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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 rich.pretty import pprint
from relik.common import upload
from relik.common.log import get_console_logger, get_logger
from relik.common.utils import (
from_cache,
is_remote_url,
is_str_a_path,
relative_to_absolute_path,
sapienzanlp_model_urls,
)
from relik.retriever.data.labels import Labels
# from relik.retriever.models.model import GoldenRetriever, RetrievedSample
logger = get_logger(__name__)
console_logger = get_console_logger()
@dataclass
class IndexerOutput:
indices: Union[torch.Tensor, numpy.ndarray]
distances: Union[torch.Tensor, numpy.ndarray]
class BaseDocumentIndex:
CONFIG_NAME = "config.yaml"
DOCUMENTS_FILE_NAME = "documents.json"
EMBEDDINGS_FILE_NAME = "embeddings.pt"
def __init__(
self,
documents: Union[str, List[str], Labels, os.PathLike, List[os.PathLike]] = None,
embeddings: Optional[torch.Tensor] = None,
name_or_dir: Optional[Union[str, os.PathLike]] = None,
) -> None:
if documents is not None:
if isinstance(documents, Labels):
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:
_documents += [line.strip() for line in f.readlines()]
# remove duplicates
documents = list(set(_documents))
self.documents = Labels()
self.documents.add_labels(documents)
else:
self.documents = Labels()
self.embeddings = embeddings
self.name_or_dir = name_or_dir
@property
def config(self) -> Dict[str, Any]:
"""
The configuration of the document index.
Returns:
`Dict[str, Any]`: The configuration of the retriever.
"""
def obj_to_dict(obj):
match obj:
case dict():
data = {}
for k, v in obj.items():
data[k] = obj_to_dict(v)
return data
case list() | tuple():
return [obj_to_dict(x) for x in obj]
case object(__dict__=_):
data = {
"_target_": f"{obj.__class__.__module__}.{obj.__class__.__name__}",
}
for k, v in obj.__dict__.items():
if not k.startswith("_"):
data[k] = obj_to_dict(v)
return data
case _:
return obj
return obj_to_dict(self)
def index(
self,
retriever,
*args,
**kwargs,
) -> "BaseDocumentIndex":
raise NotImplementedError
def search(self, query: Any, k: int = 1, *args, **kwargs) -> List:
raise NotImplementedError
def get_index_from_passage(self, document: str) -> int:
"""
Get the index of the passage.
Args:
document (`str`):
The document to get the index for.
Returns:
`int`: The index of the document.
"""
return self.documents.get_index_from_label(document)
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.
"""
return self.documents.get_label_from_index(index)
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 save_pretrained(
self,
output_dir: Union[str, os.PathLike],
config: Optional[Dict[str, Any]] = None,
config_file_name: Optional[str] = None,
document_file_name: Optional[str] = None,
embedding_file_name: Optional[str] = None,
push_to_hub: bool = False,
**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 (`Optional[str]`, `optional`):
The name of the configuration file. Defaults to `config.yaml`.
document_file_name (`Optional[str]`, `optional`):
The name of the document file. Defaults to `documents.json`.
embedding_file_name (`Optional[str]`, `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`.
"""
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
pprint(config, console=console_logger, expand_all=True)
OmegaConf.save(config, output_dir / config_file_name)
# 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(f"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: Optional[str] = None,
config_file_name: Optional[str] = None,
document_file_name: Optional[str] = None,
embedding_file_name: Optional[str] = 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)
pprint(OmegaConf.to_container(config), console=console_logger, expand_all=True)
# 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 = Labels.from_file(documents_path)
# 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,
*args,
**kwargs,
)
return document_index
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