-
Notifications
You must be signed in to change notification settings - Fork 4.5k
/
pipeline.py
648 lines (559 loc) · 23.9 KB
/
pipeline.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
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
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
import asyncio
import multiprocessing
import re
import warnings
from concurrent.futures import ProcessPoolExecutor
from enum import Enum
from functools import partial, reduce
from hashlib import sha256
from itertools import repeat
from pathlib import Path
from typing import Any, Generator, List, Optional, Sequence, Union
from fsspec import AbstractFileSystem
from llama_index.legacy.bridge.pydantic import BaseModel, Field
from llama_index.legacy.embeddings.utils import resolve_embed_model
from llama_index.legacy.ingestion.cache import DEFAULT_CACHE_NAME, IngestionCache
from llama_index.legacy.node_parser import SentenceSplitter
from llama_index.legacy.readers.base import ReaderConfig
from llama_index.legacy.schema import (
BaseNode,
Document,
MetadataMode,
TransformComponent,
)
from llama_index.legacy.service_context import ServiceContext
from llama_index.legacy.storage.docstore import BaseDocumentStore, SimpleDocumentStore
from llama_index.legacy.storage.storage_context import DOCSTORE_FNAME
from llama_index.legacy.utils import concat_dirs
from llama_index.legacy.vector_stores.types import BasePydanticVectorStore
def remove_unstable_values(s: str) -> str:
"""Remove unstable key/value pairs.
Examples include:
- <__main__.Test object at 0x7fb9f3793f50>
- <function test_fn at 0x7fb9f37a8900>
"""
pattern = r"<[\w\s_\. ]+ at 0x[a-z0-9]+>"
return re.sub(pattern, "", s)
def get_transformation_hash(
nodes: List[BaseNode], transformation: TransformComponent
) -> str:
"""Get the hash of a transformation."""
nodes_str = "".join(
[str(node.get_content(metadata_mode=MetadataMode.ALL)) for node in nodes]
)
transformation_dict = transformation.to_dict()
transform_string = remove_unstable_values(str(transformation_dict))
return sha256((nodes_str + transform_string).encode("utf-8")).hexdigest()
def run_transformations(
nodes: List[BaseNode],
transformations: Sequence[TransformComponent],
in_place: bool = True,
cache: Optional[IngestionCache] = None,
cache_collection: Optional[str] = None,
**kwargs: Any,
) -> List[BaseNode]:
"""Run a series of transformations on a set of nodes.
Args:
nodes: The nodes to transform.
transformations: The transformations to apply to the nodes.
Returns:
The transformed nodes.
"""
if not in_place:
nodes = list(nodes)
for transform in transformations:
if cache is not None:
hash = get_transformation_hash(nodes, transform)
cached_nodes = cache.get(hash, collection=cache_collection)
if cached_nodes is not None:
nodes = cached_nodes
else:
nodes = transform(nodes, **kwargs)
cache.put(hash, nodes, collection=cache_collection)
else:
nodes = transform(nodes, **kwargs)
return nodes
async def arun_transformations(
nodes: List[BaseNode],
transformations: Sequence[TransformComponent],
in_place: bool = True,
cache: Optional[IngestionCache] = None,
cache_collection: Optional[str] = None,
**kwargs: Any,
) -> List[BaseNode]:
"""Run a series of transformations on a set of nodes.
Args:
nodes: The nodes to transform.
transformations: The transformations to apply to the nodes.
Returns:
The transformed nodes.
"""
if not in_place:
nodes = list(nodes)
for transform in transformations:
if cache is not None:
hash = get_transformation_hash(nodes, transform)
cached_nodes = cache.get(hash, collection=cache_collection)
if cached_nodes is not None:
nodes = cached_nodes
else:
nodes = await transform.acall(nodes, **kwargs)
cache.put(hash, nodes, collection=cache_collection)
else:
nodes = await transform.acall(nodes, **kwargs)
return nodes
def arun_transformations_wrapper(
nodes: List[BaseNode],
transformations: Sequence[TransformComponent],
in_place: bool = True,
cache: Optional[IngestionCache] = None,
cache_collection: Optional[str] = None,
**kwargs: Any,
) -> List[BaseNode]:
"""Wrapper for async run_transformation. To be used in loop.run_in_executor
within a ProcessPoolExecutor.
"""
loop = asyncio.new_event_loop()
nodes = loop.run_until_complete(
arun_transformations(
nodes=nodes,
transformations=transformations,
in_place=in_place,
cache=cache,
cache_collection=cache_collection,
**kwargs,
)
)
loop.close()
return nodes
class DocstoreStrategy(str, Enum):
"""Document de-duplication strategy."""
UPSERTS = "upserts"
DUPLICATES_ONLY = "duplicates_only"
UPSERTS_AND_DELETE = "upserts_and_delete"
class IngestionPipeline(BaseModel):
"""An ingestion pipeline that can be applied to data."""
transformations: List[TransformComponent] = Field(
description="Transformations to apply to the data"
)
documents: Optional[Sequence[Document]] = Field(description="Documents to ingest")
reader: Optional[ReaderConfig] = Field(description="Reader to use to read the data")
vector_store: Optional[BasePydanticVectorStore] = Field(
description="Vector store to use to store the data"
)
cache: IngestionCache = Field(
default_factory=IngestionCache,
description="Cache to use to store the data",
)
docstore: Optional[BaseDocumentStore] = Field(
default=None,
description="Document store to use for de-duping with a vector store.",
)
docstore_strategy: DocstoreStrategy = Field(
default=DocstoreStrategy.UPSERTS, description="Document de-dup strategy."
)
disable_cache: bool = Field(default=False, description="Disable the cache")
class Config:
arbitrary_types_allowed = True
def __init__(
self,
transformations: Optional[List[TransformComponent]] = None,
reader: Optional[ReaderConfig] = None,
documents: Optional[Sequence[Document]] = None,
vector_store: Optional[BasePydanticVectorStore] = None,
cache: Optional[IngestionCache] = None,
docstore: Optional[BaseDocumentStore] = None,
docstore_strategy: DocstoreStrategy = DocstoreStrategy.UPSERTS,
disable_cache: bool = False,
) -> None:
if transformations is None:
transformations = self._get_default_transformations()
super().__init__(
transformations=transformations,
reader=reader,
documents=documents,
vector_store=vector_store,
cache=cache or IngestionCache(),
docstore=docstore,
docstore_strategy=docstore_strategy,
disable_cache=disable_cache,
)
@classmethod
def from_service_context(
cls,
service_context: ServiceContext,
reader: Optional[ReaderConfig] = None,
documents: Optional[Sequence[Document]] = None,
vector_store: Optional[BasePydanticVectorStore] = None,
cache: Optional[IngestionCache] = None,
docstore: Optional[BaseDocumentStore] = None,
disable_cache: bool = False,
) -> "IngestionPipeline":
transformations = [
*service_context.transformations,
service_context.embed_model,
]
return cls(
transformations=transformations,
reader=reader,
documents=documents,
vector_store=vector_store,
cache=cache,
docstore=docstore,
disable_cache=disable_cache,
)
def persist(
self,
persist_dir: str = "./pipeline_storage",
fs: Optional[AbstractFileSystem] = None,
cache_name: str = DEFAULT_CACHE_NAME,
docstore_name: str = DOCSTORE_FNAME,
) -> None:
"""Persist the pipeline to disk."""
if fs is not None:
persist_dir = str(persist_dir) # NOTE: doesn't support Windows here
docstore_path = concat_dirs(persist_dir, docstore_name)
cache_path = concat_dirs(persist_dir, cache_name)
else:
persist_path = Path(persist_dir)
docstore_path = str(persist_path / docstore_name)
cache_path = str(persist_path / cache_name)
self.cache.persist(cache_path, fs=fs)
if self.docstore is not None:
self.docstore.persist(docstore_path, fs=fs)
def load(
self,
persist_dir: str = "./pipeline_storage",
fs: Optional[AbstractFileSystem] = None,
cache_name: str = DEFAULT_CACHE_NAME,
docstore_name: str = DOCSTORE_FNAME,
) -> None:
"""Load the pipeline from disk."""
if fs is not None:
self.cache = IngestionCache.from_persist_path(
concat_dirs(persist_dir, cache_name), fs=fs
)
self.docstore = SimpleDocumentStore.from_persist_path(
concat_dirs(persist_dir, docstore_name), fs=fs
)
else:
self.cache = IngestionCache.from_persist_path(
str(Path(persist_dir) / cache_name)
)
self.docstore = SimpleDocumentStore.from_persist_path(
str(Path(persist_dir) / docstore_name)
)
def _get_default_transformations(self) -> List[TransformComponent]:
return [
SentenceSplitter(),
resolve_embed_model("default"),
]
def _prepare_inputs(
self, documents: Optional[List[Document]], nodes: Optional[List[BaseNode]]
) -> List[Document]:
input_nodes: List[BaseNode] = []
if documents is not None:
input_nodes += documents
if nodes is not None:
input_nodes += nodes
if self.documents is not None:
input_nodes += self.documents
if self.reader is not None:
input_nodes += self.reader.read()
return input_nodes
def _handle_duplicates(
self,
nodes: List[BaseNode],
store_doc_text: bool = True,
) -> List[BaseNode]:
"""Handle docstore duplicates by checking all hashes."""
assert self.docstore is not None
existing_hashes = self.docstore.get_all_document_hashes()
current_hashes = []
nodes_to_run = []
for node in nodes:
if node.hash not in existing_hashes and node.hash not in current_hashes:
self.docstore.set_document_hash(node.id_, node.hash)
nodes_to_run.append(node)
current_hashes.append(node.hash)
self.docstore.add_documents(nodes_to_run, store_text=store_doc_text)
return nodes_to_run
def _handle_upserts(
self,
nodes: List[BaseNode],
store_doc_text: bool = True,
) -> List[BaseNode]:
"""Handle docstore upserts by checking hashes and ids."""
assert self.docstore is not None
existing_doc_ids_before = set(self.docstore.get_all_document_hashes().values())
doc_ids_from_nodes = set()
deduped_nodes_to_run = {}
for node in nodes:
ref_doc_id = node.ref_doc_id if node.ref_doc_id else node.id_
doc_ids_from_nodes.add(ref_doc_id)
existing_hash = self.docstore.get_document_hash(ref_doc_id)
if not existing_hash:
# document doesn't exist, so add it
self.docstore.set_document_hash(ref_doc_id, node.hash)
deduped_nodes_to_run[ref_doc_id] = node
elif existing_hash and existing_hash != node.hash:
self.docstore.delete_ref_doc(ref_doc_id, raise_error=False)
if self.vector_store is not None:
self.vector_store.delete(ref_doc_id)
self.docstore.set_document_hash(ref_doc_id, node.hash)
deduped_nodes_to_run[ref_doc_id] = node
else:
continue # document exists and is unchanged, so skip it
if self.docstore_strategy == DocstoreStrategy.UPSERTS_AND_DELETE:
# Identify missing docs and delete them from docstore and vector store
doc_ids_to_delete = existing_doc_ids_before - doc_ids_from_nodes
for ref_doc_id in doc_ids_to_delete:
self.docstore.delete_document(ref_doc_id)
if self.vector_store is not None:
self.vector_store.delete(ref_doc_id)
nodes_to_run = list(deduped_nodes_to_run.values())
self.docstore.add_documents(nodes_to_run, store_text=store_doc_text)
return nodes_to_run
@staticmethod
def _node_batcher(
num_batches: int, nodes: Union[List[BaseNode], List[Document]]
) -> Generator[Union[List[BaseNode], List[Document]], Any, Any]:
"""Yield successive n-sized chunks from lst."""
batch_size = max(1, int(len(nodes) / num_batches))
for i in range(0, len(nodes), batch_size):
yield nodes[i : i + batch_size]
def run(
self,
show_progress: bool = False,
documents: Optional[List[Document]] = None,
nodes: Optional[List[BaseNode]] = None,
cache_collection: Optional[str] = None,
in_place: bool = True,
store_doc_text: bool = True,
num_workers: Optional[int] = None,
**kwargs: Any,
) -> Sequence[BaseNode]:
"""
Args:
show_progress (bool, optional): Shows execution progress bar(s). Defaults to False.
documents (Optional[List[Document]], optional): Set of documents to be transformed. Defaults to None.
nodes (Optional[List[BaseNode]], optional): Set of nodes to be transformed. Defaults to None.
cache_collection (Optional[str], optional): Cache for transformations. Defaults to None.
in_place (bool, optional): Whether transformations creates a new list for transformed nodes or modifies the
array passed to `run_transformations`. Defaults to True.
num_workers (Optional[int], optional): The number of parallel processes to use.
If set to None, then sequential compute is used. Defaults to None.
Returns:
Sequence[BaseNode]: The set of transformed Nodes/Documents
"""
input_nodes = self._prepare_inputs(documents, nodes)
# check if we need to dedup
if self.docstore is not None and self.vector_store is not None:
if self.docstore_strategy in (
DocstoreStrategy.UPSERTS,
DocstoreStrategy.UPSERTS_AND_DELETE,
):
nodes_to_run = self._handle_upserts(
input_nodes, store_doc_text=store_doc_text
)
elif self.docstore_strategy == DocstoreStrategy.DUPLICATES_ONLY:
nodes_to_run = self._handle_duplicates(
input_nodes, store_doc_text=store_doc_text
)
else:
raise ValueError(f"Invalid docstore strategy: {self.docstore_strategy}")
elif self.docstore is not None and self.vector_store is None:
if self.docstore_strategy == DocstoreStrategy.UPSERTS:
print(
"Docstore strategy set to upserts, but no vector store. "
"Switching to duplicates_only strategy."
)
self.docstore_strategy = DocstoreStrategy.DUPLICATES_ONLY
elif self.docstore_strategy == DocstoreStrategy.UPSERTS_AND_DELETE:
print(
"Docstore strategy set to upserts and delete, but no vector store. "
"Switching to duplicates_only strategy."
)
self.docstore_strategy = DocstoreStrategy.DUPLICATES_ONLY
nodes_to_run = self._handle_duplicates(
input_nodes, store_doc_text=store_doc_text
)
else:
nodes_to_run = input_nodes
if num_workers and num_workers > 1:
if num_workers > multiprocessing.cpu_count():
warnings.warn(
"Specified num_workers exceed number of CPUs in the system. "
"Setting `num_workers` down to the maximum CPU count."
)
with multiprocessing.get_context("spawn").Pool(num_workers) as p:
node_batches = self._node_batcher(
num_batches=num_workers, nodes=nodes_to_run
)
nodes_parallel = p.starmap(
run_transformations,
zip(
node_batches,
repeat(self.transformations),
repeat(in_place),
repeat(self.cache if not self.disable_cache else None),
repeat(cache_collection),
),
)
nodes = reduce(lambda x, y: x + y, nodes_parallel, [])
else:
nodes = run_transformations(
nodes_to_run,
self.transformations,
show_progress=show_progress,
cache=self.cache if not self.disable_cache else None,
cache_collection=cache_collection,
in_place=in_place,
**kwargs,
)
if self.vector_store is not None:
self.vector_store.add([n for n in nodes if n.embedding is not None])
return nodes
# ------ async methods ------
async def _ahandle_duplicates(
self,
nodes: List[BaseNode],
store_doc_text: bool = True,
) -> List[BaseNode]:
"""Handle docstore duplicates by checking all hashes."""
assert self.docstore is not None
existing_hashes = await self.docstore.aget_all_document_hashes()
current_hashes = []
nodes_to_run = []
for node in nodes:
if node.hash not in existing_hashes and node.hash not in current_hashes:
await self.docstore.aset_document_hash(node.id_, node.hash)
nodes_to_run.append(node)
current_hashes.append(node.hash)
await self.docstore.async_add_documents(nodes_to_run, store_text=store_doc_text)
return nodes_to_run
async def _ahandle_upserts(
self,
nodes: List[BaseNode],
store_doc_text: bool = True,
) -> List[BaseNode]:
"""Handle docstore upserts by checking hashes and ids."""
assert self.docstore is not None
existing_doc_ids_before = set(
(await self.docstore.aget_all_document_hashes()).values()
)
doc_ids_from_nodes = set()
deduped_nodes_to_run = {}
for node in nodes:
ref_doc_id = node.ref_doc_id if node.ref_doc_id else node.id_
doc_ids_from_nodes.add(ref_doc_id)
existing_hash = await self.docstore.aget_document_hash(ref_doc_id)
if not existing_hash:
# document doesn't exist, so add it
await self.docstore.aset_document_hash(ref_doc_id, node.hash)
deduped_nodes_to_run[ref_doc_id] = node
elif existing_hash and existing_hash != node.hash:
await self.docstore.adelete_ref_doc(ref_doc_id, raise_error=False)
if self.vector_store is not None:
await self.vector_store.adelete(ref_doc_id)
await self.docstore.aset_document_hash(ref_doc_id, node.hash)
deduped_nodes_to_run[ref_doc_id] = node
else:
continue # document exists and is unchanged, so skip it
if self.docstore_strategy == DocstoreStrategy.UPSERTS_AND_DELETE:
# Identify missing docs and delete them from docstore and vector store
doc_ids_to_delete = existing_doc_ids_before - doc_ids_from_nodes
for ref_doc_id in doc_ids_to_delete:
await self.docstore.adelete_document(ref_doc_id)
if self.vector_store is not None:
await self.vector_store.adelete(ref_doc_id)
nodes_to_run = list(deduped_nodes_to_run.values())
await self.docstore.async_add_documents(nodes_to_run, store_text=store_doc_text)
return nodes_to_run
async def arun(
self,
show_progress: bool = False,
documents: Optional[List[Document]] = None,
nodes: Optional[List[BaseNode]] = None,
cache_collection: Optional[str] = None,
in_place: bool = True,
store_doc_text: bool = True,
num_workers: Optional[int] = None,
**kwargs: Any,
) -> Sequence[BaseNode]:
input_nodes = self._prepare_inputs(documents, nodes)
# check if we need to dedup
if self.docstore is not None and self.vector_store is not None:
if self.docstore_strategy in (
DocstoreStrategy.UPSERTS,
DocstoreStrategy.UPSERTS_AND_DELETE,
):
nodes_to_run = await self._ahandle_upserts(
input_nodes, store_doc_text=store_doc_text
)
elif self.docstore_strategy == DocstoreStrategy.DUPLICATES_ONLY:
nodes_to_run = await self._ahandle_duplicates(
input_nodes, store_doc_text=store_doc_text
)
else:
raise ValueError(f"Invalid docstore strategy: {self.docstore_strategy}")
elif self.docstore is not None and self.vector_store is None:
if self.docstore_strategy == DocstoreStrategy.UPSERTS:
print(
"Docstore strategy set to upserts, but no vector store. "
"Switching to duplicates_only strategy."
)
self.docstore_strategy = DocstoreStrategy.DUPLICATES_ONLY
elif self.docstore_strategy == DocstoreStrategy.UPSERTS_AND_DELETE:
print(
"Docstore strategy set to upserts and delete, but no vector store. "
"Switching to duplicates_only strategy."
)
self.docstore_strategy = DocstoreStrategy.DUPLICATES_ONLY
nodes_to_run = await self._ahandle_duplicates(
input_nodes, store_doc_text=store_doc_text
)
else:
nodes_to_run = input_nodes
if num_workers and num_workers > 1:
if num_workers > multiprocessing.cpu_count():
warnings.warn(
"Specified num_workers exceed number of CPUs in the system. "
"Setting `num_workers` down to the maximum CPU count."
)
loop = asyncio.get_event_loop()
with ProcessPoolExecutor(max_workers=num_workers) as p:
node_batches = self._node_batcher(
num_batches=num_workers, nodes=nodes_to_run
)
tasks = [
loop.run_in_executor(
p,
partial(
arun_transformations_wrapper,
transformations=self.transformations,
in_place=in_place,
cache=self.cache if not self.disable_cache else None,
cache_collection=cache_collection,
),
batch,
)
for batch in node_batches
]
result: List[List[BaseNode]] = await asyncio.gather(*tasks)
nodes = reduce(lambda x, y: x + y, result, [])
else:
nodes = await arun_transformations(
nodes_to_run,
self.transformations,
show_progress=show_progress,
cache=self.cache if not self.disable_cache else None,
cache_collection=cache_collection,
in_place=in_place,
**kwargs,
)
if self.vector_store is not None:
await self.vector_store.async_add(
[n for n in nodes if n.embedding is not None]
)
return nodes