Update app.py

app.py

@@ -33,15 +33,58 @@ from aimakerspace.openai_utils.prompts import (
     AssistantRolePrompt,
 )
 
-# Chainlit App
-# ChatOpenAI Templates
-system_template = """You are a helpful assistant who always speaks in a pleasant tone!
+RAQA_PROMPT_TEMPLATE = """
+Use the provided context to answer the user's query. 
+
+You may not answer the user's query unless there is specific context in the following text.
+
+If you do not know the answer, or cannot answer, please respond with "I don't know".
+
+Context:
+{context}
 """
 
-user_template = """{input}
-Think through your response step by step.
+raqa_prompt = SystemRolePrompt(RAQA_PROMPT_TEMPLATE)
+
+USER_PROMPT_TEMPLATE = """
+User Query:
+{user_query}
 """
 
+user_prompt = UserRolePrompt(USER_PROMPT_TEMPLATE)
+
+class RetrievalAugmentedQAPipeline:
+    def __init__(self, llm: ChatOpenAI(), vector_db_retriever: VectorDatabase) -> None:
+        self.llm = llm
+        self.vector_db_retriever = vector_db_retriever
+
+    def run_pipeline(self, user_query: str) -> str:
+        context_list = self.vector_db_retriever.search_by_text(user_query, k=4)
+        
+        context_prompt = ""
+        for context in context_list:
+            context_prompt += context[0] + "\n"
+
+        formatted_system_prompt = raqa_prompt.create_message(context=context_prompt)
+
+        formatted_user_prompt = user_prompt.create_message(user_query=user_query)
+        
+        return self.llm.run([formatted_system_prompt, formatted_user_prompt])
+    
+
+chat_openai = ChatOpenAI()
+
+retrieval_augmented_qa_pipeline = RetrievalAugmentedQAPipeline(
+    vector_db_retriever=vector_db,
+    llm=chat_openai
+)
+
+# Chainlit App
+# ChatOpenAI Templates
+system_template = RAQA_PROMPT_TEMPLATE
+
+user_template = USER_PROMPT_TEMPLATE
+
 @cl.on_chat_start  # marks a function that will be executed at the start of a user session
 async def start_chat():
     settings = {