This document provides an overview and explanation of the code used to create a Semantic Kernel-based tool that integrates with Azure AI Search for Retrieval-Augmented Generation (RAG). In this document we have an AI agent that retrieves inflation rates from an Azure AI Search index, augments user queries with semantic search results, and streams detailed answers about the consumer price index.

A juypter notebook is used to run this

Importing the necessary packages

import json
import os

from typing import Annotated

from IPython.display import display, HTML

from dotenv import load_dotenv

from azure.core.credentials import AzureKeyCredential
from azure.search.documents import SearchClient
from azure.search.documents.indexes import SearchIndexClient
from azure.search.documents.indexes.models import SearchIndex, SimpleField, SearchFieldDataType, SearchableField

from openai import AsyncOpenAI

from semantic_kernel.agents import ChatCompletionAgent, ChatHistoryAgentThread
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
from semantic_kernel.contents import FunctionCallContent,FunctionResultContent, StreamingTextContent
from semantic_kernel.functions import kernel_function

Creating the Semantic Kernel and AI Service

A Semantic Kernel instance is created and configured with an asynchronous OpenAI chat completion service. The service is added to the kernel for use in generating responses.

load_dotenv()
# Initialize the asynchronous OpenAI client
client = AsyncOpenAI(
    api_key=os.environ["GITHUB_TOKEN"],
    base_url="https://models.inference.ai.azure.com/"
)

# Create the OpenAI Chat Completion Service
chat_completion_service = OpenAIChatCompletion(
    ai_model_id="gpt-4o-mini",
    async_client=client,
)

Defining the Prompt Plugin

The PromptPlugin is a native plugin that defines a function to build an augmented prompt using retrieval context

class SearchPlugin:

    def __init__(self, search_client: SearchClient):
        self.search_client = search_client

    @kernel_function(
        name="build_augmented_prompt",
        description="Build an augmented prompt using retrieval context or function results.",
    )
    def build_augmented_prompt(self, query: str, retrieval_context: str) -> str:
        return (
            f"Retrieved Context:\n{retrieval_context}\n\n"
            f"User Query: {query}\n\n"
            "First review the retrieved context, if this does not answer the query, try calling an available plugin functions that might give you an answer. If no context is available, say so."
        )
    
    @kernel_function(
        name="retrieve_documents",
        description="Retrieve documents from the Azure Search service.",
    )
    def get_retrieval_context(self, query: str) -> str:
        results = self.search_client.search(query)
        context_strings = []
        for result in results:
            context_strings.append(f"Document: {result['content']}")
        return "\n\n".join(context_strings) if context_strings else "No results found"

class EggPriceInfoPlugin:
    """A Plugin that provides the egg price in USA for this year."""

    def __init__(self):
        # Dictionary of month and the cost of egg
        self.eggprice_month = {
            "january ": "$4.95",
            "february ": "$5.90",
            "march ": "$6.23",
            "april ": "$5.12",
            "may ": "$4.548",
            "june ": "$3.775",
            "july ": "$3.599",
            "august ": "$3.49"
        }

    @kernel_function(description="Get the egg price for specific month.")
    def get_eggprice_month(self, eggprice_month: str) -> Annotated[str, "Returns the eggprice for the month."]:
        """Get the eggprice for the month."""
        # Normalize the input month (lowercase)
        normalized_eggprice_month = eggprice_month.lower()

        # Look up the price for the month
        if normalized_eggprice_month in self.eggprice_month:
            return f"The price in {eggprice_month} is {self.eggprice_month[normalized_eggprice_month]}."
        else:
            return f"Sorry, I don't have price information for {eggprice_month}. Available months are: January, February, March, April, May, June, July, August"

Vector Database Initialization

We initialize Azure AI Search with persistent storage and add enhanced sample documents. Azure AI Search will be used to store and retrieve documents that provide context for generating accurate responses.

# Initialize Azure AI Search with persistent storage
search_service_endpoint = os.getenv("AZURE_SEARCH_SERVICE_ENDPOINT")
search_api_key = os.getenv("AZURE_SEARCH_API_KEY")
index_name = "price-documents"

search_client = SearchClient(
    endpoint=search_service_endpoint,
    index_name=index_name,
    credential=AzureKeyCredential(search_api_key)
)

index_client = SearchIndexClient(
    endpoint=search_service_endpoint,
    credential=AzureKeyCredential(search_api_key)
)

# Define the index schema
fields = [
    SimpleField(name="id", type=SearchFieldDataType.String, key=True),
    SearchableField(name="content", type=SearchFieldDataType.String)
]

index = SearchIndex(name=index_name, fields=fields)

# Check if index already exists if not, create it
try:
    existing_index = index_client.get_index(index_name)
    print(f"Index '{index_name}' already exists, using the existing index.")
except Exception:
    # Create the index if it doesn't exist
    print(f"Creating new index '{index_name}'...")
    index_client.create_index(index)


# Enhanced sample documents
documents = [
    {"id": "1", "content": " In January a significant monthly increase of 0.5% was driven by a broad rise in prices for shelter, food, and energy.."},
    {"id": "2", "content": "In February The annual inflation rate started to slow, reaching 2.8% year-over-year."},
    {"id": "3", "content": "In March The annual inflation rate cooled sharply to 2.4%, a six-month low.."},
    {"id": "4", "content": "In April Inflation continued its decline, reaching 2.3% year-over-year, the smallest 12-month increase since February 2021.."},
    {"id": "5", "content": "In May 	The annual rate ticked up slightly to 2.4%, but core CPI was cooler than expected, suggesting minimal tariff impact.."},
    {"id": "5", "content": "In June The annual inflation rate rose to 2.7%, with higher food and shelter prices outpacing a decline in gasoline costs.."},
    {"id": "5", "content": "In July 	Inflation held steady at 2.7% annually, as falling gasoline prices were offset by continued increases in core inflation, partly due to tariffs.."},
    {"id": "5", "content": "In Aug 	Economists forecast a 2.9% annual increase, expecting tariffs to have a more apparent impact on prices. Official data is scheduled for release on Thursday, September 11, 2025."},
    
]

# Add documents to the index
search_client.upload_documents(documents)

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