# Run PrivateGPT with IPEX-LLM on Intel GPU
[PrivateGPT](https://github.com/zylon-ai/private-gpt) is a production-ready AI project that allows users to chat over documents, etc.; by integrating it with [`ipex-llm`](https://github.com/intel-analytics/ipex-llm), users can now easily leverage local LLMs running on Intel GPU (e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max).
*See the demo of privateGPT running Mistral:7B on Intel Arc A770 below.*
## Quickstart
### 1. Install and Start `Ollama` Service on Intel GPU
Follow the steps in [Run Ollama on Intel GPU Guide](./ollama_quickstart.md) to install and run Ollama on Intel GPU. Ensure that `ollama serve` is running correctly and can be accessed through a local URL (e.g., `https://127.0.0.1:11434`) or a remote URL (e.g., `http://your_ip:11434`).
### 2. Install PrivateGPT
#### Download PrivateGPT
You can either clone the repository or download the source zip from [github](https://github.com/zylon-ai/private-gpt/archive/refs/heads/main.zip):
```bash
git clone https://github.com/zylon-ai/private-gpt
```
#### Install Dependencies
Execute the following commands in a terminal to install the dependencies of PrivateGPT:
```cmd
cd private-gpt
pip install poetry
pip install ffmpy==0.3.1
poetry install --extras "ui llms-ollama embeddings-ollama vector-stores-qdrant"
```
For more details, refer to the [PrivateGPT installation Guide](https://docs.privategpt.dev/installation/getting-started/main-concepts).
### 3. Start PrivateGPT
#### Configure PrivateGPT
Change PrivateGPT settings by modifying `settings.yaml` and `settings-ollama.yaml`.
* `settings.yaml` is always loaded and contains the default configuration. In order to run PrivateGPT locally, you need to replace the tokenizer path under the `llm` option with your local path.
* `settings-ollama.yaml` is loaded if the ollama profile is specified in the PGPT_PROFILES environment variable. It can override configuration from the default `settings.yaml`. You can modify the settings in this file according to your preference. It is worth noting that to use the options `llm_model: ` and `embedding_model: `, you need to first use `ollama pull` to fetch the models locally.
To learn more about the configuration of PrivatePGT, please refer to [PrivateGPT Main Concepts](https://docs.privategpt.dev/installation/getting-started/main-concepts).
#### Start the service
Please ensure that the Ollama server continues to run in a terminal while you're using the PrivateGPT.
Run below commands to start the service in another terminal:
```eval_rst
.. tabs::
.. tab:: Linux
.. code-block:: bash
export no_proxy=localhost,127.0.0.1
PGPT_PROFILES=ollama make run
.. note:
Setting ``PGPT_PROFILES=ollama`` will load the configuration from ``settings.yaml`` and ``settings-ollama.yaml``.
.. tab:: Windows
.. code-block:: bash
set no_proxy=localhost,127.0.0.1
set PGPT_PROFILES=ollama
make run
.. note:
Setting ``PGPT_PROFILES=ollama`` will load the configuration from ``settings.yaml`` and ``settings-ollama.yaml``.
```
### 4. Using PrivateGPT
#### Chat with the Model
To chat with the LLM, select the "LLM Chat" option located in the upper left corner of the page. Type your messages at the bottom of the page and click the "Submit" button to receive responses from the model.
#### Chat over Documents (RAG)
To interact with documents, select the "Query Files" option in the upper left corner of the page. Click the "Upload File(s)" button to upload documents. After the documents have been vectorized, you can type your messages at the bottom of the page and click the "Submit" button to receive responses from the model based on the uploaded content.
