Update app.py

app.py

@@ -38,10 +38,20 @@ from api_docs_mck import api_docs_str
 from personvernspolicy import instruction_text_priv, personvernspolicy_data
 from frequently_asked_questions import instruction_text_faq, faq 
 
-
 OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
 
-# If you don't know the answer, just say that you don't know, don't try to make up an answer.
+class LLMChainConfig(BaseModel):
+    model_config = ConfigDict(extra='allow')  
+    instruction_text_faq: str 
+    faq: dict  
+    instruction_text_priv: str
+    personvernspolicy_data: dict
+
+    instruction_text_faq = instruction_text_faq,
+    faq = faq,
+    instruction_text_priv = instruction_text_priv,
+    personvernspolicy_data = personvernspolicy_data
+
 daysoff_assistant_template = """
 You are a customer support assistant (’kundeservice AI assistent’) for Daysoff. 
 By default, you respond in Norwegian language, using a warm, direct, and professional tone. 
@@ -74,8 +84,6 @@ API URL:
 """
 api_url_prompt = PromptTemplate(input_variables=['api_docs', 'question'],
                                 template=api_url_template)
-
-# If the response includes booking information, provide the information verbatim (do not summarize it.)
     
 api_response_template = """
 With the API Documentation for Daysoff's official API: {api_docs} in mind,
@@ -93,19 +101,6 @@ api_response_prompt = PromptTemplate(
     template=api_response_template
 )
 
-# ---------------------------------------------------------------------------------------------------------
-# 100 tokens ≃ 75 words
-# system prompt(s), total = 330 tokens
-# average api response = 250-300 tokens (current)
-# user input "reserved" = 400 tokens (300 words max. /English; Polish, Norwegian {..}?@tiktokenizer), could be reduc3d to 140 tokens ≃ 105 words
-# model output (max_tokens) = 2048
-
-# ConversationBufferMemory = maintains raw chat history; crucial for "nuanced" follow-ups (e.g. "nuanced" ~ for non-English inputs)
-# ConversationTokenBufferMemory (max_token_limit) = 1318 (gives space in chat_history for approximately 10-15 exchanges, assuming ~100 tokens/exchange) 
-# ConversationSummaryMemory = scalable approach, especially useful for extended or complex interactions, caveat: loss of granular context
-# ---------------------------------------------------------------------------------------------------------
-
-
 @cl.on_chat_start
 def setup_multiple_chains():
 
@@ -118,31 +113,11 @@ def setup_multiple_chains():
         frequency_penalty=0.1,
         presence_penalty=0.1   
     )
-
-    # --ConversationBufferMemory
+    
     conversation_memory = ConversationBufferMemory(memory_key="chat_history", 
                                                    max_len=30,  # --retains only the last 30 exchanges
                                                    return_messages=True,
     )
-    
-    # --ConversationTokenBufferMemory
-    #conversation_memory = ConversationTokenBufferMemory(memory_key="chat_history",
-                                                        #max_token_limit=1318,
-                                                        #return_messages=True,
-    #)
-
-    # --ConversationSummaryMemory
-    #conversation_memory = ConversationSummaryMemory(memory_key="chat_history",
-                                                    #return_messages=True,
-    #)
-
-
-class LLMChainConfig(BaseModel):
-    model_config = ConfigDict(extra='allow')  
-    instruction_text_faq: str 
-    faq: dict  
-    instruction_text_priv: str
-    personvernspolicy_data: dict
 
     llm_chain = LLMChain(
         llm=llm,