Update app.py

app.py

@@ -24,7 +24,7 @@ if not groq_api_key:
 st.set_page_config(page_title="General Knowledge Assistant")
 st.title("General Knowledge Assistant")
 
-# Initialize the LLM (Groq API - llama-3.1-70b)
+# Initialize the LLM (Groq API - deepseek-r1-distill-llama-70b)
 llm = ChatGroq(model="deepseek-r1-distill-llama-70b", groq_api_key=groq_api_key)
 
 # Initialize Wikipedia tool for information retrieval
@@ -32,14 +32,19 @@ wikipedia_wrapper = WikipediaAPIWrapper()
 wikipedia_tool = Tool(
     name="Wikipedia",
     func=wikipedia_wrapper.run,
-    description="A tool for searching the Internet to find information on various topics, including general knowledge."
+    description="A tool for searching Wikipedia to retrieve up-to-date and detailed information on various topics."
 )
 
-# Prompt template for general knowledge questions
+# Updated prompt template with explicit instructions for ReAct chaining
 prompt = """
-You are a knowledgeable assistant. Your task is to answer the user's questions accurately, using your general knowledge.
-If the answer is not readily available in your knowledge base, search Wikipedia for relevant information.
-Your information should be accurate and up to date. Whenever I tell you to write essay give a title also to the essay.
+You are a knowledgeable assistant. Your task is to answer the user's questions accurately using your general knowledge.
+If you believe that your internal knowledge may be outdated or insufficient, follow these steps:
+1. Write your internal thought process beginning with 'Thought:'.
+2. If you determine that you need updated information, output an 'Action:' line in the following format:
+   Action: Wikipedia[search query]
+3. Once you receive additional information, integrate it into your final answer.
+Ensure that you follow this format strictly. Also, whenever I ask you to write an essay, provide a title for the essay.
+
 Question: {question}
 Answer:
 """
@@ -57,7 +62,7 @@ chain = LLMChain(llm=llm, prompt=prompt_template)
 reasoning_tool = Tool(
     name="Reasoning tool",
     func=chain.run,
-    description="A tool for answering general knowledge questions using logical reasoning and factual information. Try to use the latest information"
+    description="A tool for answering general knowledge questions using logical reasoning and factual information. Use this tool and consult Wikipedia if necessary."
 )
 
 # Initialize the agent with the tools and LLM
@@ -77,22 +82,25 @@ if "messages" not in st.session_state:
 
 # Display the conversation history
 for msg in st.session_state.messages:
-    st.chat_message(msg["role"]).write(msg['content'])
+    st.chat_message(msg["role"]).write(msg["content"])
 
 # Get the user's question
 question = st.text_area("Enter your question:", "Please enter your general knowledge question here")
 
 # Handle the button click to process the question
-if st.button("find my answer"):
+if st.button("Find my answer"):
     if question:
-        with st.spinner("Generate response.."):
+        with st.spinner("Generating response..."):
+            # Append the user question to the conversation history and display it
             st.session_state.messages.append({"role": "user", "content": question})
             st.chat_message("user").write(question)
 
             st_cb = StreamlitCallbackHandler(st.container(), expand_new_thoughts=False)
-            response = assistant_agent.run(st.session_state.messages, callbacks=[st_cb])
+            # Pass the question string directly to the agent (instead of the full conversation history)
+            response = assistant_agent.run(question, callbacks=[st_cb])
             st.session_state.messages.append({'role': 'assistant', "content": response})
+            
             st.write('### Response:')
             st.success(response)
     else:
-        st.warning("Please enter the question")
+        st.warning("Please enter your question")