[Doc] Move examples into categories (#11840)

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

docs/source/features/disagg_prefill.md

@@ -21,7 +21,7 @@ Disaggregated prefill DOES NOT improve throughput.
 
 ## Usage example
 
-Please refer to `examples/disaggregated_prefill.sh` for the example usage of disaggregated prefilling.
+Please refer to `examples/online_serving/disaggregated_prefill.sh` for the example usage of disaggregated prefilling.
 
 ## Benchmarks
 

docs/source/features/lora.md

@@ -47,7 +47,7 @@ outputs = llm.generate(
 )
 ```
 
-Check out <gh-file:examples/multilora_inference.py> for an example of how to use LoRA adapters with the async engine and how to use more advanced configuration options.
+Check out <gh-file:examples/offline_inference/multilora_inference.py> for an example of how to use LoRA adapters with the async engine and how to use more advanced configuration options.
 
 ## Serving LoRA Adapters
 

docs/source/features/quantization/auto_awq.md

@@ -47,7 +47,7 @@ print(f'Model is quantized and saved at "{quant_path}"')
 To run an AWQ model with vLLM, you can use [TheBloke/Llama-2-7b-Chat-AWQ](https://huggingface.co/TheBloke/Llama-2-7b-Chat-AWQ) with the following command:
 
 ```console
-$ python examples/llm_engine_example.py --model TheBloke/Llama-2-7b-Chat-AWQ --quantization awq
+$ python examples/offline_inference/llm_engine_example.py --model TheBloke/Llama-2-7b-Chat-AWQ --quantization awq
 ```
 
 AWQ models are also supported directly through the LLM entrypoint:

docs/source/features/quantization/fp8_e4m3_kvcache.md

@@ -28,7 +28,7 @@ Here is an example of how to enable this feature:
 
 ```python
 # two float8_e4m3fn kv cache scaling factor files are provided under tests/fp8_kv, please refer to
-# https://github.com/vllm-project/vllm/blob/main/examples/fp8/README.md to generate kv_cache_scales.json of your own.
+# https://github.com/vllm-project/vllm/blob/main/examples/other/fp8/README.md to generate kv_cache_scales.json of your own.
 
 from vllm import LLM, SamplingParams
 sampling_params = SamplingParams(temperature=1.3, top_p=0.8)

docs/source/features/structured_outputs.md

@@ -131,7 +131,7 @@ completion = client.chat.completions.create(
 print(completion.choices[0].message.content)
 ```
 
-Full example: <gh-file:examples/openai_chat_completion_structured_outputs.py>
+Full example: <gh-file:examples/online_serving/openai_chat_completion_structured_outputs.py>
 
 ## Experimental Automatic Parsing (OpenAI API)
 
@@ -257,4 +257,4 @@ outputs = llm.generate(
 print(outputs[0].outputs[0].text)
 ```
 
-Full example: <gh-file:examples/offline_inference_structured_outputs.py>
+Full example: <gh-file:examples/offline_inference/offline_inference_structured_outputs.py>