thepipe
Extract clean markdown from PDFs, URLs, Word docs, slides, videos, and more, ready for any LLM. ⚡
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The Pipe is a multimodal-first tool for feeding files and web pages into vision-language models such as GPT-4V. It is best for LLM and RAG applications that require a deep understanding of tricky data sources. The Pipe is available as a hosted API at thepi.pe, or it can be set up locally.
README:
thepi.pe is a package that can scrape clean markdown and extract structured data from tricky sources, like PDFs. It uses vision-language models (VLMs) under the hood, and works out-of-the-box with any LLM, VLM, or vector database. It can be used right away on a hosted cloud, or it can be run locally.
- Scrape clean markdown, tables, and images from any document or webpage
- Works out-of-the-box with LLMs, vector databases, and RAG frameworks
- AI-native filetype detection, layout analysis, and structured data extraction
- Accepts a wide range of sources, including Word docs, Powerpoints, Python notebooks, GitHub repos, videos, audio, and more
thepi.pe can read a wide range of filetypes and web sources, so it requires a few dependencies. It also requires vision-language model inference for AI extraction features. For these reasons, we host an API that works out-of-the-box. For more detailed setup instructions, view the docs.
pip install thepipe-apiYou can get an API key by signing up for a free account at thepi.pe. It is completely free to try out. The, simply set the THEPIPE_API_KEY environment variable to your API key.
from thepipe.scraper import scrape_file
from thepipe.core import chunks_to_messages
from openai import OpenAI
# scrape clean markdown
chunks = scrape_file(filepath="paper.pdf", ai_extraction=False)
# call LLM with scraped chunks
client = OpenAI()
response = client.chat.completions.create(
model="gpt-4o",
messages=chunks_to_messages(chunks),
)For a local installation, you can use the following command:
pip install thepipe-api[local]You must have a local LLM server setup and running for AI extraction features. You can use any local LLM server that follows OpenAI format (such as LiteLLM) or a provider (such as OpenRouter or OpenAI). Next, set the LLM_SERVER_BASE_URL environment variable to your LLM server's endpoint URL and set LLM_SERVER_API_KEY. the DEFAULT_AI_MODEL environment variable can be set to your VLM of choice. For example, you would use openai/gpt-4o-mini if using OpenRouter or gpt-4o-mini if using OpenAI.
For full functionality with media-rich sources, you will need to install the following dependencies:
apt-get update && apt-get install -y git ffmpeg tesseract-ocr
python -m playwright install --with-deps chromiumWhen using thepi.pe locally, be sure to append local=True to your function calls:
chunks = scrape_url(url="https://example.com", local=True)You can also use thepi.pe from the command line:
thepipe path/to/folder --include_regex .*\.tsx --local| 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 content extraction from the webpage's screenshot |
.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 |
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 withtext_only=True, or embed the chunks into vector database.
Thank you to Cal.com for sponsoring this project.
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