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220 | class BGEM3Index(BaseIndex[IndexDict]):
"""
Store for BGE-M3 with PLAID indexing.
BGE-M3 is a multilingual embedding model with multi-functionality:
Dense retrieval, Sparse retrieval and Multi-vector retrieval.
Parameters:
index_path: directory containing PLAID index files.
model_name: BGE-M3 hugging face model name.
Default: "BAAI/bge-m3".
show_progress: whether to show progress bar when building index.
Default: False. noop for BGE-M3 for now.
doc_maxlen: max document length. Default: 120.
query_maxlen: max query length. Default: 60.
"""
def __init__(
self,
nodes: Optional[Sequence[BaseNode]] = None,
objects: Optional[Sequence[IndexNode]] = None,
index_struct: Optional[IndexDict] = None,
storage_context: Optional[StorageContext] = None,
model_name: str = "BAAI/bge-m3",
index_name: str = "",
show_progress: bool = False,
pooling_method: str = "cls",
normalize_embeddings: bool = True,
use_fp16: bool = False,
batch_size: int = 32,
doc_maxlen: int = 8192,
query_maxlen: int = 8192,
weights_for_different_modes: List[float] = None,
**kwargs: Any,
) -> None:
self.index_path = "storage/bge_m3_index"
self.index_name = index_name
self.batch_size = batch_size
self.doc_maxlen = doc_maxlen
self.query_maxlen = query_maxlen
self.weights_for_different_modes = weights_for_different_modes
self._multi_embed_store = None
self._docs_pos_to_node_id: Dict[int, str] = {}
try:
from FlagEmbedding import BGEM3FlagModel
except ImportError as exc:
raise ImportError(
"Please install FlagEmbedding to use this feature from the repo:",
"https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3",
) from exc
self.model = BGEM3FlagModel(
model_name,
pooling_method=pooling_method,
normalize_embeddings=normalize_embeddings,
use_fp16=use_fp16,
)
super().__init__(
nodes=nodes,
index_struct=index_struct,
index_name=index_name,
storage_context=storage_context,
show_progress=show_progress,
objects=objects,
**kwargs,
)
def _insert(self, nodes: Sequence[BaseNode], **insert_kwargs: Any) -> None:
raise NotImplementedError("BGEM3Index does not support insertion yet.")
def _delete_node(self, node_id: str, **delete_kwargs: Any) -> None:
raise NotImplementedError("BGEM3Index does not support deletion yet.")
def as_retriever(self, **kwargs: Any) -> BaseRetriever:
from .retriever import BGEM3Retriever
return BGEM3Retriever(index=self, object_map=self._object_map, **kwargs)
@property
def ref_doc_info(self) -> Dict[str, RefDocInfo]:
raise NotImplementedError("BGEM3Index does not support ref_doc_info.")
def _build_index_from_nodes(
self, nodes: Sequence[BaseNode], **kwargs: Any
) -> IndexDict:
"""Generate a PLAID index from the BGE-M3 checkpoint via its hugging face
model_name.
"""
index_struct = IndexDict()
docs_list = []
for i, node in enumerate(nodes):
docs_list.append(node.get_content())
self._docs_pos_to_node_id[i] = node.node_id
index_struct.add_node(node, text_id=str(i))
self._multi_embed_store = self.model.encode(
docs_list,
batch_size=self.batch_size,
max_length=self.doc_maxlen,
return_dense=True,
return_sparse=True,
return_colbert_vecs=True,
)
return index_struct
def persist(self, persist_dir: str) -> None:
# Check if the destination directory exists
if os.path.exists(persist_dir):
# Remove the existing destination directory
shutil.rmtree(persist_dir)
self._storage_context.persist(persist_dir=persist_dir)
# Save _multi_embed_store
pickle.dump(
self._multi_embed_store,
open(Path(persist_dir) / "multi_embed_store.pkl", "wb"),
)
@classmethod
def load_from_disk(
cls,
persist_dir: str,
model_name: str = "BAAI/bge-m3",
index_name: str = "",
weights_for_different_modes: List[float] = None,
) -> "BGEM3Index":
sc = StorageContext.from_defaults(persist_dir=persist_dir)
index = BGEM3Index(
model_name=model_name,
index_name=index_name,
index_struct=sc.index_store.index_structs()[0],
storage_context=sc,
weights_for_different_modes=weights_for_different_modes,
)
docs_pos_to_node_id = {
int(k): v for k, v in index.index_struct.nodes_dict.items()
}
index._docs_pos_to_node_id = docs_pos_to_node_id
index._multi_embed_store = pickle.load(
open(Path(persist_dir) / "multi_embed_store.pkl", "rb")
)
return index
def query(self, query_str: str, top_k: int = 10) -> List[NodeWithScore]:
"""
Query the BGE-M3 + Plaid store.
Returns: list of NodeWithScore.
"""
query_embed = self.model.encode(
query_str,
batch_size=self.batch_size,
max_length=self.query_maxlen,
return_dense=True,
return_sparse=True,
return_colbert_vecs=True,
)
dense_scores = np.matmul(
query_embed["dense_vecs"], self._multi_embed_store["dense_vecs"].T
)
sparse_scores = np.array(
[
self.model.compute_lexical_matching_score(
query_embed["lexical_weights"], doc_lexical_weights
)
for doc_lexical_weights in self._multi_embed_store["lexical_weights"]
]
)
colbert_scores = np.array(
[
self.model.colbert_score(
query_embed["colbert_vecs"], doc_colbert_vecs
).item()
for doc_colbert_vecs in self._multi_embed_store["colbert_vecs"]
]
)
if self.weights_for_different_modes is None:
weights_for_different_modes = [1.0, 1.0, 1.0]
weight_sum = 3.0
else:
weights_for_different_modes = self.weights_for_different_modes
weight_sum = sum(weights_for_different_modes)
combined_scores = (
dense_scores * weights_for_different_modes[0]
+ sparse_scores * weights_for_different_modes[1]
+ colbert_scores * weights_for_different_modes[2]
) / weight_sum
topk_indices = np.argsort(combined_scores)[::-1][:top_k]
topk_scores = [combined_scores[idx] for idx in topk_indices]
node_doc_ids = [self._docs_pos_to_node_id[idx] for idx in topk_indices]
nodes = self.docstore.get_nodes(node_doc_ids)
nodes_with_score = []
for node, score in zip(nodes, topk_scores):
nodes_with_score.append(NodeWithScore(node=node, score=score))
return nodes_with_score
|
