Update actions/actions.py

actions/actions.py

@@ -14,7 +14,7 @@ import sys
 import openai
 
 # Add "/app/actions" to the sys.path
-actions_path = os.path.abspath("/app/actions")
+actions_path = os.path.abspath("/workspaces/Omdena-faq-chatbot-RASA/RASA_OpenAI_actions_server/actions")
 sys.path.insert(0, actions_path)
 
 print("-#-System-path-#-")
@@ -32,43 +32,54 @@ 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=15, temperature=0.5):
+def generate_openai_response(user_queries, model_engine="gpt-3.5-turbo", max_tokens=100, 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(user_queries[-1])
-
-    # 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}"
-
-    # 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 tasked to find out the latest user intent and generate the question related to omdena"},
+    messages_0=[
+    {"role": "system", "content": f"Extract and return the question the user is trying to ask, use the previous questions as context only if necessory"}
              ] 
-    max_user_queries_to_include = 4
-
-    for i, query in enumerate(reversed(user_queries)):
+    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.append({"role": "user", "content": query})
+            messages_0.append({"role": "user", "content": user_queries[-max_user_queries_to_include+i]})
 
     # Generate a response using the OpenAI API
-    response = openai.ChatCompletion.create(
+    response_0 = openai.ChatCompletion.create(
         model="gpt-3.5-turbo",
-        messages= messages,
-        max_tokens=max_tokens,
-        temperature=temperature,
+        messages= messages_0,
+        max_tokens=20,
+        temperature=0,
         top_p=1,
         frequency_penalty=0,
         presence_penalty=0
     )
+    # Run the main function from search_content.py and store the results in a variable
+    extracted_query = response_0.choices[0]['message']['content'].strip()
+    results = main_search(extracted_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 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=0,
+    top_p=1,
+    frequency_penalty=0,
+    presence_penalty=0
+    )
+    
 
-    return response.choices[0]['message']['content'].strip()
+    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)
 
 class GetOpenAIResponse(Action):