Update app.py

app.py

@@ -3,17 +3,6 @@
 # ver01.01-5.workload-----app.py
 # ===========================================
 
-
-
-#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. 
-#Your expertise is exclusively in retrieving booking information for a given booking ID assistance related to
-#to this.
-#You do not provide information outside of this scope. If a question is not about this topic, respond with 
-#"Ooops da, jeg driver faktisk kun med henvendelser omkring bestillingsinformasjon. Gjelder det andre henvendelser, 
-#må du nok kontakte kundeservice på [email protected]😊"
-
-
 import asyncio
 import os
 import re
@@ -22,8 +11,6 @@ import json
 
 import chainlit as cl
 
-#from tiktoken import encoding_for_model
-
 from pydantic import BaseModel, ConfigDict
 
 from langchain import hub
@@ -42,14 +29,14 @@ OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
 
 class LLMChainConfig(BaseModel):
     model_config = ConfigDict(extra='allow')  
-    instruction_text_faq: str 
-    faq: dict  
+    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,
+    instruction_text_faq = instruction_text_faq
+    faq = faq
+    instruction_text_priv = instruction_text_priv
     personvernspolicy_data = personvernspolicy_data
 
 daysoff_assistant_template = """
@@ -104,70 +91,60 @@ api_response_prompt = PromptTemplate(
 @cl.on_chat_start
 def setup_multiple_chains():
 
-    llm = OpenAI(
-        model='gpt-3.5-turbo-instruct',
-        temperature=0.7, 
-        openai_api_key=OPENAI_API_KEY,
-        max_tokens=2048, 
-        top_p=0.9,  
-        frequency_penalty=0.1,
-        presence_penalty=0.1   
-    )
-    
-    conversation_memory = ConversationBufferMemory(memory_key="chat_history", 
-                                                   max_len=30,  # --retains only the last 30 exchanges
-                                                   return_messages=True,
-    )
-
-    llm_chain = LLMChain(
-        llm=llm,
-        prompt=daysoff_assistant_prompt,
-        memory=conversation_memory,
-        **LLMChainConfig(
-            instruction_text_faq=instruction_text_faq, 
-            faq=faq, 
-            instruction_text_priv=instruction_text_priv, 
-            personvernspolicy_data=personvernspolicy_data
-        ).model_dump()
-    )
-    
-    cl.user_session.set("llm_chain", llm_chain)
-
-    api_chain = APIChain.from_llm_and_api_docs(
-        llm=llm,
-        api_docs=api_docs_str,
-        api_url_prompt=api_url_prompt,
-        api_response_prompt=api_response_prompt,
-        verbose=True,
-        limit_to_domains=None 
-    )
-
-    cl.user_session.set("api_chain", api_chain)
+ try:
+        llm_chain = LLMChain(
+            llm=llm,
+            prompt=daysoff_assistant_prompt,
+            memory=conversation_memory,
+            **LLMChainConfig(
+                instruction_text_faq=instruction_text_faq,
+                faq=faq,
+                instruction_text_priv=instruction_text_priv,
+                personvernspolicy_data=personvernspolicy_data
+            ).model_dump()
+        )
+        cl.user_session.set("llm_chain", llm_chain)
+    except Exception as e:
+        print(f"Error setting LLMChain: {e}")
+
+    try:
+        api_chain = APIChain.from_llm_and_api_docs(
+            llm=llm,
+            api_docs=api_docs_str,
+            api_url_prompt=api_url_prompt,
+            api_response_prompt=api_response_prompt,
+            verbose=True,
+            limit_to_domains=None
+        )
+        cl.user_session.set("api_chain", api_chain)
+    except Exception as e:
+        print(f"Error setting APIChain: {e}")
 
 @cl.on_message
 async def handle_message(message: cl.Message):
-    user_message = message.content #.lower()
+    user_message = message.content
     llm_chain = cl.user_session.get("llm_chain")
     api_chain = cl.user_session.get("api_chain")
-    
-    booking_pattern = r'\b[A-Z]{6}\d{6}\b' 
+
+    if llm_chain is None or api_chain is None:
+        await cl.Message("Error: LLMChain or APIChain not initialized properly.").send()
+        return
+
+    booking_pattern = r'\b[A-Z]{6}\d{6}\b'
     endpoint_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 {endpoint_url}?search={bestillingskode}"
-   
-        response = await api_chain.acall(
-            {
-                "bestillingskode": bestillingskode,
-                "question": question
-              
-            },
-            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()
-    return message.content
+    try:
+        if re.search(booking_pattern, user_message):
+            bestillingskode = re.search(booking_pattern, user_message).group(0)
+            question = f"Retrieve information for booking ID {endpoint_url}?search={bestillingskode}"
+            response = await api_chain.acall(
+                {"bestillingskode": bestillingskode, "question": question},
+                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()
+    except Exception as e:
+        await cl.Message(f"Error during message handling: {e}").send()