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| import os | |
| import re | |
| from datetime import datetime, timedelta | |
| from typing import TypedDict, Annotated | |
| import sympy as sp | |
| import math | |
| from langchain_openai import ChatOpenAI | |
| from langchain_community.tools.tavily_search import TavilySearchResults | |
| from langchain_core.messages import HumanMessage, SystemMessage | |
| from langgraph.graph import StateGraph, MessagesState, START, END | |
| from langgraph.prebuilt import ToolNode | |
| from langgraph.checkpoint.memory import MemorySaver | |
| import json | |
| # Load environment variables | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| def read_system_prompt(): | |
| """Read the system prompt from file""" | |
| try: | |
| with open('system_prompt.txt', 'r') as f: | |
| return f.read().strip() | |
| except FileNotFoundError: | |
| return """You are a helpful assistant tasked with answering questions using a set of tools. | |
| Now, I will ask you a question. Report your thoughts, and finish your answer with the following template: | |
| FINAL ANSWER: [YOUR FINAL ANSWER]. | |
| YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string. | |
| Your answer should only start with "FINAL ANSWER: ", then follows with the answer.""" | |
| def math_calculator(expression: str) -> str: | |
| """ | |
| Advanced mathematical calculator that can handle complex expressions, | |
| equations, symbolic math, calculus, and more using SymPy. | |
| """ | |
| try: | |
| # Clean the expression | |
| expression = expression.strip() | |
| # Handle common mathematical operations and functions | |
| expression = expression.replace('^', '**') # Convert ^ to ** | |
| expression = expression.replace('ln', 'log') # Natural log | |
| # Try to evaluate as a symbolic expression first | |
| try: | |
| result = sp.sympify(expression) | |
| # If it's a symbolic expression that can be simplified | |
| simplified = sp.simplify(result) | |
| # Try to get numerical value | |
| try: | |
| numerical = float(simplified.evalf()) | |
| return str(numerical) | |
| except: | |
| return str(simplified) | |
| except: | |
| # Fall back to basic evaluation | |
| # Replace common math functions | |
| safe_expression = expression | |
| for func in ['sin', 'cos', 'tan', 'sqrt', 'log', 'exp', 'abs']: | |
| safe_expression = safe_expression.replace(func, f'math.{func}') | |
| # Evaluate safely | |
| result = eval(safe_expression, {"__builtins__": {}}, { | |
| "math": math, | |
| "pi": math.pi, | |
| "e": math.e | |
| }) | |
| return str(result) | |
| except Exception as e: | |
| return f"Error calculating '{expression}': {str(e)}" | |
| def date_time_processor(query: str) -> str: | |
| """ | |
| Process date and time related queries, calculations, and conversions. | |
| """ | |
| try: | |
| current_time = datetime.now() | |
| query_lower = query.lower() | |
| # Current date/time queries | |
| if 'current' in query_lower or 'today' in query_lower or 'now' in query_lower: | |
| if 'date' in query_lower: | |
| return current_time.strftime('%Y-%m-%d') | |
| elif 'time' in query_lower: | |
| return current_time.strftime('%H:%M:%S') | |
| else: | |
| return current_time.strftime('%Y-%m-%d %H:%M:%S') | |
| # Day of week queries | |
| if 'day of week' in query_lower or 'what day' in query_lower: | |
| return current_time.strftime('%A') | |
| # Year queries | |
| if 'year' in query_lower and 'current' in query_lower: | |
| return str(current_time.year) | |
| # Month queries | |
| if 'month' in query_lower and 'current' in query_lower: | |
| return current_time.strftime('%B') | |
| # Date arithmetic (simple cases) | |
| if 'days ago' in query_lower: | |
| days_match = re.search(r'(\d+)\s+days?\s+ago', query_lower) | |
| if days_match: | |
| days = int(days_match.group(1)) | |
| past_date = current_time - timedelta(days=days) | |
| return past_date.strftime('%Y-%m-%d') | |
| if 'days from now' in query_lower or 'days later' in query_lower: | |
| days_match = re.search(r'(\d+)\s+days?\s+(?:from now|later)', query_lower) | |
| if days_match: | |
| days = int(days_match.group(1)) | |
| future_date = current_time + timedelta(days=days) | |
| return future_date.strftime('%Y-%m-%d') | |
| # If no specific pattern matched, return current datetime | |
| return f"Current date and time: {current_time.strftime('%Y-%m-%d %H:%M:%S')}" | |
| except Exception as e: | |
| return f"Error processing date/time query: {str(e)}" | |
| # Define the agent state | |
| class AgentState(TypedDict): | |
| messages: Annotated[list, "The messages in the conversation"] | |
| class GAIAAgent: | |
| def __init__(self): | |
| # Check for required API keys | |
| openai_key = os.getenv("OPENAI_API_KEY") | |
| tavily_key = os.getenv("TAVILY_API_KEY") | |
| if not openai_key: | |
| raise ValueError("OPENAI_API_KEY environment variable is required") | |
| if not tavily_key: | |
| print("⚠️ TAVILY_API_KEY not found - web search will be disabled") | |
| self.has_search = False | |
| else: | |
| self.has_search = True | |
| print("✅ Initializing GAIA agent...") | |
| # Initialize LLM (using OpenAI GPT-4) | |
| self.llm = ChatOpenAI( | |
| model="gpt-4o-mini", | |
| temperature=0, | |
| openai_api_key=openai_key | |
| ) | |
| # Initialize tools only if we have Tavily key | |
| self.tools = [] | |
| if self.has_search: | |
| self.search_tool = TavilySearchResults( | |
| max_results=5, | |
| tavily_api_key=tavily_key | |
| ) | |
| self.tools = [self.search_tool] | |
| # Create LLM with tools (only if we have tools) | |
| if self.tools: | |
| self.llm_with_tools = self.llm.bind_tools(self.tools) | |
| else: | |
| self.llm_with_tools = self.llm | |
| # Build the graph | |
| self.graph = self._build_graph() | |
| self.system_prompt = read_system_prompt() | |
| def _build_graph(self): | |
| """Build the LangGraph workflow""" | |
| def agent_node(state: AgentState): | |
| """Main agent reasoning node""" | |
| messages = state["messages"] | |
| # Add system message if not present at the beginning | |
| if not any(isinstance(msg, SystemMessage) for msg in messages): | |
| system_msg = SystemMessage(content=self.system_prompt) | |
| messages = [system_msg] + messages | |
| # Get the original question (the first HumanMessage) | |
| original_question = None | |
| for msg in messages: | |
| if isinstance(msg, HumanMessage): | |
| original_question = msg.content | |
| break | |
| # Check if this is a fresh question (not after tool calls) | |
| last_msg = messages[-1] | |
| is_fresh_question = isinstance(last_msg, HumanMessage) | |
| # Only do special processing for fresh questions | |
| if is_fresh_question and original_question: | |
| # Check if this is a math problem | |
| if self._is_math_problem(original_question): | |
| try: | |
| math_result = math_calculator(original_question) | |
| enhanced_msg = f"Question: {original_question}\n\nMath calculation result: {math_result}\n\nBased on this calculation, provide your final answer using the format: FINAL ANSWER: [your answer]" | |
| messages[-1] = HumanMessage(content=enhanced_msg) | |
| except Exception as e: | |
| print(f"Math calculation error: {e}") | |
| # Check if this is a date/time problem | |
| elif self._is_datetime_problem(original_question): | |
| try: | |
| datetime_result = date_time_processor(original_question) | |
| enhanced_msg = f"Question: {original_question}\n\nDate/time processing result: {datetime_result}\n\nBased on this information, provide your final answer using the format: FINAL ANSWER: [your answer]" | |
| messages[-1] = HumanMessage(content=enhanced_msg) | |
| except Exception as e: | |
| print(f"DateTime processing error: {e}") | |
| try: | |
| response = self.llm_with_tools.invoke(messages) | |
| return {"messages": messages + [response]} | |
| except Exception as e: | |
| print(f"LLM invocation error: {e}") | |
| # Return a simple response on error | |
| error_response = HumanMessage(content=f"FINAL ANSWER: Error processing question: {str(e)}") | |
| return {"messages": messages + [error_response]} | |
| def tool_node(state: AgentState): | |
| """Tool execution node""" | |
| try: | |
| tool_node_instance = ToolNode(self.tools) | |
| result = tool_node_instance.invoke(state) | |
| return result | |
| except Exception as e: | |
| print(f"Tool execution error: {e}") | |
| # Add an error message and continue | |
| messages = state["messages"] | |
| error_msg = HumanMessage(content=f"Tool execution failed: {str(e)}. Please provide your best answer without tools.") | |
| return {"messages": messages + [error_msg]} | |
| def should_continue(state: AgentState): | |
| """Decide whether to continue or end""" | |
| try: | |
| last_message = state["messages"][-1] | |
| # If we don't have tools, just end | |
| if not self.tools: | |
| return "end" | |
| # If the last message has tool calls, continue to tools | |
| if hasattr(last_message, 'tool_calls') and last_message.tool_calls: | |
| return "tools" | |
| # If we have a final answer, end | |
| if (hasattr(last_message, 'content') and | |
| last_message.content and | |
| "FINAL ANSWER:" in str(last_message.content)): | |
| return "end" | |
| # Check if we've had too many iterations (prevent infinite loops) | |
| if len(state["messages"]) > 10: | |
| return "end" | |
| # Otherwise end (be conservative) | |
| return "end" | |
| except Exception as e: | |
| print(f"Should continue error: {e}") | |
| return "end" | |
| # Build the graph | |
| workflow = StateGraph(AgentState) | |
| # Add nodes | |
| workflow.add_node("agent", agent_node) | |
| workflow.add_node("tools", tool_node) | |
| # Add edges | |
| workflow.add_edge(START, "agent") | |
| workflow.add_conditional_edges("agent", should_continue, { | |
| "tools": "tools", | |
| "end": END | |
| }) | |
| workflow.add_edge("tools", "agent") | |
| # Compile without checkpointer to avoid state issues | |
| return workflow.compile() | |
| def _is_math_problem(self, text: str) -> bool: | |
| """Check if the text contains mathematical expressions""" | |
| math_indicators = [ | |
| '+', '-', '*', '/', '^', '=', 'calculate', 'compute', | |
| 'solve', 'equation', 'integral', 'derivative', 'sum', | |
| 'sqrt', 'log', 'sin', 'cos', 'tan', 'exp' | |
| ] | |
| text_lower = text.lower() | |
| return any(indicator in text_lower for indicator in math_indicators) or \ | |
| re.search(r'\d+[\+\-\*/\^]\d+', text) is not None | |
| def _is_datetime_problem(self, text: str) -> bool: | |
| """Check if the text contains date/time related queries""" | |
| datetime_indicators = [ | |
| 'date', 'time', 'day', 'month', 'year', 'today', 'yesterday', | |
| 'tomorrow', 'current', 'now', 'ago', 'later', 'when' | |
| ] | |
| text_lower = text.lower() | |
| return any(indicator in text_lower for indicator in datetime_indicators) | |
| def __call__(self, question: str) -> str: | |
| """Process a question and return the answer""" | |
| try: | |
| print(f"Processing question: {question[:100]}...") | |
| # Check for file/media requirements that we can't handle | |
| if any(indicator in question.lower() for indicator in [ | |
| 'attached', 'audio', 'video', 'image', 'file', 'mp3', 'pdf', | |
| 'excel', 'spreadsheet', 'listen to', 'watch', 'download' | |
| ]): | |
| return "Unable to process files or media attachments" | |
| # Create initial state | |
| initial_state = { | |
| "messages": [HumanMessage(content=question)] | |
| } | |
| # Run the graph | |
| final_state = self.graph.invoke(initial_state) | |
| # Extract the final answer | |
| last_message = final_state["messages"][-1] | |
| response_content = last_message.content if hasattr(last_message, 'content') else str(last_message) | |
| # Extract just the final answer part | |
| final_answer = self._extract_final_answer(response_content) | |
| print(f"Final answer: {final_answer}") | |
| return final_answer | |
| except Exception as e: | |
| print(f"Error processing question: {e}") | |
| # Try to provide a meaningful fallback | |
| if "api" in str(e).lower() or "key" in str(e).lower(): | |
| return "Error: API key configuration issue" | |
| elif "tool" in str(e).lower(): | |
| return "Error: Tool execution issue" | |
| else: | |
| return f"Unable to process question due to technical error" | |
| def _extract_final_answer(self, response: str) -> str: | |
| """Extract the final answer from the response""" | |
| if "FINAL ANSWER:" in response: | |
| # Find the final answer part | |
| parts = response.split("FINAL ANSWER:") | |
| if len(parts) > 1: | |
| answer = parts[-1].strip() | |
| # Remove any trailing punctuation or explanations | |
| answer = answer.split('\n')[0].strip() | |
| return answer | |
| # If no FINAL ANSWER format found, return the whole response | |
| return response.strip() | |
| # Create a function to get the agent (for use in app.py) | |
| def create_agent(): | |
| """Factory function to create the GAIA agent""" | |
| return GAIAAgent() |