Update app.py

app.py

@@ -7,6 +7,7 @@ from langchain.prompts import PromptTemplate
 from langchain import LLMChain
 from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain_community.document_loaders import WebBaseLoader
+from langchain_community.tools import DuckDuckGoSearchRun
 
 os.environ["GOOGLE_API_KEY"] = os.getenv("GOOGLE_API_KEY")
 genai.configure(api_key=os.environ["GOOGLE_API_KEY"])
@@ -14,8 +15,14 @@ genai.configure(api_key=os.environ["GOOGLE_API_KEY"])
 llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash-latest",
                              temperature=0.2)
 
+search_engine = DuckDuckGoSearchRun()
 
-template = """You are a friendly chat assistant called "CRETA" having a conversation with a human and you are created by Pachaiappan [portfolio](https://mr-vicky-01.github.io/Portfolio/) an AI Specialist.
+def get_web_result(question):
+    result = search_engine.invoke(question)
+    return result.strip()
+
+
+template = """You are a friendly chat assistant called "CRETA" having a conversation with a human and you are created by Pachaiappan [portfolio](https://mr-vicky-01.github.io/Portfolio/) an AI Specialist. If the question pertains to information that you do not have access to, respond with string 'search_query' only nothing else.
 provided document:
 {provided_docs}
 previous_chat:
@@ -30,10 +37,32 @@ prompt = PromptTemplate(
 llm_chain = LLMChain(
     llm=llm,
     prompt=prompt,
+    # verbose=True,
+)
+
+template_2 = """You are a friendly chat assistant called "CRETA" having a conversation with a human and you are created by Pachaiappan [portfolio](https://mr-vicky-01.github.io/Portfolio/) an AI Specialist.
+web_result:
+{web_result}
+provided document:
+{provided_docs}
+previous_chat:
+{chat_history}
+Human: {human_input}
+Chatbot:"""
+
+
+prompt2 = PromptTemplate(
+    input_variables=["web_result", "chat_history", "human_input", "provided_docs"], template=template_2
+)
+
+llm_chain_2 = LLMChain(
+    llm=llm,
+    prompt=prompt2,
     verbose=True,
 )
 
 
+
 previous_response = ""
 provided_docs = ""
 def conversational_chat(query):
@@ -43,6 +72,12 @@ def conversational_chat(query):
             previous_response += f"Human: {i[0]}\n Chatbot: {i[1]}\n"
     provided_docs = "".join(st.session_state["docs"])
     result = llm_chain.predict(chat_history=previous_response, human_input=query, provided_docs=provided_docs)
+
+    if  'search_query'in result.strip():
+        web_result = get_web_result(query)
+        print("searching on web....")
+        result = llm_chain_2.predict(web_result= web_result,chat_history=previous_response, human_input=query, provided_docs=provided_docs)
+
     st.session_state['history'].append((query, result))
     
     # Keep only the last 5 history entries