chore: import upstream snapshot with attribution

python/samples/concepts/rag/rag_with_vector_collection.py

@@ -0,0 +1,105 @@
+# Copyright (c) Microsoft. All rights reserved.
+import asyncio
+from dataclasses import dataclass
+from typing import Annotated
+
+from semantic_kernel import Kernel
+from semantic_kernel.connectors.ai import FunctionChoiceBehavior
+from semantic_kernel.connectors.ai.open_ai import (
+    OpenAIChatCompletion,
+    OpenAIChatPromptExecutionSettings,
+    OpenAITextEmbedding,
+)
+from semantic_kernel.connectors.in_memory import InMemoryCollection
+from semantic_kernel.data.vector import VectorStoreField, vectorstoremodel
+from semantic_kernel.functions import KernelArguments
+
+"""
+This sample shows a really easy way to have RAG with a vector store.
+It creates a simple datamodel, and then creates a collection with that datamodel.
+Then we create a function that can search the collection.
+Finally, in two different ways we call the function to search the collection.
+"""
+
+
+# Define a data model for the collection
+# This model will be used to store the information in the collection
+@vectorstoremodel(collection_name="budget")
+@dataclass
+class BudgetItem:
+    id: Annotated[str, VectorStoreField("key")]
+    text: Annotated[str, VectorStoreField("data")]
+    embedding: Annotated[
+        list[float] | str | None,
+        VectorStoreField("vector", dimensions=1536, embedding_generator=OpenAITextEmbedding()),
+    ] = None
+
+    def __post_init__(self):
+        if self.embedding is None:
+            self.embedding = self.text
+
+
+async def main():
+    kernel = Kernel()
+
+    kernel.add_service(OpenAIChatCompletion())
+
+    async with InMemoryCollection(record_type=BudgetItem) as collection:
+        await collection.ensure_collection_exists()
+        # Add information to the collection
+        await collection.upsert(
+            [
+                BudgetItem(id="info1", text="My budget for 2022 is $50,000"),
+                BudgetItem(id="info1", text="My budget for 2023 is $75,000"),
+                BudgetItem(id="info1", text="My budget for 2024 is $100,000"),
+                BudgetItem(id="info2", text="My budget for 2025 is $150,000"),
+            ],
+        )
+        # Create a function to search the collection
+        # note the string_mapper, this is used to map the result of the search to a string
+        kernel.add_function(
+            "memory",
+            collection.create_search_function(
+                function_name="recall",
+                description="Recalls the budget information.",
+                string_mapper=lambda x: x.record.text,
+            ),
+        )
+
+        # Call the search function directly from from a template.
+        result = await kernel.invoke_prompt(
+            function_name="budget",
+            plugin_name="BudgetPlugin",
+            prompt="{{memory.recall 'budget by year'}} What is my budget for 2024?",
+        )
+        print("Called from template")
+        print(result)
+        print("======================")
+        # Let the LLM choose the function to call
+        result = await kernel.invoke_prompt(
+            function_name="budget",
+            plugin_name="BudgetPlugin",
+            prompt="What is my budget for 2024?",
+            arguments=KernelArguments(
+                settings=OpenAIChatPromptExecutionSettings(
+                    function_choice_behavior=FunctionChoiceBehavior.Auto(),
+                ),
+            ),
+        )
+        print("Called from LLM")
+        print(result)
+
+
+"""
+Output:
+
+Called from template
+Your budget for 2024 is $100,000.
+======================
+Called from LLM
+Your budget for 2024 is $100,000.
+
+"""
+
+if __name__ == "__main__":
+    asyncio.run(main())