from __future__ import annotations import gc import logging import shutil from pathlib import Path from typing import Dict, List, Union import faiss import numpy as np import torch from datasets import Dataset from faiss.contrib.ondisk import merge_ondisk from huggingface_hub import snapshot_download from sentence_transformers import SentenceTransformer from tqdm import auto as tqdm logger = logging.getLogger(__name__) class OnDiskIVFHelper: def __init__(self, path: Union[str, Path]): self.path = path @property def path(self): return self._path @path.setter def path(self, value: Union[str, Path]): value = Path(value) value.mkdir(parents=True, exist_ok=True) self._path = value def train(self, xt: np.ndarray): index = faiss.index_factory(xt.shape[1], "IVF4096,Flat") logger.info("Training index...") index.train(xt) train_index_path = str(self.path / "faiss.index") logger.info(f"Write {train_index_path}") faiss.write_index(index, train_index_path) def add_with_ids(self, xb: np.ndarray, ix: np.ndarray | int): if isinstance(ix, int): ix = np.arange(ix, ix + xb.shape[0]) block_num = ix[0] train_index_path = str(self.path / "faiss.index") index = faiss.read_index(train_index_path) logger.info("Adding vectors to index...") index.add_with_ids(xb, ix) block_index_path = str(self.path / f"block_{block_num}.index") logger.info(f"Write {block_index_path}") faiss.write_index(index, block_index_path) def merge(self): logger.info("Loading trained index") train_index_path = str(self.path / "faiss.index") index = faiss.read_index(train_index_path) block_fnames = [str(p) for p in self.path.glob("block_*.index")] if len(block_fnames) == 0: return merged_index_path = str(self.path / "merged_index.ivfdata") merge_ondisk(index, block_fnames, merged_index_path) populated_index_path = str(self.path / "populated.index") logger.info(f"Write {populated_index_path}") faiss.write_index(index, populated_index_path) def search(self, xq: np.ndarray, top_k: int = 5, nprobe: int = 16): populated_index_path = str(self.path / "populated.index") logger.info("Read " + populated_index_path) index = faiss.read_index(populated_index_path) index.nprobe = nprobe D, I = index.search(xq, top_k) # noqa: E741 return D, I def delete(self): shutil.rmtree(self.path) @property def ntotal(self) -> int: populated_index_path = str(self.path / "populated.index") logger.info("Read " + populated_index_path) index = faiss.read_index(populated_index_path) return index.ntotal def __len__(self) -> int: return self.ntotal @property def is_trained(self) -> bool: index_file_path = self.path / "populated.index" if not index_file_path.exists(): return False index_file_path = str(index_file_path) index = faiss.read_index(index_file_path) return index.is_trained class DatasetIndex: def __init__(self, path: Union[str, Path]): self._context_embedding_model = None self._query_embedding_model = None self.context_model = "facebook-dpr-ctx_encoder-multiset-base" self.query_model = "facebook-dpr-question_encoder-multiset-base" # Ensure we're using CPU if ( hasattr(torch.backends, "mps") and torch.backends.mps.is_available() ): self.device = torch.device("mps") elif torch.cuda.is_available(): self.device = torch.device("cuda") else: self.device = torch.device("cpu") self.index = OnDiskIVFHelper(path) @property def context_embedding_model(self) -> SentenceTransformer: if self._context_embedding_model is None: self._query_embedding_model = None self._context_embedding_model = SentenceTransformer( self.context_model, device=self.device ) return self._context_embedding_model @property def query_embedding_model(self) -> SentenceTransformer: if self._query_embedding_model is None: self._context_embedding_model = None self._query_embedding_model = SentenceTransformer( self.query_model, device=self.device ) return self._query_embedding_model @property def ntotal(self) -> int: try: ntotal = len(self.index) except Exception: try: self.index.merge() ntotal = len(self.index) except Exception: ntotal = 0 return ntotal def create_index( self, dataset: Dataset | List[Dict] | List[str], /, *, column: str | None = None, batch_size: int = 32, block_size: int = 159744, force: bool = False, ): """Build FAISS index for the given documents""" n_documents = len(dataset) n_index = self.ntotal if not force and n_index == n_documents: logging.info("Found existing index. Skipping...") return self.index.delete() logging.info("Training index/Adding vectors to index...") # Add vectors to index for block_start in tqdm.trange( 0, n_documents, block_size, desc="Blocks" ): # logging.info(f"Starting encoding of {n_documents} documents...") # Train index embeddings = self.context_embedding_model.encode( ( dataset[block_start : block_start + block_size] if column is None else dataset[block_start : block_start + block_size][ column ] ), batch_size=batch_size, convert_to_numpy=True, show_progress_bar=True, device=self.device, ) batch_end = min(block_start + block_size, n_documents) assert len(embeddings) == batch_end - block_start if block_start == 0: self.index.train(embeddings) self.index.add_with_ids(embeddings, block_start) self.index.merge() print("Number of indexed documents:", len(self.index)) # Clear memory del embeddings gc.collect() if self.device.type == "cuda": torch.cuda.empty_cache() logging.info("Indexing complete!") def search(self, query: str, k: int = 5) -> List[Dict]: """Search the index for similar documents""" if self.index is None: raise ValueError("Index not built yet!") # Encode query if isinstance(query, str): query = [query] query_vector = self.query_embedding_model.encode( query, convert_to_numpy=True ) # Search D, I = self.index.search(query_vector, k) # noqa: E741 # Format results results = [] for i in range(len(query)): results.append([ (idx, score) for idx, score in zip(I[i], D[i]) if idx >= 0 ]) return results @classmethod def from_pretrained(cls, model_id: str) -> DatasetIndex: """Download and load a pre-trained index""" snapshot_path = snapshot_download( repo_id=model_id, repo_type="dataset", allow_patterns="index/*", ) snapshot_path = Path(snapshot_path) self = cls(snapshot_path / "index") # make sure the index is merged _ = self.ntotal return self