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-0.1.2.tar.gz (423.6 kB view details)

Uploaded Source

Built Distribution

docparser_feb-0.1.2-py3-none-any.whl (436.3 kB view details)

Uploaded Python 3

File details

Details for the file docparser_feb-0.1.2.tar.gz.

File metadata

  • Download URL: docparser_feb-0.1.2.tar.gz
  • Upload date:
  • Size: 423.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.5

File hashes

Hashes for docparser_feb-0.1.2.tar.gz
Algorithm Hash digest
SHA256 99c981f7e16545656b24fcdedc929e566504120630d46d9bd66d147aac87cde1
MD5 d183d023bf472dcd7762734921137115
BLAKE2b-256 4e0a1950dc2cbb4d524eb781851e7d7265cd1a86e75b78c2cac284d3933a2135

See more details on using hashes here.

File details

Details for the file docparser_feb-0.1.2-py3-none-any.whl.

File metadata

File hashes

Hashes for docparser_feb-0.1.2-py3-none-any.whl
Algorithm Hash digest
SHA256 ad01f3d0388bf4c548a08c3838bd8713b3e13b0261344a598a916f62d5284f2d
MD5 7ffa63e950e5eccc817e3c9dacfe29a5
BLAKE2b-256 249b041c8549c1647b4789b68d6e13f73155f9d195cfda49ea1fb49e92a40bc6

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