Semantic Kernel with Azure AI Search

pip install semantic-kernel openai

import os
import semantic_kernel as sk
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
from semantic_kernel.connectors.memory.azure_search import AzureCognitiveSearchMemory

# Initialize kernel
kernel = sk.Kernel()

# Add OpenAI chat completion service
kernel.add_service(OpenAIChatCompletion(
    service_id="chat",
    api_key=os.environ["OPENAI_API_KEY"],
    ai_model_id="gpt-4o-mini"
))

# Configure Azure Cognitive Search memory retriever
search_endpoint = os.environ["AZURE_SEARCH_ENDPOINT"]
search_api_key = os.environ["AZURE_SEARCH_API_KEY"]
search_index_name = "your-index-name"  # Replace with your Azure Search index name

azure_search_memory = AzureCognitiveSearchMemory(
    endpoint=search_endpoint,
    api_key=search_api_key,
    index_name=search_index_name
)

# Add the Azure Search memory to kernel
kernel.register_memory(azure_search_memory)

# Example: Add a document to Azure Search (optional, requires index setup)
# azure_search_memory.upload_documents([
#     {"id": "1", "content": "Azure AI Search enables semantic search capabilities."}
# ])

# Query the Azure Search memory
query = "What is Azure AI Search?"
results = azure_search_memory.search(query, top_k=3)

print("Search results:")
for doc in results:
    print(f"- {doc['content']}")

# Use kernel chat service to generate a completion based on retrieved docs
context = "\n".join(doc["content"] for doc in results)
prompt = f"Answer the question based on the following context:\n{context}\nQuestion: {query}"

response = kernel.run("chat", prompt)
print("\nChat completion:")
print(response)

import asyncio

async def async_search_and_chat():
    kernel = sk.Kernel()
    kernel.add_service(OpenAIChatCompletion(
        service_id="chat",
        api_key=os.environ["OPENAI_API_KEY"],
        ai_model_id="gpt-4o-mini"
    ))
    azure_search_memory = AzureCognitiveSearchMemory(
        endpoint=os.environ["AZURE_SEARCH_ENDPOINT"],
        api_key=os.environ["AZURE_SEARCH_API_KEY"],
        index_name="your-index-name"
    )
    kernel.register_memory(azure_search_memory)

    query = "Explain semantic search"
    results = await azure_search_memory.search_async(query, top_k=3)

    context = "\n".join(doc["content"] for doc in results)
    prompt = f"Based on the context:\n{context}\nExplain semantic search."

    response = await kernel.run_async("chat", prompt)
    print(response)

# asyncio.run(async_search_and_chat())