Revert actions to 3 days ago

actions/actions.py

@@ -31,25 +31,15 @@ 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(conversation_data, model_engine="gpt-3.5-turbo", max_tokens=256, temperature=0.5):
+def generate_openai_response(query, model_engine="text-davinci-002", max_tokens=124, temperature=0.8):
     """Generate a response using the OpenAI API."""
     
     # Run the main function from search_content.py and store the results in a variable
-    
-    #results = main_search(query)
-    results = main_search(conversation_data["current_user_query"]+conversation_data["previous_user_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
-    
-    #prompt_template = f"Relevant context: {context}\n\n Answer the question in detail: {query}"
-    previous_user_query = conversation_data["previous_user_query"]
-    previous_bot_response = conversation_data["previous_bot_response"]
-    current_user_query = conversation_data["current_user_query"]
-
-    # Create the prompt template
-    context = f"Using Relevant context:{context}\n\n and Previous User Query: {previous_user_query}\n\n Answer the next question in detail:{current_user_query}"
+    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(
@@ -74,9 +64,8 @@ class GetOpenAIResponse(Action):
             domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:
 
         # Use OpenAI API to generate a response
-        #query = tracker.latest_message.get('text')
-        conversation_data = [FollowupAction("action_extract_history")]
-        response = generate_openai_response(conversation_data)
+        query = tracker.latest_message.get('text')
+        response = generate_openai_response(query)
                 
         # Output the generated response to user
         dispatcher.utter_message(text=response)
@@ -221,50 +210,4 @@ class SayHelloWorld(Action):
 
         # Output the generated response to user
         generated_text = response.choices[0].text
-        dispatcher.utter_message(text=generated_text)
-
-class ExtractConversationhistory(Action):
-    def name(self) -> Text:
-        return "action_extract_history"
-
-    def run(self, dispatcher: CollectingDispatcher, tracker: Tracker, domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:
-        conversation_history = tracker.events
-
-        user_queries = []
-        bot_responses = []
-        current_user_query = ""
-        previous_user_query = None
-        previous_bot_response = None
-
-        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"))
-
-        if user_queries:
-            if len(user_queries) >= 2:
-                previous_user_query = user_queries[-2]
-            else:
-                pass
-
-            try:
-                current_user_query = user_queries[-1]
-            except:
-                pass
-
-        if bot_responses:
-            if len(bot_responses) >= 2:
-                previous_bot_response = bot_responses[-2]
-            else:
-                pass
-        else:
-            pass
-
-        conversation_data = {
-            "previous_user_query": previous_user_query,
-            "previous_bot_response": previous_bot_response,
-            "current_user_query": current_user_query
-        }
-        # Now you can use the conversation_data dictionary as needed.
-        return conversation_data
+        dispatcher.utter_message(text=generated_text)