revert context feature

actions/actions.py

@@ -14,7 +14,7 @@ import sys
 import openai
 
 # Add "/app/actions" to the sys.path
-actions_path = os.path.abspath("/workspaces/Omdena-faq-chatbot-RASA/RASA_OpenAI_actions_server/actions")
+actions_path = os.path.abspath("/app/actions")
 sys.path.insert(0, actions_path)
 
 print("-#-System-path-#-")
@@ -31,54 +31,27 @@ secret_value_0 = os.environ.get("openai")
 openai.api_key = secret_value_0
 # Provide your OpenAI API key
 
-#model_engine="text-davinci-002"
-def generate_openai_response(user_queries, model_engine="gpt-3.5-turbo", max_tokens=100, temperature=0.5):
+def generate_openai_response(query, model_engine="text-davinci-003", max_tokens=124, temperature=0.8):
     """Generate a response using the OpenAI API."""
     
-
-    
-    
-    messages_0=[
-    {"system": f"Output the final question the user is asking, using the previous user questions as context only if necessory"}
-             ] 
-    max_user_queries_to_include = min(3,len(user_queries))
-    # The latest query is at the end of the list
-    for i in range(len(user_queries)):
-        if i<max_user_queries_to_include:
-            messages_0.append({f"user_question_{i}": user_queries[-max_user_queries_to_include+i]})
-
-    #Generate a response using the OpenAI API
-    response_0 = openai.Completion.create(
-    model="text-davinci-003",
-    prompt=str(messages_0),
-    max_tokens=20
-)
-    
-    
-
     # Run the main function from search_content.py and store the results in a variable
-    extracted_query = response_0.choices[0].text.strip()#--3.5---#response_0.choices[0]['message']['content'].strip()
-    results = main_search(extracted_query)
-  
+    results = main_search(query)
+
     # Create context from the results
     context = "".join([f"#{str(i)}" for i in results])[:2014] # Trim the context to 2014 characters - Modify as necessory
-    
-    messages_1=[
-    {"role": "system", "content": f"You are tasked to answer user queries using only the following Relevant context: {context}"},
-    {"role": "user", "content": extracted_query}]
-             
-    response_1 = openai.ChatCompletion.create(
-    model="gpt-3.5-turbo",
-    messages= messages_1,
-    max_tokens=254,
-    temperature=temperature,
-    top_p=1,
-    frequency_penalty=0,
-    presence_penalty=0
+    prompt_template = f"Relevant context: {context}\n\n Answer the question in detail: {query}"
+
+    # Generate a response using the OpenAI API
+    response = openai.Completion.create(
+        engine=model_engine,
+        prompt=prompt_template,
+        max_tokens=max_tokens,
+        temperature=temperature,
+        n=1,
+        stop=None,
     )
-    
 
-    return response_1.choices[0]['message']['content'].strip()#+"Debug INFO: -------------------- Openai-input_1:     "+str(messages_1)+"         Openai_input_0"+str( messages_0)+"  user_queries: "+str(user_queries)
+    return response.choices[0].text.strip()
 
 class GetOpenAIResponse(Action):
 
@@ -90,24 +63,12 @@ class GetOpenAIResponse(Action):
             tracker: Tracker,
             domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:
 
-        # Extract conversation data
-        conversation_history = tracker.events
-
-        user_queries = []
-        bot_responses = []
-
-        for event in conversation_history:
-            if event.get("event") == "user":
-                user_queries.append(event.get("text"))
-            elif event.get("event") == "bot":
-                bot_responses.append(event.get("text"))
-
         # Use OpenAI API to generate a response
-        #query = tracker.latest_message.get('text')
-        response = generate_openai_response(user_queries)
+        query = tracker.latest_message.get('text')
+        response = generate_openai_response(query)
                 
         # Output the generated response to user
-        dispatcher.utter_message(text=str(response))
+        dispatcher.utter_message(text=response)
                 
 class GeneralHelp(Action):
     def name(self) -> Text:
@@ -249,4 +210,4 @@ class SayHelloWorld(Action):
 
         # Output the generated response to user
         generated_text = response.choices[0].text
-        dispatcher.utter_message(text=generated_text)
+        dispatcher.utter_message(text=generated_text)