Update pages/4_AutoEngage.py

pages/4_AutoEngage.py

@@ -12,6 +12,14 @@ from langchain.text_splitter import CharacterTextSplitter
 from langchain.agents.agent_toolkits import create_retriever_tool
 from langchain.agents.agent_toolkits import create_conversational_retrieval_agent
 from langchain.chat_models import ChatOpenAI
+from langchain.chains import ConversationChain
+from langchain import OpenAI
+from langchain.chains.conversation.memory import ConversationBufferMemory
+from langchain.callbacks import get_openai_callback
+
+with open('docs/ae_ontology.ttl', 'r') as file:
+    # Read the entire contents of the file into a string
+    ttl_file_contents = file.read()
 
 import streamlit as st 
 
@@ -65,10 +73,47 @@ if OpenAI_Key:
 
         if result_2:
             st.subheader('JSON-LD Description')
-            prompt_3 = """### Instruction: Please use AEO ontology to map the  plan given below to the JSON-LD description. ### Plan: """ + result_2['output']
-            result_3 = agent_executor({'input': prompt_3}) 
+            # lets make gpt to digest our ontology by calling the 16k-model to avoid token limit
+            # first initialize the large language model
+            llm = OpenAI(
+                temperature=0,
+                openai_api_key= OpenAI_Key,
+                model_name="gpt-3.5-turbo-16k"
+            )
+            conversation = ConversationChain(llm=llm)
+            
+
+
+            conversation_buf = ConversationChain(
+                llm=llm,
+                memory=ConversationBufferMemory()
+            )
+            #internal purpose
+            def count_tokens(chain, query):
+                with get_openai_callback() as cb:
+                    result = chain.run(query)
+                    print(f'Spent a total of {cb.total_tokens} tokens')
+
+                return result
+            
+            count_tokens( conversation_buf, 
+                    f""" Please digest this ttl file content and simply acknowledge that you have understand it.
+                    No need to say anything else.\n {ttl_file_contents}  """
+                )
+            
+            result_3 = count_tokens( conversation_buf, 
+            f""" I'll provide the deception strategy plan to you. 
+            Kindly generate a json-ld portrayal of the strategy, adhering to the ontology you've acquired. 
+            Ensure the json-ld aligns with the ontology framework, and refrain from utilizing any classes not included in the established ontology.
+
+             {result_2['output']}
+            """
+             )
+            
+            
+            result_3
             
-            st.write(result_3['output'])
+            st.write(result_3)