Tools renewed with if financials exist clauses

app.py

@@ -506,7 +506,15 @@ with strategies_container:
         if "ESG_analysis_button_key" in st.session_state.results and st.session_state.results["ESG_analysis_button_key"]:
             
             doc_retriever_ESG, query_engine = create_vector_database_ESG()
-            doc_retriever_financials, query_engine_financials = create_vector_database_financials()
+            # Define the file path
+            file_path = os.path.join("data", "parsed_data_financials.pkl")
+
+            # Check if the file exists before running the function
+            if os.path.exists(file_path):
+                doc_retriever_financials, query_engine_financials = create_vector_database_financials()
+            else:
+                print(f"The file {file_path} does not exist. Skipping vector database creation.")
+                
             memory = ConversationBufferMemory(memory_key="chat_history", k=3, return_messages=True)
             search = SerpAPIWrapper()
 
@@ -519,7 +527,7 @@ with strategies_container:
             prompt_financials = PromptTemplate.from_template(
                 template="""
                     You are a seasoned corporate finance specialist.
-                    Use figures, numerical, and statistical data when possible. Never give false information, numbers or data.
+                    Use figures, and numerical, and statistical data when possible. Never give false information, numbers, or data.
 
                     Conversation history:
                     {chat_history}
@@ -531,7 +539,7 @@ with strategies_container:
             prompt_ESG = PromptTemplate.from_template(
                 template="""
                     You are a seasoned finance specialist and a specialist in environmental, social, and governance matters.
-                    Use figures, numerical, and statistical data when possible. Never give false information, numbers or data.
+                    Use figures, and numerical, and statistical data when possible. Never give false information, numbers or data.
 
                     Conversation history:
                     {chat_history}
@@ -541,17 +549,18 @@ with strategies_container:
             )
 
             # LCEL Chains with memory integration
-            financials_chain = (
-                {
-                    "context": doc_retriever_financials,
-                    # Lambda function now accepts one argument (even if unused)
-                    "chat_history": lambda _: format_chat_history(memory.load_memory_variables({})["chat_history"]),
-                    "question": RunnablePassthrough(),
-                }
-                | prompt_financials
-                | llm_tool
-                | StrOutputParser()
-            )
+            if os.path.exists(file_path)
+                financials_chain = (
+                    {
+                        "context": doc_retriever_financials,
+                        # Lambda function now accepts one argument (even if unused)
+                        "chat_history": lambda _: format_chat_history(memory.load_memory_variables({})["chat_history"]),
+                        "question": RunnablePassthrough(),
+                    }
+                    | prompt_financials
+                    | llm_tool
+                    | StrOutputParser()
+                )
 
             ESG_chain = (
                 {
@@ -572,11 +581,12 @@ with strategies_container:
             description="Useful for answering questions about specific ESG figures, data and statistics.",
             )
 
-            vector_query_tool_financials = Tool(
-            name="Vector Query Engine Financials",
-            func=lambda query: query_engine_financials.query(query),  # Use query_engine to query the vector database
-            description="Useful for answering questions about specific financial figures, data and statistics.",
-            )
+            if os.path.exists(file_path)
+                vector_query_tool_financials = Tool(
+                name="Vector Query Engine Financials",
+                func=lambda query: query_engine_financials.query(query),  # Use query_engine to query the vector database
+                description="Useful for answering questions about specific financial figures, data and statistics.",
+                )
 
             tools = [
                 Tool(
@@ -584,20 +594,24 @@ with strategies_container:
                     func=ESG_chain.invoke,
                     description="Useful for answering general questions about environmental, social, and governance (ESG) matters related to the company. ",
                 ),
-                Tool(
-                    name="Financials QA System",
-                    func=financials_chain.invoke,
-                    description="Useful for answering general questions about financial or operational information concerning the company.",
-                ),
                 Tool(
                     name="Search Tool",
                     func=search.run,
                     description="Useful when other tools do not provide the answer.",
                 ),
                 vector_query_tool_ESG,
-                vector_query_tool_financials,
             ]
 
+            if os.path.exists(file_path)
+                tools.append(
+                    Tool(
+                    name="Financials QA System",
+                    func=financials_chain.invoke,
+                    description="Useful for answering general questions about financial or operational information concerning the company.",
+                    vector_query_tool_financials,
+                )
+                )
+        
             # Initialize the agent with LCEL tools and memory
             agent = initialize_agent(
                 tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, memory=memory, handle_parsing_errors=True)