""" Report generator module for the intelligent research system. This module provides functionality to generate reports from search results by scraping documents, storing them in a database, and synthesizing them into a comprehensive report. """ import os import asyncio import logging from typing import Dict, List, Any, Optional, Tuple, Union from report.database.db_manager import get_db_manager, initialize_database from report.document_scraper import get_document_scraper from report.document_processor import get_document_processor # Configure logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') logger = logging.getLogger(__name__) class ReportGenerator: """ Report generator for the intelligent research system. This class provides methods to generate reports from search results by scraping documents, storing them in a database, and synthesizing them into a comprehensive report. """ def __init__(self): """Initialize the report generator.""" self.db_manager = get_db_manager() self.document_scraper = get_document_scraper() self.document_processor = get_document_processor() async def initialize(self): """Initialize the report generator by setting up the database.""" await initialize_database() logger.info("Report generator initialized") async def process_search_results(self, search_results: List[Dict[str, Any]]) -> List[Dict[str, Any]]: """ Process search results by scraping the URLs and storing them in the database. Args: search_results: List of search results, each containing at least a 'url' field Returns: List of processed documents """ # Extract URLs from search results urls = [result.get('url') for result in search_results if result.get('url')] # Extract relevance scores if available relevance_scores = {} for result in search_results: if result.get('url') and result.get('score') is not None: relevance_scores[result.get('url')] = result.get('score') # Scrape URLs and store in database documents = await self.document_scraper.scrape_urls(urls) # Log results logger.info(f"Processed {len(documents)} documents out of {len(urls)} URLs") return documents, relevance_scores async def get_document_by_url(self, url: str) -> Optional[Dict[str, Any]]: """ Get a document by its URL. Args: url: URL of the document Returns: Document as a dictionary, or None if not found """ return await self.db_manager.get_document_by_url(url) async def search_documents(self, query: str, limit: int = 10) -> List[Dict[str, Any]]: """ Search for documents in the database. Args: query: Search query limit: Maximum number of results to return Returns: List of matching documents """ return await self.db_manager.search_documents(query, limit) async def prepare_documents_for_report(self, search_results: List[Dict[str, Any]], token_budget: Optional[int] = None, chunk_size: int = 1000, overlap_size: int = 100) -> List[Dict[str, Any]]: """ Prepare documents for report generation by processing search results, prioritizing documents, and chunking them to fit within token budget. Args: search_results: List of search results token_budget: Maximum number of tokens to use chunk_size: Maximum number of tokens per chunk overlap_size: Number of tokens to overlap between chunks Returns: List of selected document chunks """ # Process search results to get documents and relevance scores documents, relevance_scores = await self.process_search_results(search_results) # Prioritize and chunk documents selected_chunks = self.document_processor.process_documents_for_report( documents, relevance_scores, token_budget, chunk_size, overlap_size ) return selected_chunks async def generate_report(self, search_results: List[Dict[str, Any]], query: str, token_budget: Optional[int] = None, chunk_size: int = 1000, overlap_size: int = 100) -> str: """ Generate a report from search results. Args: search_results: List of search results query: Original search query token_budget: Maximum number of tokens to use chunk_size: Maximum number of tokens per chunk overlap_size: Number of tokens to overlap between chunks Returns: Generated report as a string """ # Prepare documents for report selected_chunks = await self.prepare_documents_for_report( search_results, token_budget, chunk_size, overlap_size ) # TODO: Implement report synthesis using LLM # For now, just return a placeholder report report = f"# Report for: {query}\n\n" report += f"Based on {len(selected_chunks)} document chunks\n\n" # Add document summaries for i, chunk in enumerate(selected_chunks[:5]): # Show first 5 chunks report += f"## Document {i+1}: {chunk.get('title', 'Untitled')}\n" report += f"Source: {chunk.get('url', 'Unknown')}\n" report += f"Chunk type: {chunk.get('chunk_type', 'Unknown')}\n" report += f"Priority score: {chunk.get('priority_score', 0.0):.2f}\n\n" # Add a snippet of the content content = chunk.get('content', '') snippet = content[:200] + "..." if len(content) > 200 else content report += f"{snippet}\n\n" return report # Create a singleton instance for global use report_generator = ReportGenerator() async def initialize_report_generator(): """Initialize the report generator.""" await report_generator.initialize() def get_report_generator() -> ReportGenerator: """ Get the global report generator instance. Returns: ReportGenerator instance """ return report_generator async def test_report_generator(): """Test the report generator with sample search results.""" # Initialize the report generator await initialize_report_generator() # Sample search results search_results = [ { 'title': 'Example Document 1', 'url': 'https://example.com/doc1', 'snippet': 'This is an example document.', 'score': 0.95 }, { 'title': 'Example Document 2', 'url': 'https://example.com/doc2', 'snippet': 'This is another example document.', 'score': 0.85 }, { 'title': 'Python Documentation', 'url': 'https://docs.python.org/3/', 'snippet': 'Official Python documentation.', 'score': 0.75 } ] # Process search results documents, relevance_scores = await report_generator.process_search_results(search_results) # Print documents print(f"Processed {len(documents)} documents") for doc in documents: print(f"Document: {doc.get('title')} ({doc.get('url')})") print(f"Token count: {doc.get('token_count')}") print(f"Content snippet: {doc.get('content')[:100]}...") print() # Generate report report = await report_generator.generate_report(search_results, "Python programming") # Print report print("Generated Report:") print(report) # Run test if this module is executed directly if __name__ == "__main__": asyncio.run(test_report_generator())