correct QA math

app.py

@@ -574,18 +574,27 @@ llm = chat
 
 llm_math = LLMMathChain.from_llm(llm)
 
+llm_math_2 = LLMMathChain.from_llm(GPTfake)
+
 math_tool = Tool(
     name ='Calculator',
     func = llm_math.run,
     description ='Useful for when you need to answer questions about math.'
 )
 
+math_tool_2 = Tool(
+    name ='Calculator',
+    func = llm_math_2.run,
+    description ='Useful for when you need to answer questions about math.'
+)
 
 # openai
 tools = [DB_Search(), duckduckgo_tool, python_tool, math_tool, Text2Sound_tool]
 
 tools2 = [DB_Search2(), duckduckgo_tool2, wikipedia_tool2, python_tool2, math_tool, Text2Sound_tool2]
 
+tools_remote = [DB_Search2(), duckduckgo_tool2, wikipedia_tool2, python_tool2, math_tool_2, Text2Sound_tool2]
+
 # tools = load_tools(["Vector Database Search","Wikipedia Search","Python REPL","llm-math"], llm=llm)
 
 # Openai embedding
@@ -733,7 +742,7 @@ agent_ZEROSHOT_REACT = initialize_agent(tools2, llm,
                          
                         )
 
-agent_ZEROSHOT_REACT_2 = initialize_agent(tools2, GPTfake, 
+agent_ZEROSHOT_REACT_2 = initialize_agent(tools_remote, GPTfake, 
                         #  agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
                          agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
                          verbose = True,
@@ -778,7 +787,7 @@ agent_ZEROSHOT_AGENT = AgentExecutor.from_agent_and_tools(
 
 agent_ZEROSHOT_AGENT_2 = AgentExecutor.from_agent_and_tools(
     agent=agent_core_2, 
-    tools=tools2, 
+    tools=tools_remote, 
     verbose=True, 
     # memory=memory,
     handle_parsing_errors = True,
@@ -1409,11 +1418,13 @@ def QAQuery_p(question: str):
     retriever = vectordb_p.as_retriever()
     retriever.search_kwargs['k'] = int(os.environ["search_kwargs_k"])
     # retriever.search_kwargs['fetch_k'] = 100
-    if agent == agent_ZEROSHOT_REACT_2:
+    if agent == agent_ZEROSHOT_REACT_2 or agent == agent_ZEROSHOT_AGENT_2:
+        print("--------------- QA with Remote --------------")
         qa = RetrievalQA.from_chain_type(llm=GPTfake, chain_type="stuff", 
                                         retriever=retriever, return_source_documents = True,
                                         verbose = True)
     else:
+        print("--------------- QA with API --------------")
         qa = RetrievalQA.from_chain_type(llm=chat, chain_type="stuff", 
                                         retriever=retriever, return_source_documents = True,
                                         verbose = True)
@@ -1489,7 +1500,7 @@ if __name__ == '__main__':
     # CreatDb()
     # QAQuery("what is COFOR ?")
     # CreatDb_P()
-    # QAQuery_p("what is GST ?")
+    QAQuery_p("what is PDP ?")
     # question = "what is PDP?"
     # output = asyncio.run(start_playwright(question))