
Welcome to Docling with Ollama! This tool is combines the best of both Docling for document parsing and Ollama for local models. It enables you to use Docling and Ollama for RAG over PDF files (or any other supported file format) with LlamaIndex. It provides you a nice clean Streamlit GUI to chat with your own documents locally.
Before you begin, ensure you have met the following requirements:
To install Docling With Ollama, follow these steps:
Clone the repo:
bash
git clone https://github.com/fahdmirza/doclingwithollama
Navigate to the project directory:
bash
cd doclingwithollama
Create a virtual environment (recommended):
Use Conda: (recommended)
bash
conda create -n ai python=3.11 -y && conda activate ai
Or Use Python VENV:
bash
python3 -m venv myenv
source myenv/bin/activate # On Windows use `myenv\Scripts\activate`
Install the dependencies:
```bash pip install torch pip install git+https://github.com/huggingface/transformers
pip install llama-index-core llama-index-readers-docling llama-index-node-parser-docling llama-index-readers-file python-dotenv llama-index-llms-ollama llama-index-embeddings-huggingface llama-index-llms-huggingface-api
pip install pdfplumber numpy streamlit
```
To run Docling with Ollama, execute the following command:
streamlit run app.py
Open your browser and go to http://localhost:8501 to see the tool in action, if it doesnt open automatically.
From left panel, upload your local PDF file, and start chatting with them.
Contributions are always welcome! See CONTRIBUTING.md for ways to get started.
This project is licensed under the APACHE 2.0 License - see the LICENSE file for details.
For questions or feedback, please contact Fahd Mirza at https://www.youtube.com/@fahdmirza
$ claude mcp add doclingwithollama \
-- python -m otcore.mcp_server <graph>