search_utils.py

import numpy as np
import pandas as pd
import faiss
import zipfile
import logging
from pathlib import Path
from sentence_transformers import SentenceTransformer, util
import streamlit as st
import time
import os
from urllib.parse import quote
import requests
import shutil
import concurrent.futures
from functools import lru_cache

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[logging.StreamHandler()]
)
logger = logging.getLogger("MetadataManager")

class MetadataManager:
    def __init__(self):
        self.metadata_path = Path("combined.parquet")
        self.df = None
        self.total_docs = 0
        
        logger.info("Initializing MetadataManager")
        self._load_metadata()
        logger.info(f"Total documents indexed: {self.total_docs}")

    def _load_metadata(self):
        """Load the combined parquet file directly"""
        logger.info("Loading metadata from combined.parquet")
        try:
            # Load the parquet file
            self.df = pd.read_parquet(self.metadata_path)
            
            # Clean and format the data
            self.df['source'] = self.df['source'].apply(
                lambda x: [
                    url.strip()
                    for url in str(x).split(';') 
                    if url.strip() and url.startswith('http')
                ]
            )
                      

            self.total_docs = len(self.df)
            
            
            logger.info(f"Successfully loaded {self.total_docs} documents")
        except Exception as e:
            logger.error(f"Failed to load metadata: {str(e)}")
            raise

    def get_metadata(self, global_indices):
        """Retrieve metadata for given indices with deduplication by title"""
        if isinstance(global_indices, np.ndarray) and global_indices.size == 0:
            return pd.DataFrame(columns=["title", "summary", 'authors', "similarity", "source"])
        
        try:
            # Directly index the DataFrame
            results = self.df.iloc[global_indices].copy()
            
            # Deduplicate by title to avoid near-duplicate results
            if len(results) > 1:
                results = results.drop_duplicates(subset=["title"])
            
            return results
        except Exception as e:
            logger.error(f"Metadata retrieval failed: {str(e)}")
            return pd.DataFrame(columns=["title", "summary", "similarity", "source", 'authors'])


class SemanticSearch:
    def __init__(self):
        self.shard_dir = Path("compressed_shards")
        self.model = None
        self.index_shards = []
        self.metadata_mgr = MetadataManager()
        self.shard_sizes = []
        
        # Configure search logger
        self.logger = logging.getLogger("SemanticSearch")
        self.logger.info("Initializing SemanticSearch")
        
    @st.cache_resource
    def load_model(_self):
        return SentenceTransformer('all-MiniLM-L6-v2')

    def initialize_system(self):
        self.logger.info("Loading sentence transformer model")
        start_time = time.time()
        self.model = self.load_model()
        self.logger.info(f"Model loaded in {time.time() - start_time:.2f} seconds")
        
        self.logger.info("Loading FAISS indices")
        self._load_faiss_shards()
        
    def _load_faiss_shards(self):
        """Load all FAISS index shards"""
        self.logger.info(f"Searching for index files in {self.shard_dir}")
        
        if not self.shard_dir.exists():
            self.logger.error(f"Shard directory not found: {self.shard_dir}")
            return
            
        index_files = list(self.shard_dir.glob("*.index"))
        self.logger.info(f"Found {len(index_files)} index files")
        
        self.shard_sizes = []
        self.index_shards = []
        
        for shard_path in sorted(index_files):
            try:
                self.logger.info(f"Loading index: {shard_path}")
                start_time = time.time()
                
                # Log file size
                file_size_mb = os.path.getsize(shard_path) / (1024 * 1024)
                self.logger.info(f"Index file size: {file_size_mb:.2f} MB")
                
                index = faiss.read_index(str(shard_path))
                self.index_shards.append(index)
                self.shard_sizes.append(index.ntotal)
                
                self.logger.info(f"Loaded index with {index.ntotal} vectors in {time.time() - start_time:.2f} seconds")
            except Exception as e:
                self.logger.error(f"Failed to load index {shard_path}: {str(e)}")
        
        self.total_vectors = sum(self.shard_sizes)
        self.logger.info(f"Total loaded vectors: {self.total_vectors} across {len(self.index_shards)} shards")
    
    def _global_index(self, shard_idx, local_idx):
        """Convert local index to global index"""
        return sum(self.shard_sizes[:shard_idx]) + local_idx
        

    def search(self, query, top_k=5):
        """Search with validation"""
        self.logger.info(f"Searching for query: '{query}' (top_k={top_k})")
        start_time = time.time()
        
        if not query:
            self.logger.warning("Empty query provided")
            return pd.DataFrame()
            
        if not self.index_shards:
            self.logger.error("No index shards loaded")
            return pd.DataFrame()
        
        try:
            self.logger.info("Encoding query")
            query_embedding = self.model.encode([query], convert_to_numpy=True)
            self.logger.debug(f"Query encoded to shape {query_embedding.shape}")
        except Exception as e:
            self.logger.error(f"Query encoding failed: {str(e)}")
            return pd.DataFrame()
    
        all_distances = []
        all_global_indices = []
    
        # Search with index validation
        self.logger.info(f"Searching across {len(self.index_shards)} shards")
        for shard_idx, index in enumerate(self.index_shards):
            if index.ntotal == 0:
                self.logger.warning(f"Skipping empty shard {shard_idx}")
                continue
                
            try:
                shard_start = time.time()
                distances, indices = index.search(query_embedding, top_k)
                
                valid_mask = (indices[0] >= 0) & (indices[0] < index.ntotal)
                valid_indices = indices[0][valid_mask].tolist()
                valid_distances = distances[0][valid_mask].tolist()
                
                if len(valid_indices) != top_k:
                    self.logger.debug(f"Shard {shard_idx}: Found {len(valid_indices)} valid results out of {top_k}")
                
                global_indices = [self._global_index(shard_idx, idx) for idx in valid_indices]
                
                all_distances.extend(valid_distances)
                all_global_indices.extend(global_indices)
                
                self.logger.debug(f"Shard {shard_idx} search completed in {time.time() - shard_start:.3f}s")
            except Exception as e:
                self.logger.error(f"Search failed in shard {shard_idx}: {str(e)}")
                continue
    
        self.logger.info(f"Search found {len(all_global_indices)} results across all shards")
        
        # Process results
        results = self._process_results(
            np.array(all_distances), 
            np.array(all_global_indices), 
            top_k
        )
        
        self.logger.info(f"Search completed in {time.time() - start_time:.2f} seconds with {len(results)} final results")
        return results
    
    def _search_shard(self, shard_idx, index, query_embedding, top_k):
        """Search a single FAISS shard for the query embedding with proper error handling."""
        if index.ntotal == 0:
            self.logger.warning(f"Skipping empty shard {shard_idx}")
            return None
            
        try:
            shard_start = time.time()
            distances, indices = index.search(query_embedding, top_k)
            
            # Filter out invalid indices (-1 is returned by FAISS for insufficient results)
            valid_mask = (indices[0] >= 0) & (indices[0] < index.ntotal)
            valid_indices = indices[0][valid_mask]
            valid_distances = distances[0][valid_mask]
            
            if len(valid_indices) == 0:
                self.logger.debug(f"Shard {shard_idx}: No valid results found")
                return None
                
            if len(valid_indices) != top_k:
                self.logger.debug(f"Shard {shard_idx}: Found {len(valid_indices)} valid results out of {top_k}")
                
            global_indices = [self._global_index(shard_idx, idx) for idx in valid_indices]
            
            # Filter out any invalid global indices (could happen if _global_index validation fails)
            valid_global = [(d, i) for d, i in zip(valid_distances, global_indices) if i >= 0]
            if not valid_global:
                return None
                
            final_distances, final_indices = zip(*valid_global)
            
            self.logger.debug(f"Shard {shard_idx} search completed in {time.time() - shard_start:.3f}s")
            return final_distances, final_indices
        except Exception as e:
            self.logger.error(f"Search failed in shard {shard_idx}: {str(e)}")
            return None

    def _process_results(self, distances, global_indices, top_k):
        """Process raw search results into formatted DataFrame"""
        process_start = time.time()
        
        # Proper numpy array emptiness checks
        if global_indices.size == 0 or distances.size == 0:
            self.logger.warning("No search results to process")
            return pd.DataFrame(columns=["title", "summary", "source", "authors", "similarity"])
        
        try:
            # Get metadata for matched indices
            self.logger.info(f"Retrieving metadata for {len(global_indices)} indices")
            metadata_start = time.time()
            results = self.metadata_mgr.get_metadata(global_indices)
            self.logger.info(f"Metadata retrieved in {time.time() - metadata_start:.2f}s, got {len(results)} records")
            
            # Empty results check
            if len(results) == 0:
                self.logger.warning("No metadata found for indices")
                return pd.DataFrame(columns=["title", "summary", "source", "authors", "similarity"])
                
            # Ensure distances match results length
            if len(results) != len(distances):
                self.logger.warning(f"Mismatch between distances ({len(distances)}) and results ({len(results)})")
                
                if len(results) < len(distances):
                    self.logger.info("Truncating distances array to match results length")
                    distances = distances[:len(results)]
                else:
                    # Should not happen but handle it anyway
                    self.logger.error("More results than distances - this shouldn't happen")
                    distances = np.pad(distances, (0, len(results) - len(distances)), 'constant', constant_values=1.0)
                
            # Calculate similarity scores
            self.logger.debug("Calculating similarity scores")
            results['similarity'] = 1 - (distances / 2)
            
            # Log similarity statistics
            if not results.empty:
                self.logger.debug(f"Similarity stats: min={results['similarity'].min():.3f}, " +
                                 f"max={results['similarity'].max():.3f}, " +
                                 f"mean={results['similarity'].mean():.3f}")

            
            # Deduplicate and sort results
            pre_dedup = len(results)
            results = results.drop_duplicates(subset=["title"]).sort_values("similarity", ascending=False).head(top_k)
            post_dedup = len(results)
            
            if pre_dedup > post_dedup:
                self.logger.info(f"Removed {pre_dedup - post_dedup} duplicate results")
            
            self.logger.info(f"Results processed in {time.time() - process_start:.2f}s, returning {len(results)} items")
            return results.reset_index(drop=True)

            # Add URL resolution for final results only
            final_results = results.sort_values("similarity", ascending=False).head(top_k)
            
            # Resolve URLs for top results only
            # final_results['source'] =
    
            # Deduplicate based on title only
            final_results = final_results.drop_duplicates(subset=["title"]).head(top_k)
            
            return final_results.reset_index(drop=True)
            
        except Exception as e:
            self.logger.error(f"Result processing failed: {str(e)}", exc_info=True)
            return pd.DataFrame(columns=["title", "summary", "similarity", 'authors'])