🚧 Under Active Development 🚧
RAG application powered by Haystack AI, designed to revolutionize question-answering processes. Harnessing advanced technology, our platform seamlessly integrates with Haystack AI to deliver unparalleled accuracy and efficiency in retrieving and generating responses to a wide array of queries. Whether you're seeking insights into complex topics or quick answers to pressing questions, our RAG application is your go-to solution. Experience the future of information retrieval and generation with our user-friendly interface and cutting-edge algorithms. Say goodbye to tedious searches and hello to instant, reliable answers with our RAG application.
- Fast and Efficient: Designed with speed and efficiency at its core. DocAI ensures rapid access to your data.
- Secure: Your data, your control. Always.
- OS Compatible: Ubuntu 22 or newer.
- File Compatibility: PDF
Below are the specifications for the server environment
- RAM: 8 GB
- CPU: 4 core
- Storage: 50 GB
Ensure you have the following installed:
- Docker
- Docker Compose
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Step 1: Clone the repository:
git clone https://github.com/Nikunj3masarani/DocAI && cd DocAI
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Step 2: Copy the
sample.envfilescp sample.env .env
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Step 3: Update the
.envfilesvim .env # or emacs or vscode or nano -
Step 4 Create a volume dictionary for elastic search
mkdir elasticsearch_data
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Step 5 Create a volume dictionary for backend and download models
mkdir backend_models && cd backend_models
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Download Sentence Transformer Model from huggingface
git lfs clone https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2
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Download Rank Model from huggingface
git lfs clone https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-12-v2
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Step 6 Start the application with docker
docker-compose pull docker-compose up
This project is licensed under the Apache 2.0 License - see the LICENSE file for details