[Docs] add v1/score api to native api documentation (#16568)

docs/basic_usage/native_api.ipynb

@@ -17,6 +17,7 @@
     "- `/update_weights`\n",
     "- `/encode`(embedding model)\n",
     "- `/v1/rerank`(cross encoder rerank model)\n",
+    "- `/v1/score`(decoder-only scoring)\n",
     "- `/classify`(reward model)\n",
     "- `/start_expert_distribution_record`\n",
     "- `/stop_expert_distribution_record`\n",
@@ -129,7 +130,7 @@
     "Gets the server information including CLI arguments, token limits, and memory pool sizes.\n",
     "- Note: `get_server_info` merges the following deprecated endpoints:\n",
     "  - `get_server_args`\n",
-    "  - `get_memory_pool_size` \n",
+    "  - `get_memory_pool_size`\n",
     "  - `get_max_total_num_tokens`"
    ]
   },
@@ -366,6 +367,77 @@
     "terminate_process(reranker_process)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## v1/score (decoder-only scoring)\n",
+    "\n",
+    "Compute token probabilities for specified tokens given a query and items. This is useful for classification tasks, scoring responses, or computing log-probabilities.\n",
+    "\n",
+    "Parameters:\n",
+    "- `query`: Query text\n",
+    "- `items`: Item text(s) to score\n",
+    "- `label_token_ids`: Token IDs to compute probabilities for\n",
+    "- `apply_softmax`: Whether to apply softmax to get normalized probabilities (default: False)\n",
+    "- `item_first`: Whether items come first in concatenation order (default: False)\n",
+    "- `model`: Model name\n",
+    "\n",
+    "The response contains `scores` - a list of probability lists, one per item, each in the order of `label_token_ids`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "score_process, port = launch_server_cmd(\n",
+    "    \"\"\"\n",
+    "python3 -m sglang.launch_server --model-path qwen/qwen2.5-0.5b-instruct \\\n",
+    "    --host 0.0.0.0 --log-level warning\n",
+    "\"\"\"\n",
+    ")\n",
+    "\n",
+    "wait_for_server(f\"http://localhost:{port}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Score the probability of different completions given a query\n",
+    "query = \"The capital of France is\"\n",
+    "items = [\"Paris\", \"London\", \"Berlin\"]\n",
+    "\n",
+    "url = f\"http://localhost:{port}/v1/score\"\n",
+    "data = {\n",
+    "    \"model\": \"qwen/qwen2.5-0.5b-instruct\",\n",
+    "    \"query\": query,\n",
+    "    \"items\": items,\n",
+    "    \"label_token_ids\": [9454, 2753],  # e.g. \"Yes\" and \"No\" token ids\n",
+    "    \"apply_softmax\": True,  # Normalize probabilities to sum to 1\n",
+    "}\n",
+    "\n",
+    "response = requests.post(url, json=data)\n",
+    "response_json = response.json()\n",
+    "\n",
+    "# Display scores for each item\n",
+    "for item, scores in zip(items, response_json[\"scores\"]):\n",
+    "    print_highlight(f\"Item '{item}': probabilities = {[f'{s:.4f}' for s in scores]}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "terminate_process(score_process)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},