v0.10.1rc1

examples/offline_embed.py

@@ -0,0 +1,58 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Copyright 2023 The vLLM team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# This file is a part of the vllm-ascend project.
+# Adapted from https://www.modelscope.cn/models/Qwen/Qwen3-Embedding-0.6B
+#
+
+import os
+
+import torch
+from vllm import LLM
+
+os.environ["VLLM_USE_MODELSCOPE"] = "True"
+os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
+
+def get_detailed_instruct(task_description: str, query: str) -> str:
+    return f'Instruct: {task_description}\nQuery:{query}'
+
+
+def main():
+    # Each query must come with a one-sentence instruction that describes the task
+    task = 'Given a web search query, retrieve relevant passages that answer the query'
+
+    queries = [
+        get_detailed_instruct(task, 'What is the capital of China?'),
+        get_detailed_instruct(task, 'Explain gravity')
+    ]
+    # No need to add instruction for retrieval documents
+    documents = [
+        "The capital of China is Beijing.",
+        "Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun."
+    ]
+    input_texts = queries + documents
+
+    model = LLM(model="Qwen/Qwen3-Embedding-0.6B", task="embed")
+
+    outputs = model.embed(input_texts)
+    embeddings = torch.tensor([o.outputs.embedding for o in outputs])
+    # Calculate the similarity scores between the first two queries and the last two documents
+    scores = (embeddings[:2] @ embeddings[2:].T)
+    print(scores.tolist())
+    # [[0.7620252966880798, 0.14078938961029053], [0.1358368694782257, 0.6013815999031067]]
+
+
+if __name__ == "__main__":
+    main()