chore: import upstream snapshot with attribution

2026-07-13 13:21:23 +08:00
commit b957a53def
5423 changed files with 863745 additions and 0 deletions
@@ -0,0 +1,72 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+from uuid import uuid4
+
+import pandas as pd
+
+from semantic_kernel.connectors.ai.open_ai import OpenAITextEmbedding
+from semantic_kernel.connectors.azure_ai_search import AzureAISearchCollection
+from semantic_kernel.data.vector import VectorStoreCollectionDefinition, VectorStoreField
+
+definition = VectorStoreCollectionDefinition(
+    collection_name="pandas_test_index",
+    fields=[
+        VectorStoreField("key", name="id", type="str"),
+        VectorStoreField("data", name="title", type="str"),
+        VectorStoreField("data", name="content", type="str", is_full_text_indexed=True),
+        VectorStoreField(
+            "vector",
+            name="vector",
+            type="float",
+            dimensions=1536,
+            embedding_generator=OpenAITextEmbedding(ai_model_id="text-embedding-3-small"),
+        ),
+    ],
+    to_dict=lambda record, **_: record.to_dict(orient="records"),
+    from_dict=lambda records, **_: pd.DataFrame(records),
+    container_mode=True,
+)
+
+
+async def main():
+    # create the record collection
+    async with AzureAISearchCollection[str, pd.DataFrame](
+        record_type=pd.DataFrame,
+        definition=definition,
+    ) as collection:
+        await collection.ensure_collection_exists()
+        # create some records
+        records = [
+            {
+                "id": str(uuid4()),
+                "title": "Document about Semantic Kernel.",
+                "content": "Semantic Kernel is a framework for building AI applications.",
+            },
+            {
+                "id": str(uuid4()),
+                "title": "Document about Python",
+                "content": "Python is a programming language that lets you work quickly.",
+            },
+        ]
+
+        # create the dataframe and add the content you want to embed to a new column
+        df = pd.DataFrame(records)
+        df["vector"] = df.apply(lambda row: f"title: {row['title']}, content: {row['content']}", axis=1)
+        print(df.head(1))
+        # upsert the records (for a container, upsert and upsert_batch are equivalent)
+        await collection.upsert(df)
+
+        # retrieve a record
+        result = await collection.get(top=2)
+        if result is None:
+            print("No records found, this is sometimes because the get is too fast and the index is not ready yet.")
+        else:
+            print("Retrieved records:")
+            print(result.to_string())
+
+        await collection.ensure_collection_deleted()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())