[Docs] Reduce custom syntax used in docs (#27009)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

docs/deployment/docker.md

@@ -37,7 +37,7 @@ You can add any other [engine-args](../configuration/engine_args.md) you need af
     memory to share data between processes under the hood, particularly for tensor parallel inference.
 
 !!! note
-    Optional dependencies are not included in order to avoid licensing issues (e.g. <gh-issue:8030>).
+    Optional dependencies are not included in order to avoid licensing issues (e.g. <https://github.com/vllm-project/vllm/issues/8030>).
 
     If you need to use those dependencies (having accepted the license terms),
     create a custom Dockerfile on top of the base image with an extra layer that installs them:
@@ -66,7 +66,7 @@ You can add any other [engine-args](../configuration/engine_args.md) you need af
 
 ## Building vLLM's Docker Image from Source
 
-You can build and run vLLM from source via the provided <gh-file:docker/Dockerfile>. To build vLLM:
+You can build and run vLLM from source via the provided [docker/Dockerfile](../../docker/Dockerfile). To build vLLM:
 
 ```bash
 # optionally specifies: --build-arg max_jobs=8 --build-arg nvcc_threads=2

docs/deployment/frameworks/anyscale.md

@@ -5,7 +5,7 @@
 [Anyscale](https://www.anyscale.com) is a managed, multi-cloud platform developed by the creators of Ray.
 
 Anyscale automates the entire lifecycle of Ray clusters in your AWS, GCP, or Azure account, delivering the flexibility of open-source Ray
-without the operational overhead of maintaining Kubernetes control planes, configuring autoscalers, managing observability stacks, or manually managing head and worker nodes with helper scripts like <gh-file:examples/online_serving/run_cluster.sh>.
+without the operational overhead of maintaining Kubernetes control planes, configuring autoscalers, managing observability stacks, or manually managing head and worker nodes with helper scripts like [examples/online_serving/run_cluster.sh](../../../examples/online_serving/run_cluster.sh).
 
 When serving large language models with vLLM, Anyscale can rapidly provision [production-ready HTTPS endpoints](https://docs.anyscale.com/examples/deploy-ray-serve-llms) or [fault-tolerant batch inference jobs](https://docs.anyscale.com/examples/ray-data-llm).
 

docs/deployment/frameworks/retrieval_augmented_generation.md

@@ -36,7 +36,7 @@ pip install -U vllm \
     vllm serve qwen/Qwen1.5-0.5B-Chat --port 8001
     ```
 
-1. Use the script: <gh-file:examples/online_serving/retrieval_augmented_generation_with_langchain.py>
+1. Use the script: [examples/online_serving/retrieval_augmented_generation_with_langchain.py](../../../examples/online_serving/retrieval_augmented_generation_with_langchain.py)
 
 1. Run the script
 
@@ -74,7 +74,7 @@ pip install vllm \
     vllm serve qwen/Qwen1.5-0.5B-Chat --port 8001
     ```
 
-1. Use the script: <gh-file:examples/online_serving/retrieval_augmented_generation_with_llamaindex.py>
+1. Use the script: [examples/online_serving/retrieval_augmented_generation_with_llamaindex.py](../../../examples/online_serving/retrieval_augmented_generation_with_llamaindex.py)
 
 1. Run the script:
 

docs/deployment/frameworks/streamlit.md

@@ -20,7 +20,7 @@ pip install vllm streamlit openai
     vllm serve Qwen/Qwen1.5-0.5B-Chat
     ```
 
-1. Use the script: <gh-file:examples/online_serving/streamlit_openai_chatbot_webserver.py>
+1. Use the script: [examples/online_serving/streamlit_openai_chatbot_webserver.py](../../../examples/online_serving/streamlit_openai_chatbot_webserver.py)
 
 1. Start the streamlit web UI and start to chat: