Update build.mdx: FastAPI basic chat example

docs/quickstart/build.mdx

@@ -149,24 +149,19 @@ Let's enhance our endpoint to call OpenAI's API and stream the responses back as
 AG-UI events:
 
 ```python
+import uuid
+
+from ag_ui.core import (EventType, Message, RunAgentInput, RunFinishedEvent,
+                        RunStartedEvent, TextMessageContentEvent,
+                        TextMessageEndEvent, TextMessageStartEvent)
+from ag_ui.encoder import EventEncoder
 from fastapi import FastAPI, Request
 from fastapi.responses import StreamingResponse
-from ag_ui.core import (
-  RunAgentInput,
-  Message,
-  EventType,
-  RunStartedEvent,
-  RunFinishedEvent,
-  TextMessageStartEvent,
-  TextMessageContentEvent,
-  TextMessageEndEvent
-)
-from ag_ui.encoder import EventEncoder
-import uuid
 from openai import OpenAI
 
 app = FastAPI(title="AG-UI Endpoint")
 
+
 @app.post("/awp")
 async def my_endpoint(input_data: RunAgentInput):
     async def event_generator():
@@ -175,71 +170,68 @@ async def my_endpoint(input_data: RunAgentInput):
 
         # Send run started event
         yield encoder.encode(
-          RunStartedEvent(
-            type=EventType.RUN_STARTED,
-            thread_id=input_data.thread_id,
-            run_id=input_data.run_id
-          )
+            RunStartedEvent(
+                type=EventType.RUN_STARTED,
+                thread_id=input_data.thread_id,
+                run_id=input_data.run_id,
+            )
         )
 
         # Initialize OpenAI client
         client = OpenAI()
 
         # Generate a message ID for the assistant's response
-        message_id = uuid.uuid4()
+        message_id = str(uuid.uuid4())
 
         # Send text message start event
         yield encoder.encode(
             TextMessageStartEvent(
                 type=EventType.TEXT_MESSAGE_START,
                 message_id=message_id,
-                role="assistant"
+                role="assistant",
             )
         )
 
         # Create a streaming completion request
         stream = client.chat.completions.create(
-            model="gpt-3.5-turbo",
-            messages=openai_messages,
-            stream=True
+            model="gpt-3.5-turbo", messages=input_data.messages, stream=True
         )
 
         # Process the streaming response and send content events
         for chunk in stream:
-            if hasattr(chunk.choices[0].delta, "content") and chunk.choices[0].delta.content:
+            if (
+                hasattr(chunk.choices[0].delta, "content")
+                and chunk.choices[0].delta.content
+            ):
                 content = chunk.choices[0].delta.content
                 yield encoder.encode(
                     TextMessageContentEvent(
                         type=EventType.TEXT_MESSAGE_CONTENT,
                         message_id=message_id,
-                        delta=content
+                        delta=content,
                     )
                 )
 
         # Send text message end event
         yield encoder.encode(
-            TextMessageEndEvent(
-                type=EventType.TEXT_MESSAGE_END,
-                message_id=message_id
-            )
+            TextMessageEndEvent(type=EventType.TEXT_MESSAGE_END, message_id=message_id)
         )
 
         # Send run finished event
         yield encoder.encode(
-          RunFinishedEvent(
-            type=EventType.RUN_FINISHED,
-            thread_id=input_data.thread_id,
-            run_id=input_data.run_id
-          )
+            RunFinishedEvent(
+                type=EventType.RUN_FINISHED,
+                thread_id=input_data.thread_id,
+                run_id=input_data.run_id,
+            )
         )
 
-    return StreamingResponse(
-        event_generator(),
-        media_type="text/event-stream"
-    )
+    return StreamingResponse(event_generator(), media_type="text/event-stream")
+
 
 if __name__ == "__main__":
     import uvicorn
+
     uvicorn.run(app, host="0.0.0.0", port=8000)
 ```