mirror of
https://github.com/TheBlewish/Automated-AI-Web-Researcher-Ollama.git
synced 2025-01-19 00:47:46 +00:00
Update Self_Improving_Search.py
This commit is contained in:
Hafeez
GitHub
parent
161698a228
commit
816ea37293
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@ -1,8 +1,8 @@
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import time
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import re
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import os
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from typing import List, Dict, Tuple, Union, Optional
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from colorama import Fore, Style, init
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from typing import List, Dict, Tuple, Union
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from colorama import Fore, Style
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import logging
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import sys
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from io import StringIO
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@ -10,365 +10,425 @@ from web_scraper import get_web_content, can_fetch
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from llm_config import get_llm_config
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from llm_response_parser import UltimateLLMResponseParser
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from llm_wrapper import LLMWrapper
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from urllib.parse import urlparse, quote_plus
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import requests
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from bs4 import BeautifulSoup
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import json
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from datetime import datetime, timedelta
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import threading
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from queue import Queue
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import concurrent.futures
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# Initialize colorama
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init()
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from urllib.parse import urlparse
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# Set up logging
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log_directory = 'logs'
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if not os.path.exists(log_directory):
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os.makedirs(log_directory)
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os.makedirs(log_directory)
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# Configure logger
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.INFO)
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log_file = os.path.join(log_directory, 'search.log')
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log_file = os.path.join(log_directory, 'llama_output.log')
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file_handler = logging.FileHandler(log_file)
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formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
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file_handler.setFormatter(formatter)
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logger.handlers = []
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logger.addHandler(file_handler)
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logger.propagate = False
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class SearchResult:
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def __init__(self, title: str, url: str, snippet: str, score: float = 0.0):
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self.title = title
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self.url = url
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self.snippet = snippet
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self.score = score
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self.content: Optional[str] = None
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self.processed = False
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self.error = None
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# Suppress other loggers
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for name in ['root', 'duckduckgo_search', 'requests', 'urllib3']:
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logging.getLogger(name).setLevel(logging.WARNING)
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logging.getLogger(name).handlers = []
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logging.getLogger(name).propagate = False
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def to_dict(self) -> Dict:
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return {
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'title': self.title,
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'url': self.url,
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'snippet': self.snippet,
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'score': self.score,
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'has_content': bool(self.content),
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'processed': self.processed,
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'error': str(self.error) if self.error else None
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}
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class OutputRedirector:
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def __init__(self, stream=None):
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self.stream = stream or StringIO()
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self.original_stdout = sys.stdout
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self.original_stderr = sys.stderr
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def __enter__(self):
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sys.stdout = self.stream
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sys.stderr = self.stream
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return self.stream
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def __exit__(self, exc_type, exc_val, exc_tb):
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sys.stdout = self.original_stdout
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sys.stderr = self.original_stderr
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class EnhancedSelfImprovingSearch:
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def __init__(self, llm: LLMWrapper, parser: UltimateLLMResponseParser, max_attempts: int = 5):
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self.llm = llm
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self.parser = parser
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self.max_attempts = max_attempts
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self.llm_config = get_llm_config()
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self.last_query = ""
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self.last_time_range = ""
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self.search_cache = {}
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self.content_cache = {}
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self.max_cache_size = 100
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self.max_concurrent_requests = 5
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self.request_timeout = 15
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self.headers = {
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'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'
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}
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def __init__(self, llm: LLMWrapper, parser: UltimateLLMResponseParser, max_attempts: int = 5):
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self.llm = llm
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self.parser = parser
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self.max_attempts = max_attempts
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self.llm_config = get_llm_config()
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def search_and_improve(self, query: str, time_range: str = "auto") -> str:
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"""Main search method that includes self-improvement"""
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try:
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logger.info(f"Starting search for query: {query}")
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self.last_query = query
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self.last_time_range = time_range
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@staticmethod
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def initialize_llm():
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llm_wrapper = LLMWrapper()
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return llm_wrapper
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# Check cache first
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cache_key = f"{query}_{time_range}"
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if cache_key in self.search_cache:
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logger.info("Returning cached results")
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return self.search_cache[cache_key]
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def print_thinking(self):
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print(Fore.MAGENTA + "🧠 Thinking..." + Style.RESET_ALL)
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# Perform initial search
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results = self.perform_search(query, time_range)
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if not results:
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return "No results found."
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def print_searching(self):
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print(Fore.MAGENTA + "📝 Searching..." + Style.RESET_ALL)
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# Enhance results with content fetching
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enhanced_results = self.enhance_search_results(results)
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# Generate improved summary
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summary = self.generate_enhanced_summary(enhanced_results, query)
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# Cache the results
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self.cache_results(cache_key, summary)
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return summary
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def search_and_improve(self, user_query: str) -> str:
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attempt = 0
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while attempt < self.max_attempts:
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print(f"\n{Fore.CYAN}Search attempt {attempt + 1}:{Style.RESET_ALL}")
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self.print_searching()
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except Exception as e:
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logger.error(f"Search and improve error: {str(e)}", exc_info=True)
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return f"Error during search: {str(e)}"
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try:
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formulated_query, time_range = self.formulate_query(user_query, attempt)
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def perform_search(self, query: str, time_range: str) -> List[SearchResult]:
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"""Performs web search with improved error handling and retry logic"""
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if not query:
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return []
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print(f"{Fore.YELLOW}Original query: {user_query}{Style.RESET_ALL}")
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print(f"{Fore.YELLOW}Formulated query: {formulated_query}{Style.RESET_ALL}")
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print(f"{Fore.YELLOW}Time range: {time_range}{Style.RESET_ALL}")
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results = []
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retries = 3
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delay = 2
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if not formulated_query:
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print(f"{Fore.RED}Error: Empty search query. Retrying...{Style.RESET_ALL}")
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attempt += 1
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continue
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for attempt in range(retries):
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try:
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encoded_query = quote_plus(query)
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search_url = f"https://html.duckduckgo.com/html/?q={encoded_query}"
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response = requests.get(search_url, headers=self.headers, timeout=self.request_timeout)
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response.raise_for_status()
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soup = BeautifulSoup(response.text, 'html.parser')
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for i, result in enumerate(soup.select('.result'), 1):
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if i > 15: # Increased limit for better coverage
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break
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title_elem = result.select_one('.result__title')
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snippet_elem = result.select_one('.result__snippet')
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link_elem = result.select_one('.result__url')
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if title_elem and link_elem:
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title = title_elem.get_text(strip=True)
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snippet = snippet_elem.get_text(strip=True) if snippet_elem else ""
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url = link_elem.get('href', '')
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# Basic result scoring
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score = self.calculate_result_score(title, snippet, query)
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results.append(SearchResult(title, url, snippet, score))
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search_results = self.perform_search(formulated_query, time_range)
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if results:
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# Sort results by score
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results.sort(key=lambda x: x.score, reverse=True)
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return results
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if attempt < retries - 1:
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logger.warning(f"No results found, retrying ({attempt + 1}/{retries})...")
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time.sleep(delay)
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except Exception as e:
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logger.error(f"Search attempt {attempt + 1} failed: {str(e)}")
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if attempt < retries - 1:
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time.sleep(delay)
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else:
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raise
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if not search_results:
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print(f"{Fore.RED}No results found. Retrying with a different query...{Style.RESET_ALL}")
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attempt += 1
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continue
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return results
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self.display_search_results(search_results)
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def calculate_result_score(self, title: str, snippet: str, query: str) -> float:
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"""Calculate relevance score for search result"""
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score = 0.0
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query_terms = query.lower().split()
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# Title matching
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title_lower = title.lower()
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for term in query_terms:
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if term in title_lower:
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score += 2.0
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# Snippet matching
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snippet_lower = snippet.lower()
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for term in query_terms:
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if term in snippet_lower:
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score += 1.0
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# Exact phrase matching
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if query.lower() in title_lower:
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score += 3.0
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if query.lower() in snippet_lower:
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score += 1.5
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return score
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selected_urls = self.select_relevant_pages(search_results, user_query)
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def enhance_search_results(self, results: List[SearchResult]) -> List[SearchResult]:
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"""Enhance search results with parallel content fetching"""
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enhanced_results = []
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with concurrent.futures.ThreadPoolExecutor(max_workers=self.max_concurrent_requests) as executor:
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future_to_result = {
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executor.submit(self.fetch_and_process_content, result): result
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for result in results[:10] # Limit to top 10 results
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}
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for future in concurrent.futures.as_completed(future_to_result):
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result = future_to_result[future]
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try:
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content = future.result()
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if content:
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result.content = content
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result.processed = True
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enhanced_results.append(result)
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except Exception as e:
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logger.error(f"Error processing {result.url}: {str(e)}")
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result.error = e
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return enhanced_results
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if not selected_urls:
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print(f"{Fore.RED}No relevant URLs found. Retrying...{Style.RESET_ALL}")
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attempt += 1
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continue
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def fetch_and_process_content(self, result: SearchResult) -> Optional[str]:
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"""Fetch and process content for a search result"""
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try:
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# Check cache first
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if result.url in self.content_cache:
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return self.content_cache[result.url]
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print(Fore.MAGENTA + "⚙️ Scraping selected pages..." + Style.RESET_ALL)
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# Scraping is done without OutputRedirector to ensure messages are visible
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scraped_content = self.scrape_content(selected_urls)
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# Check if we can fetch the content
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if not can_fetch(result.url):
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logger.warning(f"Cannot fetch content from {result.url}")
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return None
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if not scraped_content:
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print(f"{Fore.RED}Failed to scrape content. Retrying...{Style.RESET_ALL}")
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attempt += 1
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continue
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content = get_web_content(result.url)
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if content:
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# Process and clean content
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cleaned_content = self.clean_content(content)
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# Cache the content
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self.cache_content(result.url, cleaned_content)
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return cleaned_content
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except Exception as e:
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logger.error(f"Error fetching content from {result.url}: {str(e)}")
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return None
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self.display_scraped_content(scraped_content)
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def clean_content(self, content: str) -> str:
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"""Clean and normalize web content"""
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# Remove HTML tags if any remained
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content = re.sub(r'<[^>]+>', '', content)
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# Remove extra whitespace
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content = re.sub(r'\s+', ' ', content)
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# Remove special characters
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content = re.sub(r'[^\w\s.,!?-]', '', content)
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# Truncate if too long
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max_length = 5000
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if len(content) > max_length:
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content = content[:max_length] + "..."
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return content.strip()
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self.print_thinking()
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def generate_enhanced_summary(self, results: List[SearchResult], query: str) -> str:
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"""Generate an enhanced summary using LLM with improved context"""
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try:
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# Prepare context from enhanced results
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context = self.prepare_summary_context(results, query)
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prompt = f"""
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Based on the following comprehensive search results for "{query}",
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provide a detailed analysis that:
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1. Synthesizes key information from multiple sources
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2. Highlights important findings and patterns
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3. Maintains factual accuracy and cites sources
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4. Presents a balanced view of different perspectives
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5. Identifies any gaps or limitations in the available information
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with OutputRedirector() as output:
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evaluation, decision = self.evaluate_scraped_content(user_query, scraped_content)
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llm_output = output.getvalue()
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logger.info(f"LLM Output in evaluate_scraped_content:\n{llm_output}")
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Context:
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{context}
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print(f"{Fore.MAGENTA}Evaluation: {evaluation}{Style.RESET_ALL}")
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print(f"{Fore.MAGENTA}Decision: {decision}{Style.RESET_ALL}")
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Please provide a well-structured analysis:
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"""
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if decision == "answer":
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return self.generate_final_answer(user_query, scraped_content)
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elif decision == "refine":
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print(f"{Fore.YELLOW}Refining search...{Style.RESET_ALL}")
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attempt += 1
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else:
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print(f"{Fore.RED}Unexpected decision. Proceeding to answer.{Style.RESET_ALL}")
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return self.generate_final_answer(user_query, scraped_content)
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summary = self.llm.generate(prompt, max_tokens=1500)
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return self.format_summary(summary)
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except Exception as e:
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print(f"{Fore.RED}An error occurred during search attempt. Check the log file for details.{Style.RESET_ALL}")
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logger.error(f"An error occurred during search: {str(e)}", exc_info=True)
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attempt += 1
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except Exception as e:
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logger.error(f"Summary generation error: {str(e)}")
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return f"Error generating summary: {str(e)}"
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return self.synthesize_final_answer(user_query)
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def prepare_summary_context(self, results: List[SearchResult], query: str) -> str:
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"""Prepare context for summary generation"""
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context = f"Query: {query}\n\n"
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for i, result in enumerate(results, 1):
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context += f"Source {i}:\n"
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context += f"Title: {result.title}\n"
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context += f"URL: {result.url}\n"
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if result.content:
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# Include relevant excerpts from content
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excerpts = self.extract_relevant_excerpts(result.content, query)
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context += f"Key Excerpts:\n{excerpts}\n"
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else:
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context += f"Summary: {result.snippet}\n"
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context += "\n"
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return context
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def evaluate_scraped_content(self, user_query: str, scraped_content: Dict[str, str]) -> Tuple[str, str]:
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user_query_short = user_query[:200]
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prompt = f"""
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Evaluate if the following scraped content contains sufficient information to answer the user's question comprehensively:
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def extract_relevant_excerpts(self, content: str, query: str, max_excerpts: int = 3) -> str:
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"""Extract relevant excerpts from content"""
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sentences = re.split(r'[.!?]+', content)
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scored_sentences = []
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query_terms = set(query.lower().split())
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for sentence in sentences:
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sentence = sentence.strip()
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if not sentence:
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continue
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score = sum(1 for term in query_terms if term in sentence.lower())
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if score > 0:
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scored_sentences.append((sentence, score))
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# Sort by relevance score and take top excerpts
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scored_sentences.sort(key=lambda x: x[1], reverse=True)
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excerpts = [sentence for sentence, _ in scored_sentences[:max_excerpts]]
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return "\n".join(f"- {excerpt}" for excerpt in excerpts)
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User's question: "{user_query_short}"
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def format_summary(self, summary: str) -> str:
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"""Format the final summary for better readability"""
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# Add section headers if not present
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if not re.search(r'^Key Findings:', summary, re.MULTILINE):
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summary = "Key Findings:\n" + summary
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# Add source attribution if not present
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if not re.search(r'^Sources:', summary, re.MULTILINE):
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summary += "\n\nSources: Based on analysis of search results"
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# Add formatting
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summary = summary.replace('Key Findings:', f"{Fore.CYAN}Key Findings:{Style.RESET_ALL}")
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summary = summary.replace('Sources:', f"\n{Fore.CYAN}Sources:{Style.RESET_ALL}")
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return summary
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Scraped Content:
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{self.format_scraped_content(scraped_content)}
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def cache_results(self, key: str, value: str) -> None:
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"""Cache search results with size limit"""
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if len(self.search_cache) >= self.max_cache_size:
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# Remove oldest entry
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oldest_key = next(iter(self.search_cache))
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del self.search_cache[oldest_key]
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self.search_cache[key] = value
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Your task:
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1. Determine if the scraped content provides enough relevant and detailed information to answer the user's question thoroughly.
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2. If the information is sufficient, decide to 'answer'. If more information or clarification is needed, decide to 'refine' the search.
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def cache_content(self, url: str, content: str) -> None:
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"""Cache web content with size limit"""
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if len(self.content_cache) >= self.max_cache_size:
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# Remove oldest entry
|
||||
oldest_key = next(iter(self.content_cache))
|
||||
del self.content_cache[oldest_key]
|
||||
|
||||
self.content_cache[url] = content
|
||||
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)}")
|
||||
|
||||
def clear_cache(self) -> None:
|
||||
"""Clear all caches"""
|
||||
self.search_cache.clear()
|
||||
self.content_cache.clear()
|
||||
logger.warning("Failed to get a valid decision in evaluate_scraped_content. Defaulting to 'refine'.")
|
||||
return "Failed to evaluate content.", "refine"
|
||||
|
||||
def get_last_query(self) -> str:
|
||||
"""Returns the last executed query"""
|
||||
return self.last_query
|
||||
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 get_last_time_range(self) -> str:
|
||||
"""Returns the last used time range"""
|
||||
return self.last_time_range
|
||||
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"
|
||||
|
||||
if __name__ == "__main__":
|
||||
pass
|
||||
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 []
|
||||
|
||||
from duckduckgo_search import DDGS
|
||||
|
||||
with DDGS() as ddgs:
|
||||
try:
|
||||
with OutputRedirector() as output:
|
||||
if time_range and time_range != 'none':
|
||||
results = list(ddgs.text(query, timelimit=time_range, max_results=10))
|
||||
else:
|
||||
results = list(ddgs.text(query, max_results=10))
|
||||
ddg_output = output.getvalue()
|
||||
logger.info(f"DDG Output in perform_search:\n{ddg_output}")
|
||||
return [{'number': i+1, **result} for i, result in enumerate(results)]
|
||||
except Exception as e:
|
||||
print(f"{Fore.RED}Search error: {str(e)}{Style.RESET_ALL}")
|
||||
return []
|
||||
|
||||
def display_search_results(self, results: List[Dict]) -> None:
|
||||
"""Display search results with minimal output"""
|
||||
try:
|
||||
if not results:
|
||||
return
|
||||
|
||||
# Only show search success status
|
||||
print(f"\nSearch query sent to DuckDuckGo: {self.last_query}")
|
||||
print(f"Time range sent to DuckDuckGo: {self.last_time_range}")
|
||||
print(f"Number of results: {len(results)}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error displaying search results: {str(e)}")
|
||||
|
||||
def select_relevant_pages(self, search_results: List[Dict], user_query: str) -> List[str]:
|
||||
prompt = f"""
|
||||
Given the following search results for the user's question: "{user_query}"
|
||||
Select the 2 most relevant results to scrape and analyze. Explain your reasoning for each selection.
|
||||
|
||||
Search Results:
|
||||
{self.format_results(search_results)}
|
||||
|
||||
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]
|
||||
"""
|
||||
|
||||
max_retries = 3
|
||||
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}")
|
||||
|
||||
parsed_response = self.parse_page_selection_response(response_text)
|
||||
if parsed_response and self.validate_page_selection_response(parsed_response, len(search_results)):
|
||||
selected_urls = [result['href'] for result in search_results if result['number'] in parsed_response['selected_results']]
|
||||
|
||||
allowed_urls = [url for url in selected_urls if can_fetch(url)]
|
||||
if allowed_urls:
|
||||
return allowed_urls
|
||||
else:
|
||||
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}")
|
||||
|
||||
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
|
||||
|
||||
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 validate_page_selection_response(self, parsed_response: Dict[str, Union[List[int], str]], num_results: int) -> bool:
|
||||
if len(parsed_response['selected_results']) != 2:
|
||||
return False
|
||||
if any(num < 1 or num > num_results for num in parsed_response['selected_results']):
|
||||
return False
|
||||
return True
|
||||
|
||||
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)
|
||||
|
||||
def scrape_content(self, urls: List[str]) -> Dict[str, str]:
|
||||
scraped_content = {}
|
||||
blocked_urls = []
|
||||
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}")
|
||||
|
||||
print(Fore.CYAN + f"Scraped content received for {len(scraped_content)} URLs" + Style.RESET_ALL)
|
||||
logger.info(f"Scraped content received for {len(scraped_content)} URLs")
|
||||
|
||||
if blocked_urls:
|
||||
print(Fore.RED + f"Warning: {len(blocked_urls)} URL(s) were not scraped due to robots.txt restrictions." + Style.RESET_ALL)
|
||||
logger.warning(f"{len(blocked_urls)} URL(s) were not scraped due to robots.txt restrictions: {', '.join(blocked_urls)}")
|
||||
|
||||
return scraped_content
|
||||
|
||||
def display_scraped_content(self, scraped_content: Dict[str, str]):
|
||||
print(f"\n{Fore.CYAN}Scraped Content:{Style.RESET_ALL}")
|
||||
for url, content in scraped_content.items():
|
||||
print(f"{Fore.GREEN}URL: {url}{Style.RESET_ALL}")
|
||||
print(f"Content: {content[:4000]}...\n")
|
||||
|
||||
def generate_final_answer(self, user_query: str, scraped_content: Dict[str, str]) -> str:
|
||||
user_query_short = user_query[:200]
|
||||
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.
|
||||
|
||||
Question: "{user_query_short}"
|
||||
|
||||
Scraped Content:
|
||||
{self.format_scraped_content(scraped_content)}
|
||||
|
||||
Important Instructions:
|
||||
1. Do not use phrases like "Based on the absence of selected results" or similar.
|
||||
2. If the scraped content does not contain enough information to answer the question, say so explicitly and explain what information is missing.
|
||||
3. Provide as much relevant detail as possible from the scraped content.
|
||||
|
||||
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
|
||||
|
||||
error_message = "I apologize, but I couldn't generate a satisfactory answer based on the available information."
|
||||
logger.warning(f"Failed to generate a response after {max_retries} attempts. Returning error message.")
|
||||
return error_message
|
||||
|
||||
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 synthesize_final_answer(self, user_query: str) -> str:
|
||||
prompt = f"""
|
||||
After multiple search attempts, we couldn't find a fully satisfactory answer to the user's question: "{user_query}"
|
||||
|
||||
Please provide the best possible answer you can, acknowledging any limitations or uncertainties.
|
||||
If appropriate, suggest ways the user might refine their question or where they might find more information.
|
||||
|
||||
Respond in a clear, concise, and informative manner.
|
||||
"""
|
||||
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."
|
||||
|
||||
# End of EnhancedSelfImprovingSearch class
|
||||
|
|
|
|||
Loading…
Reference in a new issue