Fix application quickstart (intel-analytics#12305)

* fix graphrag quickstart

* fix axolotl quickstart

* fix ragflow quickstart

* fix ragflow quickstart

* fix graphrag toc

* fix comments

* fix comment

* fix comments

docs/mddocs/Quickstart/axolotl_quickstart.md

@@ -45,10 +45,10 @@ Install [axolotl v0.4.0](https://github.com/OpenAccess-AI-Collective/axolotl/tre
 
 ```bash
 # install axolotl v0.4.0
-git clone https://github.com/OpenAccess-AI-Collective/axolotl/tree/v0.4.0
+git clone https://github.com/OpenAccess-AI-Collective/axolotl -b v0.4.0
 cd axolotl
 # replace requirements.txt
-remove requirements.txt
+rm requirements.txt
 wget -O requirements.txt https://raw.githubusercontent.com/intel-analytics/ipex-llm/main/python/llm/example/GPU/LLM-Finetuning/axolotl/requirements-xpu.txt
 pip install -e .
 pip install transformers==4.36.0

docs/mddocs/Quickstart/graphrag_quickstart.md

@@ -18,7 +18,8 @@ The [GraphRAG project](https://github.com/microsoft/graphrag) is designed to lev
 
 Follow the steps in [Run Ollama with IPEX-LLM on Intel GPU Guide](./ollama_quickstart.md) to install `ipex-llm[cpp]==2.1.0` 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`).
 
-**Please note that for GraphRAG, we highly recommand using the stable version of ipex-llm through `pip install ipex-llm[cpp]==2.1.0`**.
+> [!NOTE]
+> Please note that for GraphRAG, we highly recommand using the stable version of ipex-llm through `pip install ipex-llm[cpp]==2.1.0`.
 
 ### 2. Prepare LLM and Embedding Model
 

docs/mddocs/Quickstart/ragflow_quickstart.md

@@ -21,6 +21,7 @@
 - [Pull Model](./ragflow_quickstart.md#2-pull-model)
 - [Start `RAGFlow` Service](./ragflow_quickstart.md#3-start-ragflow-service)
 - [Using `RAGFlow`](./ragflow_quickstart.md#4-using-ragflow)
+- [Troubleshooting](./ragflow_quickstart.md#5-troubleshooting)
 
 ## Quickstart
 
@@ -71,23 +72,23 @@ Now we need to pull a model for RAG using Ollama. Here we use [Qwen/Qwen2-7B](ht
 You can either clone the repository or download the source zip from [github](https://github.com/infiniflow/ragflow/archive/refs/heads/main.zip):
 
 ```bash
-$ git clone https://github.com/infiniflow/ragflow.git
+git clone https://github.com/infiniflow/ragflow.git
 ```
 
 #### 3.2 Environment Settings
 
 Ensure `vm.max_map_count` is set to at least 262144. To check the current value of `vm.max_map_count`, use:
 
 ```bash
-$ sysctl vm.max_map_count
+sysctl vm.max_map_count
 ```
 
 ##### Changing `vm.max_map_count`
 
 To set the value temporarily, use:
 
 ```bash
-$ sudo sysctl -w vm.max_map_count=262144
+sudo sysctl -w vm.max_map_count=262144
 ```
 
 To make the change permanent and ensure it persists after a reboot, add or update the following line in `/etc/sysctl.conf`:
@@ -104,10 +105,10 @@ 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
+export no_proxy=localhost,127.0.0.1
+cd ragflow/docker
+chmod +x ./entrypoint.sh
+docker compose up -d
 ```
 
 > [!NOTE]
@@ -116,7 +117,7 @@ $ docker compose up -d
 Check the server status after having the server up and running:
 
 ```bash
-$ docker logs -f ragflow-server
+docker logs -f ragflow-server
 ```
 
 Upon successful deployment, you will see logs in the terminal similar to the following:
@@ -237,3 +238,18 @@ Input your questions into the **Message Resume Assistant** textbox at the bottom
 #### Exit
 
 To shut down the RAGFlow server, use **Ctrl+C** in the terminal where the Ragflow server is runing, then close your browser tab.
+
+### 5. Troubleshooting
+
+#### Stuck when parsing files `Node <Urllib3HttpNode(http://es01:9200)> has failed for xx times in a row, putting on 30 second timeout`
+
+This is because there's no enough space on the disk and the Docker container stop working. Please left enough space on the disk and make sure the disk usage is below 90%.
+
+#### `Max retries exceeded with url: /encodings/cl100k_base.tiktoken` while starting the RAGFlow service through Docker
+
+This may caused by network problem. To resolve this, you could try to:
+
+1. Attach to the Docker container by `docker exec -it ragflow-server /bin/bash`
+2. Set environment variables like `HTTP_PROXY`, `HTTPS_PROXY`, and `NO_PROXY` at the beginning of the `/ragflow/entrypoint.sh`.
+3. Stop the service by `docker compose stop`.
+4. Restart the service by `docker compose start`.

python/llm/example/GPU/LLM-Finetuning/axolotl/requirements-xpu.txt

@@ -40,4 +40,4 @@ s3fs
 gcsfs
 # adlfs
 
-trl>=0.7.9
+trl>=0.7.9, <=0.9.6