# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
"""
DESCRIPTION:
    This sample demonstrates how to add files to agent during the vector store creation.

USAGE:
    python sample_agents_enterprise_file_search.py

    Before running the sample:

    pip install azure-ai-projects azure-identity azure-ai-ml

    Set this environment variables with your own values:
    PROJECT_CONNECTION_STRING - the Azure AI Project connection string, as found in your AI Foundry project.
"""

import os
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import FileSearchTool, VectorStoreDataSource, VectorStoreDataSourceAssetType
from azure.identity import DefaultAzureCredential

project_client = AIProjectClient.from_connection_string(
    credential=DefaultAzureCredential(), conn_str=os.environ["PROJECT_CONNECTION_STRING"]
)

with project_client:

    # [START upload_file_and_create_agent_with_file_search]
    # We will upload the local file to Azure and will use it for vector store creation.
    _, asset_uri = project_client.upload_file("./product_info_1.md")

    # Create a vector store with no file and wait for it to be processed
    ds = VectorStoreDataSource(asset_identifier=asset_uri, asset_type=VectorStoreDataSourceAssetType.URI_ASSET)
    vector_store = project_client.agents.create_vector_store_and_poll(data_sources=[ds], name="sample_vector_store")
    print(f"Created vector store, vector store ID: {vector_store.id}")

    # Create a file search tool
    file_search_tool = FileSearchTool(vector_store_ids=[vector_store.id])

    # Notices that FileSearchTool as tool and tool_resources must be added or the assistant unable to search the file
    agent = project_client.agents.create_agent(
        model="gpt-4-1106-preview",
        name="my-assistant",
        instructions="You are helpful assistant",
        tools=file_search_tool.definitions,
        tool_resources=file_search_tool.resources,
    )
    # [END upload_file_and_create_agent_with_file_search]
    print(f"Created agent, agent ID: {agent.id}")

    thread = project_client.agents.create_thread()
    print(f"Created thread, thread ID: {thread.id}")

    message = project_client.agents.create_message(
        thread_id=thread.id, role="user", content="What feature does Smart Eyewear offer?"
    )
    print(f"Created message, message ID: {message.id}")

    run = project_client.agents.create_and_process_run(thread_id=thread.id, assistant_id=agent.id)
    print(f"Created run, run ID: {run.id}")

    project_client.agents.delete_vector_store(vector_store.id)
    print("Deleted vector store")

    project_client.agents.delete_agent(agent.id)
    print("Deleted agent")

    messages = project_client.agents.list_messages(thread_id=thread.id)
    print(f"Messages: {messages}")
