Skip to main content

Document parsing tool for LLM training and Rag

Project description

DocParser 📄

DocParser is a powerful tool for LLM traning and other application, for examples: RAG, which support to parse multi type file, includes:

Feature 🎉

File types supported for parsing:

  • Pdf: Use OCR to parse PDF documents and output text in markdown format. The parsing results can be used for LLM pretrain, RAG, etc.
  • Html: Use jina to parse multi html pages and output text in markdown.

Install

From pip:

pip install docparser_feb

From repository:

pip install git+https://github.com/feb-co/DocParser.git

Or install it directly through the installation package:

git clone https://github.com/feb-co/DocParser.git
cd DocParser
pip install -e .

API/Functional

Pdf

From CLI

You can run the following script to get the pdf parsing results:

export LOG_LEVEL="ERROR"
export DOC_PARSER_MODEL_DIR="xxx"
export DOC_PARSER_OPENAI_URL="xxx"
export DOC_PARSER_OPENAI_KEY="xxx"
export DOC_PARSER_OPENAI_MODEL="gpt-4-0125-preview"
export DOC_PARSER_OPENAI_RETRY="3"
docparser-pdf \
    --inputs path/to/file.pdf or path/to/directory \
    --output_dir output_directory \
    --page_range '0:1' --mode 'figure latex' \
    --rendering --use_llm --overwrite_result

The following is a description of the relevant parameters:

usage: docparser-pdf [-h] --inputs INPUTS --output_dir OUTPUT_DIR [--page_range PAGE_RANGE] [--mode {plain,figure placehold,figure latex}] [--rendering] [--use_llm]

options:
  -h, --help            show this help message and exit
  --inputs INPUTS       Directory where to store PDFs, or a file path to a single PDF
  --output_dir OUTPUT_DIR
                        Directory where to store the output results (md/json/images).
  --page_range PAGE_RANGE
                        The page range to parse the PDF, the format is 'start_page:end_page', that is, [start, end). Default: full.
  --mode {plain,figure placehold,figure latex}
                        The mode for parsing the PDF, to extract only the plain text or the text plus images.
  --rendering           Is it necessary to render the recognition results of the input PDF to output the recognition range? Default: False.
  --use_llm             Do you need to use LLM to format the parsing results? If so, please specify the corresponding parameters through the environment variables: DOC_PARSER_OPENAI_URL, DOC_PARSER_OPENAI_KEY, DOC_PARSER_OPENAI_MODEL. Default: False.
  --overwrite_result    If the parsed target file already exists, should it be rewritten? Default: False.

From Python

Html

From CLI

You can run the following script to get the html parsing results:

docparser-html https://github.com/mem0ai/mem0

From Python

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

docparser_feb-0.1.3.tar.gz (424.0 kB view details)

Uploaded Source

Built Distribution

docparser_feb-0.1.3-py3-none-any.whl (436.6 kB view details)

Uploaded Python 3

File details

Details for the file docparser_feb-0.1.3.tar.gz.

File metadata

  • Download URL: docparser_feb-0.1.3.tar.gz
  • Upload date:
  • Size: 424.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.5

File hashes

Hashes for docparser_feb-0.1.3.tar.gz
Algorithm Hash digest
SHA256 b26bdc3d71b77226e17bc4743819d4a51ba16d893f0bdc8e6fb02a21c54ea801
MD5 964101ac3a03fdf1ff5444a133117499
BLAKE2b-256 b7a21fa88b393565ee91e26491a7a968ee9cbfffadde1bde9f8296010d31b8fa

See more details on using hashes here.

File details

Details for the file docparser_feb-0.1.3-py3-none-any.whl.

File metadata

File hashes

Hashes for docparser_feb-0.1.3-py3-none-any.whl
Algorithm Hash digest
SHA256 1a89c3eed1f0329a3d70e3f98dbe83980db91f469704b52a31aaacc72a8d1833
MD5 82c7626acaba7cf7418999dc5af3b1e1
BLAKE2b-256 13f5dc82516a05912d8b2296bf76684d1b55328d2323eb2ed1c58a2654fe7179

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page