Updated docs.

Author: lu-ohai

docs/source/user_guide/large_language_model/langchain_models.rst

@@ -33,9 +33,9 @@ By default, the integration uses the same authentication method configured with
     llm = ChatOCIModelDeployment(
         model="odsc-llm",
         endpoint= f"https://modeldeployment.oci.customer-oci.com/<OCID>/predict",
-        # Optionally you can specify additional keyword arguments for the model, e.g. temperature and headers.
+        # Optionally you can specify additional keyword arguments for the model, e.g. temperature and default_headers.
         temperature=0.1,
-        headers={"route": "v1/chat/completions"}, # default header for chat models
+        default_headers={"route": "v1/chat/completions"}, # default route for chat models
     )
 
 Alternatively, you may use specific authentication for the model:
@@ -50,9 +50,9 @@ Alternatively, you may use specific authentication for the model:
         endpoint= f"https://modeldeployment.oci.customer-oci.com/<OCID>/predict",
         # Use security token authentication for the model
         auth=ads.auth.security_token(profile="my_profile"),
-        # Optionally you can specify additional keyword arguments for the model, e.g. temperature and headers.
+        # Optionally you can specify additional keyword arguments for the model, e.g. temperature and default_headers.
         temperature=0.1,
-        headers={"route": "v1/chat/completions"}, # default header for chat models
+        default_headers={"route": "v1/chat/completions"}, # default route for chat models
     )
 
 Completion Models
@@ -69,7 +69,7 @@ Completion models takes a text string and input and returns a string with comple
         endpoint= f"https://modeldeployment.oci.customer-oci.com/<OCID>/predict",
         # Optionally you can specify additional keyword arguments for the model.
         max_tokens=32,
-        headers={"route": "v1/completions"}, # default header for completion models
+        default_headers={"route": "v1/completions"}, # default route for completion models
     )
 
     # Invoke the LLM. The completion will be a string.
@@ -102,7 +102,7 @@ Chat models takes `chat messages <https://python.langchain.com/docs/concepts/#me
         endpoint=f"<oci_model_deployment_url>/predict",
         # Optionally you can specify additional keyword arguments for the model.
         max_tokens=32,
-        headers={"route": "v1/chat/completions"}, # default header for chat models
+        default_headers={"route": "v1/chat/completions"}, # default route for chat models
     )
 
     messages = [

docs/source/user_guide/model_registration/large_language_model.rst

@@ -2,6 +2,11 @@
 Large Language Model
 ====================
 
+.. admonition:: AI Quick Actions
+  :class: note
+
+  Oracle Data Science now has launched `AI Quick Actions <https://docs.oracle.com/en-us/iaas/data-science/using/ai-quick-actions.htm>`_, which makes it easy for you to browse foundation models, and deploy, fine-tune, and evaluate them inside Data Science notebooks.
+
 Oracle ADS (Accelerated Data Science) opens the gateway to harnessing the full potential of the Large Language models
 within Oracle Cloud Infrastructure (OCI). `Meta <https://ai.meta.com/resources/models-and-libraries/llama-downloads/>`_'s
 latest offering, `Llama 2 <https://ai.meta.com/llama/>`_, introduces a collection of pre-trained and