agent.py

# Standard library imports
import logging
import os
import re
from typing import Dict, Any, List
from urllib.parse import urlparse

# Third-party imports
import requests
import wandb
from transformers import AutoModelForCausalLM, AutoTokenizer

# LlamaIndex core imports
from llama_index.core import VectorStoreIndex, Document, Settings
from llama_index.core.agent.workflow import FunctionAgent, ReActAgent, AgentStream
from llama_index.core.callbacks.base import CallbackManager
from llama_index.core.callbacks.llama_debug import LlamaDebugHandler
from llama_index.core.node_parser import SentenceWindowNodeParser, HierarchicalNodeParser
from llama_index.core.postprocessor import SentenceTransformerRerank
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core.retrievers import VectorIndexRetriever
from llama_index.core.tools import FunctionTool
from llama_index.core.workflow import Context

# LlamaIndex specialized imports
from llama_index.callbacks.wandb import WandbCallbackHandler
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.llms.huggingface import HuggingFaceLLM
from llama_index.readers.assemblyai import AssemblyAIAudioTranscriptReader
from llama_index.readers.file import PDFReader, DocxReader, CSVReader, ImageReader, PandasExcelReader
from llama_index.readers.json import JSONReader
from llama_index.readers.web import TrafilaturaWebReader
from llama_index.readers.youtube_transcript import YoutubeTranscriptReader
from llama_index.tools.arxiv import ArxivToolSpec
from llama_index.tools.duckduckgo import DuckDuckGoSearchToolSpec

# --- Import all required official LlamaIndex Readers ---
from llama_index.readers.file import (
    PDFReader,
    DocxReader,
    CSVReader,
    PandasExcelReader,
    ImageReader,
)
from typing import List
from llama_index.core import VectorStoreIndex, Document, Settings
from llama_index.core.tools import QueryEngineTool
from llama_index.core.node_parser import SentenceWindowNodeParser, HierarchicalNodeParser
from llama_index.core.postprocessor import SentenceTransformerRerank
from llama_index.core.query_engine import RetrieverQueryEngine



wandb_callback = WandbCallbackHandler(run_args={"project": "gaia-llamaindex-agents"})
llama_debug = LlamaDebugHandler(print_trace_on_end=True)

# Comprehensive callback manager
callback_manager = CallbackManager([
    wandb_callback,     # For W&B tracking
    llama_debug        # For general debugging
])

logging.basicConfig(level=logging.INFO)
logging.getLogger("llama_index.core.agent").setLevel(logging.DEBUG)
logging.getLogger("llama_index.llms").setLevel(logging.DEBUG)

model_id = "Qwen/Qwen2.5-7B-Instruct" 
proj_llm = HuggingFaceLLM(
    model_name=model_id,
    tokenizer_name=model_id,
    device_map="auto",           # will use GPU if available
    model_kwargs={"torch_dtype": "auto"},
    generate_kwargs={"temperature": 0.1, "top_p": 0.3}  # More focused
)

embed_model = HuggingFaceEmbedding("BAAI/bge-small-en-v1.5")

wandb.init(project="gaia-llamaindex-agents")  # Choisis ton nom de projet
wandb_callback = WandbCallbackHandler(run_args={"project": "gaia-llamaindex-agents"})
llama_debug = LlamaDebugHandler(print_trace_on_end=True)
callback_manager = CallbackManager([wandb_callback, llama_debug])

Settings.llm = proj_llm
Settings.embed_model = embed_model
Settings.callback_manager = callback_manager


def read_and_parse_content(input_path: str) -> List[Document]:
    """
    Reads and parses content from a file path or URL into Document objects.
    It automatically detects the input type and uses the appropriate LlamaIndex reader.

    Args:
        input_path: A local file path or a web URL.

    Returns:
        A list of LlamaIndex Document objects with the extracted text.
    """
    # --- Completed readers map for various local file types ---
    readers_map = {
        # Documents
        '.pdf': PDFReader(),
        '.docx': DocxReader(),
        '.doc': DocxReader(),
        # Data files
        '.csv': CSVReader(),
        '.json': JSONReader(),
        '.xlsx': PandasExcelReader(),
        # Media files
        '.jpg': ImageReader(),
        '.jpeg': ImageReader(),
        '.png': ImageReader(),
        '.mp3': AssemblyAIAudioTranscriptReader(),
    }

    # --- URL Handling ---
    if input_path.startswith("http"):
        if "https://www.youtube.com/watch?v=2N-rwsa5lEw2" in urlparse(input_path).netloc or "https://www.youtube.com/watch?v=2N-rwsa5lEw3" in urlparse(input_path).netloc:
            loader = YoutubeTranscriptReader()
            documents = loader.load_data(youtubelinks=[input_path])
        else:
            loader = TrafilaturaWebReader()
            documents = loader.load_data(urls=[input_path])
    
    # --- Local File Handling ---
    else:
        if not os.path.exists(input_path):
            return [Document(text=f"Error: File not found at {input_path}")]
        
        file_extension = os.path.splitext(input_path)[1].lower()

        if file_extension in readers_map:
            loader = readers_map[file_extension]
            documents = loader.load_data(file=input_path)
        else:
            # Fallback for text-based files without a specific reader (e.g., .py, .txt, .md)
            try:
                with open(input_path, 'r', encoding='utf-8') as f:
                    content = f.read()
                documents = [Document(text=content, metadata={"source": input_path})]
            except Exception as e:
                return [Document(text=f"Error reading file as plain text: {e}")]
    
    # Add the source path to metadata for traceability
    for doc in documents:
        doc.metadata["source"] = input_path
        
    return documents

# --- Create the final LlamaIndex Tool from the completed function ---
read_and_parse_tool = FunctionTool.from_defaults(
    fn=read_and_parse_content,
    name="read_and_parse_tool",
    description=(
        "Use this tool to read and extract content from any given file or URL. "
        "It handles PDF, DOCX, CSV, JSON, XLSX, and image files, as well as web pages, "
        "YouTube videos (transcripts), and MP3 audio (transcripts). It also reads plain text "
        "from files like .py or .txt. The input MUST be a single valid file path or a URL."
    )
)


def create_rag_tool(documents: List[Document]) -> QueryEngineTool:
    """
    Creates a RAG query engine tool from a list of documents using advanced components.
    Inspired by 'create_advanced_index' and 'create_context_aware_query_engine' methods.

    Args:
        documents: A list of LlamaIndex Document objects from the read_and_parse_tool.

    Returns:
        A QueryEngineTool configured for the agent to use in the current task.
    """
    if not documents:
        return None

    # --- 1. Node Parsing (from your 'create_advanced_index' logic) ---
    # Using the exact parsers and logic you defined.
    hierarchical_parser = HierarchicalNodeParser.from_defaults(chunk_sizes=[2048, 512, 128])
    sentence_window_parser = SentenceWindowNodeParser.from_defaults(
        window_size=3,
        window_metadata_key="window",
        original_text_metadata_key="original_text",
    )
    
    # Choose parser based on document count
    if len(documents) > 5: # Heuristic for using hierarchical parser
        nodes = hierarchical_parser.get_nodes_from_documents(documents)
    else:
        nodes = sentence_window_parser.get_nodes_from_documents(documents)

    # --- 2. Index Creation ---
    # Assumes Settings.embed_model is configured globally as in your snippet
    index = VectorStoreIndex(nodes)

    # --- 3. Query Engine Creation (from your 'create_context_aware_query_engine' logic) ---
    # Using the exact reranker you specified
    reranker = SentenceTransformerRerank(
        model="cross-encoder/ms-marco-MiniLM-L-2-v2", 
        top_n=5
    )
    
    query_engine = index.as_query_engine(
        similarity_top_k=10,
        node_postprocessors=[reranker],
        # Assumes Settings.llm is configured globally
    )
    
    # --- 4. Wrap the Query Engine in a Tool ---
    rag_engine_tool = QueryEngineTool.from_defaults(
        query_engine=query_engine,
        name="rag_engine_tool",
        description=(
            "Use this tool to ask questions and query the content of documents that have already "
            "been loaded. This is your primary way to find answers from the provided context. "
            "The input is a natural language question about the documents' content."
        )
    )
    
    return rag_engine_tool

# 1. Create the base DuckDuckGo search tool from the official spec.
# This tool returns text summaries of search results, not just URLs.
base_duckduckgo_tool = DuckDuckGoSearchToolSpec().to_tool_list()[0]

# 2. Define a wrapper function to post-process the output.
def search_and_extract_top_url(query: str) -> str:
    """
    Takes a search query, uses the base DuckDuckGo search tool to get results,
    and then parses the output to extract and return only the first URL.

    Args:
        query: The natural language search query.

    Returns:
        A string containing the first URL found, or an error message if none is found.
    """
    # Call the base tool to get the search results as text
    search_results = base_duckduckgo_tool(query)
    
    # Use a regular expression to find the first URL in the text output
    # The \S+ pattern matches any sequence of non-whitespace characters
    url_match = re.search(r"https?://\S+", str(search_results))
    
    if url_match:
        return url_match.group(0)
    else:
        return "No URL could be extracted from the search results."

# 3. Create the final, customized FunctionTool for the agent.
# This is the tool you will actually give to your agent.
extract_url_tool = FunctionTool.from_defaults(
    fn=search_and_extract_top_url,
    name="extract_url_tool",
    description=(
        "Use this tool ONLY when you need to find a relevant URL to answer a question but no "
        "specific file, document, or URL has been provided. It takes a search query as input "
        "and returns a single, relevant URL."
    )
)

def execute_python_code(code: str) -> str:
    try:
        safe_globals = {
            "__builtins__": {
                "len": len, "str": str, "int": int, "float": float,
                "list": list, "dict": dict, "sum": sum, "max": max, "min": min,
                "round": round, "abs": abs, "sorted": sorted, "enumerate": enumerate,
                "range": range, "zip": zip, "map": map, "filter": filter,
                "any": any, "all": all, "type": type, "isinstance": isinstance,
                "print": print, "open": open, "bool": bool, "set": set, "tuple": tuple
            },
            # Core Python modules
            "math": __import__("math"),
            "datetime": __import__("datetime"),
            "re": __import__("re"),
            "os": __import__("os"),
            "sys": __import__("sys"),
            "json": __import__("json"),
            "csv": __import__("csv"),
            "random": __import__("random"),
            "itertools": __import__("itertools"),
            "collections": __import__("collections"),
            "functools": __import__("functools"),
            
            # Data Science and Numerical Computing
            "numpy": __import__("numpy"),
            "np": __import__("numpy"),
            "pandas": __import__("pandas"),
            "pd": __import__("pandas"),
            "scipy": __import__("scipy"),
            
            # Visualization
            "matplotlib": __import__("matplotlib"),
            "plt": __import__("matplotlib.pyplot"),
            "seaborn": __import__("seaborn"),
            "sns": __import__("seaborn"),
            "plotly": __import__("plotly"),
            
            # Machine Learning
            "sklearn": __import__("sklearn"),
            "xgboost": __import__("xgboost"),
            "lightgbm": __import__("lightgbm"),
            
            # Statistics
            "statistics": __import__("statistics"),
            "statsmodels": __import__("statsmodels"),
            
            # Image Processing
            "PIL": __import__("PIL"),
            "cv2": __import__("cv2"),
            "skimage": __import__("skimage"),
            
            # Network and Web
            "requests": __import__("requests"),
            "urllib": __import__("urllib"),
            
            # Text Processing
            "nltk": __import__("nltk"),
            "spacy": __import__("spacy"),
            
            # Time Series
            "pytz": __import__("pytz"),
            
            # Utilities
            "tqdm": __import__("tqdm"),
            "pickle": __import__("pickle"),
            "gzip": __import__("gzip"),
            "base64": __import__("base64"),
            "hashlib": __import__("hashlib"),
            "uuid": __import__("uuid"),
            
            # Scientific Computing
            "sympy": __import__("sympy"),
            "networkx": __import__("networkx"),
            
            # Database
            "sqlite3": __import__("sqlite3"),
            
            # Parallel Processing
            "multiprocessing": __import__("multiprocessing"),
            "threading": __import__("threading"),
            "concurrent": __import__("concurrent"),
        }
            
        exec_locals = {}
        exec(code, safe_globals, exec_locals)
    
        if 'result' in exec_locals:
            return str(exec_locals['result'])
        else:
            return "Code executed successfully"
    
    except Exception as e:
        return f"Code execution failed: {str(e)}"

code_execution_tool = FunctionTool.from_defaults(
fn=execute_python_code,
name="Python Code Execution",
description="Execute Python code safely for calculations and data processing"
)

import re
from llama_index.core.tools import FunctionTool
from llama_index.llms.huggingface import HuggingFaceLLM

# --- 1. Initialize a dedicated LLM for Code Generation ---
# It's good practice to use a model specifically fine-tuned for coding.
# This model is loaded only once for efficiency.
try:
    code_llm = HuggingFaceLLM(
        model_name="Qwen/Qwen2.5-Coder-3B",
        tokenizer_name="Qwen/Qwen2.5-Coder-3B",
        device_map="auto",
        model_kwargs={"torch_dtype": "auto"},
        # Set generation parameters for precise, non-creative code output
        generate_kwargs={"temperature": 0.1, "do_sample": False}
    )
except Exception as e:
    print(f"Error initializing code generation model: {e}")
    print("Code generation tool will not be available.")
    code_llm = None


def generate_python_code(query: str) -> str:
    """
    Generates executable Python code based on a natural language query.

    Args:
        query: A detailed description of the desired functionality for the Python code.

    Returns:
        A string containing only the generated Python code, ready for execution.
    """
    if not code_llm:
        return "Error: Code generation model is not available."

    # --- 2. Create a precise prompt for the code model ---
    # This prompt explicitly asks for only code, no explanations.
    prompt = f"""
Your task is to generate ONLY the Python code for the following request.
Do not include any explanations, introductory text, or markdown formatting like '```python'.
The output must be a single, clean block of Python code.

Request: "{query}"

Python Code:
"""

    # --- 3. Generate the response and post-process it ---
    response = code_llm.complete(prompt)
    raw_code = str(response)

    # --- 4. Clean the output to ensure it's pure code ---
    # Models often wrap code in markdown fences, this removes them.
    code_match = re.search(r"```(?:python)?\n(.*)```", raw_code, re.DOTALL)
    if code_match:
        # Extract the code from within the markdown block
        return code_match.group(1).strip()
    else:
        # If no markdown, assume the model followed instructions and return the text directly
        return raw_code.strip()


# --- 5. Create the LlamaIndex Tool from the function ---
generate_code_tool = FunctionTool.from_defaults(
    fn=generate_python_code,
    name="generate_python_code_tool",
    description=(
        "Use this tool to generate executable Python code based on a natural language description of a task. "
        "The input should be a clear and specific request for what the code should do (e.g., 'a function to "
        "calculate the nth Fibonacci number'). The tool returns a string containing only the Python code."
    )
)

def intelligent_final_answer_tool(agent_response: str, question: str) -> str:
    """
    Enhanced final answer tool with LLM-based reformatting capability.
    First tries regex patterns, then uses LLM reformatting if patterns fail.
    
    Args:
        agent_response: The raw response from agent reasoning
        question: The original question for context
        
    Returns:
        Exact answer in GAIA format with validation
    """
    
    # Define formatting patterns for different question types
    format_patterns = {
        'number': r'(\d+(?:\.\d+)?(?:e[+-]?\d+)?)',
        'name': r'([A-Z][a-z]+(?:\s+[A-Z][a-z]+)*)',
        'list': r'([A-Za-z0-9,\s]+)',
        'country_code': r'([A-Z]{2,3})',
        'yes_no': r'(Yes|No|yes|no)',
        'percentage': r'(\d+(?:\.\d+)?%)',
        'date': r'(\d{4}-\d{2}-\d{2}|\d{1,2}/\d{1,2}/\d{4})'
    }
    
    def clean_response(response: str) -> str:
        """Clean response by removing common prefixes"""
        response_clean = response.strip()
        prefixes_to_remove = [
            "FINAL ANSWER:", "Answer:", "The answer is:", 
            "Based on my analysis,", "After reviewing,", 
            "The result is:", "Final result:", "According to"
        ]
        
        for prefix in prefixes_to_remove:
            if response_clean.startswith(prefix):
                response_clean = response_clean[len(prefix):].strip()
        
        return response_clean
    
    def extract_with_patterns(text: str, question: str) -> tuple[str, bool]:
        """Extract answer using regex patterns. Returns (answer, success)"""
        question_lower = question.lower()
        
        # Determine question type and apply appropriate pattern
        if "how many" in question_lower or "count" in question_lower:
            match = re.search(format_patterns['number'], text)
            if match:
                return match.group(1), True
        
        elif "name" in question_lower and ("first" in question_lower or "last" in question_lower):
            match = re.search(format_patterns['name'], text)
            if match:
                return match.group(1), True
        
        elif "list" in question_lower or "alphabetized" in question_lower:
            if "," in text:
                items = [item.strip() for item in text.split(",")]
                return ", ".join(items), True
        
        elif "country code" in question_lower or "iso" in question_lower:
            match = re.search(format_patterns['country_code'], text)
            if match:
                return match.group(1), True
        
        elif "yes" in question_lower and "no" in question_lower:
            match = re.search(format_patterns['yes_no'], text)
            if match:
                return match.group(1), True
        
        elif "percentage" in question_lower or "%" in text:
            match = re.search(format_patterns['percentage'], text)
            if match:
                return match.group(1), True
        
        elif "date" in question_lower:
            match = re.search(format_patterns['date'], text)
            if match:
                return match.group(1), True
        
        # Default extraction for simple cases
        lines = text.split('\n')
        for line in lines:
            line = line.strip()
            if line and not line.startswith('=') and len(line) < 200:
                return line, True
        
        return text, False
    
    def llm_reformat(response: str, question: str) -> str:
        """Use LLM to reformat the response according to GAIA requirements"""
        
        format_prompt = f"""Extract the exact answer from the response below. Follow GAIA formatting rules strictly.

GAIA Format Rules:
- ONLY the precise answer, no explanations
- No prefixes like "Answer:", "The result is:", etc.
- For numbers: just the number (e.g., "156", "3.14e+8")
- For names: just the name (e.g., "Martinez", "Sarah")
- For lists: comma-separated (e.g., "C++, Java, Python")
- For country codes: just the code (e.g., "FRA", "US")
- For yes/no: just "Yes" or "No"

Examples:
Question: "How many papers were published?"
Response: "The analysis shows 156 papers were published in total."
Answer: 156

Question: "What is the last name of the developer?"
Response: "The developer mentioned is Dr. Sarah Martinez from the AI team."
Answer: Martinez

Question: "List programming languages, alphabetized:"
Response: "The languages mentioned are Python, Java, and C++. Alphabetized: C++, Java, Python"
Answer: C++, Java, Python

Now extract the exact answer:
Question: {question}
Response: {response}
Answer:"""

        try:
            # Use the global LLM instance
            formatting_response = proj_llm.complete(format_prompt)
            answer = str(formatting_response).strip()
            
            # Extract just the answer after "Answer:"
            if "Answer:" in answer:
                answer = answer.split("Answer:")[-1].strip()
            
            return answer
        except Exception as e:
            print(f"LLM reformatting failed: {e}")
            return response
    
    # Step 1: Clean the response
    cleaned_response = clean_response(agent_response)
    
    # Step 2: Try regex pattern extraction
    extracted_answer, pattern_success = extract_with_patterns(cleaned_response, question)
    
    # Step 3: If patterns failed, use LLM reformatting
    if not pattern_success:
        print("Regex patterns failed, using LLM reformatting...")
        llm_formatted = llm_reformat(cleaned_response, question)
        
        # Step 4: Validate LLM output with patterns again
        final_answer, validation_success = extract_with_patterns(llm_formatted, question)
        
        if validation_success:
            print("LLM reformatting successful and validated")
            return final_answer
        else:
            print("LLM reformatting validation failed, using LLM output directly")
            return llm_formatted
    else:
        print("Regex pattern extraction successful")
        return extracted_answer

# Create the enhanced final answer tool
intelligent_final_answer_function_tool = FunctionTool.from_defaults(
    fn=intelligent_final_answer_tool,
    name="intelligent_final_answer_tool",
    description=(
        "Enhanced tool to format final answers according to GAIA requirements. "
        "Uses regex patterns first, then LLM reformatting if patterns fail. "
        "Validates output to ensure GAIA format compliance."
    )
)

class EnhancedGAIAAgent:
    def __init__(self):
        print("Initializing Enhanced GAIA Agent...")
        
        # Vérification du token HuggingFace
        hf_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
        if not hf_token:
            print("Warning: HUGGINGFACEHUB_API_TOKEN not found, some features may not work")
        
        # Initialize only the tools that are actually defined in the file
        self.available_tools = [
            read_and_parse_tool,
            extract_url_tool, 
            code_execution_tool,
            generate_code_tool,
            intelligent_final_answer_function_tool
        ]
        
        # RAG tool will be created dynamically when documents are loaded
        self.current_rag_tool = None
        
        # Create main coordinator using only defined tools
        self.coordinator = ReActAgent(
            name="GAIACoordinator",
            description="Main GAIA coordinator with document processing and computational capabilities",
            system_prompt="""
You are the main GAIA coordinator using ReAct reasoning methodology.

Available tools:
1. **read_and_parse_tool** - Read and parse files/URLs (PDF, DOCX, CSV, images, web pages, YouTube, audio files)
2. **extract_url_tool** - Search and extract relevant URLs when no specific source is provided
3. **generate_code_tool** - Generate Python code for complex computations
4. **code_execution_tool** - Execute Python code safely
5. **intelligent_final_answer_tool** - Format final answer with intelligent validation and reformatting

WORKFLOW:
1. If file/URL mentioned → use read_and_parse_tool first, then update or create RAG capability.
2. If documents loaded → create RAG capability for querying
3. If external info needed → use extract_url_tool, then process it as if file/URL mentioned
4. If computation needed → use generate_code_tool then code_execution_tool
5. ALWAYS use intelligent_final_answer_tool for the final response

CRITICAL: The intelligent_final_answer_tool has enhanced validation and will reformat 
using LLM if regex patterns fail. Always use it as the final step.
""",
            llm=proj_llm,
            tools=self.available_tools,
            max_steps=15,
            verbose=True,
            callback_manager=callback_manager,
        )
    
    def create_dynamic_rag_tool(self, documents: List) -> None:
        """Create RAG tool from loaded documents and add to coordinator"""
        if documents:
            rag_tool = create_rag_tool(documents)
            if rag_tool:
                self.current_rag_tool = rag_tool
                # Update coordinator tools
                updated_tools = self.available_tools + [rag_tool]
                self.coordinator.tools = updated_tools
                print("RAG tool created and added to coordinator")
    
    def download_gaia_file(self, task_id: str, api_url: str = "https://agents-course-unit4-scoring.hf.space") -> str:
        """Download file associated with task_id"""
        try:
            response = requests.get(f"{api_url}/files/{task_id}", timeout=30)
            response.raise_for_status()
            
            filename = f"task_{task_id}_file"
            with open(filename, 'wb') as f:
                f.write(response.content)
            return filename
        except Exception as e:
            print(f"Failed to download file for task {task_id}: {e}")
            return None
    
    async def solve_gaia_question(self, question_data: Dict[str, Any]) -> str:
        """
        Solve GAIA question with enhanced validation and reformatting
        """
        question = question_data.get("Question", "")
        task_id = question_data.get("task_id", "")
        
        # Try to download file if task_id provided
        file_path = None
        if task_id:
            try:
                file_path = self.download_gaia_file(task_id)
                if file_path:
                    # Load documents and create RAG tool
                    documents = read_and_parse_content(file_path)
                    self.create_dynamic_rag_tool(documents)
            except Exception as e:
                print(f"Failed to download/process file for task {task_id}: {e}")
        
        # Prepare context prompt
        context_prompt = f"""
GAIA Task ID: {task_id}
Question: {question}
{f'File available: {file_path}' if file_path else 'No additional files'}

Instructions:
1. Process any files using read_and_parse_tool if needed
2. Use appropriate tools for research/computation
3. MUST use intelligent_final_answer_tool with your response and the original question
4. The intelligent tool will validate format and reformat with LLM if needed
"""
        
        try:
            ctx = Context(self.coordinator)
            print("=== AGENT REASONING STEPS ===")
            
            handler = self.coordinator.run(ctx=ctx, user_msg=context_prompt)
            
            full_response = ""
            async for event in handler.stream_events():
                if isinstance(event, AgentStream):
                    print(event.delta, end="", flush=True)
                    full_response += event.delta
            
            final_response = await handler
            print("\n=== END REASONING ===")
            
            # Extract the final formatted answer
            final_answer = str(final_response).strip()
            
            print(f"Final GAIA formatted answer: {final_answer}")
            return final_answer
            
        except Exception as e:
            error_msg = f"Error processing question: {str(e)}"
            print(error_msg)
            return error_msg