Update app.py

app.py

@@ -18,16 +18,13 @@ from langchain_core.prompts import PromptTemplate
 from langchain.memory.buffer import ConversationBufferMemory
 
 from api_docs_mck import api_docs_str 
-from faq_data import help1, ansatte_faq_data, utleiere_faq_data
-from personvernspolicy import help2, personvernspolicy_data
 
 OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
 
 daysoff_assistant_template = """
 You are a customer support assistant (’kundeservice AI assistent’) for Daysoff.no
 By default, you respond in Norwegian language, using a warm, direct and professional tone. 
-You can provide information associated with a given booking ID, and with {help1} in mind, you
-can also answer frequently asked questions (FAQ) about DaysOff firmahytteordning for 
+You can provide information associated with a given booking ID, answer frequently asked questions (FAQ) about DaysOff firmahytteordning for 
 employees: {ansatte_faq_data} and for employers:{utleiere_faq_data}.
 To understand how best to answer queries about privacy policy, 
 refer to {help2} and {personvernspolicy_data}
@@ -36,7 +33,7 @@ Question: {question}
 Answer:
 """
 daysoff_assistant_prompt = PromptTemplate(
-    input_variables=['chat_history', 'help1', 'question', 'utleiere_faq_data', 'personvernspolicy_data', 'ansatte_faq_data'],
+    input_variables=['chat_history', 'question'],
     template=daysoff_assistant_template
 )
 
@@ -53,16 +50,21 @@ api_url_prompt = PromptTemplate(input_variables=['api_docs', 'question'],
                                 template=api_url_template)
 
 api_response_template = """
-With the API Documentation for Daysoff's official API: {api_docs} in mind, 
-and user question: {question} in mind,
+With the API Documentation for Daysoff's official API: {api_docs} in mind,
+and  the specific user question: {question} in mind,
 and given this API URL: {api_url} for querying,
-and if the response from Daysoff's API is information associated with 
-a booking ID (’bestillingskode’): {api_response}, 
-please directly address the user's question (in Norwegian) and focus on delivering the 
-the response from Daysoff's API in a markdown table with clarity 
-and conciseness. 
-Response:
+here is the response from Daysoff's API: {api_response}.
+Please provide an summary (in Norwegian) that directly addresses the user's question,
+and focusing on delivering the answer with clarity and conciseness,
+as if a human customer service agent is providing this information.
+Summary:
 """
+## --"default"
+#Please provide an summary (in Norwegian) that directly addresses the user's question,
+#and focusing on delivering the answer with clarity and conciseness,
+#as if a human customer service agent is providing this information.
+#Summary:
+
 
 api_response_prompt = PromptTemplate(
     input_variables=['api_docs', 'question', 'api_url', 'api_response'],
@@ -113,7 +115,6 @@ async def handle_message(message: cl.Message):
     booking_pattern = r'\b[A-Z]{6}\d{6}\b' 
     base_url = "https://670dccd0073307b4ee447f2f.mockapi.io/daysoff/api/V1/booking"
 
-
     if re.search(booking_pattern, user_message):  
         bestillingskode = re.search(booking_pattern, user_message).group(0)  
         question = f"Retrieve information for booking ID {base_url}?search={bestillingskode}"
@@ -126,20 +127,8 @@ async def handle_message(message: cl.Message):
             },
             callbacks=[cl.AsyncLangchainCallbackHandler()])
         
-    else:
-        response = await llm_chain.acall(
-            {
-            'chat_history': chat_history,
-            'question': user_message,
-            'help1': help1(),  # --call function w/arguments to get the instruction
-            'ansatte_faq_data': ansatte_faq_data,
-            'utleiere_faq_data': utleiere_faq_data,
-            'personvernspolicy_data': personvernspolicy_data,
-            'help2': help2()  
-            },
-            callbacks=[cl.AsyncLangchainCallbackHandler()])
-        
-        #response = await llm_chain.acall(user_message, callbacks=[cl.AsyncLangchainCallbackHandler()])
+    else:    
+        response = await llm_chain.acall(user_message, callbacks=[cl.AsyncLangchainCallbackHandler()])
 
     response_key = "output" if "output" in response else "text"
     await cl.Message(response.get(response_key, "")).send()