"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"
@@ -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. |
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
importrequests
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."
| **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. |