docparser-feb


Namedocparser-feb JSON
Version 0.1.5 PyPI version JSON
download
home_pageNone
SummaryDocument parsing tool for LLM training and Rag
upload_time2024-10-09 15:25:37
maintainerNone
docs_urlNone
authorNone
requires_python>=3.8
licenseMIT
keywords pdf llm chatgpt transformer pytorch deep learning
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            # 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](#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](#Html): Use [jina](https://jina.ai/reader) to parse multi html pages and output text in markdown.

## Install

From pip:

```bash
pip install docparser_feb
```

From repository:

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

Or install it directly through the installation package:

```bash
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:

```shell
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:

```bash
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:

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

The following is a description of the relevant parameters:

#### From Python

            

Raw data

            {
    "_id": null,
    "home_page": null,
    "name": "docparser-feb",
    "maintainer": null,
    "docs_url": null,
    "requires_python": ">=3.8",
    "maintainer_email": null,
    "keywords": "pdf, LLM, ChatGPT, transformer, pytorch, deep learning",
    "author": null,
    "author_email": "Licheng Wang <244267620@qq.com>",
    "download_url": "https://files.pythonhosted.org/packages/95/e8/279f26a16735d0e12e45d2249f290819da4c586f347036da52ddf83efbef/docparser_feb-0.1.5.tar.gz",
    "platform": null,
    "description": "# DocParser \ud83d\udcc4\n\nDocParser is a powerful tool for LLM traning and other application, for examples: RAG, which support to parse multi type file, includes:\n\n## Feature \ud83c\udf89\n\n### File types supported for parsing:\n\n- [Pdf](#Pdf): Use OCR to parse PDF documents and output text in markdown format. The parsing results can be used for LLM pretrain, RAG, etc.\n- [Html](#Html): Use [jina](https://jina.ai/reader) to parse multi html pages and output text in markdown.\n\n## Install\n\nFrom pip:\n\n```bash\npip install docparser_feb\n```\n\nFrom repository:\n\n```bash\npip install git+https://github.com/feb-co/DocParser.git\n```\n\nOr install it directly through the installation package:\n\n```bash\ngit clone https://github.com/feb-co/DocParser.git\ncd DocParser\npip install -e .\n```\n\n## API/Functional\n\n### Pdf\n\n#### From CLI\n\nYou can run the following script to get the pdf parsing results:\n\n```shell\nexport LOG_LEVEL=\"ERROR\"\nexport DOC_PARSER_MODEL_DIR=\"xxx\"\nexport DOC_PARSER_OPENAI_URL=\"xxx\"\nexport DOC_PARSER_OPENAI_KEY=\"xxx\"\nexport DOC_PARSER_OPENAI_MODEL=\"gpt-4-0125-preview\"\nexport DOC_PARSER_OPENAI_RETRY=\"3\"\ndocparser-pdf \\\n    --inputs path/to/file.pdf or path/to/directory \\\n    --output_dir output_directory \\\n    --page_range '0:1' --mode 'figure latex' \\\n    --rendering --use_llm --overwrite_result\n```\n\nThe following is a description of the relevant parameters:\n\n```bash\nusage: docparser-pdf [-h] --inputs INPUTS --output_dir OUTPUT_DIR [--page_range PAGE_RANGE] [--mode {plain,figure placehold,figure latex}] [--rendering] [--use_llm]\n\noptions:\n  -h, --help            show this help message and exit\n  --inputs INPUTS       Directory where to store PDFs, or a file path to a single PDF\n  --output_dir OUTPUT_DIR\n                        Directory where to store the output results (md/json/images).\n  --page_range PAGE_RANGE\n                        The page range to parse the PDF, the format is 'start_page:end_page', that is, [start, end). Default: full.\n  --mode {plain,figure placehold,figure latex}\n                        The mode for parsing the PDF, to extract only the plain text or the text plus images.\n  --rendering           Is it necessary to render the recognition results of the input PDF to output the recognition range? Default: False.\n  --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.\n  --overwrite_result    If the parsed target file already exists, should it be rewritten? Default: False.\n```\n\n#### From Python\n\n\n### Html\n\n#### From CLI\n\nYou can run the following script to get the html parsing results:\n\n```bash\ndocparser-html https://github.com/mem0ai/mem0\n```\n\nThe following is a description of the relevant parameters:\n\n#### From Python\n",
    "bugtrack_url": null,
    "license": "MIT",
    "summary": "Document parsing tool for LLM training and Rag",
    "version": "0.1.5",
    "project_urls": {
        "Homepage": "https://github.com/feb-co/DocParser"
    },
    "split_keywords": [
        "pdf",
        " llm",
        " chatgpt",
        " transformer",
        " pytorch",
        " deep learning"
    ],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "4353deb7b830b2cb3f5ee73d1d4a473c615270a6beeea8f05646fbf2834819e0",
                "md5": "b6284b97d015439aa2e83faff250fd9a",
                "sha256": "30b5073ae113a410e93c36da8ae103e13d892f7188cb7a43361896c46c29e76e"
            },
            "downloads": -1,
            "filename": "docparser_feb-0.1.5-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "b6284b97d015439aa2e83faff250fd9a",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": ">=3.8",
            "size": 436956,
            "upload_time": "2024-10-09T15:25:35",
            "upload_time_iso_8601": "2024-10-09T15:25:35.484976Z",
            "url": "https://files.pythonhosted.org/packages/43/53/deb7b830b2cb3f5ee73d1d4a473c615270a6beeea8f05646fbf2834819e0/docparser_feb-0.1.5-py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "95e8279f26a16735d0e12e45d2249f290819da4c586f347036da52ddf83efbef",
                "md5": "53036b496cc2dec3bc6acec7f2507f15",
                "sha256": "c4483878537bab79e9a77de2aa39536ad9bd48f16dd79f3073e4022211c15ed2"
            },
            "downloads": -1,
            "filename": "docparser_feb-0.1.5.tar.gz",
            "has_sig": false,
            "md5_digest": "53036b496cc2dec3bc6acec7f2507f15",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=3.8",
            "size": 424251,
            "upload_time": "2024-10-09T15:25:37",
            "upload_time_iso_8601": "2024-10-09T15:25:37.064110Z",
            "url": "https://files.pythonhosted.org/packages/95/e8/279f26a16735d0e12e45d2249f290819da4c586f347036da52ddf83efbef/docparser_feb-0.1.5.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2024-10-09 15:25:37",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "github_user": "feb-co",
    "github_project": "DocParser",
    "travis_ci": false,
    "coveralls": false,
    "github_actions": true,
    "lcname": "docparser-feb"
}
        
Elapsed time: 0.30889s