[Doc] add embedding rerank doc (#7364)

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.   |