Update OCI model deployment clients to inherit from OpenAI

libs/oci/README.md

@@ -59,14 +59,16 @@ embeddings.embed_query("What is the meaning of life?")
 
 ### 1. Use a Chat Model
 
-You may instantiate the OCI Data Science model with the generic `ChatOCIModelDeployment` or framework specific class like `ChatOCIModelDeploymentVLLM`.
+The `ChatOCIModelDeployment` class is designed for deployment with OpenAI compatible APIs.
 
 ```python
-from langchain_oci.chat_models import ChatOCIModelDeployment, ChatOCIModelDeploymentVLLM
+from langchain_oci import ChatOCIModelDeployment
 
 # Create an instance of OCI Model Deployment Endpoint
+
 # Replace the endpoint uri with your own
-endpoint = "https://modeldeployment.<region>.oci.customer-oci.com/<ocid>/predict"
+# For streaming, use the /predictWithResponseStream endpoint.
+endpoint = "https://modeldeployment.<region>.oci.customer-oci.com/<ocid>/predictWithResponseStream"
 
 messages = [
     (
@@ -78,31 +80,23 @@ messages = [
 
 chat = ChatOCIModelDeployment(
     endpoint=endpoint,
-    streaming=True,
-    max_retries=1,
-    model_kwargs={
-        "temperature": 0.2,
-        "max_tokens": 512,
-    },  # other model params...
-    default_headers={
-        "route": "/v1/chat/completions",
-        # other request headers ...
-    },
+    model="odsc-llm",
+    max_tokens=512,
 )
-chat.invoke(messages)
+chat.stream(messages)
 
-chat_vllm = ChatOCIModelDeploymentVLLM(endpoint=endpoint)
-chat_vllm.invoke(messages)
 ```
 
 ### 2. Use a Completion Model
-You may instantiate the OCI Data Science model with `OCIModelDeploymentLLM` or `OCIModelDeploymentVLLM`.
+The `OCIModelDeploymentLLM` class is designed for completion endpoints.
 
 ```python
-from langchain_oci.llms import OCIModelDeploymentLLM, OCIModelDeploymentVLLM
+from langchain_oci import OCIModelDeploymentLLM
 
 # Create an instance of OCI Model Deployment Endpoint
+
 # Replace the endpoint uri and model name with your own
+# For streaming, use the /predictWithResponseStream endpoint.
 endpoint = "https://modeldeployment.<region>.oci.customer-oci.com/<ocid>/predict"
 
 llm = OCIModelDeploymentLLM(
@@ -111,10 +105,6 @@ llm = OCIModelDeploymentLLM(
 )
 llm.invoke("Who is the first president of United States?")
 
-vllm = OCIModelDeploymentVLLM(
-    endpoint=endpoint,
-)
-vllm.invoke("Who is the first president of United States?")
 ```
 
 ### 3. Use an Embedding Model

libs/oci/langchain_oci/__init__.py

@@ -1,35 +1,21 @@
 # Copyright (c) 2025 Oracle and/or its affiliates.
 # Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
-
-from langchain_oci.chat_models.oci_data_science import (
-    ChatOCIModelDeployment,
-    ChatOCIModelDeploymentTGI,
-    ChatOCIModelDeploymentVLLM,
-)
+from langchain_oci.chat_models import ChatOCIModelDeployment
 from langchain_oci.chat_models.oci_generative_ai import ChatOCIGenAI
 from langchain_oci.embeddings.oci_data_science_model_deployment_endpoint import (
     OCIModelDeploymentEndpointEmbeddings,
 )
 from langchain_oci.embeddings.oci_generative_ai import OCIGenAIEmbeddings
-from langchain_oci.llms.oci_data_science_model_deployment_endpoint import (
-    BaseOCIModelDeployment,
-    OCIModelDeploymentLLM,
-    OCIModelDeploymentTGI,
-    OCIModelDeploymentVLLM,
-)
+from langchain_oci.llms import BaseOCIModelDeployment, OCIModelDeploymentLLM
 from langchain_oci.llms.oci_generative_ai import OCIGenAI, OCIGenAIBase
 
 __all__ = [
     "ChatOCIGenAI",
     "ChatOCIModelDeployment",
-    "ChatOCIModelDeploymentTGI",
-    "ChatOCIModelDeploymentVLLM",
     "OCIGenAIEmbeddings",
     "OCIModelDeploymentEndpointEmbeddings",
     "OCIGenAIBase",
     "OCIGenAI",
     "BaseOCIModelDeployment",
     "OCIModelDeploymentLLM",
-    "OCIModelDeploymentTGI",
-    "OCIModelDeploymentVLLM",
 ]

libs/oci/langchain_oci/chat_models/__init__.py

@@ -1,16 +1,33 @@
 # Copyright (c) 2025 Oracle and/or its affiliates.
 # Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
 
-from langchain_oci.chat_models.oci_data_science import (
-    ChatOCIModelDeployment,
-    ChatOCIModelDeploymentTGI,
-    ChatOCIModelDeploymentVLLM,
-)
 from langchain_oci.chat_models.oci_generative_ai import ChatOCIGenAI
 
+try:
+    from langchain_oci.chat_models.oci_data_science import ChatOCIModelDeployment
+
+except ModuleNotFoundError as ex:
+    # Default message
+    message = ex.msg
+    # For langchain_openai, show the message with pip install command.
+    if ex.name == "langchain_openai":
+        message = (
+            "No module named langchain_openai. "
+            "Please install it with `pip install langchain_openai`"
+        )
+
+    # Create a placeholder class here so that
+    # users can import the class without error.
+    # Users will see the error message when they try to initialize an instance.
+    class ChatOCIModelDeployment:
+        """Placeholder class for ChatOCIModelDeployment
+        when langchain-openai is not installed."""
+
+        def __init__(self, *args, **kwargs):
+            raise ModuleNotFoundError(message)
+
+
 __all__ = [
     "ChatOCIGenAI",
     "ChatOCIModelDeployment",
-    "ChatOCIModelDeploymentTGI",
-    "ChatOCIModelDeploymentVLLM",
 ]