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 # 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.cache_dir = Path("unzipped_cache") self.shard_dir = self.cache_dir / "metadata_shards" self.shard_map = {} self.loaded_shards = {} self.total_docs = 0 logger.info("Initializing MetadataManager") self._ensure_directories() self._unzip_if_needed() self._build_shard_map() logger.info(f"Total documents indexed: {self.total_docs}") logger.info(f"Total shards found: {len(self.shard_map)}") def _ensure_directories(self): """Create necessary directories if they don't exist""" self.cache_dir.mkdir(parents=True, exist_ok=True) self.shard_dir.mkdir(parents=True, exist_ok=True) def _unzip_if_needed(self): """Handle ZIP extraction with validation and retries""" zip_path = Path("metadata_shards.zip") # Check if we need to unzip if not any(self.shard_dir.glob("*.parquet")): logger.info("No parquet files found, checking for zip archive") if not zip_path.exists(): raise FileNotFoundError(f"Metadata ZIP file not found at {zip_path}") logger.info(f"Extracting {zip_path} to {self.shard_dir}") try: with zipfile.ZipFile(zip_path, 'r') as zip_ref: # Validate zip contents before extraction zip_files = zip_ref.namelist() if not any(fname.endswith('.parquet') for fname in zip_files): raise ValueError("ZIP file contains no parquet files") zip_ref.extractall(self.shard_dir) logger.info(f"Extracted {len(zip_files)} files") # Verify extraction succeeded if not any(self.shard_dir.glob("*.parquet")): raise RuntimeError("Extraction completed but no parquet files found") except Exception as e: logger.error(f"Failed to extract zip file: {str(e)}") # Clean up partial extraction if any(self.shard_dir.iterdir()): for f in self.shard_dir.glob("*"): f.unlink() raise else: logger.info("Parquet files already exist in cache directory") def _build_shard_map(self): """Create validated index range to shard mapping""" logger.info("Building shard map from parquet files") parquet_files = list(self.shard_dir.glob("*.parquet")) if not parquet_files: raise FileNotFoundError("No parquet files found after extraction") # Sort files by numerical order parquet_files = sorted(parquet_files, key=lambda x: int(x.stem.split("_")[1])) # Track expected next index expected_start = 0 for f in parquet_files: try: parts = f.stem.split("_") if len(parts) != 3: raise ValueError("Invalid filename format") start = int(parts[1]) end = int(parts[2]) # Validate continuity if start != expected_start: raise ValueError(f"Non-contiguous shard start: expected {expected_start}, got {start}") # Validate range if end <= start: raise ValueError(f"Invalid shard range: {start}-{end}") self.shard_map[(start, end)] = f.name self.total_docs = end + 1 expected_start = end + 1 logger.debug(f"Mapped shard {f.name}: indices {start}-{end}") except Exception as e: logger.error(f"Error processing shard {f.name}: {str(e)}") raise RuntimeError("Invalid shard structure") from e logger.info(f"Validated {len(self.shard_map)} continuous shards") logger.info(f"Total document count: {self.total_docs}") # Log shard statistics logger.info(f"Shard map built with {len(self.shard_map)} shards") logger.info(f"Total document count: {self.total_docs}") # Validate shard boundaries for gaps or overlaps sorted_ranges = sorted(self.shard_map.keys()) for i in range(1, len(sorted_ranges)): prev_end = sorted_ranges[i-1][1] curr_start = sorted_ranges[i][0] if curr_start != prev_end + 1: logger.warning(f"Gap or overlap detected between shards: {prev_end} to {curr_start}") def get_metadata(self, global_indices): """Retrieve metadata with validation""" # Check for empty numpy array properly if isinstance(global_indices, np.ndarray) and global_indices.size == 0: logger.warning("Empty indices array passed to get_metadata") return pd.DataFrame(columns=["title", "summary", "source", "similarity"]) # Convert numpy array to list for processing indices_list = global_indices.tolist() if isinstance(global_indices, np.ndarray) else global_indices logger.info(f"Retrieving metadata for {len(indices_list)} indices") # Filter valid indices valid_indices = [idx for idx in indices_list if 0 <= idx < self.total_docs] invalid_count = len(indices_list) - len(valid_indices) if invalid_count > 0: logger.warning(f"Filtered out {invalid_count} invalid indices") if not valid_indices: logger.warning("No valid indices remain after filtering") return pd.DataFrame(columns=["title", "summary", "source", "similarity"]) # Group indices by shard with boundary check shard_groups = {} unassigned_indices = [] for idx in valid_indices: found = False for (start, end), shard in self.shard_map.items(): if start <= idx <= end: if shard not in shard_groups: shard_groups[shard] = [] shard_groups[shard].append(idx - start) found = True break if not found: unassigned_indices.append(idx) logger.warning(f"Index {idx} not found in any shard range") if unassigned_indices: logger.warning(f"Could not assign {len(unassigned_indices)} indices to any shard") # Load and process shards results = [] for shard, local_indices in shard_groups.items(): try: logger.info(f"Processing shard {shard} with {len(local_indices)} indices") start_time = time.time() if shard not in self.loaded_shards: logger.info(f"Loading shard file: {shard}") shard_path = self.shard_dir / shard # Verify file exists if not shard_path.exists(): logger.error(f"Shard file not found: {shard_path}") continue # Log file size file_size_mb = os.path.getsize(shard_path) / (1024 * 1024) logger.info(f"Shard file size: {file_size_mb:.2f} MB") # Attempt to read the parquet file try: self.loaded_shards[shard] = pd.read_parquet( shard_path, columns=["title", "summary", "source"] ) logger.info(f"Successfully loaded shard {shard} with {len(self.loaded_shards[shard])} rows") except Exception as e: logger.error(f"Failed to read parquet file {shard}: {str(e)}") # Try to read file schema for debugging try: schema = pd.read_parquet(shard_path, engine='pyarrow').dtypes logger.info(f"Parquet schema: {schema}") except: pass continue if local_indices: # Validate indices are within dataframe bounds df_len = len(self.loaded_shards[shard]) valid_local_indices = [idx for idx in local_indices if 0 <= idx < df_len] if len(valid_local_indices) != len(local_indices): logger.warning(f"Filtered {len(local_indices) - len(valid_local_indices)} out-of-bounds indices") if valid_local_indices: logger.debug(f"Retrieving rows at indices: {valid_local_indices}") chunk = self.loaded_shards[shard].iloc[valid_local_indices] results.append(chunk) logger.info(f"Retrieved {len(chunk)} records from shard {shard}") logger.info(f"Shard processing completed in {time.time() - start_time:.2f} seconds") except Exception as e: logger.error(f"Error processing shard {shard}: {str(e)}", exc_info=True) continue # Combine results if results: combined = pd.concat(results).reset_index(drop=True) logger.info(f"Combined metadata: {len(combined)} records from {len(results)} shards") return combined else: logger.warning("No metadata records retrieved") return pd.DataFrame(columns=["title", "summary", "source", "similarity"]) 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)}") total_vectors = sum(self.shard_sizes) self.logger.info(f"Total loaded vectors: {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 _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", "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", "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", "source"]).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) except Exception as e: self.logger.error(f"Result processing failed: {str(e)}", exc_info=True) return pd.DataFrame(columns=["title", "summary", "source", "similarity"])