Update query_data.py

query_data.py

@@ -2,32 +2,32 @@ from langchain.prompts.prompt import PromptTemplate
 from langchain.llms import OpenAI
 from langchain.chains import ChatVectorDBChain
 
-_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.
-You can assume the question about the most recent state of the union address.
+_template = """Paraphrase the message and encourage to share more
 Chat History:
 {chat_history}
-Follow Up Input: {question}
-Standalone question:"""
-CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
+Message: {message}
+Paraphrased message:"""
 
-template = """You are an AI assistant for answering questions about the most recent state of the union address.
-You are given the following extracted parts of a long document and a question. Provide a conversational answer.
-If you don't know the answer, just say "Hmm, I'm not sure." Don't try to make up an answer.
-If the question is not about the most recent state of the union, politely inform them that you are tuned to only answer questions about the most recent state of the union.
-Question: {question}
+PARAPHRASE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
+
+template = """You are an AI psychotherapist. You are empathic and encourage humans to share. If asked for information,
+provide it and then gently inquire if they want to talk about it. If you don't know the answer, just say 
+"Hmm, I'm not sure." Don't try to make up an answer. If the question is not about mental health or resources, 
+politely inform them that you are tuned to only answer questions about mental health and well being.
+Message: {message}
 =========
 {context}
 =========
 Answer in Markdown:"""
-QA_PROMPT = PromptTemplate(template=template, input_variables=["question", "context"])
 
+QA_PROMPT = PromptTemplate(template=template, input_variables=["message", "context"])
 
 def get_chain(vectorstore):
-    llm = OpenAI(temperature=0)
+    llm = OpenAI(temperature=0.5)
     qa_chain = ChatVectorDBChain.from_llm(
         llm,
         vectorstore,
         qa_prompt=QA_PROMPT,
-        condense_question_prompt=CONDENSE_QUESTION_PROMPT,
+        paraphrase_question_prompt=CONDENSE_QUESTION_PROMPT,
     )
     return qa_chain