Criando aplicações de IA com o Microsoft Semantic Kernel e o MongoDB Atlas Vector Search

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!python -m pip install semantic-kernel==0.3.14.dev

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import openai
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import semantic_kernel as sk
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from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion, OpenAITextEmbedding
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from semantic_kernel.connectors.memory.mongodb_atlas import MongoDBAtlasMemoryStore
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kernel = sk.Kernel()
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openai.api_key = '<your-openai-key'
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MONGODB_SEARCH_INDEX="defaultRandomFacts"
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MONGODB_DATABASE="semantic-kernel"
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MONGODB_COLLECTION="randomFacts"
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kernel.add_chat_service("chat-gpt", OpenAIChatCompletion("gpt-3.5-turbo", openai.api_key))
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kernel.add_text_embedding_generation_service("ada", OpenAITextEmbedding("text-embedding-ada-002", openai.api_key))
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kernel.register_memory_store(memory_store=MongoDBAtlasMemoryStore(index_name=MONGODB_SEARCH_INDEX, database_name=MONGODB_DATABASE, connection_string="<mongodb-uri>"))
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kernel.import_skill(sk.core_skills.TextMemorySkill())

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# Wrap your prompt in a function
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prompt = kernel.create_semantic_function("""
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As a friendly AI Copilot, answer the question: Did Albert Einstein like coffee?
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""")
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print(prompt())

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Yes, Albert Einstein was known to enjoy coffee. He was often seen with a cup of coffee in his hand and would frequently visit cafes to discuss scientific ideas with his colleagues over a cup of coffee.

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prompt = kernel.create_semantic_function("""
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As a friendly AI Copilot, answer the question: Did I like coffee?
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""")
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print(prompt())

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As an AI, I don't have personal preferences or experiences, so I can't say whether "I" liked coffee or not. However, coffee is a popular beverage enjoyed by many people around the world. It has a distinct taste and aroma that some people find appealing, while others may not enjoy it as much. Ultimately, whether someone likes coffee or not is a subjective matter and varies from person to person.

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async def populate_memory(kernel: sk.Kernel) -> None:
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# Add some documents to the semantic memory
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await kernel.memory.save_information_async(
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collection=MONGODB_COLLECTION, id="1", text="We enjoy coffee and Starbucks"
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)
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await kernel.memory.save_information_async(
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collection=MONGODB_COLLECTION, id="2", text="We are Associate Developer Advocates at MongoDB"
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)
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await kernel.memory.save_information_async(
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collection=MONGODB_COLLECTION, id="3", text="We have great coworkers and we love our teams!"
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)
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await kernel.memory.save_information_async(
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collection=MONGODB_COLLECTION, id="4", text="Our names are Anaiya and Tim"
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)
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await kernel.memory.save_information_async(
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collection=MONGODB_COLLECTION, id="5", text="We have been to New York City and Dublin"
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)

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print("Populating memory...aka adding in documents")
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await populate_memory(kernel)

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{
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  "mappings": {
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    "dynamic": true,
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    "fields": {
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      "embedding": {
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        "dimensions": 1536,
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        "similarity": "dotProduct",
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        "type": "knnVector"
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      }
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    }
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  }
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}

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result = await kernel.memory.search_async(MONGODB_COLLECTION, 'What is my job title?')
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print(f"Retrieved document: {result[0].text}, {result[0].relevance}")