Removing kernal messaging in aqua

ads/aqua/common/enums.py

@@ -24,6 +24,7 @@ class PredictEndpoints(ExtendedEnum):
     CHAT_COMPLETIONS_ENDPOINT = "/v1/chat/completions"
     TEXT_COMPLETIONS_ENDPOINT = "/v1/completions"
     EMBEDDING_ENDPOINT = "/v1/embedding"
+    RESPONSES = "/v1/responses"
 
 
 class Tags(ExtendedEnum):

ads/aqua/extension/deployment_handler.py

@@ -7,8 +7,8 @@
 
 from tornado.web import HTTPError
 
-from ads.aqua.app import logger
 from ads.aqua.client.client import Client, ExtendedRequestError
+from ads.aqua.client.openai_client import OpenAI
 from ads.aqua.common.decorator import handle_exceptions
 from ads.aqua.common.enums import PredictEndpoints
 from ads.aqua.extension.base_handler import AquaAPIhandler
@@ -221,11 +221,49 @@ def list_shapes(self):
 
 
 class AquaDeploymentStreamingInferenceHandler(AquaAPIhandler):
+
+    def _extract_text_from_choice(self, choice):
+        # choice may be a dict or an object
+        if isinstance(choice, dict):
+            # streaming chunk: {"delta": {"content": "..."}}
+            delta = choice.get("delta")
+            if isinstance(delta, dict):
+                return delta.get("content") or delta.get("text") or None
+            # non-streaming: {"message": {"content": "..."}}
+            msg = choice.get("message")
+            if isinstance(msg, dict):
+                return msg.get("content") or msg.get("text")
+            # fallback top-level fields
+            return choice.get("text") or choice.get("content")
+        # object-like choice
+        delta = getattr(choice, "delta", None)
+        if delta is not None:
+            return getattr(delta, "content", None) or getattr(delta, "text", None)
+        msg = getattr(choice, "message", None)
+        if msg is not None:
+            if isinstance(msg, str):
+                return msg
+            return getattr(msg, "content", None) or getattr(msg, "text", None)
+        return getattr(choice, "text", None) or getattr(choice, "content", None)
+
+    def _extract_text_from_chunk(self, chunk):
+        if chunk : 
+            if isinstance(chunk, dict):
+                choices = chunk.get("choices") or []
+                if choices:
+                    return self._extract_text_from_choice(choices[0])
+                # fallback top-level
+                return chunk.get("text") or chunk.get("content")
+            # object-like chunk
+            choices = getattr(chunk, "choices", None)
+            if choices:
+                return self._extract_text_from_choice(choices[0])
+            return getattr(chunk, "text", None) or getattr(chunk, "content", None)
+
     def _get_model_deployment_response(
         self,
         model_deployment_id: str,
-        payload: dict,
-        route_override_header: Optional[str],
+        payload: dict
     ):
         """
         Returns the model deployment inference response in a streaming fashion.
@@ -272,49 +310,160 @@ def _get_model_deployment_response(
         """
 
         model_deployment = AquaDeploymentApp().get(model_deployment_id)
-        endpoint = model_deployment.endpoint + "/predictWithResponseStream"
-        endpoint_type = model_deployment.environment_variables.get(
-            "MODEL_DEPLOY_PREDICT_ENDPOINT", PredictEndpoints.TEXT_COMPLETIONS_ENDPOINT
-        )
-        aqua_client = Client(endpoint=endpoint)
-
-        if PredictEndpoints.CHAT_COMPLETIONS_ENDPOINT in (
-            endpoint_type,
-            route_override_header,
-        ):
+        endpoint = model_deployment.endpoint + "/predictWithResponseStream/v1"
+        endpoint_type = payload["endpoint_type"]
+        aqua_client = OpenAI(base_url=endpoint)
+
+        allowed = {
+            "max_tokens",
+            "temperature",
+            "top_p",
+            "stop",
+            "n",
+            "presence_penalty",
+            "frequency_penalty",
+            "logprobs",
+            "user",
+            "echo",
+        }
+        responses_allowed = {
+            "temperature", "top_p"
+        }
+
+        # normalize and filter
+        if payload.get("stop") == []:
+            payload["stop"] = None
+
+        encoded_image = "NA"
+        if encoded_image in payload :
+            encoded_image = payload["encoded_image"]
+
+        model = payload.pop("model")
+        filtered = {k: v for k, v in payload.items() if k in allowed}
+        responses_filtered = {k: v for k, v in payload.items() if k in responses_allowed}
+
+        if PredictEndpoints.CHAT_COMPLETIONS_ENDPOINT == endpoint_type and encoded_image == "NA":
             try:
-                for chunk in aqua_client.chat(
-                    messages=payload.pop("messages"),
-                    payload=payload,
-                    stream=True,
-                ):
-                    try:
-                        if "text" in chunk["choices"][0]:
-                            yield chunk["choices"][0]["text"]
-                        elif "content" in chunk["choices"][0]["delta"]:
-                            yield chunk["choices"][0]["delta"]["content"]
-                    except Exception as e:
-                        logger.debug(
-                            f"Exception occurred while parsing streaming response: {e}"
-                        )
+                api_kwargs = {
+                    "model": model,
+                    "messages": [{"role": "user", "content": payload["prompt"]}],
+                    "stream": True,
+                    **filtered
+                }
+                if "chat_template" in payload:
+                    chat_template = payload.pop("chat_template")
+                    api_kwargs["extra_body"] = {"chat_template": chat_template}
+
+                stream = aqua_client.chat.completions.create(**api_kwargs)
+
+                for chunk in stream:
+                    if chunk : 
+                        piece  = self._extract_text_from_chunk(chunk)
+                        if piece  :
+                            yield piece 
             except ExtendedRequestError as ex:
                 raise HTTPError(400, str(ex))
             except Exception as ex:
                 raise HTTPError(500, str(ex))
 
+        elif (
+                    endpoint_type == PredictEndpoints.CHAT_COMPLETIONS_ENDPOINT
+                    and encoded_image != "NA"
+            ):
+                file_type = payload.pop("file_type")
+                if file_type.startswith("image"):
+                    api_kwargs = {
+                        "model": model,
+                        "messages": [
+                            {
+                                "role": "user",
+                                "content": [
+                                    {"type": "text", "text": payload["prompt"]},
+                                    {
+                                        "type": "image_url",
+                                        "image_url": {"url": f"{self.encoded_image}"},
+                                    },
+                                ],
+                            }
+                        ],
+                        "stream": True,
+                        **filtered
+                    }
+
+                    # Add chat_template for image-based chat completions
+                    if "chat_template" in payload:
+                        chat_template = payload.pop("chat_template")
+                        api_kwargs["extra_body"] = {"chat_template": chat_template}
+
+                    response = aqua_client.chat.completions.create(**api_kwargs)
+
+                elif self.file_type.startswith("audio"):
+                    api_kwargs = {
+                        "model": model,
+                        "messages": [
+                            {
+                                "role": "user",
+                                "content": [
+                                    {"type": "text", "text": payload["prompt"]},
+                                    {
+                                        "type": "audio_url",
+                                        "audio_url": {"url": f"{self.encoded_image}"},
+                                    },
+                                ],
+                            }
+                        ],
+                        "stream": True,
+                        **filtered
+                    }
+
+                    # Add chat_template for audio-based chat completions
+                    if "chat_template" in payload:
+                        chat_template = payload.pop("chat_template")
+                        api_kwargs["extra_body"] = {"chat_template": chat_template}
+
+                    response = aqua_client.chat.completions.create(**api_kwargs)
+                try:
+                    for chunk in response:
+                        piece = self._extract_text_from_chunk(chunk)
+                        if piece:
+                            print(piece, end="", flush=True)
+                except ExtendedRequestError as ex:
+                    raise HTTPError(400, str(ex))
+                except Exception as ex:
+                    raise HTTPError(500, str(ex))
         elif endpoint_type == PredictEndpoints.TEXT_COMPLETIONS_ENDPOINT:
             try:
-                for chunk in aqua_client.generate(
-                    prompt=payload.pop("prompt"),
-                    payload=payload,
-                    stream=True,
+                for chunk in aqua_client.completions.create(
+                    prompt=payload["prompt"], stream=True, model=model, **filtered
                 ):
-                    try:
-                        yield chunk["choices"][0]["text"]
-                    except Exception as e:
-                        logger.debug(
-                            f"Exception occurred while parsing streaming response: {e}"
-                        )
+                    if chunk : 
+                        piece  = self._extract_text_from_chunk(chunk)
+                        if piece  : 
+                            yield piece 
+            except ExtendedRequestError as ex:
+                raise HTTPError(400, str(ex))
+            except Exception as ex:
+                raise HTTPError(500, str(ex))
+
+        elif endpoint_type == PredictEndpoints.RESPONSES:
+            api_kwargs = {
+                    "model": model,
+                    "input": payload["prompt"],
+                    "stream": True
+                }
+
+            if "temperature" in responses_filtered:
+                api_kwargs["temperature"] = responses_filtered["temperature"]
+            if "top_p" in responses_filtered:
+                api_kwargs["top_p"] = responses_filtered["top_p"]
+
+            response = aqua_client.responses.create(**api_kwargs)
+            try:
+                for chunk in response:
+                    if chunk : 
+                        piece  = self._extract_text_from_chunk(chunk)
+                        if piece  : 
+                            yield piece 
             except ExtendedRequestError as ex:
                 raise HTTPError(400, str(ex))
             except Exception as ex:
@@ -340,19 +489,20 @@ def post(self, model_deployment_id):
         prompt = input_data.get("prompt")
         messages = input_data.get("messages")
 
+
         if not prompt and not messages:
             raise HTTPError(
                 400, Errors.MISSING_REQUIRED_PARAMETER.format("prompt/messages")
             )
         if not input_data.get("model"):
             raise HTTPError(400, Errors.MISSING_REQUIRED_PARAMETER.format("model"))
-        route_override_header = self.request.headers.get("route", None)
         self.set_header("Content-Type", "text/event-stream")
         response_gen = self._get_model_deployment_response(
-            model_deployment_id, input_data, route_override_header
+            model_deployment_id, input_data
         )
         try:
             for chunk in response_gen:
+                print(chunk)
                 self.write(chunk)
                 self.flush()
             self.finish()

tests/unitary/with_extras/aqua/test_deployment_handler.py

@@ -274,8 +274,7 @@ def test_post(self, mock_get_model_deployment_response):
 
         mock_get_model_deployment_response.assert_called_with(
             "mock-deployment-id",
-            {"prompt": "Hello", "model": "some-model"},
-            "test-route",
+            {"prompt": "Hello", "model": "some-model"}
         )
         self.handler.write.assert_any_call("chunk1")
         self.handler.write.assert_any_call("chunk2")