OpenAI Pooling Client#
Source: examples/openai_pooling_client.py.
1"""
2Example online usage of Pooling API.
3
4Run `vllm serve <model> --task <embed|classify|reward|score>`
5to start up the server in vLLM.
6"""
7import argparse
8import pprint
9
10import requests
11
12
13def post_http_request(prompt: dict, api_url: str) -> requests.Response:
14 headers = {"User-Agent": "Test Client"}
15 response = requests.post(api_url, headers=headers, json=prompt)
16 return response
17
18
19if __name__ == "__main__":
20 parser = argparse.ArgumentParser()
21 parser.add_argument("--host", type=str, default="localhost")
22 parser.add_argument("--port", type=int, default=8000)
23 parser.add_argument("--model",
24 type=str,
25 default="jason9693/Qwen2.5-1.5B-apeach")
26
27 args = parser.parse_args()
28 api_url = f"http://{args.host}:{args.port}/pooling"
29 model_name = args.model
30
31 # Input like Completions API
32 prompt = {"model": model_name, "input": "vLLM is great!"}
33 pooling_response = post_http_request(prompt=prompt, api_url=api_url)
34 print("Pooling Response:")
35 pprint.pprint(pooling_response.json())
36
37 # Input like Chat API
38 prompt = {
39 "model":
40 model_name,
41 "messages": [{
42 "role": "user",
43 "content": [{
44 "type": "text",
45 "text": "vLLM is great!"
46 }],
47 }]
48 }
49 pooling_response = post_http_request(prompt=prompt, api_url=api_url)
50 print("Pooling Response:")
51 pprint.pprint(pooling_response.json())