diff --git a/docs/readthedocs/source/_templates/sidebar_quicklinks.html b/docs/readthedocs/source/_templates/sidebar_quicklinks.html
index d1aed482..e91b5342 100644
--- a/docs/readthedocs/source/_templates/sidebar_quicklinks.html
+++ b/docs/readthedocs/source/_templates/sidebar_quicklinks.html
@@ -68,6 +68,9 @@
Run IPEX-LLM serving on Multiple Intel GPUs
using DeepSpeed AutoTP and FastApi
+
+ Run RAGFlow using Ollama with IPEX_LLM
+
diff --git a/docs/readthedocs/source/_toc.yml b/docs/readthedocs/source/_toc.yml
index 0f5383a8..e619678c 100644
--- a/docs/readthedocs/source/_toc.yml
+++ b/docs/readthedocs/source/_toc.yml
@@ -47,6 +47,7 @@ subtrees:
- file: doc/LLM/Quickstart/vLLM_quickstart
- file: doc/LLM/Quickstart/axolotl_quickstart
- file: doc/LLM/Quickstart/deepspeed_autotp_fastapi_quickstart
+ - file: doc/LLM/Quickstart/ragflow_quickstart
- file: doc/LLM/Overview/KeyFeatures/index
title: "Key Features"
subtrees:
diff --git a/docs/readthedocs/source/doc/LLM/Quickstart/index.rst b/docs/readthedocs/source/doc/LLM/Quickstart/index.rst
index 2e82acde..a5d9ed1f 100644
--- a/docs/readthedocs/source/doc/LLM/Quickstart/index.rst
+++ b/docs/readthedocs/source/doc/LLM/Quickstart/index.rst
@@ -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>`_
* `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 RAGFlow using Ollama with IPEX_LLM <./ragflow_quickstart.html>`_
.. |bigdl_llm_migration_guide| replace:: ``bigdl-llm`` Migration Guide
diff --git a/docs/readthedocs/source/doc/LLM/Quickstart/ragflow_quickstart.md b/docs/readthedocs/source/doc/LLM/Quickstart/ragflow_quickstart.md
new file mode 100644
index 00000000..384ec4e0
--- /dev/null
+++ b/docs/readthedocs/source/doc/LLM/Quickstart/ragflow_quickstart.md
@@ -0,0 +1,254 @@
+# 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.