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
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+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+
+from openai import AsyncOpenAI
+
+from semantic_kernel.connectors.ai.open_ai import OpenAITextEmbedding
+from semantic_kernel.core_plugins.text_memory_plugin import TextMemoryPlugin
+from semantic_kernel.kernel import Kernel
+from semantic_kernel.memory.semantic_text_memory import SemanticTextMemory
+from semantic_kernel.memory.volatile_memory_store import VolatileMemoryStore
+
+# This concept sample shows how to use the OpenAI connector to add memory
+# to applications with a local embedding model running in LM studio: https://lmstudio.ai/
+# Please follow the instructions here: https://lmstudio.ai/docs/local-server to set up LM studio.
+# The default model used in this sample is from nomic.ai due to its compact size.
+
+kernel = Kernel()
+
+service_id = "local-gpt"
+
+openAIClient: AsyncOpenAI = AsyncOpenAI(
+    api_key="fake_key",  # This cannot be an empty string, use a fake key
+    base_url="http://localhost:1234/v1",
+)
+kernel.add_service(
+    OpenAITextEmbedding(
+        service_id=service_id, ai_model_id="Nomic-embed-text-v1.5-Embedding-GGUF", async_client=openAIClient
+    )
+)
+
+memory = SemanticTextMemory(storage=VolatileMemoryStore(), embeddings_generator=kernel.get_service(service_id))
+kernel.add_plugin(TextMemoryPlugin(memory), "TextMemoryPlugin")
+
+
+async def populate_memory(memory: SemanticTextMemory, collection_id="generic") -> None:
+    # Add some documents to the semantic memory
+    await memory.save_information(collection=collection_id, id="info1", text="Your budget for 2024 is $100,000")
+    await memory.save_information(collection=collection_id, id="info2", text="Your savings from 2023 are $50,000")
+    await memory.save_information(collection=collection_id, id="info3", text="Your investments are $80,000")
+
+
+async def search_memory_examples(memory: SemanticTextMemory, collection_id="generic") -> None:
+    questions = [
+        "What is my budget for 2024?",
+        "What are my savings from 2023?",
+        "What are my investments?",
+    ]
+
+    for question in questions:
+        print(f"Question: {question}")
+        result = await memory.search(collection_id, question)
+        print(f"Answer: {result[0].text}\n")
+
+
+async def main() -> None:
+    await populate_memory(memory)
+    await search_memory_examples(memory)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())