Update agent.py

agent.py

@@ -1,75 +1,71 @@
-# --- IMPORTS --- #
 import os
 import re
-import ast
-import requests
-import time
-import tempfile
-
 from pathlib import Path
+from typing import Optional, Union, Dict, List, Any
 from enum import Enum
-from typing import TypedDict, Dict, List, Any
+import requests
+import tempfile
+import ast
 
 from dotenv import load_dotenv
 from langgraph.graph import StateGraph, END
-from langchain.tools import Tool as LangTool, StructuredTool
+from langchain.tools import Tool as LangTool
 from langchain_core.runnables import RunnableLambda
+from pathlib import Path
 
-# ——— Zephyr LLM setup ———
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 from langchain_community.llms import HuggingFacePipeline
 import torch
 
+from langchain.tools import StructuredTool
+
+from tools import (
+    EnhancedSearchTool,
+    EnhancedWikipediaTool,
+    excel_to_markdown,
+    image_file_info,
+    audio_file_info,
+    code_file_read,
+    extract_youtube_info)
 
-# Function to get the desired model Zephyr
 def get_zephyr_llm():
-    model_id = 'HuggingFaceH4/zephyr-7b-alpha'
+    model_id = "HuggingFaceH4/zephyr-7b-alpha"
     tokenizer = AutoTokenizer.from_pretrained(model_id)
     model = AutoModelForCausalLM.from_pretrained(
-        model_id,
-        torch_dtype = torch.float16,
-        device_map = 'auto'
+        model_id, torch_dtype=torch.float16, device_map="auto"
     )
     gen = pipeline(
-        'text-generation',
-        model = model,
-        tokenizer = tokenizer,
-        max_new_tokens = 256, 
-        temperature = 0.7, 
-        top_p = 0.9
+        "text-generation", model=model, tokenizer=tokenizer,
+        max_new_tokens=256, temperature=0.7, top_p=0.9
     )
     return HuggingFacePipeline(pipeline=gen)
 
-# Declare the llm 
+# LLM Instance
 llm = get_zephyr_llm()
 
-
-# HF APIs
+# Load environment variables
 load_dotenv()
+
+# --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 QUESTIONS_URL = f"{DEFAULT_API_URL}/questions"
-SUBMIT_URL   = f"{DEFAULT_API_URL}/submit"
-FILE_PATH    = f"{DEFAULT_API_URL}/files/"
+SUBMIT_URL = f"{DEFAULT_API_URL}/submit"
+FILE_PATH = f"{DEFAULT_API_URL}/files/"
 
-# --- TOOLS --- #
+# Initialize LLM
+llm = ChatGoogleGenerativeAI(
+    model=os.getenv("GEMINI_MODEL", "gemini-pro"),
+    google_api_key=os.getenv("GEMINI_API_KEY")
+)
 
+# ----------- Enhanced State Management -----------
+from typing import TypedDict
 
-
-# Agent Steps Class
-class AgentStep(Enum):
-    ANALYZE         = "analyze"
-    SELECT_TOOLS    = "select_tools"
-    EXECUTE_TOOLS   = "execute_tools"
-    SYNTHESIZE      = "synthesize_answer"
-    ERROR_RECOVERY  = "error_recovery"
-    COMPLETE        = "complete"
-
-
-
-# Agent State Class
 class AgentState(TypedDict):
+    """Enhanced state tracking for the agent - using TypedDict for LangGraph compatibility"""
     question: str
     original_question: str
+    conversation_history: List[Dict[str, str]]
     selected_tools: List[str]
     tool_results: Dict[str, Any]
     final_answer: str
@@ -77,63 +73,634 @@ class AgentState(TypedDict):
     error_count: int
     max_errors: int
 
-
-# Initialize state 
-def initialize_state(question: str) -> str:
+class AgentStep(Enum):
+    ANALYZE_QUESTION = "analyze_question"
+    SELECT_TOOLS = "select_tools"
+    EXECUTE_TOOLS = "execute_tools"
+    SYNTHESIZE_ANSWER = "synthesize_answer"
+    ERROR_RECOVERY = "error_recovery"
+    COMPLETE = "complete"
+
+# ----------- Helper Functions -----------
+def initialize_state(question: str) -> AgentState:
+    """Initialize agent state with default values"""
     return {
-        'question': question,
-        'original_question': question,
-        'selected_tools': [],
-        'tool_result': {},
-        'final_answer': '',
-        'current_step': AgentStep.ANALYZE.value,
+        "question": question,
+        "original_question": question,
+        "conversation_history": [],
+        "selected_tools": [],
+        "tool_results": {},
+        "final_answer": "",
+        "current_step": "start",
         "error_count": 0,
         "max_errors": 3
     }
 
-
+# Initialize vanilla tools
+from langchain.tools import DuckDuckGoSearchResults, WikipediaQueryRun
+from langchain.utilities import WikipediaAPIWrapper
+
+duckduckgo_tool = DuckDuckGoSearchResults()
+wiki_tool = WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper())
+
+
+# Initialize enhanced tools
+enhanced_search_tool = LangTool.from_function(
+    name="enhanced_web_search",
+    func=EnhancedSearchTool().run,
+    description="Enhanced web search with intelligent query processing, multiple search strategies, and result filtering. Provides comprehensive and relevant search results."
+)
+
+enhanced_wiki_tool = LangTool.from_function(
+    name="enhanced_wikipedia",
+    func=EnhancedWikipediaTool().run,
+    description="Enhanced Wikipedia search with entity extraction, multi-term search, and relevant content filtering. Provides detailed encyclopedic information."
+)
+
+excel_tool = StructuredTool.from_function(
+    name="excel_to_text",
+    func=excel_to_markdown,
+    description="Enhanced Excel analysis with metadata, statistics, and structured data preview. Inputs: 'excel_path' (str), 'sheet_name' (str, optional).",
+)
+
+image_tool = StructuredTool.from_function(
+    name="image_file_info",
+    func=image_file_info,
+    description="Enhanced image file analysis with detailed metadata and properties."
+)
+
+audio_tool = LangTool.from_function(
+    name="audio_file_info",
+    func=audio_file_info,
+    description="Enhanced audio processing with transcription, language detection, and timestamped segments."
+)
+
+code_tool = LangTool.from_function(
+    name="code_file_read",
+    func=code_file_read,
+    description="Enhanced code file analysis with language-specific insights and structure analysis."
+)
+
+youtube_tool = LangTool.from_function(
+    name="extract_youtube_info",
+    func=extract_youtube_info,
+    description="Extracts transcription from the youtube link"
+)
+
+# Enhanced tool registry
+AVAILABLE_TOOLS = {
+    "excel": excel_tool,
+    "search": wiki_tool,
+    "wikipedia": duckduckgo_tool,
+    "image": image_tool,
+    "audio": audio_tool,
+    "code": code_tool,
+    "youtube": youtube_tool
+}
+
+# ----------- Intelligent Tool Selection -----------
 def analyze_question(state: AgentState) -> AgentState:
-    state["current_step"] = AgentStep.SELECT_TOOLS.value
+    """Enhanced question analysis with better tool recommendation"""
+    analysis_prompt = f"""
+    Analyze this question and determine the best tools and approach:
+    Question: {state["question"]}
+    
+    Available enhanced tools:
+    1. excel - Enhanced Excel/CSV analysis with statistics and metadata
+    2. search - Enhanced web search with intelligent query processing and result filtering
+    3. wikipedia - Enhanced Wikipedia search with entity extraction and content filtering
+    4. image - Enhanced image analysis with what the image contains
+    5. audio - Enhanced audio processing with transcription
+    6. code - Enhanced code analysis with language-specific insights
+    7. youtube - Extracts transcription from the youtube link
+    
+    Consider:
+    - Question type (factual, analytical, current events, technical)
+    - Required information sources (files, web, encyclopedic)
+    - Time sensitivity (current vs historical information)
+    - Complexity level
+    
+    Respond with:
+    1. Question type: <type>
+    2. Primary tools needed: <tools>
+    3. Search strategy: <strategy>
+    4. Expected answer format: <format>
+    
+    Format: TYPE: <type> | TOOLS: <tools> | STRATEGY: <strategy> | FORMAT: <format>
+    """
+    
+    try:
+        response = llm.invoke(analysis_prompt).content
+        state["conversation_history"].append({"role": "analysis", "content": response})
+        state["current_step"] = AgentStep.SELECT_TOOLS.value
+    except Exception as e:
+        state["error_count"] += 1
+        state["conversation_history"].append({"role": "error", "content": f"Analysis failed: {e}"})
+        state["current_step"] = AgentStep.ERROR_RECOVERY.value
+    
     return state
 
-# Agent Class
-class GaiaAgent:
-    def __init__(self):
-        self.graph = graph 
-
-    def __call__(self, task_id: str, question: str) -> str: 
-        
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
+def select_tools(state: AgentState) -> AgentState:
+    """Enhanced tool selection with smarter logic"""
+    question = state["question"].lower()
+    selected_tools = []
+
+    # File-based tool selection
+    if any(keyword in question for keyword in ["excel", "csv", "spreadsheet", ".xlsx", ".xls"]):
+        selected_tools.append("excel")
+    if any(keyword in question for keyword in [".png", ".jpg", ".jpeg", ".bmp", ".gif", "image"]):
+        selected_tools.append("image")
+    if any(keyword in question for keyword in [".mp3", ".wav", ".ogg", "audio", "transcribe"]):
+        selected_tools.append("audio")
+    if any(keyword in question for keyword in [".py", ".ipynb", "code", "script", "function"]):
+        selected_tools.append("code")
+    if any(keyword in question for keyword in ["youtube"]):
+        selected_tools.append("youtube")
+
+    print(f"File-based tools selected: {selected_tools}")
+
+    tools_prompt = f"""
+    You are a smart assistant that selects relevant tools based on the user's natural language question.
+
+    Available tools:
+    - "search" → Use for real-time, recent, or broad web information.
+    - "wikipedia" → Use for factual or encyclopedic knowledge.
+    - "excel" → Use for spreadsheet-related questions (.xlsx, .csv).
+    - "image" → Use for image files (.png, .jpg, etc.) or image-based tasks.
+    - "audio" → Use for sound files (.mp3, .wav, etc.) or transcription.
+    - "code" → Use for programming-related questions or when files like .py are mentioned.
+    - "youtube" → Use for questions involving YouTube videos.
+
+    Return the result as a **Python list of strings**, no explanation. Use only the relevant tools.
+    If not relevant tool is found, return an empty list such as [].
+
+    ### Examples:
+
+    Q: "Show me recent news about elections in 2025"
+    A: ["search"]
+
+    Q: "Summarize this Wikipedia article about Einstein"
+    A: ["wikipedia"]
+
+    Q: "Analyze this .csv file"
+    A: ["excel"]
+
+    Q: "Transcribe this .wav audio file"
+    A: ["audio"]
+
+    Q: "Generate Python code from this prompt"
+    A: ["code"]
+
+    Q: "Who was the president of USA in 1945?"
+    A: ["wikipedia"]
+
+    Q: "Give me current weather updates"
+    A: ["search"]
+
+    Q: "Look up the history of space exploration"
+    A: ["search", "wikipedia"]
+
+    Q: "What is 2 + 2?"
+    A: []
+
+    ### Now answer:
+
+    Q: {state["question"]}
+    A:
+    """
+    
+    llm_tools = ast.literal_eval(llm.invoke(tools_prompt).content.strip())
+    if not isinstance(llm_tools, list):
+        llm_tools = []
+    print(f"LLM suggested tools: {llm_tools}")
+    selected_tools.extend(llm_tools)
+    selected_tools = list(set(selected_tools))  # Remove duplicates
+
+    print(f"Final selected tools after LLM suggestion: {selected_tools}")
+
+
+    # # Information-based tool selection
+    # current_indicators = ["recent", "current", "news", "today", "2025", "now"]
+    # encyclopedia_indicators = ["wiki", "wikipedia"]
+    
+    # if any(indicator in question for indicator in current_indicators):
+    #     selected_tools.append("search")
+    # elif any(indicator in question for indicator in encyclopedia_indicators):
+    #     selected_tools.append("wikipedia")
+    # elif any(keyword in question for keyword in ["search", "find", "look up", "information about"]):
+    #     # Use both for comprehensive coverage
+    #     selected_tools.extend(["search", "wikipedia"])
+
+    # # Default fallback
+    # if not selected_tools:
+    #     if any(word in question for word in ["who", "what", "when", "where"]):
+    #         selected_tools.append("wikipedia")
+    #         selected_tools.append("search")
+
+    # # Remove duplicates while preserving order
+    # selected_tools = list(dict.fromkeys(selected_tools))
+    
+    state["selected_tools"] = selected_tools
+    state["current_step"] = AgentStep.EXECUTE_TOOLS.value
+    return state
 
+def execute_tools(state: AgentState) -> AgentState:
+    """Enhanced tool execution with better error handling"""
+    results = {}
+
+    # Enhanced file detection
+    file_path = None
+    downloaded_file_marker = "A file was downloaded for this task and saved locally at:"
+    if downloaded_file_marker in state["question"]:
+        lines = state["question"].splitlines()
+        for i, line in enumerate(lines):
+            if downloaded_file_marker in line:
+                if i + 1 < len(lines):
+                    file_path_candidate = lines[i + 1].strip()
+                    if Path(file_path_candidate).exists():
+                        file_path = file_path_candidate
+                        print(f"Detected file path: {file_path}")
+                break
+
+    for tool_name in state["selected_tools"]:
+        try:
+            print(f"Executing tool: {tool_name}")
+            
+            # File-based tools
+            if tool_name in ["excel", "image", "audio", "code"] and file_path:
+                if tool_name == "excel":
+                    result = AVAILABLE_TOOLS["excel"].run({"excel_path": file_path, "sheet_name": None})
+                elif tool_name == "image":
+                    result = AVAILABLE_TOOLS["image"].run({"image_path": file_path, "question": state["question"]})
+                elif tool_name == "youtube":
+                    print(f"Running YouTube tool with file path: {file_path}")
+                    result = AVAILABLE_TOOLS["youtube"].run(state["question"])
+                else:
+                    result = AVAILABLE_TOOLS[tool_name].run(file_path)
+            # Information-based tools
+            else:
+                # Extract clean query for search tools
+                clean_query = state["question"]
+                if downloaded_file_marker in clean_query:
+                    clean_query = clean_query.split(downloaded_file_marker)[0].strip()
+                
+                result = AVAILABLE_TOOLS[tool_name].run(clean_query)
+
+            results[tool_name] = result
+
+            print(f"Tool {tool_name} completed successfully.")
+            print(f"Output for {tool_name}: {result}")
+            
+        except Exception as e:
+            error_msg = f"Error using {tool_name}: {str(e)}"
+            results[tool_name] = error_msg
+            state["error_count"] += 1
+            print(error_msg)
+
+    state["tool_results"] = results
+    state["current_step"] = AgentStep.SYNTHESIZE_ANSWER.value
+    return state
 
+def synthesize_answer(state: AgentState) -> AgentState:
+    """Enhanced answer synthesis with better formatting"""
 
+    tool_results_str = "\n".join([f"=== {tool.upper()} RESULTS ===\n{result}\n" for tool, result in state["tool_results"].items()])
 
+    cot_prompt = f"""You are a precise assistant tasked with analyzing the user's question{" using the available tool outputs" if state["tool_results"] else ""}.
 
+    Question:
+    {state["question"]}
 
+    {f"Available tool outputs: {tool_results_str}" if state["tool_results"] else ""}
 
+    Instructions:
+    - Think step-by-step to determine the best strategy to answer the question.
+    - Use only the given information; do not hallucinate or infer from external knowledge.
+    - If decoding, logical deduction, counting, or interpretation is required, show each step clearly.
+    - If any part of the tool output is unclear or incomplete, mention it and its impact.
+    - Do not guess. If the information is insufficient, say so clearly.
+    - Finish with a clearly marked line: `---END OF ANALYSIS---`
 
+    Your step-by-step analysis:"""
 
+    cot_response = llm.invoke(cot_prompt).content
 
+    final_answer_prompt = f"""You are a precise assistant tasked with deriving the **final answer** from the step-by-step analysis below.
 
+    Question:
+    {state["question"]}
 
+    Step-by-step analysis:
+    {cot_response}
 
+    Instructions:
+    - Read the analysis thoroughly before responding.
+    - Output ONLY the final answer. Do NOT include any reasoning or explanation.
+    - Remove any punctuation at the corners of the answer unless it is explicitly mentioned in the question.
+    - The answer must be concise and factual.
+    - If the analysis concluded that a definitive answer cannot be determined, respond with: `NA` (exactly).
 
+    Final answer:"""
 
+        
+    try:
+        response = llm.invoke(final_answer_prompt).content
+        state["final_answer"] = response
+        state["current_step"] = AgentStep.COMPLETE.value
+    except Exception as e:
+        state["error_count"] += 1
+        state["final_answer"] = f"Error synthesizing answer: {e}"
+        state["current_step"] = AgentStep.ERROR_RECOVERY.value
+    
+    return state
 
+def error_recovery(state: AgentState) -> AgentState:
+    """Enhanced error recovery with multiple fallback strategies"""
+    if state["error_count"] >= state["max_errors"]:
+        state["final_answer"] = "I encountered multiple errors and cannot complete this task reliably."
+        state["current_step"] = AgentStep.COMPLETE.value
+    else:
+        # Enhanced fallback: try with simplified approach
+        try:
+            fallback_prompt = f"""
+            Answer this question directly using your knowledge:
+            {state["original_question"]}
+            
+            Provide a helpful response even if you cannot access external tools.
+            Be clear about any limitations in your answer.
+            """
+            response = llm.invoke(fallback_prompt).content
+            state["final_answer"] = f"Using available knowledge (some tools unavailable): {response}"
+            state["current_step"] = AgentStep.COMPLETE.value
+        except Exception as e:
+            state["final_answer"] = f"All approaches failed. Error: {e}"
+            state["current_step"] = AgentStep.COMPLETE.value
+    
+    return state
 
+# ----------- Enhanced LangGraph Workflow -----------
+def route_next_step(state: AgentState) -> str:
+    """Route to next step based on current state"""
+    step_routing = {
+        "start": AgentStep.ANALYZE_QUESTION.value,
+        AgentStep.ANALYZE_QUESTION.value: AgentStep.SELECT_TOOLS.value,
+        AgentStep.SELECT_TOOLS.value: AgentStep.EXECUTE_TOOLS.value,
+        AgentStep.EXECUTE_TOOLS.value: AgentStep.SYNTHESIZE_ANSWER.value,
+        AgentStep.SYNTHESIZE_ANSWER.value: AgentStep.COMPLETE.value,
+        AgentStep.ERROR_RECOVERY.value: AgentStep.COMPLETE.value,
+        AgentStep.COMPLETE.value: END,
+    }
+    
+    return step_routing.get(state["current_step"], END)
+
+# Create enhanced workflow
+workflow = StateGraph(AgentState)
+
+# Add nodes
+workflow.add_node("analyze_question", RunnableLambda(analyze_question))
+workflow.add_node("select_tools", RunnableLambda(select_tools))
+workflow.add_node("execute_tools", RunnableLambda(execute_tools))
+workflow.add_node("synthesize_answer", RunnableLambda(synthesize_answer))
+workflow.add_node("error_recovery", RunnableLambda(error_recovery))
+
+# Set entry point
+workflow.set_entry_point("analyze_question")
+
+# Add conditional edges
+workflow.add_conditional_edges(
+    "analyze_question",
+    lambda state: "select_tools" if state["current_step"] == AgentStep.SELECT_TOOLS.value else "error_recovery"
+)
+workflow.add_edge("select_tools", "execute_tools")
+workflow.add_conditional_edges(
+    "execute_tools",
+    lambda state: "synthesize_answer" if state["current_step"] == AgentStep.SYNTHESIZE_ANSWER.value else "error_recovery"
+)
+workflow.add_conditional_edges(
+    "synthesize_answer",
+    lambda state: END if state["current_step"] == AgentStep.COMPLETE.value else "error_recovery"
+)
+workflow.add_edge("error_recovery", END)
+
+# Compile the enhanced graph
+graph = workflow.compile()
+
+# ----------- Agent Class -----------
+class GaiaAgent:
+    """GAIA Agent with tools and intelligent processing"""
+    
+    def __init__(self):
+        self.graph = graph
+        self.tool_usage_stats = {}
+        print("Enhanced GAIA Agent initialized with:")
+        print("✓ Intelligent multi-query web search")
+        print("✓ Entity-aware Wikipedia search")
+        print("✓ Enhanced file processing tools")
+        print("✓ Advanced error recovery")
+        print("✓ Comprehensive result synthesis")
+
+    def get_tool_stats(self) -> Dict[str, int]:
+        """Get usage statistics for tools"""
+        return self.tool_usage_stats.copy()
+
+    def __call__(self, task_id: str, question: str) -> str:
+        print(f"\n{'='*60}")
+        print(f"[{task_id}] ENHANCED PROCESSING: {question}")
+        
+        # Initialize state
+        processed_question = process_file(task_id, question)
+        initial_state = initialize_state(processed_question)
+        
+        try:
+            # Execute the enhanced workflow
+            result = self.graph.invoke(initial_state)
+            
+            # Extract results
+            answer = result.get("final_answer", "No answer generated")
+            selected_tools = result.get("selected_tools", [])
+            conversation_history = result.get("conversation_history", [])
+            tool_results = result.get("tool_results", {})
+            error_count = result.get("error_count", 0)
+            
+            # Update tool usage statistics
+            for tool in selected_tools:
+                self.tool_usage_stats[tool] = self.tool_usage_stats.get(tool, 0) + 1
+            
+            # Enhanced logging
+            print(f"[{task_id}] Selected tools: {selected_tools}")
+            print(f"[{task_id}] Tools executed: {list(tool_results.keys())}")
+            print(f"[{task_id}] Processing steps: {len(conversation_history)}")
+            print(f"[{task_id}] Errors encountered: {error_count}")
+            
+            # Log tool result sizes for debugging
+            for tool, result in tool_results.items():
+                result_size = len(str(result)) if result else 0
+                print(f"[{task_id}] {tool} result size: {result_size} chars")
+            
+            print(f"[{task_id}] FINAL ANSWER: {answer}")
+            print(f"{'='*60}")
+            
+            return answer
+            
+        except Exception as e:
+            error_msg = f"Critical error in enhanced agent execution: {str(e)}"
+            print(f"[{task_id}] {error_msg}")
+            
+            # Try fallback direct LLM response
+            try:
+                fallback_response = llm.invoke(f"Please answer this question: {question}").content
+                return f"Fallback response: {fallback_response}"
+            except:
+                return error_msg
+
+# ----------- Enhanced File Processing -----------
+def detect_file_type(file_path: str) -> Optional[str]:
+    """Enhanced file type detection with more formats"""
+    ext = Path(file_path).suffix.lower()
+    
+    file_type_mapping = {
+        # Spreadsheets
+        '.xlsx': 'excel', '.xls': 'excel', '.csv': 'excel',
+        # Images
+        '.png': 'image', '.jpg': 'image', '.jpeg': 'image', 
+        '.bmp': 'image', '.gif': 'image', '.tiff': 'image', '.webp': 'image',
+        # Audio
+        '.mp3': 'audio', '.wav': 'audio', '.ogg': 'audio', 
+        '.flac': 'audio', '.m4a': 'audio', '.aac': 'audio',
+        # Code
+        '.py': 'code', '.ipynb': 'code', '.js': 'code', '.html': 'code',
+        '.css': 'code', '.java': 'code', '.cpp': 'code', '.c': 'code',
+        '.sql': 'code', '.r': 'code', '.json': 'code', '.xml': 'code',
+        # Documents
+        '.txt': 'text', '.md': 'text', '.pdf': 'document',
+        '.doc': 'document', '.docx': 'document'
+    }
+    
+    return file_type_mapping.get(ext)
+
+def process_file(task_id: str, question_text: str) -> str:
+    """Enhanced file processing with better error handling and metadata"""
+    file_url = f"{FILE_PATH}{task_id}"
+    
+    try:
+        print(f"[{task_id}] Attempting to download file from: {file_url}")
+        response = requests.get(file_url, timeout=30)
+        response.raise_for_status()
+        print(f"[{task_id}] File download successful. Status: {response.status_code}")
+        
+    except requests.exceptions.RequestException as exc:
+        print(f"[{task_id}] File download failed: {str(exc)}")
+        return question_text  # Return original question if no file
+    
+    # Enhanced filename extraction
+    content_disposition = response.headers.get("content-disposition", "")
+    filename = task_id  # Default fallback
+    
+    # Try to extract filename from Content-Disposition header
+    filename_match = re.search(r'filename[*]?=(?:"([^"]+)"|([^;]+))', content_disposition)
+    if filename_match:
+        filename = filename_match.group(1) or filename_match.group(2)
+        filename = filename.strip()
+    
+    # Create enhanced temp directory structure
+    temp_storage_dir = Path(tempfile.gettempdir()) / "gaia_enhanced_files" / task_id
+    temp_storage_dir.mkdir(parents=True, exist_ok=True)
+    
+    file_path = temp_storage_dir / filename
+    file_path.write_bytes(response.content)
+    
+    # Get file metadata
+    file_size = len(response.content)
+    file_type = detect_file_type(filename)
+    
+    print(f"[{task_id}] File saved: {filename} ({file_size:,} bytes, type: {file_type})")
+    
+    # Enhanced question augmentation
+    enhanced_question = f"{question_text}\n\n"
+    enhanced_question += f"{'='*50}\n"
+    enhanced_question += f"FILE INFORMATION:\n"
+    enhanced_question += f"A file was downloaded for this task and saved locally at:\n"
+    enhanced_question += f"{str(file_path)}\n"
+    enhanced_question += f"File details:\n"
+    enhanced_question += f"- Name: {filename}\n"
+    enhanced_question += f"- Size: {file_size:,} bytes ({file_size/1024:.1f} KB)\n"
+    enhanced_question += f"- Type: {file_type or 'unknown'}\n"
+    enhanced_question += f"{'='*50}\n\n"
+    
+    return enhanced_question
+
+# ----------- Usage Examples and Testing -----------
+def run_enhanced_tests():
+    """Run comprehensive tests of the enhanced agent"""
+    agent = GaiaAgent()
+    
+    test_cases = [
+        {
+            "id": "test_search_1",
+            "question": "What are the latest developments in artificial intelligence in 2024?",
+            "expected_tools": ["search"]
+        },
+        {
+            "id": "test_wiki_1", 
+            "question": "Tell me about Albert Einstein's contributions to physics",
+            "expected_tools": ["wikipedia"]
+        },
+        {
+            "id": "test_combined_1",
+            "question": "What is machine learning and what are recent breakthroughs?",
+            "expected_tools": ["wikipedia", "search"]
+        },
+        {
+            "id": "test_excel_1",
+            "question": "Analyze the data in the Excel file sales_data.xlsx",
+            "expected_tools": ["excel"]
+        }
+    ]
+    
+    print("\n" + "="*80)
+    print("RUNNING ENHANCED AGENT TESTS")
+    print("="*80)
+    
+    for test_case in test_cases:
+        print(f"\nTest Case: {test_case['id']}")
+        print(f"Question: {test_case['question']}")
+        print(f"Expected tools: {test_case['expected_tools']}")
+        
+        try:
+            result = agent(test_case['id'], test_case['question'])
+            print(f"Result length: {len(result)} characters")
+            print(f"Result preview: {result[:200]}...")
+        except Exception as e:
+            print(f"Test failed: {e}")
+        
+        print("-" * 60)
+    
+    # Print tool usage statistics
+    print(f"\nTool Usage Statistics:")
+    for tool, count in agent.get_tool_stats().items():
+        print(f"  {tool}: {count} times")
+
+# Usage example
+if __name__ == "__main__":
+    # Create enhanced agent
+    agent = GaiaAgent()
+    
+    # Example usage
+    sample_questions = [
+        "What is the current population of Tokyo and how has it changed recently?",
+        "Explain quantum computing and its recent developments",
+        "Tell me about the history of machine learning and current AI trends",
+    ]
+    
+    print("\n" + "="*80)
+    print("ENHANCED GAIA AGENT DEMONSTRATION")
+    print("="*80)
+    
+    for i, question in enumerate(sample_questions):
+        print(f"\nExample {i+1}: {question}")
+        result = agent(f"demo_{i}", question)
+        print(f"Answer: {result[:300]}...")
+        print("-" * 60)
+    
+    # Uncomment to run comprehensive tests
+    # run_enhanced_tests()