added template to use Vertex Vector Search for q&a (#12622)

added template to use Vertex Vector Search for q&a

templates/rag-matching-engine/rag_matching_engine/chain.py

@@ -0,0 +1,69 @@
+import os
+
+from langchain.embeddings import VertexAIEmbeddings
+from langchain.llms import VertexAI
+from langchain.prompts import PromptTemplate
+from langchain.schema.output_parser import StrOutputParser
+from langchain.schema.runnable import RunnableParallel, RunnablePassthrough
+from langchain.vectorstores import MatchingEngine
+
+# you need to preate the index first, for example, as described here:
+# https://github.com/GoogleCloudPlatform/generative-ai/blob/main/language/use-cases/document-qa/question_answering_documents_langchain_matching_engine.ipynb
+expected_variables = [
+    "project_id",
+    "me_region",
+    "gcs_bucket",
+    "me_index_id",
+    "me_endpoint_id",
+]
+variables = []
+for variable_name in expected_variables:
+    variable = os.environ.get(variable_name.upper())
+    if not variable:
+        raise Exception(f"Missing `{variable_name}` environment variable.")
+    variables.append(variable)
+
+project_id, me_region, gcs_bucket, me_index_id, me_endpoint_id = variables
+
+
+vectorstore = MatchingEngine.from_components(
+    project_id=project_id,
+    region=me_region,
+    gcs_bucket_name=gcs_bucket,
+    embedding=VertexAIEmbeddings(),
+    index_id=me_index_id,
+    endpoint_id=me_endpoint_id,
+)
+
+model = VertexAI()
+
+template = (
+    "SYSTEM: You are an intelligent assistant helping the users with their questions"
+    "on research papers.\n\n"
+    "Question: {question}\n\n"
+    "Strictly Use ONLY the following pieces of context to answer the question at the "
+    "end. Think step-by-step and then answer.\n\n"
+    "Do not try to make up an answer:\n"
+    "- If the answer to the question cannot be determined from the context alone, "
+    'say \n"I cannot determine the answer to that."\n'
+    '- If the context is empty, just say "I do not know the answer to that."\n\n'
+    "=============\n{context}\n=============\n\n"
+    "Question: {question}\nHelpful Answer: "
+)
+
+prompt = PromptTemplate.from_template(template)
+
+retriever = vectorstore.as_retriever(
+    search_type="similarity",
+    search_kwargs={
+        "k": 10,
+        "search_distance": 0.6,
+    },
+)
+
+chain = (
+    RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
+    | prompt
+    | model
+    | StrOutputParser()
+)