia.org/wiki/ISO_639-1) format. This option can be used to add test for encoding errors. If the extracted text is not one of the valid languages, then it might likely be encoding error resulting in garbled text.
• encoding: Select the file encoding (default is utf-8)

Returns:

Dict of format {"text": "The text from file", "meta": meta}}