# pylint: disable=line-too-long,useless-suppression
# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------

"""
DESCRIPTION:
    This sample demonstrates how to use the File Search Tool with streaming responses using the async client.
    It combines file search capabilities with response streaming to provide real-time
    search results from uploaded documents.

USAGE:
    python sample_agent_file_search_in_stream_async.py

    Before running the sample:

    pip install "azure-ai-projects>=2.0.0b1" azure-identity openai python-dotenv aiohttp

    Set these environment variables with your own values:
    1) AZURE_AI_PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
       page of your Microsoft Foundry portal.
    2) AZURE_AI_MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in
       the "Models + endpoints" tab in your Microsoft Foundry project.
"""

import asyncio
import os
from dotenv import load_dotenv
from azure.identity.aio import DefaultAzureCredential
from azure.ai.projects.aio import AIProjectClient
from azure.ai.projects.models import PromptAgentDefinition, FileSearchTool

load_dotenv()

# Load the file to be indexed for search
asset_file_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../assets/product_info.md"))


async def main() -> None:

    credential = DefaultAzureCredential()

    async with credential:

        project_client = AIProjectClient(
            endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
            credential=credential,
        )

        async with project_client:
            # Get the OpenAI client for vector store operations
            openai_client = await project_client.get_openai_client()

            print("Setting up file search with streaming responses...")

            # Create vector store for file search
            vector_store = await openai_client.vector_stores.create(name="ProductInfoStreamStore")
            print(f"Vector store created (id: {vector_store.id})")

            # Upload file to vector store
            try:
                file = await openai_client.vector_stores.files.upload_and_poll(
                    vector_store_id=vector_store.id, file=open(asset_file_path, "rb")
                )
                print(f"File uploaded to vector store (id: {file.id})")
            except FileNotFoundError:
                print(f"Warning: Asset file not found at {asset_file_path}")
                print("Creating vector store without file for demonstration...")

            # Create agent with file search tool
            agent = await project_client.agents.create_version(
                agent_name="StreamingFileSearchAgent",
                definition=PromptAgentDefinition(
                    model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
                    instructions="You are a helpful assistant that can search through product information and provide detailed responses. Use the file search tool to find relevant information before answering.",
                    tools=[FileSearchTool(vector_store_ids=[vector_store.id])],
                ),
                description="File search agent with streaming response capabilities.",
            )
            print(f"Agent created (id: {agent.id}, name: {agent.name}, version: {agent.version})")

            # Create a conversation for the agent interaction
            conversation = await openai_client.conversations.create()
            print(f"Created conversation (id: {conversation.id})")

            print("\n" + "=" * 60)
            print("Starting file search with streaming response...")
            print("=" * 60)

            # Create a streaming response with file search capabilities
            stream_response = await openai_client.responses.create(
                stream=True,
                conversation=conversation.id,
                input=[
                    {
                        "role": "user",
                        "content": "Tell me about Contoso products and their features in detail. Please search through the available documentation.",
                    },
                ],
                tool_choice="required",
                extra_body={"agent": {"name": agent.name, "type": "agent_reference"}},
            )

            print("Processing streaming file search results...\n")

            # Process streaming events as they arrive
            async for event in stream_response:
                if event.type == "response.created":
                    print(f"Stream response created with ID: {event.response.id}")
                elif event.type == "response.output_text.delta":
                    print(f"Delta: {event.delta}")
                elif event.type == "response.text.done":
                    print(f"\nResponse done with full message: {event.text}")
                elif event.type == "response.completed":
                    print(f"\nResponse completed!")
                    print(f"Full response: {event.response.output_text}")

            print("\n" + "=" * 60)
            print("Demonstrating follow-up query with streaming...")
            print("=" * 60)

            # Demonstrate a follow-up query in the same conversation
            stream_response = await openai_client.responses.create(
                stream=True,
                conversation=conversation.id,
                input=[
                    {
                        "role": "user",
                        "content": "Tell me about Smart Eyewear and its features.",
                    },
                ],
                tool_choice="required",
                extra_body={"agent": {"name": agent.name, "type": "agent_reference"}},
            )

            print("Processing follow-up streaming response...\n")

            # Process streaming events for the follow-up
            async for event in stream_response:
                if event.type == "response.created":
                    print(f"Follow-up response created with ID: {event.response.id}")
                elif event.type == "response.output_text.delta":
                    print(f"Delta: {event.delta}")
                elif event.type == "response.text.done":
                    print(f"\nFollow-up response done!")
                elif event.type == "response.output_item.done":
                    if event.item.type == "message":
                        item = event.item
                        if item.content[-1].type == "output_text":
                            text_content = item.content[-1]
                            for annotation in text_content.annotations:
                                if annotation.type == "file_citation":
                                    print(
                                        f"File Citation - Filename: {annotation.filename}, File ID: {annotation.file_id}"
                                    )
                elif event.type == "response.completed":
                    print(f"\nFollow-up completed!")
                    print(f"Full response: {event.response.output_text}")

            # Clean up resources
            print("\n" + "=" * 60)
            print("Cleaning up resources...")
            print("=" * 60)

            # Delete the agent
            await project_client.agents.delete_version(agent_name=agent.name, agent_version=agent.version)
            print("Agent deleted")

            # Clean up vector store
            try:
                await openai_client.vector_stores.delete(vector_store.id)
                print("Vector store deleted")
            except Exception as e:
                print(f"Warning: Could not delete vector store: {e}")

    print("\nFile search streaming sample completed!")


if __name__ == "__main__":
    asyncio.run(main())
