The following is a sample to retrieve drone images using agentic retrieval:

#!/usr/bin/python

# azure-ai-agents==1.0.0

# azure-ai-projects==1.0.0b11

# azure-ai-vision-imageanalysis==1.0.0

# azure-common==1.1.28

# azure-core==1.34.0

# azure-identity==1.22.0

# azure-search-documents==11.6.0b12

# azure-storage-blob==12.25.1

# azure_ai_services==0.1.0

from dotenv import load_dotenv

from azure.identity import DefaultAzureCredential, get_bearer_token_provider

from azure.ai.agents import AgentsClient

from azure.core.credentials import AzureKeyCredential

from azure.ai.projects import AIProjectClient

from azure.ai.agents.models import AzureAISearchTool, AzureAISearchQueryType, MessageRole, ListSortOrder

import os

load_dotenv(override=True)

project_endpoint = os.environ["AZURE_PROJECT_ENDPOINT"]

project_api_key = os.environ["AZURE_PROJECT_API_KEY"]

agent_model = os.getenv("AZURE_AGENT_MODEL", "gpt-4o-mini")

search_endpoint = os.environ["AZURE_SEARCH_SERVICE_ENDPOINT"]

api_version = os.getenv("AZURE_SEARCH_API_VERSION")

search_api_key = os.getenv("AZURE_SEARCH_ADMIN_KEY")

credential = AzureKeyCredential(search_api_key)

token_provider = get_bearer_token_provider(DefaultAzureCredential(), "https://search.azure.com/.default")

index_name = os.getenv("AZURE_SEARCH_INDEX_NAME", "index00")

azure_openai_endpoint = os.environ["AZURE_OPENAI_ENDPOINT"]

azure_openai_api_key = os.getenv("AZURE_OPENAI_API_KEY")

azure_openai_gpt_deployment = os.getenv("AZURE_OPENAI_GPT_DEPLOYMENT", "gpt-4o-mini")

azure_openai_gpt_model = os.getenv("AZURE_OPENAI_GPT_MODEL", "gpt-4o-mini")

azure_openai_embedding_deployment = os.getenv("AZURE_OPENAI_EMBEDDING_DEPLOYMENT", "text-embedding-ada-002")

azure_openai_embedding_model = os.getenv("AZURE_OPENAI_EMBEDDING_MODEL", "text-embedding-ada-002")

chat_agent_name = os.getenv("AZURE_CHAT_AGENT_NAME", "chat-agent-in-a-team")

search_agent_name = os.getenv("AZURE_SEARCH_AGENT_NAME", "search-agent-in-a-team")

search_connection_id = os.getenv("AI_AZURE_AI_CONNECTION_ID","/subscriptions/656e67c6-f810-4ea6-8b89-636dd0b6774c/resourceGroups/rg-ctl-2/providers/Microsoft.CognitiveServices/accounts/found-vision-1/projects/droneimage/connections/srchvision01")

api_version = "2025-05-01-Preview"

agent_max_output_tokens=10000

from azure.search.documents.indexes.models import KnowledgeAgent, KnowledgeAgentAzureOpenAIModel, KnowledgeAgentTargetIndex, KnowledgeAgentRequestLimits, AzureOpenAIVectorizerParameters

from azure.search.documents.indexes import SearchIndexClient

from azure.ai.projects import AIProjectClient

project_client = AIProjectClient(endpoint=project_endpoint, credential=DefaultAzureCredential())

instructions = """

You are an AI assistant that answers questions about the stored and indexed drone images and objects in search index index02.

The data source is an Azure AI Search resource where the schema has JSON description field, a vector field and an id field and this id field must be cited in your answer.

If you do not find a match for the query, respond with "I don't know", otherwise cite references with the value of the id field.

"""

messages = [

    {

        "role":"system",

        "content": instructions

    }

]

search_tool = AzureAISearchTool(

    index_connection_id=search_connection_id,

    index_name=index_name,

    query_type=AzureAISearchQueryType.VECTOR_SEMANTIC_HYBRID,

    filter="", # Optional filter expression

    top_k=5 # Number of results to return

)

agent = None

for existing_agent in list(project_client.agents.list_agents()):

    if existing_agent.name == search_agent_name:

        print(existing_agent.id)

        agent = existing_agent

if agent == None:

    agent = project_client.agents.create_agent(

        model=azure_openai_gpt_model,

        # deployment=azure_openai_gpt_deployment,

        name=search_agent_name,

        instructions=instructions,

        tools=search_tool.definitions,

        tool_resources=search_tool.resources,

        top_p=1

    )

# agent = project_client.agents.get_agent("asst_lsH8uwS4hrg4v1lRpXm6sdtR")

print(f"AI agent '{search_agent_name}' created or retrieved successfully:{agent}")

from azure.ai.agents.models import FunctionTool, ToolSet, ListSortOrder

from azure.search.documents.agent import KnowledgeAgentRetrievalClient

from azure.search.documents.agent.models import KnowledgeAgentRetrievalRequest, KnowledgeAgentMessage, KnowledgeAgentMessageTextContent, KnowledgeAgentIndexParams

agent_client = KnowledgeAgentRetrievalClient(endpoint=search_endpoint, agent_name=search_agent_name, credential=credential)

# query_text = "How many parking lots are empty when compared to all the parking lots?"

query_text = "How many red cars can be found near the building with a roof that has a circular structure?"

messages.append({

    "role": "user",

    "content": query_text

    #"How many parking lots are empty when compared to all the parking lots?"

})

thread = project_client.agents.threads.create()

retrieval_results = {}

def agentic_retrieval() -> str:

    # Searches drone images about objects detected and their facts.

    # The returned string is in a JSON format that contains the reference id.

    # Be sure to use the same format in your agent's response

    # You must refer to references by id number

    # Take the last 5 messages in the conversation

    messages = project_client.agents.messages.list(thread.id, limit=5, order=ListSortOrder.DESCENDING)

    # Reverse the order so the most recent message is last

    messages = list(messages)

    messages.reverse()

    retrieval_result = agent_client.retrieve(

        retrieval_request=KnowledgeAgentRetrievalRequest(

            messages=[KnowledgeAgentMessage(role=msg["role"], content=[KnowledgeAgentMessageTextContent(text=msg["content"])]) for msg in messages if msg["role"] != "system"],

            target_index_params=[KnowledgeAgentIndexParams(index_name=index_name, reranker_threshold=2.5, include_reference_source_data=True)] # add filter_add_on here

        )

    )

    # Associate the retrieval results with the last message in the conversation

    last_message = messages[-1]

    retrieval_results[last_message.id] = retrieval_result

    # Return the grounding response to the agent

    return retrieval_result.response[0].content[0].text

# https://learn.microsoft.com/en-us/azure/ai-services/agents/how-to/tools/function-calling

functions = FunctionTool({ agentic_retrieval })

toolset = ToolSet()

toolset.add(functions)

toolset.add(search_tool)

project_client.agents.enable_auto_function_calls(toolset)

from azure.ai.agents.models import AgentsNamedToolChoice, AgentsNamedToolChoiceType, FunctionName

message = project_client.agents.messages.create(

    thread_id=thread.id,

    role="user",

    content = query_text

    # "How many red cars can be found near a building with a roof that has a circular structure?"

    # content= "How many parking lots are empty when compared to all the parking lots?"

)

run = project_client.agents.runs.create_and_process(

    thread_id=thread.id,

    agent_id=agent.id,

    tool_choice=AgentsNamedToolChoice(type=AgentsNamedToolChoiceType.FUNCTION, function=FunctionName(name="agentic_retrieval")),

    toolset=toolset)

if run.status == "failed":

    raise RuntimeError(f"Run failed: {run.last_error}")

output = project_client.agents.messages.get_last_message_text_by_role(thread_id=thread.id, role="assistant").text.value

print("Agent response:", output.replace(".", "\n"))

import json

retrieval_result = retrieval_results.get(message.id)

if retrieval_result is None:

    raise RuntimeError(f"No retrieval results found for message {message.id}")

print("Retrieval activity")

print(json.dumps([activity.as_dict() for activity in retrieval_result.activity], indent=2))

print("Retrieval results")

print(json.dumps([reference.as_dict() for reference in retrieval_result.references], indent=2))