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README.md

@@ -55,6 +55,36 @@ Output:
           microsoft/IMAGE_UNDERSTANDING      6       1833
 ```
 
+### Example: Vector similarity search
+
+```python
+import lance
+import numpy as np
+
+ds = lance.dataset("hf://datasets/julien-c/hub-stats-lance/datasets.lance")
+
+# Get an embedding to use as query (e.g., from microsoft/ms_marco)
+query_row = ds.to_table(filter="id = 'microsoft/ms_marco'").to_pandas()
+query_embedding = np.array(query_row["embedding"].iloc[0])
+
+# Find 10 nearest neighbors
+results = ds.to_table(
+    nearest={"column": "embedding", "q": query_embedding, "k": 10}
+).to_pandas()
+
+print(results[["id", "likes", "downloads", "_distance"]])
+```
+
+Output:
+```
+                                id  likes  downloads  _distance
+                microsoft/ms_marco    221      11120       2.23
+               jiwonii97/atalk_as3      0          0      10.61
+                 AI-Art-Collab/ae5      0          1      10.85
+                 wgwgwgwgw/dbbdbbd      0          9      10.90
+                      1FDSFS/56803      0          8      10.94
+```
+
 ## Why Lance?
 
 Lance is a modern columnar data format optimized for ML workflows: