Skip to content

Latest commit

 

History

History
219 lines (140 loc) · 6.63 KB

README.md

File metadata and controls

219 lines (140 loc) · 6.63 KB

image

License Release

Massive Multimodal Open RAG & Extraction

A scalable multimodal pipeline for processing, indexing, and querying multimodal documents

Ever needed to take 8000 PDFs, 2000 videos, and 500 spreadsheets and feed them to an LLM as a knowledge base? Well, MMORE is here to help you!

MMORE Installation Guide

Installation Option 1: pip (recommended)

To install all dependencies, run:

pip install -e '.[all]'

To install only processor-related dependencies, run:

pip install -e '.[processor]'

To install only RAG-related dependencies, run:

pip install -e '.[rag]'

Minimal Example

from mmore.process.processors.pdf_processor import PDFProcessor 
from mmore.process.processors.base import ProcessorConfig
from mmore.type import MultimodalSample

pdf_file_paths = ["examples/sample_data/pdf/calendar.pdf"]
out_file = "results/example.jsonl"

pdf_processor_config = ProcessorConfig(custom_config={"output_path": "results"})
pdf_processor = PDFProcessor(config=pdf_processor_config)
result_pdf = pdf_processor.process_batch(pdf_file_paths, True, 1) # args: file_paths, fast mode (True/False), num_workers

MultimodalSample.to_jsonl(out_file, result_pdf)

Installation Option 2: uv

Step 1: Install system dependencies

sudo apt update
sudo apt install -y ffmpeg libsm6 libxext6 chromium-browser libnss3 \
  libgconf-2-4 libxi6 libxrandr2 libxcomposite1 libxcursor1 libxdamage1 \
  libxext6 libxfixes3 libxrender1 libasound2 libatk1.0-0 libgtk-3-0 libreoffice

Step 2: Install uv

Refer to the uv installation guide for detailed instructions.

curl -LsSf https://astral.sh/uv/install.sh | sh

Step 3: Clone this repository

git clone https://github.com/swiss-ai/mmore
cd mmore

Step 4: Install project and dependencies

uv sync

For CPU-only installation, use:

uv sync --extra cpu

Step 5: Run a test command

Activate the virtual environment before running commands:

source .venv/bin/activate

Alternatively, prepend each command with uv run:

# Run processing
python -m mmore process --config_file examples/process_config.yaml

# Run indexer
python -m mmore index --config-file ./examples/index/indexer_config.yaml

# Run RAG
python -m mmore rag --config-file ./examples/rag/rag_config_local.yaml

Installation Option 3: Docker

Note: For manual installation without Docker, refer to the section below.

Step 1: Install Docker

Follow the official Docker installation guide.

Step 2: Build the Docker image

docker build . --tag mmore

To build for CPU-only platforms (results in a smaller image size):

docker build --build-arg PLATFORM=cpu -t mmore .

Step 3: Start an interactive session

docker run -it -v ./test_data:/app/test_data mmore

Note: The test_data folder is mapped to /app/test_data inside the container, corresponding to the default path in examples/process_config.yaml.

Step 4: Run the application inside the container

# Run processing
mmore process --config-file examples/process/config.yaml

# Run indexer
mmore index --config-file ./examples/index/indexer_config.yaml

# Run RAG
mmore rag --config-file ./examples/rag/rag_config_local.yaml

Usage

To launch the MMORE pipeline follow the specialised instructions in the docs.

The MMORE pipelines archicture

  1. 📄 Input Documents
    Upload your multimodal documents (PDFs, videos, spreadsheets, and more) into the pipeline.

  2. 🔍 Process Extracts and standardizes text, metadata, and multimedia content from diverse file formats. Easily extensible ! Add your own processors to handle new file types.
    Supports fast processing for specific types.

  3. 📁 Index Organizes extracted data into a hybrid retrieval-ready Vector Store DB, combining dense and sparse indexing through Milvus. Your vector DB can also be remotely hosted and only need to provide a standard API.

  4. 🤖 RAG Use the indexed documents inside a Retrieval-Augmented Generation (RAG) system that provides a LangChain interface. Plug in any LLM with a compatible interface or add new ones through an easy-to-use interface. Supports API hosting or local inference.

  5. 🎉 Evaluation
    Coming soon An easy way to evaluate the performance of your RAG system using Ragas

See the /docs directory for additional details on each modules and hands-on tutorials on parts of the pipeline.

🚧 Supported File Types

Category File Types Supported Device Fast Mode
Text Documents DOCX, MD, PPTX, XLSX, TXT, EML CPU ❌
PDFs PDF GPU/CPU ✅
Media Files MP4, MOV, AVI, MKV, MP3, WAV, AAC GPU/CPU ✅
Web Content (TBD) Webpages GPU/CPU ✅

Contributing

We welcome contributions to improve the current state of the pipeline, feel free to:

  • Open an issue to report a bug or ask for a new feature
  • Open a pull request to fix a bug or add a new feature
  • You can find ongoing new features and bugs in the [Issues]

Don't hesitate to star the project ⭐ if you find it interesting! (you would be our star)

License

This project is licensed under the Apache 2.0 License, see the LICENSE 🎓 file for details.

Acknowledgements

This project is part of the OpenMeditron initiative developed in LiGHT lab at EPFL/Yale/CMU Africa in collaboration with the SwissAI initiative. Thank you Scott Mahoney, Mary-Anne Hartley