Update docs.

Author: qiuosier

docs/source/user_guide/large_language_model/index.rst

@@ -19,6 +19,9 @@ Integration with LangChain
 **************************
 ADS is designed to work with LangChain, enabling developers to incorporate various LangChain components and models deployed on OCI seamlessly into their applications. Additionally, ADS can package LangChain applications and deploy it as a REST API endpoint using OCI Data Science Model Deployment.
 
+* `Bridging cloud and conversational AI: LangChain and OCI Data Science platform <https://blogs.oracle.com/ai-and-datascience/post/cloud-conversational-ai-langchain-oci-data-science>`_
+* `Deploy LangChain applications as OCI model deployments <https://blogs.oracle.com/ai-and-datascience/post/deploy-langchain-application-as-model-deployment>`_
+
 
 .. admonition:: Installation
   :class: note

docs/source/user_guide/large_language_model/langchain_models.rst

@@ -44,7 +44,7 @@ Here is an example of using prompt template and OCI generative AI LLM to build a
     map_input = RunnableParallel(text=RunnablePassthrough())
     # Template for the input text.
     template = PromptTemplate.from_template(
-        "Translate the text into French.\nText:{text}\nFrench translation: "
+        "Translate English into French. Do not ask any questions.\nEnglish: Hello!\nFrench: "
     )
     llm = GenerativeAI(
         compartment_id="<compartment_ocid>",
@@ -58,8 +58,8 @@ Here is an example of using prompt template and OCI generative AI LLM to build a
     translation_app = map_input | template | llm
 
     # Now you have a translation app.
-    translation_app.invoke("How are you?")
-    # "Comment ça va?"
+    translation_app.invoke("Hello!")
+    # "Bonjour!"
 
 Similarly, you can use the embedding model:
 
@@ -80,6 +80,11 @@ Similarly, you can use the embedding model:
 Integration with Model Deployment
 =================================
 
+.. admonition:: Available in LangChain
+  :class: note
+
+  The same ``OCIModelDeploymentVLLM`` and ``ModelDeploymentTGI`` classes are also `available from LangChain <https://python.langchain.com/docs/integrations/llms/oci_model_deployment_endpoint>`_.
+
 If you deploy open-source or your own LLM on OCI model deployment service using `vLLM <https://docs.vllm.ai/en/latest/>`_ or `HuggingFace TGI <https://huggingface.co/docs/text-generation-inference/index>`_ , you can use the ``ModelDeploymentVLLM`` or ``ModelDeploymentTGI`` to integrate your model with LangChain.
 
 .. code-block:: python3