support sse for vertex (#49)

Author: wilsonccccc

app/services/providers/vertex_adapter.py

@@ -1,6 +1,7 @@
 import asyncio
 import json
 import time
+import uuid
 from collections.abc import AsyncGenerator
 from datetime import datetime, timezone
 from typing import Any
@@ -236,15 +237,62 @@ def error_handler(error_text: str, http_status: int):
             # vertex doesn't do actual streaming, it just returns a stream of json objects
             url = f"{self._base_url}/v1/projects/{self.project_id}/locations/{self.location}/publishers/{self.publisher}/models/{model_name}:streamRawPredict"
             async def custom_stream_response(url, headers, anthropic_payload, model_name):
+                """Call Vertex streamRawPredict and convert the *single* SSE frame into OpenAI chunk format."""
+
                 async def stream_response() -> AsyncGenerator[bytes, None]:
-                    resp = await AnthropicAdapter.process_regular_response(url, headers, anthropic_payload, model_name, error_handler)
-                    resp['object'] = 'chat.completion.chunk'
-                    for choice in resp['choices']:
-                        choice['delta'] = choice['message']
-                        del choice['message']
-                    yield f"data: {json.dumps(resp)}\n\n".encode()
-                    yield b"data: [DONE]\n\n"
+                    async with aiohttp.ClientSession() as session:
+                        async with session.post(url, headers=headers, json=anthropic_payload) as response:
+                            if response.status != 200:
+                                error_text = await response.text()
+                                error_handler(error_text, response.status)
+
+                            # Read the entire event-stream; Vertex currently responds with a few data: lines
+                            body = await response.text()
+
+                            # Extract JSON payload(s) from SSE lines that start with "data: "
+                            payloads: list[dict[str, Any]] = []
+                            for line in body.splitlines():
+                                line = line.strip()
+                                if not line.startswith("data:"):
+                                    continue
+                                data_part = line[len("data:"):].strip()
+                                if data_part == "[DONE]":
+                                    continue
+                                try:
+                                    payloads.append(json.loads(data_part))
+                                except json.JSONDecodeError:
+                                    continue
+
+                            if not payloads:
+                                raise ProviderAPIException("Vertex", response.status, "Empty response from Vertex streamRawPredict")
+
+                            # Vertex typically returns a single JSON object – use the first
+                            vertex_resp = payloads[0]
+
+                            # Convert to OpenAI chunk structure expected by Forge callers
+                            openai_chunk = {
+                                "id": vertex_resp.get("responseId", f"chatcmpl-{uuid.uuid4().hex}"),
+                                "object": "chat.completion.chunk",
+                                "created": int(time.time()),
+                                "model": model_name,
+                                "choices": [
+                                    {
+                                        "index": 0,
+                                        "delta": {
+                                            "role": "assistant",
+                                            "content": vertex_resp.get("candidates", [{}])[0].get("content", "")
+                                        },
+                                        "finish_reason": None,
+                                    }
+                                ],
+                            }
+
+                            # Yield the chunk then DONE, mimicking OpenAI stream format
+                            yield f"data: {json.dumps(openai_chunk)}\n\n".encode()
+                            yield b"data: [DONE]\n\n"
+
                 return stream_response()
+
             return await custom_stream_response(url, headers, anthropic_payload, model_name)
         else:
             url = f"{self._base_url}/v1/projects/{self.project_id}/locations/{self.location}/publishers/{self.publisher}/models/{model_name}:rawPredict"