Add rag google vertex ai search template (#13294)

- **Description:** This is a template demonstrating how to utilize
Google Vertex AI Search in conjunction with ChatVertexAI()

templates/rag-google-cloud-vertexai-search/rag_google_cloud_vertexai_search/chain.py

@@ -0,0 +1,50 @@
+import os
+
+from langchain.chat_models import ChatVertexAI
+from langchain.prompts import ChatPromptTemplate
+from langchain.pydantic_v1 import BaseModel
+from langchain.retrievers import GoogleVertexAISearchRetriever
+from langchain.schema.output_parser import StrOutputParser
+from langchain.schema.runnable import RunnableParallel, RunnablePassthrough
+
+# Get region and profile from env
+project_id = os.environ.get("GOOGLE_CLOUD_PROJECT_ID")
+data_store_id = os.environ.get("DATA_STORE_ID")
+model_type = os.environ.get("MODEL_TYPE")
+
+if not data_store_id:
+    raise ValueError(
+        "No value provided in env variable 'DATA_STORE_ID'. "
+        "A  data store is required to run this application."
+    )
+
+# Set LLM and embeddings
+model = ChatVertexAI(model_name=model_type, temperature=0.0)
+
+# Create Kendra retriever
+retriever = GoogleVertexAISearchRetriever(
+    project_id=project_id, search_engine_id=data_store_id
+)
+
+# RAG prompt
+template = """Answer the question based only on the following context:
+{context}
+Question: {question}
+"""
+prompt = ChatPromptTemplate.from_template(template)
+
+# RAG
+chain = (
+    RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
+    | prompt
+    | model
+    | StrOutputParser()
+)
+
+
+# Add typing for input
+class Question(BaseModel):
+    __root__: str
+
+
+chain = chain.with_types(input_type=Question)