[Doc] add embedding rerank doc (#7364)

docs/backend/native_api.ipynb

@@ -16,6 +16,7 @@
     "- `/flush_cache`\n",
     "- `/update_weights`\n",
     "- `/encode`(embedding model)\n",
+    "- `/v1/rerank`(cross encoder rerank model)\n",
     "- `/classify`(reward model)\n",
     "- `/start_expert_distribution_record`\n",
     "- `/stop_expert_distribution_record`\n",
@@ -307,6 +308,63 @@
     "terminate_process(embedding_process)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## v1/rerank (cross encoder rerank model)\n",
+    "Rerank a list of documents given a query using a cross-encoder model. Note that this API is only available for cross encoder model like [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) with `attention-backend` `triton` and `torch_native`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reranker_process, port = launch_server_cmd(\n",
+    "    \"\"\"\n",
+    "python3 -m sglang.launch_server --model-path BAAI/bge-reranker-v2-m3 \\\n",
+    "    --host 0.0.0.0 --disable-radix-cache --chunked-prefill-size -1 --attention-backend triton --is-embedding\n",
+    "\"\"\"\n",
+    ")\n",
+    "\n",
+    "wait_for_server(f\"http://localhost:{port}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute rerank scores for query and documents\n",
+    "\n",
+    "url = f\"http://localhost:{port}/v1/rerank\"\n",
+    "data = {\n",
+    "    \"model\": \"BAAI/bge-reranker-v2-m3\",\n",
+    "    \"query\": \"what is panda?\",\n",
+    "    \"documents\": [\n",
+    "        \"hi\",\n",
+    "        \"The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.\",\n",
+    "    ],\n",
+    "}\n",
+    "\n",
+    "response = requests.post(url, json=data)\n",
+    "response_json = response.json()\n",
+    "for item in response_json:\n",
+    "    print_highlight(f\"Score: {item['score']:.2f} - Document: '{item['document']}'\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "terminate_process(reranker_process)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -322,8 +380,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "terminate_process(embedding_process)\n",
-    "\n",
     "# Note that SGLang now treats embedding models and reward models as the same type of models.\n",
     "# This will be updated in the future.\n",
     "\n",

docs/supported_models/embedding_models.md

@@ -51,3 +51,4 @@ print("Embeddings:", [x.get("embedding") for x in response.get("data", [])])
 | **GTE (QwenEmbeddingModel)**                    | `Alibaba-NLP/gte-Qwen2-7B-instruct`           | N/A           | Alibaba’s general text embedding model (7B), achieving state‑of‑the‑art multilingual performance in English and Chinese.             |
 | **GME (MultimodalEmbedModel)**                  | `Alibaba-NLP/gme-Qwen2-VL-2B-Instruct`        | `gme-qwen2-vl`          | Multimodal embedding model (2B) based on Qwen2‑VL, encoding image + text into a unified vector space for cross‑modal retrieval.      |
 | **CLIP (CLIPEmbeddingModel)**                   | `openai/clip-vit-large-patch14-336`           | N/A           | OpenAI’s CLIP model (ViT‑L/14) for embedding images (and text) into a joint latent space; widely used for image similarity search.   |
+| **BGE (BgeEmbeddingModel)**                     | `BAAI/bge-large-en-v1.5`                        | N/A                 | Currently only support `attention-backend`   `triton` and `torch_native`. BAAI's BGE embedding models optimized for retrieval and reranking tasks. |

docs/supported_models/rerank_models.md

@@ -0,0 +1,49 @@
+# Rerank Models
+
+SGLang offers comprehensive support for rerank models by incorporating optimized serving frameworks with a flexible programming interface. This setup enables efficient processing of cross-encoder reranking tasks, improving the accuracy and relevance of search result ordering. SGLang’s design ensures high throughput and low latency during reranker model deployment, making it ideal for semantic-based result refinement in large-scale retrieval systems.
+
+```{important}
+They are executed with `--is-embedding` and some may require `--trust-remote-code`
+```
+
+## Example Launch Command
+
+```shell
+python3 -m sglang.launch_server \
+  --model-path BAAI/bge-reranker-v2-m3 \
+  --host 0.0.0.0 \
+  --disable-radix-cache \
+  --chunked-prefill-size -1 \
+  --attention-backend triton \
+  --is-embedding \
+  --port 30000
+```
+
+## Example Client Request
+
+```python
+import requests
+
+url = "http://127.0.0.1:30000/v1/rerank"
+
+payload = {
+    "model": "BAAI/bge-reranker-v2-m3",
+    "query": "what is panda?",
+    "documents": [
+        "hi",
+        "The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China."
+    ]
+}
+
+response = requests.post(url, json=payload)
+response_json = response.json()
+
+for item in response_json:
+    print(f"Score: {item['score']:.2f} - Document: '{item['document']}'")
+```
+
+## Supported rerank models
+
+| Model Family (Rerank)                          | Example HuggingFace Identifier       | Chat Template | Description                                                                                                                      |
+|------------------------------------------------|--------------------------------------|---------------|----------------------------------------------------------------------------------------------------------------------------------|
+| **BGE-Reranker (BgeRerankModel)**              | `BAAI/bge-reranker-v2-m3`            | N/A           | Currently only support `attention-backend`   `triton` and `torch_native`.  high-performance cross-encoder reranker model from BAAI. Suitable for reranking search results based on semantic relevance.   |