127 lines
4.8 KiB
Markdown
127 lines
4.8 KiB
Markdown
# Embedding Models
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SGLang provides robust support for embedding models by integrating efficient serving mechanisms with its flexible programming interface. This integration allows for streamlined handling of embedding tasks, facilitating faster and more accurate retrieval and semantic search operations. SGLang's architecture enables better resource utilization and reduced latency in embedding model deployment.
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```{important}
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Embedding models are executed with `--is-embedding` flag and some may require `--trust-remote-code`
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```
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## Quick Start
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### Launch Server
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```shell
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python3 -m sglang.launch_server \
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--model-path Qwen/Qwen3-Embedding-4B \
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--is-embedding \
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--host 0.0.0.0 \
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--port 30000
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```
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### Client Request
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```python
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import requests
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url = "http://127.0.0.1:30000"
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payload = {
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"model": "Qwen/Qwen3-Embedding-4B",
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"input": "What is the capital of France?",
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"encoding_format": "float"
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}
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response = requests.post(url + "/v1/embeddings", json=payload).json()
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print("Embedding:", response["data"][0]["embedding"])
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```
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## Multimodal Embedding Example
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For multimodal models like GME that support both text and images:
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```shell
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python3 -m sglang.launch_server \
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--model-path Alibaba-NLP/gme-Qwen2-VL-2B-Instruct \
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--is-embedding \
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--chat-template gme-qwen2-vl \
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--host 0.0.0.0 \
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--port 30000
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```
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```python
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import requests
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url = "http://127.0.0.1:30000"
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text_input = "Represent this image in embedding space."
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image_path = "https://huggingface.co/datasets/liuhaotian/llava-bench-in-the-wild/resolve/main/images/023.jpg"
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payload = {
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"model": "gme-qwen2-vl",
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"input": [
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{
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"text": text_input
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},
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{
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"image": image_path
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}
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],
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}
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response = requests.post(url + "/v1/embeddings", json=payload).json()
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print("Embeddings:", [x.get("embedding") for x in response.get("data", [])])
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```
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## Matryoshka Embedding Example
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[Matryoshka Embeddings](https://sbert.net/examples/sentence_transformer/training/matryoshka/README.html#matryoshka-embeddings) or [Matryoshka Representation Learning (MRL)](https://arxiv.org/abs/2205.13147) is a technique used in training embedding models. It allows user to trade off between performance and cost.
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### 1. Launch a Matryoshka‑capable model
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If the model config already includes `matryoshka_dimensions` or `is_matryoshka` then no override is needed. Otherwise, you can use `--json-model-override-args` as below:
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```shell
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python3 -m sglang.launch_server \
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--model-path Qwen/Qwen3-Embedding-0.6B \
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--is-embedding \
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--host 0.0.0.0 \
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--port 30000 \
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--json-model-override-args '{"matryoshka_dimensions": [128, 256, 512, 1024, 1536]}'
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```
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1. Setting `"is_matryoshka": true` allows truncating to any dimension. Otherwise, the server will validate that the specified dimension in the request is one of `matryoshka_dimensions`.
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2. Omitting `dimensions` in a request returns the full vector.
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### 2. Make requests with different output dimensions
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```python
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import requests
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url = "http://127.0.0.1:30000"
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# Request a truncated (Matryoshka) embedding by specifying a supported dimension.
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payload = {
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"model": "Qwen/Qwen3-Embedding-0.6B",
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"input": "Explain diffusion models simply.",
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"dimensions": 512 # change to 128 / 1024 / omit for full size
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}
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response = requests.post(url + "/v1/embeddings", json=payload).json()
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print("Embedding:", response["data"][0]["embedding"])
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```
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## Supported Models
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| Model Family | Example Model | Chat Template | Description |
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| ------------------------------------------ | -------------------------------------- | ------------- | --------------------------------------------------------------------------- |
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| **E5 (Llama/Mistral based)** | `intfloat/e5-mistral-7b-instruct` | N/A | High-quality text embeddings based on Mistral/Llama architectures |
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| **GTE-Qwen2** | `Alibaba-NLP/gte-Qwen2-7B-instruct` | N/A | Alibaba's text embedding model with multilingual support |
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| **Qwen3-Embedding** | `Qwen/Qwen3-Embedding-4B` | N/A | Latest Qwen3-based text embedding model for semantic representation |
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| **BGE** | `BAAI/bge-large-en-v1.5` | N/A | BAAI's text embeddings (requires `attention-backend` triton/torch_native) |
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| **GME (Multimodal)** | `Alibaba-NLP/gme-Qwen2-VL-2B-Instruct`| `gme-qwen2-vl`| Multimodal embedding for text and image cross-modal tasks |
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| **CLIP** | `openai/clip-vit-large-patch14-336` | N/A | OpenAI's CLIP for image and text embeddings |
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