# Run RAGFlow using Ollama with IPEX_LLM [ollama/ollama](https://github.com/ollama/ollama) is popular framework designed to build and run language models on a local machine; you can now use the C++ interface of [`ipex-llm`](https://github.com/intel-analytics/ipex-llm) as an accelerated backend for `ollama` running on Intel **GPU** *(e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max)*. See the demo of running Qwen2-7B on Intel Arc GPU below.

```eval_rst .. note:: `ipex-llm[cpp]==2.5.0b20240527` is consistent with `v0.1.34 `_ of ollama. Our current version is consistent with `v0.1.39 `_ of ollama. ``` ## Quickstart ### 0 Prerequisites - CPU >= 4 cores - RAM >= 16 GB - Disk >= 50 GB - Docker >= 24.0.0 & Docker Compose >= v2.26.1 - Ollama service initialized ### 1. Install and Run Ollama Serve Visit [Run Ollama with IPEX-LLM on Intel GPU](./ollama_quickstart.html), and follow the steps 1) [Install IPEX-LLM for Ollama](./ollama_quickstart.html#install-ipex-llm-for-ollama), 2) [Initialize Ollama](./ollama_quickstart.html#initialize-ollama) 3) [Run Ollama Serve](./ollama_quickstart.html#run-ollama-serve) to install, init and start the Ollama Service. ```eval_rst .. important:: If the `Ragflow` is not deployed on the same machine where Ollama is running (which means `Ragflow` needs to connect to a remote Ollama service), you must configure the Ollama service to accept connections from any IP address. To achieve this, set or export the environment variable `OLLAMA_HOST=0.0.0.0` before executing the command `ollama serve`. .. tip:: If your local LLM is running on Intel Arc™ A-Series Graphics with Linux OS (Kernel 6.2), it is recommended to additionaly set the following environment variable for optimal performance before executing `ollama serve`: .. code-block:: bash export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1 ``` ### 2. Pull and Prepare the Model #### 2.1 Pull Model Now we need to pull a model for coding. Here we use [Qwen/Qwen2-7B](https://huggingface.co/Qwen/Qwen2-7B) model as an example. Open a new terminal window, run the following command to pull [`qwen2:latest`](https://ollama.com/library/qwen2). ```eval_rst .. tabs:: .. tab:: Linux .. code-block:: bash export no_proxy=localhost,127.0.0.1 ./ollama pull qwen2:latest .. tab:: Windows Please run the following command in Miniforge Prompt. .. code-block:: cmd set no_proxy=localhost,127.0.0.1 ollama pull qwen2:latest .. seealso:: Besides Qwen2, there are other coding models you might want to explore, such as Magicoder, Wizardcoder, Codellama, Codegemma, Starcoder, Starcoder2, and etc. You can find these models in the `Ollama model library `_. Simply search for the model, pull it in a similar manner, and give it a try. ``` ### 3. Initialize Ragflow Ensure `vm.max_map_count` >= 262144: > To check the value of `vm.max_map_count`: > > ```bash > $ sysctl vm.max_map_count > ``` > > Reset `vm.max_map_count` to a value at least 262144 if it is not. > > ```bash > # In this case, we set it to 262144: > $ sudo sysctl -w vm.max_map_count=262144 > ``` > > This change will be reset after a system reboot. To ensure your change remains permanent, add or update the `vm.max_map_count` value in **/etc/sysctl.conf** accordingly: > > ```bash > vm.max_map_count=262144 > ``` Clone the repo: ```bash $ git clone https://github.com/infiniflow/ragflow.git ``` ### 4. Start up Ragflow server from Docker Build the pre-built Docker images and start up the server: > Running the following commands automatically downloads the *dev* version RAGFlow Docker image. To download and run a specified Docker version, update `RAGFLOW_VERSION` in **docker/.env** to the intended version, for example `RAGFLOW_VERSION=v0.7.0`, before running the following commands. ```bash $ export no_proxy=localhost,127.0.0.1 $ cd ragflow/docker $ chmod +x ./entrypoint.sh $ docker compose up -d ``` > The core image is about 9 GB in size and may take a while to load. Check the server status after having the server up and running: ```bash $ docker logs -f ragflow-server ``` _The following output confirms a successful launch of the system:_ ```bash ____ ______ __ / __ \ ____ _ ____ _ / ____// /____ _ __ / /_/ // __ `// __ `// /_ / // __ \| | /| / / / _, _// /_/ // /_/ // __/ / // /_/ /| |/ |/ / /_/ |_| \__,_/ \__, //_/ /_/ \____/ |__/|__/ /____/ * Running on all addresses (0.0.0.0) * Running on http://127.0.0.1:9380 * Running on http://x.x.x.x:9380 INFO:werkzeug:Press CTRL+C to quit ``` > If you skip this confirmation step and directly log in to RAGFlow, your browser may prompt a `network anomaly` error because, at that moment, your RAGFlow may not be fully initialized. In your web browser, enter the IP address of your server and log in to RAGFlow. > With the default settings, you only need to enter `http://IP_OF_YOUR_MACHINE` (**sans** port number) as the default HTTP serving port `80` can be omitted when using the default configurations. In [service_conf.yaml](./docker/service_conf.yaml), select the desired LLM factory in `user_default_llm` and update the `API_KEY` field with the corresponding API key. > See [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup) for more information. ### 5. Using the Ragflow ```eval_rst .. note:: For detailed information about how to use RAGFlow, visit the README of `RAGFlow official repository `_. ``` #### Log-in If this is your first time using it, you need to register. After registering, log in with the registered account to access the interface.

#### Configure `Ollama` service URL Access the Ollama settings through **Settings -> Model Providers** in the menu. Fill out the and **Base url**, and then hit the **OK** button at the bottom.

If the connection is successful, you will see the model listed down **Show more models** as illustrated below.

```eval_rst .. note:: If you want to use an Ollama server hosted at a different URL, simply update the **Ollama Base URL** to the new URL and press the **OK** button again to re-confirm the connection to Ollama. ``` #### Create Knowledge Base Go to **Knowledge Base** after clicking **Knowledge Base** at the top bar. Hit the **+Create knowledge base** button on the right. You will be prompted to input a name for the knowledge base.

#### Edit Knowledge Base After inputting a name, you will be directed to edit the knowledge base. Hit the **Dataset** on the left, and then hit **+ Add file -> Local files**.

Choose the file you want to train, and hit the green start button marked to start parsing the file.

It will show **SUCCESS** when the parsing is completed.

Then you can go to **Configuration** and hit **Save** at the bottom to save the changes.

#### Chat with the Model Start new conversations with **Chat** at the top navbar. On the left-side, create a conversation by clicking **Create an Assistant**. Under **Assistant Setting**, give it a name and select your Knowledgebases.

Then go to **Model Setting**, choose your model added by Ollama. Make sure to disable the **Max Tokens** toggle and hit **OK** to start.

Input your questions into the **Message Resume Assistant** textbox at the bottom, and click the button on the right to get responses.

#### Exit RAGFlow To shut down the RAGFlow server, use **Ctrl+C** in the terminal where the Ragflow server is runing, then close your browser tab.