Update model to gpt-3.5

actions/actions.py

@@ -31,24 +31,36 @@ 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):
+#model_engine="text-davinci-002"
+def generate_openai_response(conversation_data, model_engine="gpt-3.5-turbo", max_tokens=256, temperature=0.5):
     """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(conversation_data["current_user_query"]+conversation_data["previous_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}"
+    #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
+    #prompt_template = 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}"
+    messages=[
+    {"role": "system", "content": f"You are Omdi, a helpful assistant answers Omdena questions Using Relevant context:{context}"},
+    {"role": "user", "content": conversation_data["previous_user_query"]},
+    {"role": "user", "content": conversation_data["current_user_query"]}
+  ]
     # Generate a response using the OpenAI API
-    response = openai.Completion.create(
-        engine=model_engine,
-        prompt=prompt_template,
+    response = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo",
+        messages= prompt_template,
         max_tokens=max_tokens,
-        temperature=temperature,
-        n=1,
-        stop=None,
+        temperature=temperature
     )
 
     return response.choices[0].text.strip()
@@ -64,8 +76,9 @@ class GetOpenAIResponse(Action):
             domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:
 
         # Use OpenAI API to generate a response
-        query = tracker.latest_message.get('text')
-        response = generate_openai_response(query)
+        #query = tracker.latest_message.get('text')
+        conversation_data = [FollowupAction("action_extract_history")]
+        response = generate_openai_response(conversation_data)
                 
         # Output the generated response to user
         dispatcher.utter_message(text=response)
@@ -210,4 +223,50 @@ 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)
+
+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