Update app.py

app.py

@@ -1,8 +1,12 @@
-
-# ===========================================
-# ver01.01-5.workload-----app.py
-# ===========================================
-
+# ===================================================
+# "the-very-latest-latest-POST-it"-----app.py✍🏽 👾
+# ===================================================
+"""
+    Change from APIChain to EnhancedRequestsPostTool was heisted by the need to handle POST requests.
+    APIChain internally was responsible for URL construction, API interaction, and response formatting:
+    EnhancedRequestsPostTool is an enabler for POST handling@RequestsPostTool.
+    Routing mechanisms for prompts (api_url_prompt+api_response_prompt) remain are being used across distinct components.
+"""
 import asyncio
 import os
 import re
@@ -10,58 +14,66 @@ import time
 import json
 
 import chainlit as cl
+from dotenv import load_dotenv
 
-#from pydantic import BaseModel, ConfigDict
+from pydantic import BaseModel, PrivateAttr
 
 from langchain import hub
 from langchain_openai import OpenAI
-from langchain.chains import LLMChain, APIChain
+from tiktoken import encoding_for_model
+from langchain.chains import LLMChain #APIChain
 from langchain_core.prompts import PromptTemplate
+
+from langchain_community.tools.requests.tool import RequestsPostTool
+from langchain_community.utilities.requests import TextRequestsWrapper
+
 from langchain.memory.buffer import ConversationBufferMemory
 from langchain.memory import ConversationTokenBufferMemory
 from langchain.memory import ConversationSummaryMemory
 
-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 
+from api_docs import api_docs_str
 
+load_dotenv()
 OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
+auth_token = os.getenv("DAYSOFF_API_TOKEN")
 
-#class LLMChainConfig(BaseModel):
-    #model_config = ConfigDict(extra='allow')
 
-    #instruction_text_faq: str = instruction_text_faq
-    #faq: dict = faq
-    #instruction_text_priv: str = instruction_text_priv
-    #personvernspolicy_data: dict = personvernspolicy_data
 
-#faq_combined = f"{instruction_text_faq}\n\n{faq}"
-#privacy_combined = f"{instruction_text_priv}\n\n{personvernspolicy_data}"     
+class EnhancedRequestsPostTool(RequestsPostTool, BaseModel):
+    api_docs: str = api_docs_str
 
-# {faq_combined}, {privacy_combined}
-"""
-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. 
-Your expertise is exclusively in retrieving booking information for a given booking id and answering
-questions about firmahytteorning and personvernspolicy.
-If a question does not involve booking information for a given booking id, ask: "Gjelder spørsmålet firmahytteordning?",
-upon user confirmation, do your best to try to answer accordingly by referring to {instruction_text_faq} and {faq}.
-If a query does not involve booking information for a given booking id or firmahytteordning, ask: "Gjelder spørsmålet personvernspolicy?"
-upon user confirmation, do your best to provide a precise privacy-related response by referring to: {instruction_text_priv} and {personvernspolicy_data}.
-If the query does not involve booking information for a given booking id, firmahytteordning or personvernspolicy,
-respond with: "Jeg driver faktisk kun med henvendelser omkring bestillingsinformasjon og ofte-stilte-spørsmål i forbindelse 
-med DaysOff firmahytteordning (inkludert personvernspolicyn). Gjelder det andre henvendelser, må du nok kontakte kundeservice på [email protected]😊"
-Chat History: {chat_history}
-Question: {question}
-Answer:
-"""
+    # --PrivateAttr@dynanmc init attrbts
+    _url_chain: LLMChain = PrivateAttr()
+    _response_chain: LLMChain = PrivateAttr()
 
+    def __init__(self, requests_wrapper, llm, api_docs_str, api_url_prompt, api_response_prompt):
+        super().__init__(requests_wrapper=requests_wrapper, allow_dangerous_requests=True)
+        object.__setattr__(self, 'api_docs', api_docs_str) # self.api_docs = api_docs_str
+        object.__setattr__(self, 'url_chain', LLMChain(llm=llm, prompt=api_url_prompt))  # --dynanmc init1
+        object.__setattr__(self, 'response_chain', LLMChain(llm=llm, prompt=api_response_prompt)) # --dynanmc init2
 
+    async def ainvoke(self, input_data, callbacks=None):
+        # -- create API URL
+        url_response = await self.url_chain.ainvoke({
+            "api_docs": self.api_docs,
+            "question": input_data.get("question")
+        })
+
+        api_response = await super().ainvoke(input_data, callbacks) # --make POST request
+
+        # --form(at) response/:response_chain
+        formatted_response = await self.response_chain.ainvoke({
+            "api_docs": self.api_docs,
+            "question": input_data.get("question"),
+            "api_url": url_response.get("text"),
+            "api_response": api_response
+        })
+
+        return formatted_response
 
 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.
+#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
@@ -87,7 +99,7 @@ API URL:
 """
 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 the specific user question: {question},
@@ -95,7 +107,7 @@ and given this API URL: {api_url} for querying,
 and response from Daysoff's API: {api_response},
 never refer the user to the API URL as your answer!
 You should always provide a clear and concise summary (in Norwegian) of the booking information retrieved.
-This way you directly address the user's question in a manner that reflects the professionalism and warmth 
+This way you directly address the user's question in a manner that reflects the professionalism and warmth
 of a human customer service agent.
 Summary:
 """
@@ -107,86 +119,77 @@ api_response_prompt = PromptTemplate(
 
 @cl.on_chat_start
 def setup_multiple_chains():
-
     llm = OpenAI(
         model='gpt-3.5-turbo-instruct',
-        temperature=0.7, 
+        temperature=0.7,
         openai_api_key=OPENAI_API_KEY,
-        max_tokens=2048, 
-        top_p=0.9,  
+        max_tokens=2048,
+        top_p=0.9,
         frequency_penalty=0.1,
-        presence_penalty=0.1   
+        presence_penalty=0.1
     )
 
-    # --ConversationBufferMemory
-    conversation_memory = ConversationBufferMemory(memory_key="chat_history", 
-                                                   max_len=30,  # --retains only the last 30 exchanges
+    conversation_memory = ConversationBufferMemory(memory_key="chat_history",
+                                                   max_len=30,
                                                    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,
-    #)
 
     llm_chain = LLMChain(
         llm=llm,
         prompt=daysoff_assistant_prompt,
-        memory=conversation_memory
+        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)
+    requests_wrapper = TextRequestsWrapper(
+        headers={
+            "Authorization": auth_token,
+            "Content-Type": "application/json"
+        }
+    )
 
-    api_chain = APIChain.from_llm_and_api_docs(
+    post_tool = EnhancedRequestsPostTool(
+        requests_wrapper=requests_wrapper,
         llm=llm,
-        api_docs=api_docs_str,
+        api_docs_str=api_docs_str,
         api_url_prompt=api_url_prompt,
-        api_response_prompt=api_response_prompt,
-        verbose=True,
-        limit_to_domains=None 
+        api_response_prompt=api_response_prompt
     )
 
-    cl.user_session.set("api_chain", api_chain)
+    cl.user_session.set("llm_chain", llm_chain)
+    cl.user_session.set("post_tool", post_tool)
+
 
 @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' 
-    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()])
+    post_tool = cl.user_session.get("post_tool")
+
+    booking_pattern = r'\b[A-Z]{6}\d{6}\b'
+    endpoint_url = "https://aivisions.no/data/daysoff/api/v1/booking/"
+
+    match = re.search(booking_pattern, user_message)
+    if match:
+        bestillingskode = match.group()
+        post_data = {
+            "url": endpoint_url,
+            "body": {
+                "booking_id": bestillingskode
+            }
+        }
+
+        response = await post_tool.arun(
+            json.dumps(post_data),
+            callbacks=[cl.AsyncLangchainCallbackHandler()]
+        )
+
+    else:
+        response = await llm_chain.ainvoke(
+            user_message,                                # {"chat_history": "", "question": user_message}
+        )
+        await cl.Message(content=response).send()
 
     response_key = "output" if "output" in response else "text"
+
     await cl.Message(response.get(response_key, "")).send()
     return message.content