migrated to fast api but need to work on bicep

Author: howieleung

README.md

@@ -58,7 +58,8 @@ To achieve this, when users submit a message to the web server, the web server w
 5. Start the services with this command:
 
     ```shell
-    python -m quart --app src.quartapp run --port 50505 --reload
+    cd src
+    python -m uvicorn "api.main:create_app" --port 50505 --reload
     ```
 
 6. Click 'http://localhost:50505' in the browser to run the application.

src/api/__init__.py

@@ -0,0 +1,112 @@
+import contextlib
+import logging
+import os
+
+import fastapi
+from azure.ai.projects.aio import AIProjectClient
+from dotenv import load_dotenv
+from fastapi.staticfiles import StaticFiles
+
+
+from typing import AsyncGenerator, Dict, Optional, Tuple
+
+
+import os
+from azure.ai.projects.aio import AIProjectClient
+from azure.identity import DefaultAzureCredential
+
+from azure.ai.projects.models import (
+    MessageDeltaChunk,
+    ThreadMessage,
+    FileSearchTool,
+    AsyncToolSet,
+    FilePurpose,
+    ThreadMessage,
+    StreamEventData,
+    AsyncAgentEventHandler,
+    Agent,
+    VectorStore
+)
+
+from .shared import bp
+
+
+
+logger = logging.getLogger("azureaiapp")
+logger.setLevel(logging.INFO)
+
+
+@contextlib.asynccontextmanager
+async def lifespan(app: fastapi.FastAPI):
+    
+
+
+    ai_client = AIProjectClient.from_connection_string(
+        credential=DefaultAzureCredential(exclude_shared_token_cache_credential=True),
+        conn_str=os.environ["PROJECT_CONNECTION_STRING"],
+    )
+    
+    # TODO: add more files are not supported for citation at the moment
+    file_names = ["product_info_1.md", "product_info_2.md"]
+    files: Dict[str, str] = {}
+    for file_name in file_names:
+        file_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'files', file_name))
+        print(f"Uploading file {file_path}")
+        file = await ai_client.agents.upload_file_and_poll(file_path=file_path, purpose=FilePurpose.AGENTS)
+        files.update({file.id: file_path})
+    
+    vector_store = await ai_client.agents.create_vector_store_and_poll(file_ids=list(files.keys()), name="sample_store")
+
+    file_search_tool = FileSearchTool(vector_store_ids=[vector_store.id])
+    
+    tool_set = AsyncToolSet()
+    tool_set.add(file_search_tool)
+    
+    print(f"ToolResource: {tool_set.resources}")
+        
+    agent = await ai_client.agents.create_agent(
+        model="gpt-4o-mini", name="my-assistant", instructions="You are helpful assistant", tools = tool_set.definitions, tool_resources=tool_set.resources
+    )
+    
+    print(f"Created agent, agent ID: {agent.id}")
+
+    bp.ai_client = ai_client
+    bp.agent = agent
+    bp.vector_store = vector_store
+    bp.files = files
+    
+    yield
+
+    await stop_server()  
+        
+
+async def stop_server():
+    for file_id in bp.files.keys():
+        await bp.ai_client.agents.delete_file(file_id)
+        print(f"Deleted file {file_id}")
+    
+    await bp.ai_client.agents.delete_vector_store(bp.vector_store.id)
+    print(f"Deleted vector store {bp.vector_store.id}")
+    
+    await bp.ai_client.agents.delete_agent(bp.agent.id)
+
+    print(f"Deleted agent {bp.agent.id}")        
+    
+    await bp.ai_client.close()
+    print("Closed AIProjectClient")
+
+
+
+def create_app():
+    if not os.getenv("RUNNING_IN_PRODUCTION"):
+        logger.info("Loading .env file")
+        load_dotenv(override=True)
+
+    app = fastapi.FastAPI(lifespan=lifespan)
+    app.mount("/static", StaticFiles(directory="api/static"), name="static")
+
+    from . import routes  # noqa
+
+    app.include_router(routes.router)
+
+    return app

src/quartapp/config_helper.py renamed to src/api/config_helper.py

@@ -2,35 +2,33 @@
 # Licensed under the MIT license. See LICENSE.md file in the project root for full license information.
 
 from typing import AsyncGenerator, Dict, Optional, Tuple
-from quart import Blueprint, jsonify, request, Response, render_template, current_app
 
 import asyncio
 import json, os
 
-import os
-from azure.ai.projects.aio import AIProjectClient
-from azure.identity import DefaultAzureCredential
 
 from azure.ai.projects.models import (
     MessageDeltaChunk,
     ThreadMessage,
-    FileSearchTool,
-    AsyncToolSet,
-    FilePurpose,
     ThreadMessage,
-    StreamEventData,
     AsyncAgentEventHandler,
-    Agent,
-    VectorStore
 )
 
-class ChatBlueprint(Blueprint):
-    ai_client: AIProjectClient
-    agent: Agent
-    files: Dict[str, str]
-    vector_store: VectorStore
 
-bp = ChatBlueprint("chat", __name__, template_folder="templates", static_folder="static")
+import json
+
+import fastapi
+from fastapi import Request
+from fastapi.responses import HTMLResponse
+from fastapi.templating import Jinja2Templates
+
+from .shared import bp
+
+
+router = fastapi.APIRouter()
+templates = Jinja2Templates(directory="api/templates")
+
+
 
 class MyEventHandler(AsyncAgentEventHandler[str]):
 
@@ -62,66 +60,10 @@ async def on_done(
 
 
 
-@bp.before_app_serving
-async def start_server():
-    
-    ai_client = AIProjectClient.from_connection_string(
-        credential=DefaultAzureCredential(exclude_shared_token_cache_credential=True),
-        conn_str=os.environ["PROJECT_CONNECTION_STRING"],
-    )
-    
-    # TODO: add more files are not supported for citation at the moment
-    file_names = ["product_info_1.md", "product_info_2.md"]
-    files: Dict[str, str] = {}
-    for file_name in file_names:
-        file_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'files', file_name))
-        print(f"Uploading file {file_path}")
-        file = await ai_client.agents.upload_file_and_poll(file_path=file_path, purpose=FilePurpose.AGENTS)
-        files.update({file.id: file_path})
-    
-    vector_store = await ai_client.agents.create_vector_store_and_poll(file_ids=list(files.keys()), name="sample_store")
 
-    file_search_tool = FileSearchTool(vector_store_ids=[vector_store.id])
-    
-    tool_set = AsyncToolSet()
-    tool_set.add(file_search_tool)
-    
-    print(f"ToolResource: {tool_set.resources}")
-        
-    agent = await ai_client.agents.create_agent(
-        model="gpt-4o-mini", name="my-assistant", instructions="You are helpful assistant", tools = tool_set.definitions, tool_resources=tool_set.resources
-    )
-    
-    print(f"Created agent, agent ID: {agent.id}")
-
-    bp.ai_client = ai_client
-    bp.agent = agent
-    bp.vector_store = vector_store
-    bp.files = files
-        
-
-@bp.after_app_serving
-async def stop_server():
-    for file_id in bp.files.keys():
-        await bp.ai_client.agents.delete_file(file_id)
-        print(f"Deleted file {file_id}")
-    
-    await bp.ai_client.agents.delete_vector_store(bp.vector_store.id)
-    print(f"Deleted vector store {bp.vector_store.id}")
-    
-    await bp.ai_client.agents.delete_agent(bp.agent.id)
-
-    print(f"Deleted agent {bp.agent.id}")        
-    
-    await bp.ai_client.close()
-    print("Closed AIProjectClient")
-
-
-
-
-@bp.get("/")
-async def index():
-    return await render_template("index.html")
+@router.get("/", response_class=HTMLResponse)
+async def index(request: Request):
+    return templates.TemplateResponse("index.html", {"request": request})
 
 
 
@@ -135,8 +77,8 @@ async def get_result(thread_id: str, agent_id: str) -> AsyncGenerator[str, None]
             if event_func_return_val:
                 yield event_func_return_val
 
-@bp.route('/chat', methods=['POST'])
-async def chat():
+@router.post("/chat")
+async def chat(request: Request):
     thread_id = request.cookies.get('thread_id')
     agent_id = request.cookies.get('agent_id')
     thread = None
@@ -146,16 +88,18 @@ async def chat():
         try:
             thread = await bp.ai_client.agents.get_thread(thread_id)
         except Exception as e:
-            current_app.logger.error(f"Failed to retrieve thread with ID {thread_id}: {e}")
+            return fastapi.responses.JSONResponse(content={"error": f"Failed to retrieve thread with ID {thread_id}: {e}"}, status_code=400)
     if thread is None:
         thread = await bp.ai_client.agents.create_thread()    
 
     thread_id = thread.id
     agent_id = bp.agent.id    
-    user_message = await request.get_json()
+    user_message = await request.json()
+    
+    print(f"user_message: {user_message}")
 
     if not hasattr(bp, 'ai_client'):
-        return jsonify({"error": "Agent is not initialized"}), 500
+        return fastapi.responses.JSONResponse(content={"error": "Agent is not initialized"}, status_code=500)
 
     message = await bp.ai_client.agents.create_message(
         thread_id=thread.id, role="user", content=user_message['message']
@@ -170,25 +114,24 @@ async def chat():
         'Content-Type': 'text/event-stream'
     }
 
-    response = Response(get_result(thread_id, agent_id), headers=headers)
+    response = fastapi.responses.StreamingResponse(get_result(thread_id, agent_id), headers=headers)
     response.set_cookie('thread_id', thread_id)
     response.set_cookie('agent_id', agent_id)    
     return response
 
-@bp.route('/fetch-document', methods=['GET'])
-async def fetch_document():
-    file_id = request.args.get('file_id')
-    current_app.logger.info(f"Fetching document: {file_id}")
+@router.get("/fetch-document")
+async def fetch_document(request: Request):
+    file_id = request.query_params.get('file_id')
     if not file_id:
-        return jsonify({"error": "file_id is required"}), 400
+        raise fastapi.HTTPException(status_code=400, detail="file_id is required")
 
     try:
         # Read the file content asynchronously using asyncio.to_thread
         data = await asyncio.to_thread(read_file, bp.files[file_id])
-        return Response(data, content_type='text/plain')
+        return fastapi.responses.PlainTextResponse(data)
 
     except Exception as e:
-        return jsonify({"error": str(e)}), 500
+        return fastapi.responses.JSONResponse(content={"error": str(e)}, status_code=500)
 
 def read_file(path):
     with open(path, 'r') as file:

src/api/main.py

@@ -0,0 +1,20 @@
+from typing import Dict
+from azure.ai.projects.aio import AIProjectClient
+
+
+from azure.ai.projects.models import (
+    Agent,
+    VectorStore
+)
+
+
+class ChatBlueprint():
+    ai_client: AIProjectClient
+    agent: Agent
+    files: Dict[str, str]
+    vector_store: VectorStore
+    
+    
+bp = ChatBlueprint()
+
+__all__ = ["bp"]