mirror of
https://github.com/microsoft/semantic-kernel.git
synced 2026-10-12 02:19:06 +00:00
Tao Chen
ea2ba47ea4
### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> We need a README in the connectors/ai directory to list all the existing AI connectors. ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> ### Contribution Checklist 1. Add a README 2. Refactor some connector package paths <!-- Before submitting this PR, please make sure: --> - [x] The code builds clean without any errors or warnings - [x] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [x] All unit tests pass, and I have added new tests where possible - [x] I didn't break anyone 😄
63 lines
2.3 KiB
Python
63 lines
2.3 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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from openai import AsyncOpenAI
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from semantic_kernel.connectors.ai.open_ai import OpenAITextEmbedding
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from semantic_kernel.core_plugins.text_memory_plugin import TextMemoryPlugin
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from semantic_kernel.kernel import Kernel
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from semantic_kernel.memory.semantic_text_memory import SemanticTextMemory
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from semantic_kernel.memory.volatile_memory_store import VolatileMemoryStore
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# This concept sample shows how to use the OpenAI connector to add memory
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# to applications with a local embedding model running in LM studio: https://lmstudio.ai/
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# Please follow the instructions here: https://lmstudio.ai/docs/local-server to set up LM studio.
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# The default model used in this sample is from nomic.ai due to its compact size.
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kernel = Kernel()
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service_id = "local-gpt"
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openAIClient: AsyncOpenAI = AsyncOpenAI(
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api_key="fake_key", # This cannot be an empty string, use a fake key
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base_url="http://localhost:1234/v1",
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)
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kernel.add_service(
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OpenAITextEmbedding(
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service_id=service_id, ai_model_id="Nomic-embed-text-v1.5-Embedding-GGUF", async_client=openAIClient
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)
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)
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memory = SemanticTextMemory(storage=VolatileMemoryStore(), embeddings_generator=kernel.get_service(service_id))
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kernel.add_plugin(TextMemoryPlugin(memory), "TextMemoryPlugin")
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async def populate_memory(memory: SemanticTextMemory, collection_id="generic") -> None:
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# Add some documents to the semantic memory
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await memory.save_information(collection=collection_id, id="info1", text="Your budget for 2024 is $100,000")
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await memory.save_information(collection=collection_id, id="info2", text="Your savings from 2023 are $50,000")
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await memory.save_information(collection=collection_id, id="info3", text="Your investments are $80,000")
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async def search_memory_examples(memory: SemanticTextMemory, collection_id="generic") -> None:
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questions = [
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"What is my budget for 2024?",
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"What are my savings from 2023?",
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"What are my investments?",
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]
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for question in questions:
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print(f"Question: {question}")
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result = await memory.search(collection_id, question)
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print(f"Answer: {result[0].text}\n")
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async def main() -> None:
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await populate_memory(memory)
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await search_memory_examples(memory)
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if __name__ == "__main__":
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asyncio.run(main())
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