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LLM: Add RAGFlow with Ollama Example QuickStart (#11338)

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docs/readthedocs/source/_templates/sidebar_quicklinks.html
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<a href="doc/LLM/Quickstart/deepspeed_autotp_fastapi_quickstart.html">Run IPEX-LLM serving on Multiple Intel GPUs <a href="doc/LLM/Quickstart/deepspeed_autotp_fastapi_quickstart.html">Run IPEX-LLM serving on Multiple Intel GPUs
using DeepSpeed AutoTP and FastApi</a> using DeepSpeed AutoTP and FastApi</a>
</li> </li>
<li>
<a href="doc/LLM/Quickstart/ragflow_quickstart.html">Run RAGFlow using Ollama with IPEX_LLM</a>
</li>
</ul> </ul>
</li> </li>
<li> <li>

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- file: doc/LLM/Quickstart/vLLM_quickstart - file: doc/LLM/Quickstart/vLLM_quickstart
- file: doc/LLM/Quickstart/axolotl_quickstart - file: doc/LLM/Quickstart/axolotl_quickstart
- file: doc/LLM/Quickstart/deepspeed_autotp_fastapi_quickstart - file: doc/LLM/Quickstart/deepspeed_autotp_fastapi_quickstart
- file: doc/LLM/Quickstart/ragflow_quickstart
- file: doc/LLM/Overview/KeyFeatures/index - file: doc/LLM/Overview/KeyFeatures/index
title: "Key Features" title: "Key Features"
subtrees: subtrees:

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docs/readthedocs/source/doc/LLM/Quickstart/index.rst
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@ -27,6 +27,7 @@ This section includes efficient guide to show you how to:
* `Run IPEX-LLM Serving with vLLM on Intel GPU <./vLLM_quickstart.html>`_ * `Run IPEX-LLM Serving with vLLM on Intel GPU <./vLLM_quickstart.html>`_
* `Finetune LLM with Axolotl on Intel GPU <./axolotl_quickstart.html>`_ * `Finetune LLM with Axolotl on Intel GPU <./axolotl_quickstart.html>`_
* `Run IPEX-LLM serving on Multiple Intel GPUs using DeepSpeed AutoTP and FastApi <./deepspeed_autotp_fastapi_quickstart.html>`_ * `Run IPEX-LLM serving on Multiple Intel GPUs using DeepSpeed AutoTP and FastApi <./deepspeed_autotp_fastapi_quickstart.html>`_
* `Run RAGFlow using Ollama with IPEX_LLM <./ragflow_quickstart.html>`_
.. |bigdl_llm_migration_guide| replace:: ``bigdl-llm`` Migration Guide .. |bigdl_llm_migration_guide| replace:: ``bigdl-llm`` Migration Guide

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# 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.
<video src="https://llm-assets.readthedocs.io/en/latest/_images/ragflow-record.mp4" width="100%" controls></video>
```eval_rst
.. note::
`ipex-llm[cpp]==2.5.0b20240527` is consistent with `v0.1.34 <https://github.com/ollama/ollama/releases/tag/v0.1.34>`_ of ollama.
Our current version is consistent with `v0.1.39 <https://github.com/ollama/ollama/releases/tag/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 <https://ollama.com/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 <https://github.com/infiniflow/ragflow>`_.
```
#### 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.
<a href="https://llm-assets.readthedocs.io/en/latest/_images/ragflow-login.png" target="_blank">
<img src="https://llm-assets.readthedocs.io/en/latest/_images/ragflow-login.png" width="100%" />
</a>
<a href="https://llm-assets.readthedocs.io/en/latest/_images/ragflow-login2.png" target="_blank">
<img src="https://llm-assets.readthedocs.io/en/latest/_images/ragflow-login2.png" width="100%" />
</a>
#### 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.
<a href="https://llm-assets.readthedocs.io/en/latest/_images/ragflow-add-ollama.png" target="_blank">
<img src="https://llm-assets.readthedocs.io/en/latest/_images/ragflow-add-ollama.png" width="100%" />
</a>
If the connection is successful, you will see the model listed down **Show more models** as illustrated below.
<a href="https://llm-assets.readthedocs.io/en/latest/_images/ragflow-add-ollama2.png" target="_blank">
<img src="https://llm-assets.readthedocs.io/en/latest/_images/ragflow-add-ollama2.png" width="100%" />
</a>
```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.
<a href="https://llm-assets.readthedocs.io/en/latest/_images/ragflow-knowledgebase.png" target="_blank">
<img src="https://llm-assets.readthedocs.io/en/latest/_images/ragflow-knowledgebase.png" width="100%" />
</a>
#### 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**.
<a href="https://llm-assets.readthedocs.io/en/latest/_images/ragflow-knowledgebase2.png" target="_blank">
<img src="https://llm-assets.readthedocs.io/en/latest/_images/ragflow-knowledgebase2.png" width="100%" />
</a>
Choose the file you want to train, and hit the green start button marked to start parsing the file.
<a href="https://llm-assets.readthedocs.io/en/latest/_images/ragflow-knowledgebase3.png" target="_blank">
<img src="https://llm-assets.readthedocs.io/en/latest/_images/ragflow-knowledgebase3.png" width="100%" />
</a>
<a href="https://llm-assets.readthedocs.io/en/latest/_images/ragflow-knowledgebase4.png" target="_blank">
<img src="https://llm-assets.readthedocs.io/en/latest/_images/ragflow-knowledgebase4.png" width="100%" />
</a>
It will show **SUCCESS** when the parsing is completed.
<a href="https://llm-assets.readthedocs.io/en/latest/_images/ragflow-knowledgebase5.png" target="_blank">
<img src="https://llm-assets.readthedocs.io/en/latest/_images/ragflow-knowledgebase5.png" width="100%" />
</a>
Then you can go to **Configuration** and hit **Save** at the bottom to save the changes.
<a href="https://llm-assets.readthedocs.io/en/latest/_images/ragflow-knowledgebase6.png" target="_blank">
<img src="https://llm-assets.readthedocs.io/en/latest/_images/ragflow-knowledgebase6.png" width="100%" />
</a>
#### 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.
<a href="https://llm-assets.readthedocs.io/en/latest/_images/ragflow-chat.png" target="_blank">
<img src="https://llm-assets.readthedocs.io/en/latest/_images/ragflow-chat.png" width="100%" />
</a>
Then go to **Model Setting**, choose your model added by Ollama. Make sure to disable the **Max Tokens** toggle and hit **OK** to start.
<a href="https://llm-assets.readthedocs.io/en/latest/_images/ragflow-chat2.png" target="_blank">
<img src="https://llm-assets.readthedocs.io/en/latest/_images/ragflow-chat2.png" width="100%" />
</a>
<br/>
Input your questions into the **Message Resume Assistant** textbox at the bottom, and click the button on the right to get responses.
<a href="https://llm-assets.readthedocs.io/en/latest/_images/ragflow-chat3.png" target="_blank">
<img src="https://llm-assets.readthedocs.io/en/latest/_images/ragflow-chat3.png" width="100%" />
</a>
#### 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.