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chore: import upstream snapshot with attribution
2026-07-13 12:38:16 +08:00

186 lines
6.0 KiB
Python

"""
Example usage of Qwen3-VL-Reranker with SGLang.
This example demonstrates how to use the Qwen3-VL-Reranker model for multimodal
reranking tasks, supporting text, images, and videos.
Server Launch:
python -m sglang.launch_server \
--model-path Qwen/Qwen3-VL-Reranker-2B \
--served-model-name Qwen3-VL-Reranker-2B \
--trust-remote-code \
--disable-radix-cache \
--chat-template examples/chat_template/qwen3_vl_reranker.jinja
Client Usage:
python examples/runtime/qwen3_vl_reranker.py
"""
import requests
# Server URL
BASE_URL = "http://localhost:30000"
def rerank_text_only():
"""Example: Text-only reranking (backward compatible)."""
print("=" * 60)
print("Text-only reranking example")
print("=" * 60)
request_data = {
"query": "What is machine learning?",
"documents": [
"Machine learning is a branch of artificial intelligence that enables computers to learn from data.",
"The weather in Paris is usually mild with occasional rain.",
"Deep learning is a subset of machine learning using neural networks with many layers.",
],
"instruct": "Retrieve passages that answer the question.",
"return_documents": True,
}
response = requests.post(f"{BASE_URL}/v1/rerank", json=request_data)
results = response.json()
print("Results (sorted by relevance):")
for i, result in enumerate(results):
print(f" {i+1}. Score: {result['score']:.4f} - {result['document'][:60]}...")
print()
def rerank_with_images():
"""Example: Query is text, documents contain images."""
print("=" * 60)
print("Image reranking example")
print("=" * 60)
request_data = {
"query": "A woman playing with her dog on a beach at sunset.",
"documents": [
# Document 1: Text description
"A woman shares a joyful moment with her golden retriever on a sun-drenched beach at sunset.",
# Document 2: Image URL
[
{
"type": "image_url",
"image_url": {
"url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"
},
}
],
# Document 3: Text + Image (mixed)
[
{
"type": "text",
"text": "A joyful scene at the beach:",
},
{
"type": "image_url",
"image_url": {
"url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"
},
},
],
],
"instruct": "Retrieve images or text relevant to the user's query.",
"return_documents": False,
}
response = requests.post(f"{BASE_URL}/v1/rerank", json=request_data)
results = response.json()
# Debug: print raw response if it's an error
if isinstance(results, dict) and "message" in results:
print(f"Error: {results['message']}")
return
if isinstance(results, str):
print(f"Error: {results}")
return
print("Results (sorted by relevance):")
for i, result in enumerate(results):
print(f" {i+1}. Index: {result['index']}, Score: {result['score']:.4f}")
print()
def rerank_multimodal_query():
"""Example: Query contains both text and image."""
print("=" * 60)
print("Multimodal query reranking example")
print("=" * 60)
request_data = {
# Query with text and image
"query": [
{"type": "text", "text": "Find similar images to this:"},
{
"type": "image_url",
"image_url": {
"url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"
},
},
],
"documents": [
"A cat sleeping on a couch.",
"A woman and her dog enjoying the sunset at the beach.",
"A busy city street with cars and pedestrians.",
[
{
"type": "image_url",
"image_url": {
"url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"
},
}
],
],
"instruct": "Find images or descriptions similar to the query image.",
}
response = requests.post(f"{BASE_URL}/v1/rerank", json=request_data)
results = response.json()
# Debug: print raw response if it's an error
if isinstance(results, dict) and "message" in results:
print(f"Error: {results['message']}")
return
if isinstance(results, str):
print(f"Error: {results}")
return
print("Results (sorted by relevance):")
for i, result in enumerate(results):
print(f" {i+1}. Index: {result['index']}, Score: {result['score']:.4f}")
print()
def main():
"""Run all examples."""
print("\nQwen3-VL-Reranker Examples")
print("Make sure the server is running with the correct model and template.\n")
# Check if server is available
try:
response = requests.get(f"{BASE_URL}/health")
if response.status_code != 200:
print(f"Server health check failed: {response.status_code}")
return
except requests.exceptions.ConnectionError:
print(f"Cannot connect to server at {BASE_URL}")
print("Please start the server first with:")
print(" python -m sglang.launch_server \\")
print(" --model-path Qwen/Qwen3-VL-Reranker-2B \\")
print(" --served-model-name Qwen3-VL-Reranker-2B \\")
print(" --trust-remote-code \\")
print(" --disable-radix-cache \\")
print(" --chat-template examples/chat_template/qwen3_vl_reranker.jinja")
return
# Run examples
rerank_text_only()
rerank_with_images()
rerank_multimodal_query()
if __name__ == "__main__":
main()