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import json
import os
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import hydra
import numpy
import torch
from omegaconf import OmegaConf
from pprintpp import pformat
from relik.common.log import get_logger
from relik.common.upload import upload
from relik.common.utils import (
from_cache,
is_str_a_path,
relative_to_absolute_path,
to_config,
)
from relik.retriever.indexers.document import Document, DocumentStore
logger = get_logger(__name__)
@dataclass
class IndexerOutput:
indices: Union[torch.Tensor, numpy.ndarray]
distances: Union[torch.Tensor, numpy.ndarray]
class BaseDocumentIndex:
"""
Base class for document indexes.
Args:
documents (:obj:`str`, :obj:`List[str]`, :obj:`os.PathLike`, :obj:`List[os.PathLike]`, :obj:`DocumentStore`, `optional`):
The documents to index. If `None`, an empty document store will be created. Defaults to `None`.
embeddings (:obj:`torch.Tensor`, `optional`):
The embeddings of the documents. If `None`, the documents will not be indexed. Defaults to `None`.
name_or_path (:obj:`str`, :obj:`os.PathLike`, `optional`):
The name or directory of the retriever.
"""
CONFIG_NAME = "config.yaml"
DOCUMENTS_FILE_NAME = "documents.jsonl"
EMBEDDINGS_FILE_NAME = "embeddings.pt"
def __init__(
self,
documents: str
| List[str]
| os.PathLike
| List[os.PathLike]
| DocumentStore
| None = None,
embeddings: torch.Tensor | None = None,
metadata_fields: List[str] | None = None,
separator: str | None = None,
name_or_path: str | os.PathLike | None = None,
device: str = "cpu",
) -> None:
if metadata_fields is None:
metadata_fields = []
self.metadata_fields = metadata_fields
self.separator = separator
self.document_path: List[str | os.PathLike] = []
if documents is not None:
if isinstance(documents, DocumentStore):
self.documents = documents
else:
documents_are_paths = False
# normalize the documents to list if not already
if not isinstance(documents, list):
documents = [documents]
# now check if the documents are a list of paths (either str or os.PathLike)
if isinstance(documents[0], str) or isinstance(
documents[0], os.PathLike
):
# check if the str is a path
documents_are_paths = is_str_a_path(documents[0])
# if the documents are a list of paths, then we load them
if documents_are_paths:
logger.info("Loading documents from paths")
_documents = []
for doc in documents:
with open(relative_to_absolute_path(doc)) as f:
self.document_path.append(doc)
_documents += [
Document.from_dict(json.loads(line))
for line in f.readlines()
]
# remove duplicates
documents = _documents
self.documents = DocumentStore(documents)
else:
self.documents = DocumentStore()
self.embeddings = embeddings
self.name_or_path = name_or_path
# store the device in case embeddings are not provided
self.device_in_init = device
def __iter__(self):
# make this class iterable
for i in range(len(self)):
yield self[i]
def __len__(self):
return len(self.documents)
def __getitem__(self, index):
return self.get_passage_from_index(index)
def to(
self, device_or_precision: str | torch.device | torch.dtype
) -> "BaseDocumentIndex":
"""
Move the retriever to the specified device or precision.
Args:
device_or_precision (`str` | `torch.device` | `torch.dtype`):
The device or precision to move the retriever to.
Returns:
`BaseDocumentIndex`: The retriever.
"""
if self.embeddings is not None:
if isinstance(device_or_precision, torch.dtype) and self.device != "cpu":
# if the device is a dtype, then we need to move the embeddings to cpu
# first before converting to the dtype to avoid OOM
previous_device = self.embeddings.device
self.embeddings = self.embeddings.cpu()
self.embeddings = self.embeddings.to(device_or_precision)
self.embeddings = self.embeddings.to(previous_device)
else:
if isinstance(device_or_precision, torch.device):
self.embeddings = self.embeddings.to(device_or_precision)
else:
if device_or_precision != self.embeddings.dtype and self.device != "cpu":
self.embeddings = self.embeddings.to(device_or_precision)
# self.embeddings = self.embeddings.to(device_or_precision)
return self
@property
def device(self):
return (
self.embeddings.device
if self.embeddings is not None
else self.device_in_init
)
@property
def config(self) -> Dict[str, Any]:
"""
The configuration of the document index.
Returns:
`Dict[str, Any]`: The configuration of the retriever.
"""
config = {
"_target_": f"{self.__class__.__module__}.{self.__class__.__name__}",
"metadata_fields": self.metadata_fields,
"separator": self.separator,
"name_or_path": self.name_or_path,
}
if len(self.document_path) > 0:
config["documents"] = self.document_path
return config
def index(
self,
retriever,
*args,
**kwargs,
) -> "BaseDocumentIndex":
raise NotImplementedError
def search(self, query: Any, k: int = 1, *args, **kwargs) -> List:
raise NotImplementedError
def get_document_from_passage(self, passage: str) -> Document | None:
"""
Get the document label from the passage.
Args:
passage (`str`):
The document to get the label for.
Returns:
`str`: The document label.
"""
# get the text from the document
if self.separator:
text = passage.split(self.separator)[0]
else:
text = passage
return self.documents.get_document_from_text(text)
def get_index_from_passage(self, passage: str) -> int:
"""
Get the index of the passage.
Args:
passage (`str`):
The document to get the index for.
Returns:
`int`: The index of the document.
"""
# get the text from the document
doc = self.get_document_from_passage(passage)
if doc is None:
raise ValueError(f"Document `{passage}` not found.")
return doc.id
def get_document_from_index(self, index: int) -> Document | None:
"""
Get the document from the index.
Args:
index (`int`):
The index of the document.
Returns:
`str`: The document.
"""
return self.documents.get_document_from_id(index)
def get_passage_from_index(self, index: int) -> str:
"""
Get the document from the index.
Args:
index (`int`):
The index of the document.
Returns:
`str`: The document.
"""
document = self.get_document_from_index(index)
# build the passage using the metadata fields
passage = document.text
for field in self.metadata_fields:
passage += f"{self.separator}{document.metadata[field]}"
return passage
def get_passage_from_document(self, document: Document) -> str:
passage = document.text
for field in self.metadata_fields:
passage += f"{self.separator}{document.metadata[field]}"
return passage
def get_embeddings_from_index(self, index: int) -> torch.Tensor:
"""
Get the document vector from the index.
Args:
index (`int`):
The index of the document.
Returns:
`torch.Tensor`: The document vector.
"""
if self.embeddings is None:
raise ValueError(
"The documents must be indexed before they can be retrieved."
)
if index >= self.embeddings.shape[0]:
raise ValueError(
f"The index {index} is out of bounds. The maximum index is {len(self.embeddings) - 1}."
)
return self.embeddings[index]
def get_embeddings_from_passage(self, document: str) -> torch.Tensor:
"""
Get the document vector from the document label.
Args:
document (`str`):
The document to get the vector for.
Returns:
`torch.Tensor`: The document vector.
"""
if self.embeddings is None:
raise ValueError(
"The documents must be indexed before they can be retrieved."
)
return self.get_embeddings_from_index(self.get_index_from_passage(document))
def get_embeddings_from_document(self, document: str) -> torch.Tensor:
"""
Get the document vector from the document label.
Args:
document (`str`):
The document to get the vector for.
Returns:
`torch.Tensor`: The document vector.
"""
if self.embeddings is None:
raise ValueError(
"The documents must be indexed before they can be retrieved."
)
return self.get_embeddings_from_index(self.get_index_from_document(document))
def get_passages(self, documents: DocumentStore | None = None) -> List[str]:
"""
Get the passages from the document store.
Returns:
`List[str]`: The passages.
"""
documents = documents or self.documents
# construct the passages from the documents
# return [self.get_passage_from_index(i) for i in range(len(documents))]
return [self.get_passage_from_document(doc) for doc in documents]
def save_pretrained(
self,
output_dir: Union[str, os.PathLike],
config: Optional[Dict[str, Any]] = None,
config_file_name: str | None = None,
document_file_name: str | None = None,
embedding_file_name: str | None = None,
push_to_hub: bool = False,
model_id: str | None = None,
**kwargs,
):
"""
Save the retriever to a directory.
Args:
output_dir (`str`):
The directory to save the retriever to.
config (`Optional[Dict[str, Any]]`, `optional`):
The configuration to save. If `None`, the current configuration of the retriever will be
saved. Defaults to `None`.
config_file_name (`str | None`, `optional`):
The name of the configuration file. Defaults to `config.yaml`.
document_file_name (`str | None`, `optional`):
The name of the document file. Defaults to `documents.json`.
embedding_file_name (`str | None`, `optional`):
The name of the embedding file. Defaults to `embeddings.pt`.
push_to_hub (`bool`, `optional`):
Whether to push the saved retriever to the hub. Defaults to `False`.
model_id (`str | None`, `optional`):
The id of the model to push to the hub. If `None`, the name of the output
directory will be used. Defaults to `None`.
**kwargs:
Additional keyword arguments to pass to `upload`.
"""
if config is None:
# create a default config
config = self.config
config_file_name = config_file_name or self.CONFIG_NAME
document_file_name = document_file_name or self.DOCUMENTS_FILE_NAME
embedding_file_name = embedding_file_name or self.EMBEDDINGS_FILE_NAME
# create the output directory
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
logger.info(f"Saving retriever to {output_dir}")
logger.info(f"Saving config to {output_dir / config_file_name}")
# pretty print the config
OmegaConf.save(config, output_dir / config_file_name)
logger.info(pformat(config))
# save the current state of the retriever
embedding_path = output_dir / embedding_file_name
logger.info(f"Saving retriever state to {output_dir / embedding_path}")
torch.save(self.embeddings, embedding_path)
# save the passage index
documents_path = output_dir / document_file_name
logger.info(f"Saving passage index to {documents_path}")
self.documents.save(documents_path)
logger.info("Saving document index to disk done.")
if push_to_hub:
# push to hub
logger.info("Pushing to hub")
model_id = model_id or output_dir.name
upload(output_dir, model_id, **kwargs)
@classmethod
def from_pretrained(
cls,
name_or_dir: Union[str, os.PathLike],
device: str = "cpu",
precision: str | None = None,
config_file_name: str | None = None,
document_file_name: str | None = None,
embedding_file_name: str | None = None,
*args,
**kwargs,
) -> "BaseDocumentIndex":
cache_dir = kwargs.pop("cache_dir", None)
force_download = kwargs.pop("force_download", False)
config_file_name = config_file_name or cls.CONFIG_NAME
document_file_name = document_file_name or cls.DOCUMENTS_FILE_NAME
embedding_file_name = embedding_file_name or cls.EMBEDDINGS_FILE_NAME
model_dir = from_cache(
name_or_dir,
filenames=[config_file_name, document_file_name, embedding_file_name],
cache_dir=cache_dir,
force_download=force_download,
)
config_path = model_dir / config_file_name
if not config_path.exists():
raise FileNotFoundError(
f"Model configuration file not found at {config_path}."
)
config = OmegaConf.load(config_path)
# override the config with the kwargs
# if config_kwargs is not None:
config = OmegaConf.merge(config, OmegaConf.create(kwargs))
logger.info("Loading Index from config:")
logger.info(pformat(OmegaConf.to_container(config)))
# load the documents
documents_path = model_dir / document_file_name
if not documents_path.exists():
raise ValueError(f"Document file `{documents_path}` does not exist.")
logger.info(f"Loading documents from {documents_path}")
documents = DocumentStore.from_file(documents_path)
# TODO: probably is better to do the opposite and iterate over the config
# check for each possible attribute ind DocumentStore
for attr in dir(documents):
if attr.startswith("__"):
continue
if attr not in config:
continue
# set the attribute
setattr(documents, attr, config[attr])
# load the passage embeddings
embedding_path = model_dir / embedding_file_name
# run some checks
embeddings = None
if embedding_path.exists():
logger.info(f"Loading embeddings from {embedding_path}")
embeddings = torch.load(embedding_path, map_location="cpu")
else:
logger.warning(f"Embedding file `{embedding_path}` does not exist.")
document_index = hydra.utils.instantiate(
config,
documents=documents,
embeddings=embeddings,
device=device,
precision=precision,
name_or_dir=name_or_dir,
_convert_="partial",
*args,
**kwargs,
)
return document_index
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