fix: Updating preview python streaming to use v5

api/index.py

@@ -1,12 +1,13 @@
 import os
 import json
+import uuid
 from typing import List
 from openai.types.chat.chat_completion_message_param import ChatCompletionMessageParam
 from pydantic import BaseModel
 from dotenv import load_dotenv
 from fastapi import FastAPI, Query
 from fastapi.responses import StreamingResponse
-from openai import OpenAI
+from openai import AsyncOpenAI
 from .utils.prompt import ClientMessage, convert_to_openai_messages
 from .utils.tools import get_current_weather
 
@@ -15,7 +16,7 @@
 
 app = FastAPI()
 
-client = OpenAI(
+client = AsyncOpenAI(
     api_key=os.environ.get("OPENAI_API_KEY"),
 )
 
@@ -28,126 +29,144 @@ class Request(BaseModel):
     "get_current_weather": get_current_weather,
 }
 
-def do_stream(messages: List[ChatCompletionMessageParam]):
-    stream = client.chat.completions.create(
-        messages=messages,
-        model="gpt-4o",
-        stream=True,
-        tools=[{
-            "type": "function",
-            "function": {
-                "name": "get_current_weather",
-                "description": "Get the current weather at a location",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "latitude": {
-                            "type": "number",
-                            "description": "The latitude of the location",
-                        },
-                        "longitude": {
-                            "type": "number",
-                            "description": "The longitude of the location",
-                        },
-                    },
-                    "required": ["latitude", "longitude"],
-                },
-            },
-        }]
-    )
-
-    return stream
-
-def stream_text(messages: List[ChatCompletionMessageParam], protocol: str = 'data'):
-    draft_tool_calls = []
-    draft_tool_calls_index = -1
-
-    stream = client.chat.completions.create(
-        messages=messages,
-        model="gpt-4o",
-        stream=True,
-        tools=[{
-            "type": "function",
-            "function": {
-                "name": "get_current_weather",
-                "description": "Get the current weather at a location",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "latitude": {
-                            "type": "number",
-                            "description": "The latitude of the location",
-                        },
-                        "longitude": {
-                            "type": "number",
-                            "description": "The longitude of the location",
+async def stream_text(messages: List[ChatCompletionMessageParam], protocol: str = 'data'):
+    message_id = f"msg_{uuid.uuid4().hex}"
+
+    yield f'data: {json.dumps({"type": "start", "messageId": message_id})}\n\n'
+
+    conversation_messages = list(messages)
+
+    while True:
+        text_id = f"text_{uuid.uuid4().hex}"
+        text_started = False
+        draft_tool_calls = []
+        draft_tool_calls_index = -1
+
+        stream = await client.chat.completions.create(
+            messages=conversation_messages,
+            model="gpt-4o",
+            stream=True,
+            tools=[{
+                "type": "function",
+                "function": {
+                    "name": "get_current_weather",
+                    "description": "Get the current weather at a location",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "latitude": {
+                                "type": "number",
+                                "description": "The latitude of the location",
+                            },
+                            "longitude": {
+                                "type": "number",
+                                "description": "The longitude of the location",
+                            },
                         },
+                        "required": ["latitude", "longitude"],
                     },
-                    "required": ["latitude", "longitude"],
                 },
-            },
-        }]
-    )
-
-    for chunk in stream:
-        for choice in chunk.choices:
-            if choice.finish_reason == "stop":
-                continue
-
-            elif choice.finish_reason == "tool_calls":
-                for tool_call in draft_tool_calls:
-                    yield '9:{{"toolCallId":"{id}","toolName":"{name}","args":{args}}}\n'.format(
-                        id=tool_call["id"],
-                        name=tool_call["name"],
-                        args=tool_call["arguments"])
-
-                for tool_call in draft_tool_calls:
-                    tool_result = available_tools[tool_call["name"]](
-                        **json.loads(tool_call["arguments"]))
-
-                    yield 'a:{{"toolCallId":"{id}","toolName":"{name}","args":{args},"result":{result}}}\n'.format(
-                        id=tool_call["id"],
-                        name=tool_call["name"],
-                        args=tool_call["arguments"],
-                        result=json.dumps(tool_result))
-
-            elif choice.delta.tool_calls:
-                for tool_call in choice.delta.tool_calls:
-                    id = tool_call.id
-                    name = tool_call.function.name
-                    arguments = tool_call.function.arguments
-
-                    if (id is not None):
-                        draft_tool_calls_index += 1
-                        draft_tool_calls.append(
-                            {"id": id, "name": name, "arguments": ""})
-
-                    else:
-                        draft_tool_calls[draft_tool_calls_index]["arguments"] += arguments
-
-            else:
-                yield '0:{text}\n'.format(text=json.dumps(choice.delta.content))
-
-        if chunk.choices == []:
-            usage = chunk.usage
-            prompt_tokens = usage.prompt_tokens
-            completion_tokens = usage.completion_tokens
-
-            yield 'e:{{"finishReason":"{reason}","usage":{{"promptTokens":{prompt},"completionTokens":{completion}}},"isContinued":false}}\n'.format(
-                reason="tool-calls" if len(
-                    draft_tool_calls) > 0 else "stop",
-                prompt=prompt_tokens,
-                completion=completion_tokens
-            )
+            }]
+        )
+
+        finish_reason = None
+
+        async for chunk in stream:
+            for choice in chunk.choices:
+                if choice.delta.tool_calls:
+                    for tool_call in choice.delta.tool_calls:
+                        id = tool_call.id
+                        name = tool_call.function.name
+                        arguments = tool_call.function.arguments
+
+                        if (id is not None):
+                            draft_tool_calls_index += 1
+                            draft_tool_calls.append(
+                                {"id": id, "name": name, "arguments": ""})
+
+                            yield f'data: {json.dumps({"type": "tool-input-start", "toolCallId": id, "toolName": name})}\n\n'
+
+                        if arguments:
+                            draft_tool_calls[draft_tool_calls_index]["arguments"] += arguments
+                            yield f'data: {json.dumps({"type": "tool-input-delta", "toolCallId": draft_tool_calls[draft_tool_calls_index]["id"], "inputTextDelta": arguments})}\n\n'
+
+                if choice.delta.content:
+                    if not text_started:
+                        yield f'data: {json.dumps({"type": "text-start", "id": text_id})}\n\n'
+                        text_started = True
+
+                    yield f'data: {json.dumps({"type": "text-delta", "id": text_id, "delta": choice.delta.content})}\n\n'
+
+                if choice.finish_reason:
+                    finish_reason = choice.finish_reason
+
+        if text_started:
+            yield f'data: {json.dumps({"type": "text-end", "id": text_id})}\n\n'
+            text_started = False
+
+        if finish_reason == "tool_calls":
+            tool_calls_for_message = [
+                {
+                    "id": tc["id"],
+                    "type": "function",
+                    "function": {
+                        "name": tc["name"],
+                        "arguments": tc["arguments"]
+                    }
+                }
+                for tc in draft_tool_calls
+            ]
+
+            conversation_messages.append({
+                "role": "assistant",
+                "tool_calls": tool_calls_for_message
+            })
+
+            for tool_call in draft_tool_calls:
+                parsed_args = json.loads(tool_call["arguments"])
+
+                yield f'data: {json.dumps({"type": "tool-input-available", "toolCallId": tool_call["id"], "toolName": tool_call["name"], "input": parsed_args})}\n\n'
+
+                tool_result = available_tools[tool_call["name"]](**parsed_args)
+
+                yield f'data: {json.dumps({"type": "tool-output-available", "toolCallId": tool_call["id"], "output": tool_result})}\n\n'
+
+                conversation_messages.append({
+                    "role": "tool",
+                    "tool_call_id": tool_call["id"],
+                    "content": json.dumps(tool_result)
+                })
+
+            yield f'data: {json.dumps({"type": "finish-step"})}\n\n'
+            continue
+
+        elif finish_reason == "stop":
+            break
+
+    yield f'data: {json.dumps({"type": "finish"})}\n\n'
+    yield f'data: [DONE]\n\n'
 
 
 
 
 @app.post("/api/chat")
 async def handle_chat_data(request: Request, protocol: str = Query('data')):
-    messages = request.messages
-    openai_messages = convert_to_openai_messages(messages)
-
-    response = StreamingResponse(stream_text(openai_messages, protocol))
-    response.headers['x-vercel-ai-data-stream'] = 'v1'
-    return response
+    try:
+        messages = request.messages
+        openai_messages = convert_to_openai_messages(messages)
+
+        return StreamingResponse(
+            stream_text(openai_messages, protocol),
+            media_type="text/event-stream",
+            headers={
+                'x-vercel-ai-ui-message-stream': 'v1',
+                'Cache-Control': 'no-cache, no-transform',
+                'X-Accel-Buffering': 'no',
+                'Connection': 'keep-alive',
+                'Content-Type': 'text/event-stream',
+            }
+        )
+    except Exception as e:
+        import traceback
+        traceback.print_exc()
+        raise