thepipe-api 1.1.5

AI-native scraper for multimodal LLMs.

thepi.pe

Extract markdown and visuals from PDFs URLs, slides, videos, and more, ready for multimodal LLMs. ⚡

thepi.pe is an API that can scrape multimodal data via thepipe.scrape or extract structured data via thepipe.extract from a wide range of sources. It is built to interface with vision-language models such as GPT-4o, and works out-of-the-box with any LLM or vector database. It can be used right away with a hosted GPU cloud, or it can be self-hosted.

Features 🌟

• Extract markdown, tables, and images from any document or webpage
• Extract complex structured data from any document or webpage
• Works out-of-the-box with all LLMs and RAG frameworks
• AI-native filetype detection, layout analysis, and structured data extraction
• Multimodal scraping for video, audio, and image sources

Get started in 5 minutes 🚀

thepi.pe can read a wide range of filetypes and web sources, so it requires a few dependencies. It also requires a strong machine (16GB+ VRAM for optimal PDF & video response times) for AI extraction features. For these reasons, we host a REST API that works out-of-the-box at thepi.pe.

For detailed setup instructions, view the docs.

pip install thepipe-api

Hosted API (Python)

from thepipe.scraper import scrape_file
from thepipe.core import chunks_to_messages
from openai import OpenAI

# scrape markdown, tables, visuals
chunks = scrape_file(filepath="paper.pdf")

# call LLM with clean, comprehensive data
client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4o",
    messages=chunks_to_messages(chunks),
)

Local Installation (Python)

For a local installation, you can use the following command:

pip install -r thepipe-api[local]

And append local=True to your API calls:

chunks = scrape_url(url="https://example.com", local=True)

You can also use The Pipe from the command line:

thepipe path/to/folder --include_regex .*\.tsx

Supported File Types 📚

Source	Input types	Multimodal	Notes
Webpage	URLs starting with http, https, ftp	✔️	Scrapes markdown, images, and tables from web pages. ai_extraction available for AI layout analysis
PDF	.pdf	✔️	Extracts page markdown and page images. ai_extraction available for AI layout analysis
Word Document	.docx	✔️	Extracts text, tables, and images
PowerPoint	.pptx	✔️	Extracts text and images from slides
Video	.mp4, .mov, .wmv	✔️	Uses Whisper for transcription and extracts frames
Audio	.mp3, .wav	✔️	Uses Whisper for transcription
Jupyter Notebook	.ipynb	✔️	Extracts markdown, code, outputs, and images
Spreadsheet	.csv, .xls, .xlsx	❌	Converts each row to JSON format, including row index for each
Plaintext	.txt, .md, .rtf, etc	❌	Simple text extraction
Image	.jpg, .jpeg, .png	✔️	Uses pytesseract for OCR in text-only mode
ZIP File	.zip	✔️	Extracts and processes contained files
Directory	any path/to/folder	✔️	Recursively processes all files in directory
YouTube Video (known issues)	YouTube video URLs starting with https://youtube.com or https://www.youtube.com.	✔️	Uses pytube for video download and Whisper for transcription. For consistent extraction, you may need to modify your pytube installation to send a valid user agent header (see this issue).
Tweet	URLs starting with https://twitter.com or https://x.com	✔️	Uses unofficial API, may break unexpectedly
GitHub Repository	GitHub repo URLs starting with https://github.com or https://www.github.com	✔️	Requires GITHUB_TOKEN environment variable

How it works 🛠️

thepi.pe uses computer vision models and heuristics to extract clean content from the source and process it for downstream use with language models, or vision transformers. The output from thepi.pe is a list of chunks containing all content within the source document. These chunks can easily be converted to a prompt format that is compatible with any LLM or multimodal model with thepipe.core.chunks_to_messages, which gives the following format:

[
  {
    "role": "user",
    "content": [
      {
        "type": "text",
        "text": "..."
      },
      {
        "type": "image_url",
        "image_url": {
          "url": "data:image/jpeg;base64,..."
        }
      }
    ]
  }
]

You can feed these messages directly into the model, or alternatively you can use chunker.chunk_by_document, chunker.chunk_by_page, chunker.chunk_by_section, chunker.chunk_semantic to chunk these messages for a vector database such as ChromaDB or a RAG framework. A chunk can be converted to LlamaIndex Document/ImageDocument with .to_llamaindex.

⚠️ It is important to be mindful of your model's token limit. GPT-4o does not work with too many images in the prompt (see discussion here). To remedy this issue, either use an LLM with a larger context window, extract larger documents with text_only=True, or embed the chunks into vector database.

Sponsors

Thank you to Cal.com for sponsoring this project. Contact [e-mail] for sponsorship information.