👉 DocuMind: Your documents, your AI-powered mind. 🌿
Turn your documents into dynamic knowledge sources with DocuMind! Simply upload a document (currently supports only pdf format), ask questions, and get instant, accurate responses. Powered by advanced Retrieval-Augmented Generation (RAG) technology, DocuMind understands the content and provides clear and insightful answers. Whether it’s contracts, research papers, reports, or technical manuals — DocuMind helps you access information in seconds.
- Ask questions about your documents and get instant, accurate responses.
- Provides relevant, fact-based answers using RAG.
- Documents are processed securely on your local machine with no data leakage.
- Using Axum rust server at backend to expose REST Apis and connect with Ollama server for inference & Qdrant vector database for storage.
- Tauri app with React UI for frontend.
Make sure you have following installed
- Rust
- Ollama
- Docker
- Node (v18.19 or later) & yarn
docker volume create qdrant_data
docker run -d \
--name qdrant \
-p 6333:6333 \
-v qdrant_data:/qdrant/storage \
qdrant/qdrant
ollama pull nomic-embed-text:latest
ollama pull llama3.1:8b
# Clone the repo
git clone https://github.com/Harry-027/DocuMind
# Change the directory
cd DocuMind
# Run the server
make app_server
# Run the tauri client in a new terminal
make tauri_client
# Run the app client in another new terminal
make app_client
In the env.yaml
, you can configure the following -
- Chunking Strategy: You can configure the chunk size (
embedding_model_chunk_size
). - Embedding Model: Customize the embedding model (
embedding_model_name
) for better document understanding. - LLM Model: Customize the LLM model (
generate_model_name
) for better document understanding.
Screenshots
DocuMind is licensed under the MIT License. See the LICENSE file for details.
Feel free to open issues or submit pull requests.
For support or inquiries, reach out at harishmmp@gmail.com