Update README.md

README.md

@@ -216,6 +216,88 @@ print(similarity)
 ```
 </details>
 
+<details>
+  <summary>via <a href="https://github.com/vllm-project/vllm">vLLM</a></summary>
+
+```python
+
+import torch
+import torch.nn.functional as F
+from vllm import LLM
+
+INSTRUCTION_CONFIG = {
+    "nl2code": {
+        "query": "Find the most relevant code snippet given the following query:\n",
+        "passage": "Candidate code snippet:\n"
+    },
+    "qa": {
+        "query": "Find the most relevant answer given the following question:\n",
+        "passage": "Candidate answer:\n"
+    },
+    "code2code": {
+        "query": "Find an equivalent code snippet given the following code snippet:\n",
+        "passage": "Candidate code snippet:\n"
+    },
+    "code2nl": {
+        "query": "Find the most relevant comment given the following code snippet:\n",
+        "passage": "Candidate comment:\n"
+    },
+    "code2completion": {
+        "query": "Find the most relevant completion given the following start of code snippet:\n",
+        "passage": "Candidate completion:\n"
+    }
+}
+
+def add_instruction(instruction, text):
+    return f"{instruction}{text}"
+
+def cosine_similarity(x, y):
+    x = F.normalize(x, p=2, dim=1)
+    y = F.normalize(y, p=2, dim=1)
+    return x @ y.T
+
+# Build the queries and documents
+queries = [
+    add_instruction(INSTRUCTION_CONFIG["nl2code"]["query"], "print hello world in python"),
+    add_instruction(INSTRUCTION_CONFIG["nl2code"]["query"], "initialize array of 5 zeros in c++"),
+]
+documents = [
+    add_instruction(INSTRUCTION_CONFIG["nl2code"]["passage"], "print('Hello World!')"),
+    add_instruction(INSTRUCTION_CONFIG["nl2code"]["passage"], "int arr[5] = {0, 0, 0, 0, 0};"),
+]
+all_inputs = queries + documents
+
+# vLLM embedding model
+llm = LLM(
+    model="jinaai/jina-code-embeddings-0.5b",
+    hf_overrides={"architectures": ["Qwen2ForCausalLM"]},
+    task="embed"
+)
+
+# Encode with vLLM
+outputs = llm.encode(all_inputs)
+
+# Collect embeddings into a single tensor
+emb_list = []
+for out in outputs:
+    vec = out.outputs.data.detach()
+    emb_list.append(vec)
+embeddings = torch.stack(emb_list, dim=0)
+
+# Split into query and passage embeddings
+n_q = len(queries)
+query_embeddings = embeddings[:n_q]
+passage_embeddings = embeddings[n_q:]
+
+# Cosine similarity matrix (queries x documents)
+scores = cosine_similarity(query_embeddings, passage_embeddings)
+print(scores)
+# tensor([[0.8171, 0.1230],
+#         [0.1207, 0.5513]])
+```
+
+</details>
+
 ## Training & Evaluation
 
 Please refer to our [technical report of jina-code-embeddings](https://arxiv.org/abs/2508.21290) for training details and benchmarks.