Sync from v0.13

examples/pooling/embed/embedding_requests_base64_client.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Example Python client for embedding API using vLLM API server
+NOTE:
+    start a supported embeddings model server with `vllm serve`, e.g.
+    vllm serve intfloat/e5-small
+"""
+
+import argparse
+import base64
+
+import requests
+import torch
+
+from vllm.utils.serial_utils import (
+    EMBED_DTYPE_TO_TORCH_DTYPE,
+    ENDIANNESS,
+    binary2tensor,
+)
+
+
+def post_http_request(prompt: dict, api_url: str) -> requests.Response:
+    headers = {"User-Agent": "Test Client"}
+    response = requests.post(api_url, headers=headers, json=prompt)
+    return response
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--host", type=str, default="localhost")
+    parser.add_argument("--port", type=int, default=8000)
+    parser.add_argument("--model", type=str, default="intfloat/e5-small")
+
+    return parser.parse_args()
+
+
+def main(args):
+    api_url = f"http://{args.host}:{args.port}/v1/embeddings"
+    model_name = args.model
+
+    # The OpenAI client does not support the embed_dtype and endianness parameters.
+    for embed_dtype in EMBED_DTYPE_TO_TORCH_DTYPE:
+        for endianness in ENDIANNESS:
+            prompt = {
+                "model": model_name,
+                "input": "vLLM is great!",
+                "encoding_format": "base64",
+                "embed_dtype": embed_dtype,
+                "endianness": endianness,
+            }
+            response = post_http_request(prompt=prompt, api_url=api_url)
+
+            embedding = []
+            for data in response.json()["data"]:
+                binary = base64.b64decode(data["embedding"])
+                tensor = binary2tensor(binary, (-1,), embed_dtype, endianness)
+                embedding.append(tensor.to(torch.float32))
+            embedding = torch.cat(embedding)
+            print(embed_dtype, endianness, embedding.shape)
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    main(args)