Update Self_Improving_Search.py

Self_Improving_Search.py

@@ -1,8 +1,8 @@
 import time
 import re
 import os
-from typing import List, Dict, Tuple, Union, Optional
+from typing import List, Dict, Tuple, Union
-from colorama import Fore, Style, init
+from colorama import Fore, Style
 import logging
 import sys
 from io import StringIO
@@ -10,51 +10,44 @@ from web_scraper import get_web_content, can_fetch
 from llm_config import get_llm_config
 from llm_response_parser import UltimateLLMResponseParser
 from llm_wrapper import LLMWrapper
-from urllib.parse import urlparse, quote_plus
+from urllib.parse import urlparse
-import requests
-from bs4 import BeautifulSoup
-import json
-from datetime import datetime, timedelta
-import threading
-from queue import Queue
-import concurrent.futures
-
-# Initialize colorama
-init()
 
 # Set up logging
 log_directory = 'logs'
 if not os.path.exists(log_directory):
     os.makedirs(log_directory)
 
+# Configure logger
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.INFO)
-log_file = os.path.join(log_directory, 'search.log')
+log_file = os.path.join(log_directory, 'llama_output.log')
 file_handler = logging.FileHandler(log_file)
 formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
 file_handler.setFormatter(formatter)
+logger.handlers = []
 logger.addHandler(file_handler)
+logger.propagate = False
 
-class SearchResult:
+# Suppress other loggers
-  def __init__(self, title: str, url: str, snippet: str, score: float = 0.0):
+for name in ['root', 'duckduckgo_search', 'requests', 'urllib3']:
-      self.title = title
+    logging.getLogger(name).setLevel(logging.WARNING)
-      self.url = url
+    logging.getLogger(name).handlers = []
-      self.snippet = snippet
+    logging.getLogger(name).propagate = False
-      self.score = score
-      self.content: Optional[str] = None
-      self.processed = False
-      self.error = None
 
-  def to_dict(self) -> Dict:
+class OutputRedirector:
-      return {
+    def __init__(self, stream=None):
-          'title': self.title,
+        self.stream = stream or StringIO()
-          'url': self.url,
+        self.original_stdout = sys.stdout
-          'snippet': self.snippet,
+        self.original_stderr = sys.stderr
-          'score': self.score,
+
-          'has_content': bool(self.content),
+    def __enter__(self):
-          'processed': self.processed,
+        sys.stdout = self.stream
-          'error': str(self.error) if self.error else None
+        sys.stderr = self.stream
-      }
+        return self.stream
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        sys.stdout = self.original_stdout
+        sys.stderr = self.original_stderr
 
 class EnhancedSelfImprovingSearch:
     def __init__(self, llm: LLMWrapper, parser: UltimateLLMResponseParser, max_attempts: int = 5):
@@ -62,313 +55,380 @@ class EnhancedSelfImprovingSearch:
         self.parser = parser
         self.max_attempts = max_attempts
         self.llm_config = get_llm_config()
-      self.last_query = ""
-      self.last_time_range = ""
-      self.search_cache = {}
-      self.content_cache = {}
-      self.max_cache_size = 100
-      self.max_concurrent_requests = 5
-      self.request_timeout = 15
-      self.headers = {
-          'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
-      }
 
-  def search_and_improve(self, query: str, time_range: str = "auto") -> str:
+    @staticmethod
-      """Main search method that includes self-improvement"""
+    def initialize_llm():
+        llm_wrapper = LLMWrapper()
+        return llm_wrapper
+
+    def print_thinking(self):
+        print(Fore.MAGENTA + "🧠 Thinking..." + Style.RESET_ALL)
+
+    def print_searching(self):
+        print(Fore.MAGENTA + "📝 Searching..." + Style.RESET_ALL)
+
+    def search_and_improve(self, user_query: str) -> str:
+        attempt = 0
+        while attempt < self.max_attempts:
+            print(f"\n{Fore.CYAN}Search attempt {attempt + 1}:{Style.RESET_ALL}")
+            self.print_searching()
+
             try:
-          logger.info(f"Starting search for query: {query}")
+                formulated_query, time_range = self.formulate_query(user_query, attempt)
-          self.last_query = query
-          self.last_time_range = time_range
 
-          # Check cache first
+                print(f"{Fore.YELLOW}Original query: {user_query}{Style.RESET_ALL}")
-          cache_key = f"{query}_{time_range}"
+                print(f"{Fore.YELLOW}Formulated query: {formulated_query}{Style.RESET_ALL}")
-          if cache_key in self.search_cache:
+                print(f"{Fore.YELLOW}Time range: {time_range}{Style.RESET_ALL}")
-              logger.info("Returning cached results")
-              return self.search_cache[cache_key]
 
-          # Perform initial search
+                if not formulated_query:
-          results = self.perform_search(query, time_range)
+                    print(f"{Fore.RED}Error: Empty search query. Retrying...{Style.RESET_ALL}")
-          if not results:
+                    attempt += 1
-              return "No results found."
+                    continue
 
-          # Enhance results with content fetching
+                search_results = self.perform_search(formulated_query, time_range)
-          enhanced_results = self.enhance_search_results(results)
 
-          # Generate improved summary
+                if not search_results:
-          summary = self.generate_enhanced_summary(enhanced_results, query)
+                    print(f"{Fore.RED}No results found. Retrying with a different query...{Style.RESET_ALL}")
+                    attempt += 1
+                    continue
 
-          # Cache the results
+                self.display_search_results(search_results)
-          self.cache_results(cache_key, summary)
 
-          return summary
+                selected_urls = self.select_relevant_pages(search_results, user_query)
+
+                if not selected_urls:
+                    print(f"{Fore.RED}No relevant URLs found. Retrying...{Style.RESET_ALL}")
+                    attempt += 1
+                    continue
+
+                print(Fore.MAGENTA + "⚙️ Scraping selected pages..." + Style.RESET_ALL)
+                # Scraping is done without OutputRedirector to ensure messages are visible
+                scraped_content = self.scrape_content(selected_urls)
+
+                if not scraped_content:
+                    print(f"{Fore.RED}Failed to scrape content. Retrying...{Style.RESET_ALL}")
+                    attempt += 1
+                    continue
+
+                self.display_scraped_content(scraped_content)
+
+                self.print_thinking()
+
+                with OutputRedirector() as output:
+                    evaluation, decision = self.evaluate_scraped_content(user_query, scraped_content)
+                llm_output = output.getvalue()
+                logger.info(f"LLM Output in evaluate_scraped_content:\n{llm_output}")
+
+                print(f"{Fore.MAGENTA}Evaluation: {evaluation}{Style.RESET_ALL}")
+                print(f"{Fore.MAGENTA}Decision: {decision}{Style.RESET_ALL}")
+
+                if decision == "answer":
+                    return self.generate_final_answer(user_query, scraped_content)
+                elif decision == "refine":
+                    print(f"{Fore.YELLOW}Refining search...{Style.RESET_ALL}")
+                    attempt += 1
+                else:
+                    print(f"{Fore.RED}Unexpected decision. Proceeding to answer.{Style.RESET_ALL}")
+                    return self.generate_final_answer(user_query, scraped_content)
 
             except Exception as e:
-          logger.error(f"Search and improve error: {str(e)}", exc_info=True)
+                print(f"{Fore.RED}An error occurred during search attempt. Check the log file for details.{Style.RESET_ALL}")
-          return f"Error during search: {str(e)}"
+                logger.error(f"An error occurred during search: {str(e)}", exc_info=True)
+                attempt += 1
 
-  def perform_search(self, query: str, time_range: str) -> List[SearchResult]:
+        return self.synthesize_final_answer(user_query)
-      """Performs web search with improved error handling and retry logic"""
+
+    def evaluate_scraped_content(self, user_query: str, scraped_content: Dict[str, str]) -> Tuple[str, str]:
+        user_query_short = user_query[:200]
+        prompt = f"""
+Evaluate if the following scraped content contains sufficient information to answer the user's question comprehensively:
+
+User's question: "{user_query_short}"
+
+Scraped Content:
+{self.format_scraped_content(scraped_content)}
+
+Your task:
+1. Determine if the scraped content provides enough relevant and detailed information to answer the user's question thoroughly.
+2. If the information is sufficient, decide to 'answer'. If more information or clarification is needed, decide to 'refine' the search.
+
+Respond using EXACTLY this format:
+Evaluation: [Your evaluation of the scraped content]
+Decision: [ONLY 'answer' if content is sufficient, or 'refine' if more information is needed]
+"""
+        max_retries = 3
+        for attempt in range(max_retries):
+            try:
+                response_text = self.llm.generate(prompt, max_tokens=200, stop=None)
+                evaluation, decision = self.parse_evaluation_response(response_text)
+                if decision in ['answer', 'refine']:
+                    return evaluation, decision
+            except Exception as e:
+                logger.warning(f"Error in evaluate_scraped_content (attempt {attempt + 1}): {str(e)}")
+
+        logger.warning("Failed to get a valid decision in evaluate_scraped_content. Defaulting to 'refine'.")
+        return "Failed to evaluate content.", "refine"
+
+    def parse_evaluation_response(self, response: str) -> Tuple[str, str]:
+        evaluation = ""
+        decision = ""
+        for line in response.strip().split('\n'):
+            if line.startswith('Evaluation:'):
+                evaluation = line.split(':', 1)[1].strip()
+            elif line.startswith('Decision:'):
+                decision = line.split(':', 1)[1].strip().lower()
+        return evaluation, decision
+
+    def formulate_query(self, user_query: str, attempt: int) -> Tuple[str, str]:
+        user_query_short = user_query[:200]
+        prompt = f"""
+Based on the following user question, formulate a concise and effective search query:
+"{user_query_short}"
+Your task:
+1. Create a search query of 2-5 words that will yield relevant results.
+2. Determine if a specific time range is needed for the search.
+Time range options:
+- 'd': Limit results to the past day. Use for very recent events or rapidly changing information.
+- 'w': Limit results to the past week. Use for recent events or topics with frequent updates.
+- 'm': Limit results to the past month. Use for relatively recent information or ongoing events.
+- 'y': Limit results to the past year. Use for annual events or information that changes yearly.
+- 'none': No time limit. Use for historical information or topics not tied to a specific time frame.
+Respond in the following format:
+Search query: [Your 2-5 word query]
+Time range: [d/w/m/y/none]
+Do not provide any additional information or explanation.
+"""
+        max_retries = 3
+        for retry in range(max_retries):
+            with OutputRedirector() as output:
+                response_text = self.llm.generate(prompt, max_tokens=50, stop=None)
+            llm_output = output.getvalue()
+            logger.info(f"LLM Output in formulate_query:\n{llm_output}")
+            query, time_range = self.parse_query_response(response_text)
+            if query and time_range:
+                return query, time_range
+        return self.fallback_query(user_query), "none"
+
+    def parse_query_response(self, response: str) -> Tuple[str, str]:
+        query = ""
+        time_range = "none"
+        for line in response.strip().split('\n'):
+            if ":" in line:
+                key, value = line.split(":", 1)
+                key = key.strip().lower()
+                value = value.strip()
+                if "query" in key:
+                    query = self.clean_query(value)
+                elif "time" in key or "range" in key:
+                    time_range = self.validate_time_range(value)
+        return query, time_range
+
+    def clean_query(self, query: str) -> str:
+        query = re.sub(r'["\'\[\]]', '', query)
+        query = re.sub(r'\s+', ' ', query)
+        return query.strip()[:100]
+
+    def validate_time_range(self, time_range: str) -> str:
+        valid_ranges = ['d', 'w', 'm', 'y', 'none']
+        time_range = time_range.lower()
+        return time_range if time_range in valid_ranges else 'none'
+
+    def fallback_query(self, user_query: str) -> str:
+        words = user_query.split()
+        return " ".join(words[:5])
+
+    def perform_search(self, query: str, time_range: str) -> List[Dict]:
         if not query:
             return []
 
-      results = []
+        from duckduckgo_search import DDGS
-      retries = 3
-      delay = 2
 
-      for attempt in range(retries):
+        with DDGS() as ddgs:
             try:
-              encoded_query = quote_plus(query)
+                with OutputRedirector() as output:
-              search_url = f"https://html.duckduckgo.com/html/?q={encoded_query}"
+                    if time_range and time_range != 'none':
-              
+                        results = list(ddgs.text(query, timelimit=time_range, max_results=10))
-              response = requests.get(search_url, headers=self.headers, timeout=self.request_timeout)
-              response.raise_for_status()
-              
-              soup = BeautifulSoup(response.text, 'html.parser')
-              
-              for i, result in enumerate(soup.select('.result'), 1):
-                  if i > 15:  # Increased limit for better coverage
-                      break
-                      
-                  title_elem = result.select_one('.result__title')
-                  snippet_elem = result.select_one('.result__snippet')
-                  link_elem = result.select_one('.result__url')
-                  
-                  if title_elem and link_elem:
-                      title = title_elem.get_text(strip=True)
-                      snippet = snippet_elem.get_text(strip=True) if snippet_elem else ""
-                      url = link_elem.get('href', '')
-                      
-                      # Basic result scoring
-                      score = self.calculate_result_score(title, snippet, query)
-                      
-                      results.append(SearchResult(title, url, snippet, score))
-
-              if results:
-                  # Sort results by score
-                  results.sort(key=lambda x: x.score, reverse=True)
-                  return results
-              
-              if attempt < retries - 1:
-                  logger.warning(f"No results found, retrying ({attempt + 1}/{retries})...")
-                  time.sleep(delay)
-                  
-          except Exception as e:
-              logger.error(f"Search attempt {attempt + 1} failed: {str(e)}")
-              if attempt < retries - 1:
-                  time.sleep(delay)
                     else:
-                  raise
+                        results = list(ddgs.text(query, max_results=10))
-
+                ddg_output = output.getvalue()
-      return results
+                logger.info(f"DDG Output in perform_search:\n{ddg_output}")
-
+                return [{'number': i+1, **result} for i, result in enumerate(results)]
-  def calculate_result_score(self, title: str, snippet: str, query: str) -> float:
-      """Calculate relevance score for search result"""
-      score = 0.0
-      query_terms = query.lower().split()
-      
-      # Title matching
-      title_lower = title.lower()
-      for term in query_terms:
-          if term in title_lower:
-              score += 2.0
-              
-      # Snippet matching
-      snippet_lower = snippet.lower()
-      for term in query_terms:
-          if term in snippet_lower:
-              score += 1.0
-              
-      # Exact phrase matching
-      if query.lower() in title_lower:
-          score += 3.0
-      if query.lower() in snippet_lower:
-          score += 1.5
-          
-      return score
-
-  def enhance_search_results(self, results: List[SearchResult]) -> List[SearchResult]:
-      """Enhance search results with parallel content fetching"""
-      enhanced_results = []
-      
-      with concurrent.futures.ThreadPoolExecutor(max_workers=self.max_concurrent_requests) as executor:
-          future_to_result = {
-              executor.submit(self.fetch_and_process_content, result): result 
-              for result in results[:10]  # Limit to top 10 results
-          }
-          
-          for future in concurrent.futures.as_completed(future_to_result):
-              result = future_to_result[future]
-              try:
-                  content = future.result()
-                  if content:
-                      result.content = content
-                      result.processed = True
-                      enhanced_results.append(result)
             except Exception as e:
-                  logger.error(f"Error processing {result.url}: {str(e)}")
+                print(f"{Fore.RED}Search error: {str(e)}{Style.RESET_ALL}")
-                  result.error = e
+                return []
 
-      return enhanced_results
+    def display_search_results(self, results: List[Dict]) -> None:
-
+        """Display search results with minimal output"""
-  def fetch_and_process_content(self, result: SearchResult) -> Optional[str]:
-      """Fetch and process content for a search result"""
         try:
-          # Check cache first
+            if not results:
-          if result.url in self.content_cache:
+                return
-              return self.content_cache[result.url]
 
-          # Check if we can fetch the content
+            # Only show search success status
-          if not can_fetch(result.url):
+            print(f"\nSearch query sent to DuckDuckGo: {self.last_query}")
-              logger.warning(f"Cannot fetch content from {result.url}")
+            print(f"Time range sent to DuckDuckGo: {self.last_time_range}")
-              return None
+            print(f"Number of results: {len(results)}")
-
-          content = get_web_content(result.url)
-          if content:
-              # Process and clean content
-              cleaned_content = self.clean_content(content)
-              
-              # Cache the content
-              self.cache_content(result.url, cleaned_content)
-              
-              return cleaned_content
 
         except Exception as e:
-          logger.error(f"Error fetching content from {result.url}: {str(e)}")
+            logger.error(f"Error displaying search results: {str(e)}")
-          return None
-
-  def clean_content(self, content: str) -> str:
-      """Clean and normalize web content"""
-      # Remove HTML tags if any remained
-      content = re.sub(r'<[^>]+>', '', content)
-      
-      # Remove extra whitespace
-      content = re.sub(r'\s+', ' ', content)
-      
-      # Remove special characters
-      content = re.sub(r'[^\w\s.,!?-]', '', content)
-      
-      # Truncate if too long
-      max_length = 5000
-      if len(content) > max_length:
-          content = content[:max_length] + "..."
-      return content.strip()
-
-  def generate_enhanced_summary(self, results: List[SearchResult], query: str) -> str:
-      """Generate an enhanced summary using LLM with improved context"""
-      try:
-          # Prepare context from enhanced results
-          context = self.prepare_summary_context(results, query)
 
+    def select_relevant_pages(self, search_results: List[Dict], user_query: str) -> List[str]:
         prompt = f"""
-          Based on the following comprehensive search results for "{query}",
+Given the following search results for the user's question: "{user_query}"
-          provide a detailed analysis that:
+Select the 2 most relevant results to scrape and analyze. Explain your reasoning for each selection.
-          1. Synthesizes key information from multiple sources
-          2. Highlights important findings and patterns
-          3. Maintains factual accuracy and cites sources
-          4. Presents a balanced view of different perspectives
-          5. Identifies any gaps or limitations in the available information
 
-          Context:
+Search Results:
-          {context}
+{self.format_results(search_results)}
 
-          Please provide a well-structured analysis:
+Instructions:
+1. You MUST select exactly 2 result numbers from the search results.
+2. Choose the results that are most likely to contain comprehensive and relevant information to answer the user's question.
+3. Provide a brief reason for each selection.
+
+You MUST respond using EXACTLY this format and nothing else:
+
+Selected Results: [Two numbers corresponding to the selected results]
+Reasoning: [Your reasoning for the selections]
 """
 
-          summary = self.llm.generate(prompt, max_tokens=1500)
+        max_retries = 3
-          return self.format_summary(summary)
+        for retry in range(max_retries):
+            with OutputRedirector() as output:
+                response_text = self.llm.generate(prompt, max_tokens=200, stop=None)
+            llm_output = output.getvalue()
+            logger.info(f"LLM Output in select_relevant_pages:\n{llm_output}")
 
-      except Exception as e:
+            parsed_response = self.parse_page_selection_response(response_text)
-          logger.error(f"Summary generation error: {str(e)}")
+            if parsed_response and self.validate_page_selection_response(parsed_response, len(search_results)):
-          return f"Error generating summary: {str(e)}"
+                selected_urls = [result['href'] for result in search_results if result['number'] in parsed_response['selected_results']]
 
-  def prepare_summary_context(self, results: List[SearchResult], query: str) -> str:
+                allowed_urls = [url for url in selected_urls if can_fetch(url)]
-      """Prepare context for summary generation"""
+                if allowed_urls:
-      context = f"Query: {query}\n\n"
+                    return allowed_urls
-      
-      for i, result in enumerate(results, 1):
-          context += f"Source {i}:\n"
-          context += f"Title: {result.title}\n"
-          context += f"URL: {result.url}\n"
-          
-          if result.content:
-              # Include relevant excerpts from content
-              excerpts = self.extract_relevant_excerpts(result.content, query)
-              context += f"Key Excerpts:\n{excerpts}\n"
                 else:
-              context += f"Summary: {result.snippet}\n"
+                    print(f"{Fore.YELLOW}Warning: All selected URLs are disallowed by robots.txt. Retrying selection.{Style.RESET_ALL}")
+            else:
+                print(f"{Fore.YELLOW}Warning: Invalid page selection. Retrying.{Style.RESET_ALL}")
 
-          context += "\n"
+        print(f"{Fore.YELLOW}Warning: All attempts to select relevant pages failed. Falling back to top allowed results.{Style.RESET_ALL}")
+        allowed_urls = [result['href'] for result in search_results if can_fetch(result['href'])][:2]
+        return allowed_urls
 
-      return context
+    def parse_page_selection_response(self, response: str) -> Dict[str, Union[List[int], str]]:
+        lines = response.strip().split('\n')
+        parsed = {}
+        for line in lines:
+            if line.startswith('Selected Results:'):
+                parsed['selected_results'] = [int(num.strip()) for num in re.findall(r'\d+', line)]
+            elif line.startswith('Reasoning:'):
+                parsed['reasoning'] = line.split(':', 1)[1].strip()
+        return parsed if 'selected_results' in parsed and 'reasoning' in parsed else None
 
-  def extract_relevant_excerpts(self, content: str, query: str, max_excerpts: int = 3) -> str:
+    def validate_page_selection_response(self, parsed_response: Dict[str, Union[List[int], str]], num_results: int) -> bool:
-      """Extract relevant excerpts from content"""
+        if len(parsed_response['selected_results']) != 2:
-      sentences = re.split(r'[.!?]+', content)
+            return False
-      scored_sentences = []
+        if any(num < 1 or num > num_results for num in parsed_response['selected_results']):
+            return False
+        return True
 
-      query_terms = set(query.lower().split())
+    def format_results(self, results: List[Dict]) -> str:
+        formatted_results = []
+        for result in results:
+            formatted_result = f"{result['number']}. Title: {result.get('title', 'N/A')}\n"
+            formatted_result += f"   Snippet: {result.get('body', 'N/A')[:200]}...\n"
+            formatted_result += f"   URL: {result.get('href', 'N/A')}\n"
+            formatted_results.append(formatted_result)
+        return "\n".join(formatted_results)
 
-      for sentence in sentences:
+    def scrape_content(self, urls: List[str]) -> Dict[str, str]:
-          sentence = sentence.strip()
+        scraped_content = {}
-          if not sentence:
+        blocked_urls = []
-              continue
+        for url in urls:
+            robots_allowed = can_fetch(url)
+            if robots_allowed:
+                content = get_web_content([url])
+                if content:
+                    scraped_content.update(content)
+                    print(Fore.YELLOW + f"Successfully scraped: {url}" + Style.RESET_ALL)
+                    logger.info(f"Successfully scraped: {url}")
+                else:
+                    print(Fore.RED + f"Robots.txt disallows scraping of {url}" + Style.RESET_ALL)
+                    logger.warning(f"Robots.txt disallows scraping of {url}")
+            else:
+                blocked_urls.append(url)
+                print(Fore.RED + f"Warning: Robots.txt disallows scraping of {url}" + Style.RESET_ALL)
+                logger.warning(f"Robots.txt disallows scraping of {url}")
 
-          score = sum(1 for term in query_terms if term in sentence.lower())
+        print(Fore.CYAN + f"Scraped content received for {len(scraped_content)} URLs" + Style.RESET_ALL)
-          if score > 0:
+        logger.info(f"Scraped content received for {len(scraped_content)} URLs")
-              scored_sentences.append((sentence, score))
 
-      # Sort by relevance score and take top excerpts
+        if blocked_urls:
-      scored_sentences.sort(key=lambda x: x[1], reverse=True)
+            print(Fore.RED + f"Warning: {len(blocked_urls)} URL(s) were not scraped due to robots.txt restrictions." + Style.RESET_ALL)
-      excerpts = [sentence for sentence, _ in scored_sentences[:max_excerpts]]
+            logger.warning(f"{len(blocked_urls)} URL(s) were not scraped due to robots.txt restrictions: {', '.join(blocked_urls)}")
 
-      return "\n".join(f"- {excerpt}" for excerpt in excerpts)
+        return scraped_content
 
-  def format_summary(self, summary: str) -> str:
+    def display_scraped_content(self, scraped_content: Dict[str, str]):
-      """Format the final summary for better readability"""
+        print(f"\n{Fore.CYAN}Scraped Content:{Style.RESET_ALL}")
-      # Add section headers if not present
+        for url, content in scraped_content.items():
-      if not re.search(r'^Key Findings:', summary, re.MULTILINE):
+            print(f"{Fore.GREEN}URL: {url}{Style.RESET_ALL}")
-          summary = "Key Findings:\n" + summary
+            print(f"Content: {content[:4000]}...\n")
 
-      # Add source attribution if not present
+    def generate_final_answer(self, user_query: str, scraped_content: Dict[str, str]) -> str:
-      if not re.search(r'^Sources:', summary, re.MULTILINE):
+        user_query_short = user_query[:200]
-          summary += "\n\nSources: Based on analysis of search results"
+        prompt = f"""
+You are an AI assistant. Provide a comprehensive and detailed answer to the following question using ONLY the information provided in the scraped content. Do not include any references or mention any sources. Answer directly and thoroughly.
 
-      # Add formatting
+Question: "{user_query_short}"
-      summary = summary.replace('Key Findings:', f"{Fore.CYAN}Key Findings:{Style.RESET_ALL}")
-      summary = summary.replace('Sources:', f"\n{Fore.CYAN}Sources:{Style.RESET_ALL}")
 
-      return summary
+Scraped Content:
+{self.format_scraped_content(scraped_content)}
 
-  def cache_results(self, key: str, value: str) -> None:
+Important Instructions:
-      """Cache search results with size limit"""
+1. Do not use phrases like "Based on the absence of selected results" or similar.
-      if len(self.search_cache) >= self.max_cache_size:
+2. If the scraped content does not contain enough information to answer the question, say so explicitly and explain what information is missing.
-          # Remove oldest entry
+3. Provide as much relevant detail as possible from the scraped content.
-          oldest_key = next(iter(self.search_cache))
-          del self.search_cache[oldest_key]
 
-      self.search_cache[key] = value
+Answer:
+"""
+        max_retries = 3
+        for attempt in range(max_retries):
+            with OutputRedirector() as output:
+                response_text = self.llm.generate(prompt, max_tokens=1024, stop=None)
+            llm_output = output.getvalue()
+            logger.info(f"LLM Output in generate_final_answer:\n{llm_output}")
+            if response_text:
+                logger.info(f"LLM Response:\n{response_text}")
+                return response_text
 
-  def cache_content(self, url: str, content: str) -> None:
+        error_message = "I apologize, but I couldn't generate a satisfactory answer based on the available information."
-      """Cache web content with size limit"""
+        logger.warning(f"Failed to generate a response after {max_retries} attempts. Returning error message.")
-      if len(self.content_cache) >= self.max_cache_size:
+        return error_message
-          # Remove oldest entry
-          oldest_key = next(iter(self.content_cache))
-          del self.content_cache[oldest_key]
 
-      self.content_cache[url] = content
+    def format_scraped_content(self, scraped_content: Dict[str, str]) -> str:
+        formatted_content = []
+        for url, content in scraped_content.items():
+            content = re.sub(r'\s+', ' ', content)
+            formatted_content.append(f"Content from {url}:\n{content}\n")
+        return "\n".join(formatted_content)
 
-  def clear_cache(self) -> None:
+    def synthesize_final_answer(self, user_query: str) -> str:
-      """Clear all caches"""
+        prompt = f"""
-      self.search_cache.clear()
+After multiple search attempts, we couldn't find a fully satisfactory answer to the user's question: "{user_query}"
-      self.content_cache.clear()
 
-  def get_last_query(self) -> str:
+Please provide the best possible answer you can, acknowledging any limitations or uncertainties.
-      """Returns the last executed query"""
+If appropriate, suggest ways the user might refine their question or where they might find more information.
-      return self.last_query
 
-  def get_last_time_range(self) -> str:
+Respond in a clear, concise, and informative manner.
-      """Returns the last used time range"""
+"""
-      return self.last_time_range
+        try:
+            with OutputRedirector() as output:
+                response_text = self.llm.generate(prompt, max_tokens=self.llm_config.get('max_tokens', 1024), stop=self.llm_config.get('stop', None))
+            llm_output = output.getvalue()
+            logger.info(f"LLM Output in synthesize_final_answer:\n{llm_output}")
+            if response_text:
+                return response_text.strip()
+        except Exception as e:
+            logger.error(f"Error in synthesize_final_answer: {str(e)}", exc_info=True)
+        return "I apologize, but after multiple attempts, I wasn't able to find a satisfactory answer to your question. Please try rephrasing your question or breaking it down into smaller, more specific queries."
 
-if __name__ == "__main__":
+# End of EnhancedSelfImprovingSearch class
-  pass