Vectara RAG template (#12975)

- **Description:** RAG template using Vectara
  - **Twitter handle:** @ofermend

templates/rag-vectara/rag_vectara/chain.py

@@ -0,0 +1,49 @@
+import os
+
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts import ChatPromptTemplate
+from langchain.pydantic_v1 import BaseModel
+from langchain.schema.output_parser import StrOutputParser
+from langchain.schema.runnable import RunnableParallel, RunnablePassthrough
+from langchain.vectorstores import Vectara
+
+if os.environ.get("VECTARA_CUSTOMER_ID", None) is None:
+    raise Exception("Missing `VECTARA_CUSTOMER_ID` environment variable.")
+if os.environ.get("VECTARA_CORPUS_ID", None) is None:
+    raise Exception("Missing `VECTARA_CORPUS_ID` environment variable.")
+if os.environ.get("VECTARA_API_KEY", None) is None:
+    raise Exception("Missing `VECTARA_API_KEY` environment variable.")
+
+# If you want to ingest data then use this code.
+# Note that no document chunking is needed, as this is
+# done efficiently in the Vectara backend.
+#   loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
+#   docs = loader.load()
+#   vec_store = Vectara.from_document(docs)
+#   retriever = vec_store.as_retriever()
+# Otherwise, if data is already loaded into Vectara then use this code:
+retriever = Vectara().as_retriever()
+
+# RAG prompt
+template = """Answer the question based only on the following context:
+{context}
+Question: {question}
+"""
+prompt = ChatPromptTemplate.from_template(template)
+
+# RAG
+model = ChatOpenAI()
+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)