20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194 | class ColbertIndex(BaseIndex[IndexDict]):
"""
Store for ColBERT v2 with PLAID indexing.
ColBERT is a neural retrieval method that tends to work
well in a zero-shot setting on out of domain datasets, due
to it's use of token-level encodings (rather than sentence or
chunk level)
Parameters:
index_path: directory containing PLAID index files.
model_name: ColBERT hugging face model name.
Default: "colbert-ir/colbertv2.0".
show_progress: whether to show progress bar when building index.
Default: False. noop for ColBERT for now.
nbits: number of bits to quantize the residual vectors. Default: 2.
kmeans_niters: number of kmeans clustering iterations. Default: 1.
gpus: number of GPUs to use for indexing. Default: 0.
rank: number of ranks to use for indexing. Default: 1.
doc_maxlen: max document length. Default: 120.
query_maxlen: max query length. Default: 60.
kmeans_niters: number of kmeans iterations. Default: 4.
"""
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 = "colbert-ir/colbertv2.0",
index_name: str = "",
show_progress: bool = False,
nbits: int = 2,
gpus: int = 0,
ranks: int = 1,
doc_maxlen: int = 120,
query_maxlen: int = 60,
kmeans_niters: int = 4,
# deprecated
service_context: Optional[ServiceContext] = None,
**kwargs: Any,
) -> None:
self.model_name = model_name
self.index_path = "storage/colbert_index"
self.index_name = index_name
self.nbits = nbits
self.gpus = gpus
self.ranks = ranks
self.doc_maxlen = doc_maxlen
self.query_maxlen = query_maxlen
self.kmeans_niters = kmeans_niters
self._docs_pos_to_node_id: Dict[int, str] = {}
try:
pass
except ImportError as exc:
raise ImportError(
"Please install colbert to use this feature from the repo:",
"https://github.com/stanford-futuredata/ColBERT",
) from exc
super().__init__(
nodes=nodes,
index_struct=index_struct,
index_name=index_name,
service_context=service_context,
storage_context=storage_context,
show_progress=show_progress,
objects=objects,
**kwargs,
)
def _insert(self, nodes: Sequence[BaseNode], **insert_kwargs: Any) -> None:
raise NotImplementedError("ColbertStoreIndex does not support insertion yet.")
def _delete_node(self, node_id: str, **delete_kwargs: Any) -> None:
raise NotImplementedError("ColbertStoreIndex does not support deletion yet.")
def as_retriever(self, **kwargs: Any) -> BaseRetriever:
from .retriever import ColbertRetriever
return ColbertRetriever(index=self, object_map=self._object_map, **kwargs)
@property
def ref_doc_info(self) -> Dict[str, RefDocInfo]:
raise NotImplementedError("ColbertStoreIndex does not support ref_doc_info.")
def _build_index_from_nodes(self, nodes: Sequence[BaseNode]) -> IndexDict:
"""Generate a PLAID index from the ColBERT checkpoint via its hugging face
model_name.
"""
from colbert import Indexer, Searcher
from colbert.infra import ColBERTConfig, Run, RunConfig
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))
with Run().context(
RunConfig(index_root=self.index_path, nranks=self.ranks, gpus=self.gpus)
):
config = ColBERTConfig(
doc_maxlen=self.doc_maxlen,
query_maxlen=self.query_maxlen,
nbits=self.nbits,
kmeans_niters=self.kmeans_niters,
)
indexer = Indexer(checkpoint=self.model_name, config=config)
indexer.index(name=self.index_name, collection=docs_list, overwrite=True)
self.store = Searcher(
index=self.index_name, collection=docs_list, checkpoint=self.model_name
)
return index_struct
# @staticmethod
# def _normalize_scores(docs: List[Document]) -> None:
# "Normalizing the MaxSim scores using softmax."
# Z = sum(math.exp(doc.score) for doc in docs)
# for doc in docs:
# doc.score = math.exp(doc.score) / Z
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)
# Copy PLAID vectors
shutil.copytree(
Path(self.index_path) / self.index_name, Path(persist_dir) / self.index_name
)
self._storage_context.persist(persist_dir=persist_dir)
@classmethod
def load_from_disk(cls, persist_dir: str, index_name: str = "") -> "ColbertIndex":
from colbert import Searcher
from colbert.infra import ColBERTConfig
colbert_config = ColBERTConfig.load_from_index(Path(persist_dir) / index_name)
searcher = Searcher(
index=index_name, index_root=persist_dir, config=colbert_config
)
sc = StorageContext.from_defaults(persist_dir=persist_dir)
colbert_index = ColbertIndex(
index_struct=sc.index_store.index_structs()[0], storage_context=sc
)
docs_pos_to_node_id = {
int(k): v for k, v in colbert_index.index_struct.nodes_dict.items()
}
colbert_index._docs_pos_to_node_id = docs_pos_to_node_id
colbert_index.store = searcher
return colbert_index
def query(self, query_str: str, top_k: int = 10) -> List[NodeWithScore]:
"""
Query the Colbert v2 + Plaid store.
Returns: list of NodeWithScore.
"""
doc_ids, _, scores = self.store.search(text=query_str, k=top_k)
node_doc_ids = [self._docs_pos_to_node_id[id] for id in doc_ids]
nodes = self.docstore.get_nodes(node_doc_ids)
nodes_with_score = []
for node, score in zip(nodes, scores):
nodes_with_score.append(NodeWithScore(node=node, score=score))
return nodes_with_score
|