[Doc] Move examples into categories (#11840)

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

examples/offline_inference/offline_inference_structured_outputs.py

@@ -0,0 +1,78 @@
+from enum import Enum
+
+from pydantic import BaseModel
+
+from vllm import LLM, SamplingParams
+from vllm.sampling_params import GuidedDecodingParams
+
+llm = LLM(model="Qwen/Qwen2.5-3B-Instruct", max_model_len=100)
+
+# Guided decoding by Choice (list of possible options)
+guided_decoding_params = GuidedDecodingParams(choice=["Positive", "Negative"])
+sampling_params = SamplingParams(guided_decoding=guided_decoding_params)
+outputs = llm.generate(
+    prompts="Classify this sentiment: vLLM is wonderful!",
+    sampling_params=sampling_params,
+)
+print(outputs[0].outputs[0].text)
+
+# Guided decoding by Regex
+guided_decoding_params = GuidedDecodingParams(regex="\w+@\w+\.com\n")
+sampling_params = SamplingParams(guided_decoding=guided_decoding_params,
+                                 stop=["\n"])
+prompt = ("Generate an email address for Alan Turing, who works in Enigma."
+          "End in .com and new line. Example result:"
+          "alan.turing@enigma.com\n")
+outputs = llm.generate(prompts=prompt, sampling_params=sampling_params)
+print(outputs[0].outputs[0].text)
+
+
+# Guided decoding by JSON using Pydantic schema
+class CarType(str, Enum):
+    sedan = "sedan"
+    suv = "SUV"
+    truck = "Truck"
+    coupe = "Coupe"
+
+
+class CarDescription(BaseModel):
+    brand: str
+    model: str
+    car_type: CarType
+
+
+json_schema = CarDescription.model_json_schema()
+
+guided_decoding_params = GuidedDecodingParams(json=json_schema)
+sampling_params = SamplingParams(guided_decoding=guided_decoding_params)
+prompt = ("Generate a JSON with the brand, model and car_type of"
+          "the most iconic car from the 90's")
+outputs = llm.generate(
+    prompts=prompt,
+    sampling_params=sampling_params,
+)
+print(outputs[0].outputs[0].text)
+
+# Guided decoding by Grammar
+simplified_sql_grammar = """
+    ?start: select_statement
+
+    ?select_statement: "SELECT " column_list " FROM " table_name
+
+    ?column_list: column_name ("," column_name)*
+
+    ?table_name: identifier
+
+    ?column_name: identifier
+
+    ?identifier: /[a-zA-Z_][a-zA-Z0-9_]*/
+"""
+guided_decoding_params = GuidedDecodingParams(grammar=simplified_sql_grammar)
+sampling_params = SamplingParams(guided_decoding=guided_decoding_params)
+prompt = ("Generate an SQL query to show the 'username' and 'email'"
+          "from the 'users' table.")
+outputs = llm.generate(
+    prompts=prompt,
+    sampling_params=sampling_params,
+)
+print(outputs[0].outputs[0].text)