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253 | class BM25Retriever(BaseRetriever):
r"""
A BM25 retriever that uses the BM25 algorithm to retrieve nodes.
Args:
nodes (List[BaseNode], optional):
The nodes to index. If not provided, an existing BM25 object must be passed.
stemmer (Stemmer.Stemmer, optional):
The stemmer to use. Defaults to an english stemmer.
language (str, optional):
The language to use for stopword removal. Defaults to "en".
existing_bm25 (bm25s.BM25, optional):
An existing BM25 object to use. If not provided, nodes must be passed.
similarity_top_k (int, optional):
The number of results to return. Defaults to DEFAULT_SIMILARITY_TOP_K.
callback_manager (CallbackManager, optional):
The callback manager to use. Defaults to None.
objects (List[IndexNode], optional):
The objects to retrieve. Defaults to None.
object_map (dict, optional):
A map of object IDs to nodes. Defaults to None.
token_pattern (str, optional):
The token pattern to use. Defaults to (?u)\\b\\w\\w+\\b.
skip_stemming (bool, optional):
Whether to skip stemming. Defaults to False.
verbose (bool, optional):
Whether to show progress. Defaults to False.
"""
def __init__(
self,
nodes: Optional[List[BaseNode]] = None,
stemmer: Optional[Stemmer.Stemmer] = None,
language: str = "en",
existing_bm25: Optional[bm25s.BM25] = None,
similarity_top_k: int = DEFAULT_SIMILARITY_TOP_K,
callback_manager: Optional[CallbackManager] = None,
objects: Optional[List[IndexNode]] = None,
object_map: Optional[dict] = None,
verbose: bool = False,
skip_stemming: bool = False,
token_pattern: str = r"(?u)\b\w\w+\b",
filters: Optional[MetadataFilters] = None,
corpus_weight_mask: Optional[List[int]] = None,
) -> None:
self.stemmer = stemmer or Stemmer.Stemmer("english")
self.similarity_top_k = similarity_top_k
self.token_pattern = token_pattern
self.skip_stemming = skip_stemming
if existing_bm25 is not None:
self.bm25 = existing_bm25
self.corpus = existing_bm25.corpus
else:
if nodes is None:
raise ValueError("Please pass nodes or an existing BM25 object.")
self.corpus = [
node_to_metadata_dict(node) | {"node_id": node.node_id}
for node in nodes
]
corpus_tokens = bm25s.tokenize(
[node.get_content(metadata_mode=MetadataMode.EMBED) for node in nodes],
stopwords=language,
stemmer=self.stemmer if not skip_stemming else None,
token_pattern=self.token_pattern,
show_progress=verbose,
)
self.bm25 = bm25s.BM25()
self.bm25.index(corpus_tokens, show_progress=verbose)
if (
self.bm25.scores.get("num_docs")
and int(self.bm25.scores["num_docs"]) < self.similarity_top_k
):
if int(self.bm25.scores["num_docs"]) == 0:
raise ValueError(
"No nodes added to the retriever kindly add more data."
)
logger.warning(
"As bm25s.BM25 requires k less than or equal to number of nodes added. Overriding the value of similarity_top_k to number of nodes added."
)
self.similarity_top_k = int(self.bm25.scores["num_docs"])
self.corpus_weight_mask = corpus_weight_mask or None
if filters and self.corpus:
# Build a weight mask for each corpus to filter out only relevant nodes
_corpus_dict = {
corpus_token["node_id"]: corpus_token for corpus_token in self.corpus
}
_query_filter_fn = build_metadata_filter_fn(
lambda node_id: _corpus_dict[node_id], filters
)
self.corpus_weight_mask = [
int(_query_filter_fn(corpus_token["node_id"]))
for corpus_token in self.corpus
]
super().__init__(
callback_manager=callback_manager,
object_map=object_map,
objects=objects,
verbose=verbose,
)
@classmethod
def from_defaults(
cls,
index: Optional[VectorStoreIndex] = None,
nodes: Optional[List[BaseNode]] = None,
docstore: Optional[BaseDocumentStore] = None,
stemmer: Optional[Stemmer.Stemmer] = None,
language: str = "en",
similarity_top_k: int = DEFAULT_SIMILARITY_TOP_K,
verbose: bool = False,
skip_stemming: bool = False,
token_pattern: str = r"(?u)\b\w\w+\b",
filters: Optional[MetadataFilters] = None,
# deprecated
tokenizer: Optional[Callable[[str], List[str]]] = None,
) -> "BM25Retriever":
if tokenizer is not None:
logger.warning(
"The tokenizer parameter is deprecated and will be removed in a future release. "
"Use a stemmer from PyStemmer instead."
)
# ensure only one of index, nodes, or docstore is passed
if sum(bool(val) for val in [index, nodes, docstore]) != 1:
raise ValueError("Please pass exactly one of index, nodes, or docstore.")
if index is not None:
docstore = index.docstore
if docstore is not None:
nodes = cast(List[BaseNode], list(docstore.docs.values()))
assert nodes is not None, (
"Please pass exactly one of index, nodes, or docstore."
)
return cls(
nodes=nodes,
stemmer=stemmer,
language=language,
similarity_top_k=similarity_top_k,
verbose=verbose,
skip_stemming=skip_stemming,
token_pattern=token_pattern,
filters=filters,
)
def get_persist_args(self) -> Dict[str, Any]:
"""Get Persist Args Dict to Save."""
return {
DEFAULT_PERSIST_ARGS[key]: getattr(self, key)
for key in DEFAULT_PERSIST_ARGS
if hasattr(self, key)
}
def persist(self, path: str, encoding: str = "utf-8", **kwargs: Any) -> None:
"""Persist the retriever to a directory."""
self.bm25.save(path, corpus=self.corpus, **kwargs)
with open(
os.path.join(path, DEFAULT_PERSIST_FILENAME), "w", encoding=encoding
) as f:
json.dump(self.get_persist_args(), f, indent=2)
@classmethod
def from_persist_dir(
cls, path: str, encoding: str = "utf-8", **kwargs: Any
) -> "BM25Retriever":
"""Load the retriever from a directory."""
bm25 = bm25s.BM25.load(path, load_corpus=True, **kwargs)
with open(os.path.join(path, DEFAULT_PERSIST_FILENAME), encoding=encoding) as f:
retriever_data = json.load(f)
return cls(existing_bm25=bm25, **retriever_data)
def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
query = query_bundle.query_str
tokenized_query = bm25s.tokenize(
query,
stemmer=self.stemmer if not self.skip_stemming else None,
token_pattern=self.token_pattern,
show_progress=self._verbose,
)
indexes, scores = self.bm25.retrieve(
tokenized_query,
k=self.similarity_top_k,
show_progress=self._verbose,
weight_mask=np.array(self.corpus_weight_mask)
if self.corpus_weight_mask
else None,
)
# batched, but only one query
indexes = indexes[0]
scores = scores[0]
nodes: List[NodeWithScore] = []
for idx, score in zip(indexes, scores):
# idx can be an int or a dict of the node
if isinstance(idx, dict):
node = metadata_dict_to_node(idx)
else:
node_dict = self.corpus[int(idx)]
node = metadata_dict_to_node(node_dict)
nodes.append(NodeWithScore(node=node, score=float(score)))
return nodes
|