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gptpdf ex LLMs: Introductio ad gptpdf, institutionem et usus methodos, ac accuratiorem ducem ad applicationes casus

Tabula contentorum

Introductio ad gptpdf

1. Processus processus

Primus gradus est bibliothecam PyMuPDF uti ad omnes regiones non-textas PDF et notare eas, ut:

In secundo gradu, magnum exemplar visuale (ut GPT-4o) utere ut fasciculi notae ad parse et obtinendum.

Quomodo institutionem et usum gptpdf

1. institutionem

2. usus

Sacri test.py codice

3、API

Causa applicationis gptpdf

---

Introductio ad gptpdf

gptpdf instrumentum est quo exempla magna lingua visuali maxime utitur (ut GPT-4o) ad parse PDF in markdownum.Accessus noster valde simplex est (modo 293 lineas codicis) sed ferePerfecte parse typographiae, formulae mathematicae, tabulae, tabulae, chartae, etc. . Pretium mediocris in pagina tantum $0.013 est, et utimur ad mutuam cum OpenAI API GeneralAgenti lib. pdfgpt-ui instrumentum visualisationis est secundum gptpdf.

Github oratio：GitHub - CosmosShadow/gptpdf: Using GPT ad parse PDF

1、Processus fluxus

Primus gradus est bibliothecam PyMuPDF uti ad omnes regiones non-textas PDF et notare eas, ut:

In secundo gradu, magnum exemplar visuale (ut GPT-4o) utere ut fasciculi notae ad parse et obtinendum.

Quomodo institutionem et usum gptpdf

1、install

pip install gptpdf

2、usus

from gptpdf import parse_pdf

api_key = 'Your OpenAI API Key'
content, image_paths = parse_pdf(pdf_path, api_key=api_key)
print(content)

Pro maiori, vide test/test.py

inscriptio:https://github.com/CosmosShadow/gptpdf/blob/main/test/test.py

Sacri test.py codice

import os

# 从 .env 文件中加载环境变量
import dotenv
dotenv.load_dotenv()

def test_use_api_key():
    from gptpdf import parse_pdf
    pdf_path = '../examples/attention_is_all_you_need.pdf'
    output_dir = '../examples/attention_is_all_you_need/'
    # 从环境变量中获取 OPENAI_API_KEY 和 OPENAI_API_BASE
    api_key = os.getenv('OPENAI_API_KEY')
    base_url = os.getenv('OPENAI_API_BASE')
    # 手动提供 OPENAI_API_KEY 和 OPENAI_API_BASE
    content, image_paths = parse_pdf(pdf_path, output_dir=output_dir, api_key=api_key, base_url=base_url, model='gpt-4o', gpt_worker=6)
    # 输出解析后的内容和图像路径
    print(content)
    print(image_paths)
    # 同时会生成 output_dir/output.md 文件

def test_use_env():
    from gptpdf import parse_pdf
    pdf_path = '../examples/attention_is_all_you_need.pdf'
    output_dir = '../examples/attention_is_all_you_need/'
    # 使用环境变量中的 OPENAI_API_KEY 和 OPENAI_API_BASE
    content, image_paths = parse_pdf(pdf_path, output_dir=output_dir, model='gpt-4o', verbose=True)
    # 输出解析后的内容和图像路径
    print(content)
    print(image_paths)
    # 同时会生成 output_dir/output.md 文件

def test_azure():
    from gptpdf import parse_pdf
    # Azure API Key
    api_key = '8ef0b4df45e444079cd5a4xxxxx' 
    # Azure API 基础 URL
    base_url = 'https://xxx.openai.azure.com/' 
    # Azure 部署的模型 ID 名称（不是 OpenAI 模型名称）
    model = 'azure_xxxx'

    pdf_path = '../examples/attention_is_all_you_need.pdf'
    output_dir = '../examples/attention_is_all_you_need/'
    # 使用提供的 Azure API Key 和基础 URL
    content, image_paths = parse_pdf(pdf_path, output_dir=output_dir, api_key=api_key, base_url=base_url, model=model, verbose=True)
    # 输出解析后的内容和图像路径
    print(content)
    print(image_paths)

if __name__ == '__main__':
    # 取消注释以运行特定的测试函数
    # test_use_api_key()
    # test_use_env()
    test_azure()

3、API

parse_pdf(pdf_path, output_dir='./', api_key=Nulla, base_url=Nulla, exemplar='gpt-4o', verbose=False)
Parse fasciculi pdf in tabella notae et notae notae et index omnium imaginum viarum redde.

• pdf_path:pdf file path

• output_dir : presul output.Repone omnes imagines et markdownum files

• api_key : OpenAI API clavis (libitum). Si non provisum est, ambitus OPENAI_API_KEY variabilis adhibetur.

• base_url : OpenAI basis URL. (libitum). Si non provisum, OPENAI_BASE_URL ambitus variabilis adhibetur.

• exemplar : Multi modale magnum exemplar in OpenAI API forma, default est "auimus-4o". Si opus est ut alia exempla ut

  Antecedens deinde