Update actions/actions.py

actions/actions.py

@@ -31,15 +31,19 @@ secret_value_0 = os.environ.get("openai")
 openai.api_key = secret_value_0
 # Provide your OpenAI API key
 
-def generate_openai_response(query, model_engine="text-davinci-002", max_tokens=124, temperature=0.8):
+def generate_openai_response(query, model_engine="text-davinci-002", max_tokens=256, 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(query)
+    results = main_search(query["current_user_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}"
+    
+    #prompt_template = f"Relevant context: {context}\n\n Answer the question in detail: {query}"
+    prompt_template = f"Relevant context: {context}\n\nPrevious User Query: {conversation_data['previous_user_query']}\nPrevious Bot Response: {conversation_data['previous_bot_response']}\n\n Answer the current_user_query in detail:Current User Query: {conversation_data['current_user_query']}"
 
     # Generate a response using the OpenAI API
     response = openai.Completion.create(
@@ -64,7 +68,8 @@ class GetOpenAIResponse(Action):
             domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:
 
         # Use OpenAI API to generate a response
-        query = tracker.latest_message.get('text')
+        #query = tracker.latest_message.get('text')
+        query = [FollowupAction("action_extract_history")]
         response = generate_openai_response(query)
                 
         # Output the generated response to user
@@ -211,3 +216,49 @@ 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