mirror of
https://github.com/TheBlewish/Automated-AI-Web-Researcher-Ollama.git
synced 2025-01-19 00:47:46 +00:00
375 lines
13 KiB
Python
375 lines
13 KiB
Python
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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import logging
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import sys
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from io import StringIO
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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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# 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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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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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.addHandler(file_handler)
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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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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 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 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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# 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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# 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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# 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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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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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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results = []
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retries = 3
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delay = 2
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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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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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return 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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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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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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# 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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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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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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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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Context:
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{context}
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Please provide a well-structured analysis:
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"""
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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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logger.error(f"Summary generation error: {str(e)}")
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return f"Error generating summary: {str(e)}"
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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 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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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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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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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
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oldest_key = next(iter(self.content_cache))
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del self.content_cache[oldest_key]
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self.content_cache[url] = content
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def clear_cache(self) -> None:
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"""Clear all caches"""
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self.search_cache.clear()
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self.content_cache.clear()
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def get_last_query(self) -> str:
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"""Returns the last executed query"""
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return self.last_query
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def get_last_time_range(self) -> str:
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"""Returns the last used time range"""
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return self.last_time_range
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if __name__ == "__main__":
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pass
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