llm_call.py

from langchain_openai import ChatOpenAI
from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
from dotenv import load_dotenv
import os
import json

load_dotenv()

def get_chat_response(messages, context_data):
    """
    Get a response from the LLM based on the chat history and code context.
    
    Args:
        messages: List of dictionaries [{'role': 'user'|'assistant', 'content': '...'}]
        context_data: Dictionary containing line_content, line_number, variables, context_code
    """
    api_key = os.getenv("KEYWORDS_AI_API_KEY")
    if not api_key:
        return "Error: KEYWORDS_AI_API_KEY not found in .env file."

    llm = ChatOpenAI(
        base_url="https://api.keywordsai.co/api/",
        api_key=api_key,
        model="gpt-4o-mini",
        streaming=False,
    )

    # Construct System Prompt
    line_content = context_data.get('line_content', '')
    line_number = context_data.get('line_number', 0)
    variables = context_data.get('variables', {})
    stack = context_data.get('stack', [])
    stdout = context_data.get('stdout', '')
    exec_count = context_data.get('exec_count', 0)
    context_code = context_data.get('context_code', '')
    error_message = context_data.get('error_message')

    if error_message:
        system_prompt = f"""You are a friendly and simplified coding assistant for non-technical users.
        
        CRITICAL ERROR DETECTED:
        The user's code has failed to run due to the following error:
        {error_message}
        
        Execution State at Crash:
        --------------------------
        Line {line_number}: {line_content}
        Execution Count: {exec_count}
        
        Call Stack:
        {json.dumps(stack, indent=2) if stack else "Empty"}
        
        Output So Far:
        {stdout}
        
        Full Source Code:
        {context_code if context_code else "Not provided"}
        
        Your Goal:
        1. Explain the error in extremely simple, non-technical language (avoid jargon like "traceback", "stack frame", "syntax error" unless defined simply).
        2. Use analogies if helpful (e.g., "The computer got confused because it expected a number but got a word").
        3. Point out exactly where the mistake is.
        4. Provide a clear, step-by-step fix.
        5. Be concise and efficient. Do not write long paragraphs.
        """
    else:
        system_prompt = f"""You are a friendly and simplified coding assistant for non-technical users.
        
        Current Execution Context:
        --------------------------
        Full Source Code:
        {context_code if context_code else "Not provided"}

        Target Line {line_number} (Currently executing): 
        {line_content}
        Execution Count: {exec_count}
        
        Call Stack (Most recent call last):
        {json.dumps(stack, indent=2) if stack else "Empty"}
        
        Output So Far:
        {stdout}
        --------------------------
        
        Your Goal:
        1. Explain what is happening in the code in simple, everyday language suitable for a beginner or non-technical person.
        2. Avoid complex technical jargon. If you must use a term, explain it briefly.
        3. Focus on the "logic" and "flow" rather than memory addresses or implementation details.
        4. Be concise and efficient. Get straight to the point.
        """

    # Build LangChain message list
    lc_messages = [SystemMessage(content=system_prompt)]
    
    for msg in messages:
        role = msg.get('role')
        content = msg.get('content')
        if role == 'user':
            lc_messages.append(HumanMessage(content=content))
        elif role == 'assistant':
            lc_messages.append(AIMessage(content=content))
    
    # If no messages yet (initial call), add the default prompt
    if not messages:
        if error_message:
            lc_messages.append(HumanMessage(content=f"Please explain this error: {error_message}"))
        else:
            lc_messages.append(HumanMessage(content=f"Explain what line {line_number} is doing."))

    try:
        response = llm.invoke(lc_messages)
        return response.content
    except Exception as e:
        return f"Error calling LLM: {str(e)}"