chore: import upstream snapshot with attribution

docs_new/docs/basic_usage/send_request.mdx

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+---
+title: "Tutorial: Sending a request"
+metatags:
+    description: "This notebook provides a quick-start guide to use SGLang in chat completions after installation. "
+---
+This notebook provides a quick-start guide to use SGLang in chat completions after installation. Once your server is running, API documentation is available at `http://localhost:30000/docs` (Swagger UI), `http://localhost:30000/redoc` (ReDoc), or `http://localhost:30000/openapi.json` (OpenAPI spec, useful for AI agents). Replace `30000` with your port if using a different one.
+
+- For Vision Language Models, see [OpenAI APIs - Vision](./openai_api_vision).
+- For Embedding Models, see [OpenAI APIs - Embedding](./openai_api_embeddings) and [Encode (embedding model)](./native_api#encode-embedding-model).
+- For Reward Models, see [Classify (reward model)](./native_api#classify-reward-model).
+
+
+## Launch A Server
+
+
+
+```python Example
+from sglang.test.doc_patch import launch_server_cmd
+from sglang.utils import wait_for_server, terminate_process
+
+# This is equivalent to running the following command in your terminal
+# python3 -m sglang.launch_server --model-path qwen/qwen2.5-0.5b-instruct --host 0.0.0.0
+
+server_process, port = launch_server_cmd(
+    """
+python3 -m sglang.launch_server --model-path qwen/qwen2.5-0.5b-instruct \
+ --host 0.0.0.0 --log-level warning
+"""
+)
+
+wait_for_server(f"http://localhost:{port}")
+```
+
+## Using cURL
+
+
+
+
+```python Example
+import subprocess, json
+
+curl_command = f"""
+curl -s http://localhost:{port}/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{{"model": "qwen/qwen2.5-0.5b-instruct", "messages": [{{"role": "user", "content": "What is the capital of France?"}}]}}'
+"""
+
+response = json.loads(subprocess.check_output(curl_command, shell=True))
+print(response)
+```
+
+## Using Python Requests
+
+
+
+```python Example
+import requests
+
+url = f"http://localhost:{port}/v1/chat/completions"
+
+data = {
+    "model": "qwen/qwen2.5-0.5b-instruct",
+    "messages": [{"role": "user", "content": "What is the capital of France?"}],
+}
+
+response = requests.post(url, json=data)
+print(response.json())
+```
+
+## Using OpenAI Python Client
+
+
+
+```python Example
+import openai
+
+client = openai.Client(base_url=f"http://127.0.0.1:{port}/v1", api_key="None")
+
+response = client.chat.completions.create(
+    model="qwen/qwen2.5-0.5b-instruct",
+    messages=[
+        {"role": "user", "content": "List 3 countries and their capitals."},
+    ],
+    temperature=0,
+    max_tokens=64,
+)
+print(response)
+```
+
+### Streaming
+
+
+
+```python Example
+import openai
+
+client = openai.Client(base_url=f"http://127.0.0.1:{port}/v1", api_key="None")
+
+# Use stream=True for streaming responses
+response = client.chat.completions.create(
+    model="qwen/qwen2.5-0.5b-instruct",
+    messages=[
+        {"role": "user", "content": "List 3 countries and their capitals."},
+    ],
+    temperature=0,
+    max_tokens=64,
+    stream=True,
+)
+
+# Handle the streaming output
+for chunk in response:
+    if chunk.choices[0].delta.content:
+        print(chunk.choices[0].delta.content, end="", flush=True)
+```
+
+## Using Native Generation APIs
+
+You can also use the native `/generate` endpoint with requests, which provides more flexibility. An API reference is available at [Sampling Parameters](./sampling_params).
+
+
+
+```python Example
+import requests
+
+response = requests.post(
+    f"http://localhost:{port}/generate",
+    json={
+        "text": "The capital of France is",
+        "sampling_params": {
+            "temperature": 0,
+            "max_new_tokens": 32,
+        },
+    },
+)
+
+print(response.json())
+```
+### Streaming
+
+
+
+```python Example
+import requests, json
+
+response = requests.post(
+    f"http://localhost:{port}/generate",
+    json={
+        "text": "The capital of France is",
+        "sampling_params": {
+            "temperature": 0,
+            "max_new_tokens": 32,
+        },
+        "stream": True,
+    },
+    stream=True,
+)
+
+prev = 0
+for chunk in response.iter_lines(decode_unicode=False):
+    chunk = chunk.decode("utf-8")
+    if chunk and chunk.startswith("data:"):
+        if chunk == "data: [DONE]":
+            break
+        data = json.loads(chunk[5:].strip("\n"))
+        output = data["text"]
+        print(output[prev:], end="", flush=True)
+        prev = len(output)
+```
+
+```python Example
+terminate_process(server_process)
+```