def main_loop()

in 4-mmrag_tooluse/mmrag_bh.py [0:0]

• 34 lines of code
• 9 McCabe index (conditional complexity)

def main_loop():
    """Interactive loop for processing user queries."""
    print("Welcome to the Financial Assistant. Type 'exit' to quit.\n")

    process_and_index_data()
    while True:
        user_query = input("User: ")
        if user_query.lower() in ["exit", "quit"]:
            print("Exiting the assistant. Goodbye!")
            break

        messages = [
            {"role": "system", "content": TRIAGE_SYSTEM_PROMPT},
            {"role": "user", "content": user_query},
        ]

        response = client.chat.completions.create(
            model='gpt-4o',
            messages=messages,
            tools=TOOLS,
            tool_choice="required")

        # Step 2: determine if the response from the model includes a tool call.
        tool_calls = response.choices[0].message.tool_calls
        if tool_calls:
            # If true the model will return the name of the tool / function to call and the argument(s)
            tool_call_id = tool_calls[0].id
            tool_function_name = tool_calls[0].function.name
            tool_query_string = json.loads(
                tool_calls[0].function.arguments)['query']

            # Step 3: Call the function and retrieve results. Append the results to the messages list.
            if tool_function_name == 'ask_database':
                results = ask_database(conn, tool_query_string)

                messages.append({
                    "role": "tool",
                    "tool_call_id": tool_call_id,
                    "name": tool_function_name,
                    "content": results
                })
            elif tool_function_name == 'query_qdrant':
                results = query_rag_system(user_query)

            print(results)