community: add new parameter default_headers

docs/docs/integrations/chat/oci_data_science.ipynb

@@ -137,7 +137,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -156,6 +156,10 @@
     "        \"temperature\": 0.2,\n",
     "        \"max_tokens\": 512,\n",
     "    },  # other model params...\n",
+    "    default_headers={\n",
+    "        \"route\": \"/v1/chat/completions\",\n",
+    "        # other request headers ...\n",
+    "    },\n",
     ")"
    ]
   },

libs/community/langchain_community/chat_models/oci_data_science.py

@@ -47,6 +47,7 @@
 )
 
 logger = logging.getLogger(__name__)
+DEFAULT_INFERENCE_ENDPOINT_CHAT = "/v1/chat/completions"
 
 
 def _is_pydantic_class(obj: Any) -> bool:
@@ -56,6 +57,13 @@ def _is_pydantic_class(obj: Any) -> bool:
 class ChatOCIModelDeployment(BaseChatModel, BaseOCIModelDeployment):
     """OCI Data Science Model Deployment chat model integration.
 
+    Prerequisite
+        The OCI Model Deployment plugins are installable only on
+        python version 3.9 and above. If you're working inside the notebook,
+        try installing the python 3.10 based conda pack and running the
+        following setup.
+
+
     Setup:
         Install ``oracle-ads`` and ``langchain-openai``.
 
@@ -90,6 +98,8 @@ class ChatOCIModelDeployment(BaseChatModel, BaseOCIModelDeployment):
     Key init args — client params:
         auth: dict
             ADS auth dictionary for OCI authentication.
+        default_headers: Optional[Dict]
+            The headers to be added to the Model Deployment request.
 
     Instantiate:
         .. code-block:: python
@@ -98,14 +108,18 @@ class ChatOCIModelDeployment(BaseChatModel, BaseOCIModelDeployment):
 
             chat = ChatOCIModelDeployment(
                 endpoint="https://modeldeployment.<region>.oci.customer-oci.com/<ocid>/predict",
-                model="odsc-llm",
+                model="odsc-llm", # this is the default model name if deployed with AQUA
                 streaming=True,
                 max_retries=3,
                 model_kwargs={
                     "max_token": 512,
                     "temperature": 0.2,
                     # other model parameters ...
                 },
+                default_headers={
+                    "route": "/v1/chat/completions",
+                    # other request headers ...
+                },
             )
 
     Invocation:
@@ -288,6 +302,25 @@ def _default_params(self) -> Dict[str, Any]:
             "stream": self.streaming,
         }
 
+    def _headers(
+        self, is_async: Optional[bool] = False, body: Optional[dict] = None
+    ) -> Dict:
+        """Construct and return the headers for a request.
+
+        Args:
+            is_async (bool, optional): Indicates if the request is asynchronous.
+                Defaults to `False`.
+            body (optional): The request body to be included in the headers if
+                the request is asynchronous.
+
+        Returns:
+            Dict: A dictionary containing the appropriate headers for the request.
+        """
+        return {
+            "route": DEFAULT_INFERENCE_ENDPOINT_CHAT,
+            **super()._headers(is_async=is_async, body=body),
+        }
+
     def _generate(
         self,
         messages: List[BaseMessage],
@@ -701,7 +734,7 @@ def _process_response(self, response_json: dict) -> ChatResult:
 
         for choice in choices:
             message = _convert_dict_to_message(choice["message"])
-            generation_info = dict(finish_reason=choice.get("finish_reason"))
+            generation_info = {"finish_reason": choice.get("finish_reason")}
             if "logprobs" in choice:
                 generation_info["logprobs"] = choice["logprobs"]
 

libs/community/langchain_community/llms/oci_data_science_model_deployment_endpoint.py

@@ -32,6 +32,7 @@
 from langchain_community.utilities.requests import Requests
 
 logger = logging.getLogger(__name__)
+DEFAULT_INFERENCE_ENDPOINT = "/v1/completions"
 
 
 DEFAULT_TIME_OUT = 300
@@ -81,6 +82,9 @@ class BaseOCIModelDeployment(Serializable):
     max_retries: int = 3
     """Maximum number of retries to make when generating."""
 
+    default_headers: Optional[Dict[str, Any]] = None
+    """The headers to be added to the Model Deployment request."""
+
     @model_validator(mode="before")
     @classmethod
     def validate_environment(cls, values: Dict) -> Dict:
@@ -120,12 +124,12 @@ def _headers(
         Returns:
             Dict: A dictionary containing the appropriate headers for the request.
         """
+        headers = self.default_headers or {}
         if is_async:
             signer = self.auth["signer"]
             _req = requests.Request("POST", self.endpoint, json=body)
             req = _req.prepare()
             req = signer(req)
-            headers = {}
             for key, value in req.headers.items():
                 headers[key] = value
 
@@ -135,7 +139,7 @@ def _headers(
                 )
             return headers
 
-        return (
+        headers.update(
             {
                 "Content-Type": DEFAULT_CONTENT_TYPE_JSON,
                 "enable-streaming": "true",
@@ -147,6 +151,8 @@ def _headers(
             }
         )
 
+        return headers
+
     def completion_with_retry(
         self, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any
     ) -> Any:
@@ -383,6 +389,10 @@ class OCIModelDeploymentLLM(BaseLLM, BaseOCIModelDeployment):
                 model="odsc-llm",
                 streaming=True,
                 model_kwargs={"frequency_penalty": 1.0},
+                headers={
+                    "route": "/v1/completions",
+                    # other request headers ...
+                }
             )
             llm.invoke("tell me a joke.")
 
@@ -426,7 +436,7 @@ def _construct_json_body(self, prompt: str, param:dict) -> dict:
     temperature: float = 0.2
     """A non-negative float that tunes the degree of randomness in generation."""
 
-    k: int = -1
+    k: int = 50
     """Number of most likely tokens to consider at each step."""
 
     p: float = 0.75
@@ -472,6 +482,25 @@ def _identifying_params(self) -> Dict[str, Any]:
             **self._default_params,
         }
 
+    def _headers(
+        self, is_async: Optional[bool] = False, body: Optional[dict] = None
+    ) -> Dict:
+        """Construct and return the headers for a request.
+
+        Args:
+            is_async (bool, optional): Indicates if the request is asynchronous.
+                Defaults to `False`.
+            body (optional): The request body to be included in the headers if
+                the request is asynchronous.
+
+        Returns:
+            Dict: A dictionary containing the appropriate headers for the request.
+        """
+        return {
+            "route": DEFAULT_INFERENCE_ENDPOINT,
+            **super()._headers(is_async=is_async, body=body),
+        }
+
     def _generate(
         self,
         prompts: List[str],

libs/community/tests/unit_tests/chat_models/test_oci_data_science.py

@@ -19,6 +19,7 @@
 CONST_ENDPOINT = "https://oci.endpoint/ocid/predict"
 CONST_PROMPT = "This is a prompt."
 CONST_COMPLETION = "This is a completion."
+CONST_COMPLETION_ROUTE = "/v1/chat/completions"
 CONST_COMPLETION_RESPONSE = {
     "id": "chat-123456789",
     "object": "chat.completion",
@@ -120,6 +121,7 @@ def mocked_requests_post(url: str, **kwargs: Any) -> MockResponse:
 def test_invoke_vllm(*args: Any) -> None:
     """Tests invoking vLLM endpoint."""
     llm = ChatOCIModelDeploymentVLLM(endpoint=CONST_ENDPOINT, model=CONST_MODEL_NAME)
+    assert llm._headers().get("route") == CONST_COMPLETION_ROUTE
     output = llm.invoke(CONST_PROMPT)
     assert isinstance(output, AIMessage)
     assert output.content == CONST_COMPLETION
@@ -132,6 +134,7 @@ def test_invoke_vllm(*args: Any) -> None:
 def test_invoke_tgi(*args: Any) -> None:
     """Tests invoking TGI endpoint using OpenAI Spec."""
     llm = ChatOCIModelDeploymentTGI(endpoint=CONST_ENDPOINT, model=CONST_MODEL_NAME)
+    assert llm._headers().get("route") == CONST_COMPLETION_ROUTE
     output = llm.invoke(CONST_PROMPT)
     assert isinstance(output, AIMessage)
     assert output.content == CONST_COMPLETION
@@ -146,6 +149,7 @@ def test_stream_vllm(*args: Any) -> None:
     llm = ChatOCIModelDeploymentVLLM(
         endpoint=CONST_ENDPOINT, model=CONST_MODEL_NAME, streaming=True
     )
+    assert llm._headers().get("route") == CONST_COMPLETION_ROUTE
     output = None
     count = 0
     for chunk in llm.stream(CONST_PROMPT):
@@ -184,6 +188,7 @@ async def test_stream_async(*args: Any) -> None:
     llm = ChatOCIModelDeploymentVLLM(
         endpoint=CONST_ENDPOINT, model=CONST_MODEL_NAME, streaming=True
     )
+    assert llm._headers().get("route") == CONST_COMPLETION_ROUTE
     with mock.patch.object(
         llm,
         "_aiter_sse",

libs/community/tests/unit_tests/llms/test_oci_model_deployment_endpoint.py

@@ -18,6 +18,7 @@
 CONST_ENDPOINT = "https://oci.endpoint/ocid/predict"
 CONST_PROMPT = "This is a prompt."
 CONST_COMPLETION = "This is a completion."
+CONST_COMPLETION_ROUTE = "/v1/completions"
 CONST_COMPLETION_RESPONSE = {
     "choices": [
         {
@@ -114,6 +115,7 @@ async def mocked_async_streaming_response(
 def test_invoke_vllm(*args: Any) -> None:
     """Tests invoking vLLM endpoint."""
     llm = OCIModelDeploymentVLLM(endpoint=CONST_ENDPOINT, model=CONST_MODEL_NAME)
+    assert llm._headers().get("route") == CONST_COMPLETION_ROUTE
     output = llm.invoke(CONST_PROMPT)
     assert output == CONST_COMPLETION
 
@@ -126,6 +128,7 @@ def test_stream_tgi(*args: Any) -> None:
     llm = OCIModelDeploymentTGI(
         endpoint=CONST_ENDPOINT, model=CONST_MODEL_NAME, streaming=True
     )
+    assert llm._headers().get("route") == CONST_COMPLETION_ROUTE
     output = ""
     count = 0
     for chunk in llm.stream(CONST_PROMPT):
@@ -143,6 +146,7 @@ def test_generate_tgi(*args: Any) -> None:
     llm = OCIModelDeploymentTGI(
         endpoint=CONST_ENDPOINT, api="/generate", model=CONST_MODEL_NAME
     )
+    assert llm._headers().get("route") == CONST_COMPLETION_ROUTE
     output = llm.invoke(CONST_PROMPT)
     assert output == CONST_COMPLETION
 
@@ -161,6 +165,7 @@ async def test_stream_async(*args: Any) -> None:
     llm = OCIModelDeploymentTGI(
         endpoint=CONST_ENDPOINT, model=CONST_MODEL_NAME, streaming=True
     )
+    assert llm._headers().get("route") == CONST_COMPLETION_ROUTE
     with mock.patch.object(
         llm,
         "_aiter_sse",