chore: import upstream snapshot with attribution

doc/source/data/doc_code/working-with-llms/embedding_example.py

@@ -0,0 +1,63 @@
+"""
+Documentation example and test for embedding model batch inference.
+
+"""
+
+import subprocess
+import sys
+
+subprocess.check_call([sys.executable, "-m", "pip", "install", "--upgrade", "ray[llm]"])
+subprocess.check_call([sys.executable, "-m", "pip", "install", "numpy==1.26.4"])
+
+
+def run_embedding_example():
+    # __embedding_example_start__
+    import ray
+    from ray.data.llm import vLLMEngineProcessorConfig, build_processor
+
+    embedding_config = vLLMEngineProcessorConfig(
+        model_source="sentence-transformers/all-MiniLM-L6-v2",
+        task_type="embed",
+        engine_kwargs=dict(
+            enable_prefix_caching=False,
+            enable_chunked_prefill=False,
+            max_model_len=256,
+            enforce_eager=True,
+        ),
+        batch_size=32,
+        concurrency=1,
+        chat_template_stage=False,  # Skip chat templating for embeddings
+        detokenize_stage=False,     # Skip detokenization for embeddings
+    )
+
+    embedding_processor = build_processor(
+        embedding_config,
+        preprocess=lambda row: dict(prompt=row["text"]),
+        postprocess=lambda row: {
+            "text": row["prompt"],
+            "embedding": row["embeddings"],
+        },
+    )
+
+    texts = [
+        "Hello world",
+        "This is a test sentence",
+        "Embedding models convert text to vectors",
+    ]
+    ds = ray.data.from_items([{"text": text} for text in texts])
+
+    embedded_ds = embedding_processor(ds)
+    embedded_ds.show(limit=1)
+    # __embedding_example_end__
+
+
+if __name__ == "__main__":
+    try:
+        import torch
+
+        if torch.cuda.is_available():
+            run_embedding_example()
+        else:
+            print("Skipping embedding example (no GPU available)")
+    except Exception as e:
+        print(f"Skipping embedding example: {e}")