Repository navigation
Expand file tree
/
Copy pathschema.py
More file actions
761 lines (599 loc) · 22.5 KB
/
Copy pathschema.py
File metadata and controls
761 lines (599 loc) · 22.5 KB
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
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
"""Base schema for data structures."""
import json
import textwrap
import uuid
from abc import abstractmethod
from dataclasses import dataclass
from enum import Enum, auto
from hashlib import sha256
from io import BytesIO
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
from dataclasses_json import DataClassJsonMixin
from typing_extensions import Self
from llama_index.bridge.pydantic import BaseModel, Field, root_validator
from llama_index.utils import SAMPLE_TEXT, truncate_text
if TYPE_CHECKING:
from haystack.schema import Document as HaystackDocument
from semantic_kernel.memory.memory_record import MemoryRecord
from llama_index.bridge.langchain import Document as LCDocument
DEFAULT_TEXT_NODE_TMPL = "{metadata_str}\n\n{content}"
DEFAULT_METADATA_TMPL = "{key}: {value}"
# NOTE: for pretty printing
TRUNCATE_LENGTH = 350
WRAP_WIDTH = 70
ImageType = Union[str, BytesIO]
class BaseComponent(BaseModel):
"""Base component object to capture class names."""
class Config:
@staticmethod
def schema_extra(schema: Dict[str, Any], model: "BaseComponent") -> None:
"""Add class name to schema."""
schema["properties"]["class_name"] = {
"title": "Class Name",
"type": "string",
"default": model.class_name(),
}
@classmethod
def class_name(cls) -> str:
"""
Get the class name, used as a unique ID in serialization.
This provides a key that makes serialization robust against actual class
name changes.
"""
return "base_component"
def json(self, **kwargs: Any) -> str:
return self.to_json(**kwargs)
def dict(self, **kwargs: Any) -> Dict[str, Any]:
data = super().dict(**kwargs)
data["class_name"] = self.class_name()
return data
def __getstate__(self) -> Dict[str, Any]:
state = super().__getstate__()
# tiktoken is not pickleable
# state["__dict__"] = self.dict()
state["__dict__"].pop("tokenizer", None)
# remove local functions
keys_to_remove = []
for key in state["__dict__"]:
if key.endswith("_fn"):
keys_to_remove.append(key)
for key in keys_to_remove:
state["__dict__"].pop(key, None)
# remove private attributes -- kind of dangerous
state["__private_attribute_values__"] = {}
return state
def __setstate__(self, state: Dict[str, Any]) -> None:
# Use the __dict__ and __init__ method to set state
# so that all variable initialize
try:
self.__init__(**state["__dict__"]) # type: ignore
except Exception:
# Fall back to the default __setstate__ method
super().__setstate__(state)
def to_dict(self, **kwargs: Any) -> Dict[str, Any]:
data = self.dict(**kwargs)
data["class_name"] = self.class_name()
return data
def to_json(self, **kwargs: Any) -> str:
data = self.to_dict(**kwargs)
return json.dumps(data)
# TODO: return type here not supported by current mypy version
@classmethod
def from_dict(cls, data: Dict[str, Any], **kwargs: Any) -> Self: # type: ignore
if isinstance(kwargs, dict):
data.update(kwargs)
data.pop("class_name", None)
return cls(**data)
@classmethod
def from_json(cls, data_str: str, **kwargs: Any) -> Self: # type: ignore
data = json.loads(data_str)
return cls.from_dict(data, **kwargs)
class TransformComponent(BaseComponent):
"""Base class for transform components."""
class Config:
arbitrary_types_allowed = True
@abstractmethod
def __call__(self, nodes: List["BaseNode"], **kwargs: Any) -> List["BaseNode"]:
"""Transform nodes."""
async def acall(self, nodes: List["BaseNode"], **kwargs: Any) -> List["BaseNode"]:
"""Async transform nodes."""
return self.__call__(nodes, **kwargs)
class NodeRelationship(str, Enum):
"""Node relationships used in `BaseNode` class.
Attributes:
SOURCE: The node is the source document.
PREVIOUS: The node is the previous node in the document.
NEXT: The node is the next node in the document.
PARENT: The node is the parent node in the document.
CHILD: The node is a child node in the document.
"""
SOURCE = auto()
PREVIOUS = auto()
NEXT = auto()
PARENT = auto()
CHILD = auto()
class ObjectType(str, Enum):
TEXT = auto()
IMAGE = auto()
INDEX = auto()
DOCUMENT = auto()
class MetadataMode(str, Enum):
ALL = auto()
EMBED = auto()
LLM = auto()
NONE = auto()
class RelatedNodeInfo(BaseComponent):
node_id: str
node_type: Optional[ObjectType] = None
metadata: Dict[str, Any] = Field(default_factory=dict)
hash: Optional[str] = None
@classmethod
def class_name(cls) -> str:
return "RelatedNodeInfo"
RelatedNodeType = Union[RelatedNodeInfo, List[RelatedNodeInfo]]
# Node classes for indexes
class BaseNode(BaseComponent):
"""Base node Object.
Generic abstract interface for retrievable nodes
"""
class Config:
allow_population_by_field_name = True
id_: str = Field(
default_factory=lambda: str(uuid.uuid4()), description="Unique ID of the node."
)
embedding: Optional[List[float]] = Field(
default=None, description="Embedding of the node."
)
""""
metadata fields
- injected as part of the text shown to LLMs as context
- injected as part of the text for generating embeddings
- used by vector DBs for metadata filtering
"""
metadata: Dict[str, Any] = Field(
default_factory=dict,
description="A flat dictionary of metadata fields",
alias="extra_info",
)
excluded_embed_metadata_keys: List[str] = Field(
default_factory=list,
description="Metadata keys that are excluded from text for the embed model.",
)
excluded_llm_metadata_keys: List[str] = Field(
default_factory=list,
description="Metadata keys that are excluded from text for the LLM.",
)
relationships: Dict[NodeRelationship, RelatedNodeType] = Field(
default_factory=dict,
description="A mapping of relationships to other node information.",
)
hash: str = Field(default="", description="Hash of the node content.")
@classmethod
@abstractmethod
def get_type(cls) -> str:
"""Get Object type."""
@abstractmethod
def get_content(self, metadata_mode: MetadataMode = MetadataMode.ALL) -> str:
"""Get object content."""
@abstractmethod
def get_metadata_str(self, mode: MetadataMode = MetadataMode.ALL) -> str:
"""Metadata string."""
@abstractmethod
def set_content(self, value: Any) -> None:
"""Set the content of the node."""
@property
def node_id(self) -> str:
return self.id_
@node_id.setter
def node_id(self, value: str) -> None:
self.id_ = value
@property
def source_node(self) -> Optional[RelatedNodeInfo]:
"""Source object node.
Extracted from the relationships field.
"""
if NodeRelationship.SOURCE not in self.relationships:
return None
relation = self.relationships[NodeRelationship.SOURCE]
if isinstance(relation, list):
raise ValueError("Source object must be a single RelatedNodeInfo object")
return relation
@property
def prev_node(self) -> Optional[RelatedNodeInfo]:
"""Prev node."""
if NodeRelationship.PREVIOUS not in self.relationships:
return None
relation = self.relationships[NodeRelationship.PREVIOUS]
if not isinstance(relation, RelatedNodeInfo):
raise ValueError("Previous object must be a single RelatedNodeInfo object")
return relation
@property
def next_node(self) -> Optional[RelatedNodeInfo]:
"""Next node."""
if NodeRelationship.NEXT not in self.relationships:
return None
relation = self.relationships[NodeRelationship.NEXT]
if not isinstance(relation, RelatedNodeInfo):
raise ValueError("Next object must be a single RelatedNodeInfo object")
return relation
@property
def parent_node(self) -> Optional[RelatedNodeInfo]:
"""Parent node."""
if NodeRelationship.PARENT not in self.relationships:
return None
relation = self.relationships[NodeRelationship.PARENT]
if not isinstance(relation, RelatedNodeInfo):
raise ValueError("Parent object must be a single RelatedNodeInfo object")
return relation
@property
def child_nodes(self) -> Optional[List[RelatedNodeInfo]]:
"""Child nodes."""
if NodeRelationship.CHILD not in self.relationships:
return None
relation = self.relationships[NodeRelationship.CHILD]
if not isinstance(relation, list):
raise ValueError("Child objects must be a list of RelatedNodeInfo objects.")
return relation
@property
def ref_doc_id(self) -> Optional[str]:
"""Deprecated: Get ref doc id."""
source_node = self.source_node
if source_node is None:
return None
return source_node.node_id
@property
def extra_info(self) -> Dict[str, Any]:
"""TODO: DEPRECATED: Extra info."""
return self.metadata
def __str__(self) -> str:
source_text_truncated = truncate_text(
self.get_content().strip(), TRUNCATE_LENGTH
)
source_text_wrapped = textwrap.fill(
f"Text: {source_text_truncated}\n", width=WRAP_WIDTH
)
return f"Node ID: {self.node_id}\n{source_text_wrapped}"
def get_embedding(self) -> List[float]:
"""Get embedding.
Errors if embedding is None.
"""
if self.embedding is None:
raise ValueError("embedding not set.")
return self.embedding
def as_related_node_info(self) -> RelatedNodeInfo:
"""Get node as RelatedNodeInfo."""
return RelatedNodeInfo(
node_id=self.node_id,
node_type=self.get_type(),
metadata=self.metadata,
hash=self.hash,
)
class TextNode(BaseNode):
text: str = Field(default="", description="Text content of the node.")
start_char_idx: Optional[int] = Field(
default=None, description="Start char index of the node."
)
end_char_idx: Optional[int] = Field(
default=None, description="End char index of the node."
)
text_template: str = Field(
default=DEFAULT_TEXT_NODE_TMPL,
description=(
"Template for how text is formatted, with {content} and "
"{metadata_str} placeholders."
),
)
metadata_template: str = Field(
default=DEFAULT_METADATA_TMPL,
description=(
"Template for how metadata is formatted, with {key} and "
"{value} placeholders."
),
)
metadata_seperator: str = Field(
default="\n",
description="Separator between metadata fields when converting to string.",
)
@classmethod
def class_name(cls) -> str:
return "TextNode"
@root_validator
def _check_hash(cls, values: dict) -> dict:
"""Generate a hash to represent the node."""
text = values.get("text", "")
metadata = values.get("metadata", {})
doc_identity = str(text) + str(metadata)
values["hash"] = str(
sha256(doc_identity.encode("utf-8", "surrogatepass")).hexdigest()
)
return values
@classmethod
def get_type(cls) -> str:
"""Get Object type."""
return ObjectType.TEXT
def get_content(self, metadata_mode: MetadataMode = MetadataMode.NONE) -> str:
"""Get object content."""
metadata_str = self.get_metadata_str(mode=metadata_mode).strip()
if not metadata_str:
return self.text
return self.text_template.format(
content=self.text, metadata_str=metadata_str
).strip()
def get_metadata_str(self, mode: MetadataMode = MetadataMode.ALL) -> str:
"""Metadata info string."""
if mode == MetadataMode.NONE:
return ""
usable_metadata_keys = set(self.metadata.keys())
if mode == MetadataMode.LLM:
for key in self.excluded_llm_metadata_keys:
if key in usable_metadata_keys:
usable_metadata_keys.remove(key)
elif mode == MetadataMode.EMBED:
for key in self.excluded_embed_metadata_keys:
if key in usable_metadata_keys:
usable_metadata_keys.remove(key)
return self.metadata_seperator.join(
[
self.metadata_template.format(key=key, value=str(value))
for key, value in self.metadata.items()
if key in usable_metadata_keys
]
)
def set_content(self, value: str) -> None:
"""Set the content of the node."""
self.text = value
def get_node_info(self) -> Dict[str, Any]:
"""Get node info."""
return {"start": self.start_char_idx, "end": self.end_char_idx}
def get_text(self) -> str:
return self.get_content(metadata_mode=MetadataMode.NONE)
@property
def node_info(self) -> Dict[str, Any]:
"""Deprecated: Get node info."""
return self.get_node_info()
# TODO: legacy backport of old Node class
Node = TextNode
class ImageNode(TextNode):
"""Node with image."""
# TODO: store reference instead of actual image
# base64 encoded image str
image: Optional[str] = None
image_path: Optional[str] = None
image_url: Optional[str] = None
text_embedding: Optional[List[float]] = Field(
default=None,
description="Text embedding of image node, if text field is filled out",
)
@classmethod
def get_type(cls) -> str:
return ObjectType.IMAGE
@classmethod
def class_name(cls) -> str:
return "ImageNode"
def resolve_image(self) -> ImageType:
"""Resolve an image such that PIL can read it."""
if self.image is not None:
return self.image
elif self.image_path is not None:
return self.image_path
elif self.image_url is not None:
# load image from URL
import requests
response = requests.get(self.image_url)
return BytesIO(response.content)
else:
raise ValueError("No image found in node.")
class IndexNode(TextNode):
"""Node with reference to any object.
This can include other indices, query engines, retrievers.
This can also include other nodes (though this is overlapping with `relationships`
on the Node class).
"""
index_id: str
@classmethod
def from_text_node(
cls,
node: TextNode,
index_id: str,
) -> "IndexNode":
"""Create index node from text node."""
# copy all attributes from text node, add index id
return cls(
**node.dict(),
index_id=index_id,
)
@classmethod
def get_type(cls) -> str:
return ObjectType.INDEX
@classmethod
def class_name(cls) -> str:
return "IndexNode"
class NodeWithScore(BaseComponent):
node: BaseNode
score: Optional[float] = None
def __str__(self) -> str:
return f"{self.node}\nScore: {self.score: 0.3f}\n"
def get_score(self, raise_error: bool = False) -> float:
"""Get score."""
if self.score is None:
if raise_error:
raise ValueError("Score not set.")
else:
return 0.0
else:
return self.score
@classmethod
def class_name(cls) -> str:
return "NodeWithScore"
##### pass through methods to BaseNode #####
@property
def node_id(self) -> str:
return self.node.node_id
@property
def id_(self) -> str:
return self.node.id_
@property
def text(self) -> str:
if isinstance(self.node, TextNode):
return self.node.text
else:
raise ValueError("Node must be a TextNode to get text.")
@property
def metadata(self) -> Dict[str, Any]:
return self.node.metadata
@property
def embedding(self) -> Optional[List[float]]:
return self.node.embedding
def get_text(self) -> str:
if isinstance(self.node, TextNode):
return self.node.get_text()
else:
raise ValueError("Node must be a TextNode to get text.")
def get_content(self, metadata_mode: MetadataMode = MetadataMode.NONE) -> str:
return self.node.get_content(metadata_mode=metadata_mode)
def get_embedding(self) -> List[float]:
return self.node.get_embedding()
# Document Classes for Readers
class Document(TextNode):
"""Generic interface for a data document.
This document connects to data sources.
"""
# TODO: A lot of backwards compatibility logic here, clean up
id_: str = Field(
default_factory=lambda: str(uuid.uuid4()),
description="Unique ID of the node.",
alias="doc_id",
)
_compat_fields = {"doc_id": "id_", "extra_info": "metadata"}
@classmethod
def get_type(cls) -> str:
"""Get Document type."""
return ObjectType.DOCUMENT
@property
def doc_id(self) -> str:
"""Get document ID."""
return self.id_
def __str__(self) -> str:
source_text_truncated = truncate_text(
self.get_content().strip(), TRUNCATE_LENGTH
)
source_text_wrapped = textwrap.fill(
f"Text: {source_text_truncated}\n", width=WRAP_WIDTH
)
return f"Doc ID: {self.doc_id}\n{source_text_wrapped}"
def get_doc_id(self) -> str:
"""TODO: Deprecated: Get document ID."""
return self.id_
def __setattr__(self, name: str, value: object) -> None:
if name in self._compat_fields:
name = self._compat_fields[name]
super().__setattr__(name, value)
def to_langchain_format(self) -> "LCDocument":
"""Convert struct to LangChain document format."""
from llama_index.bridge.langchain import Document as LCDocument
metadata = self.metadata or {}
return LCDocument(page_content=self.text, metadata=metadata)
@classmethod
def from_langchain_format(cls, doc: "LCDocument") -> "Document":
"""Convert struct from LangChain document format."""
return cls(text=doc.page_content, metadata=doc.metadata)
def to_haystack_format(self) -> "HaystackDocument":
"""Convert struct to Haystack document format."""
from haystack.schema import Document as HaystackDocument
return HaystackDocument(
content=self.text, meta=self.metadata, embedding=self.embedding, id=self.id_
)
@classmethod
def from_haystack_format(cls, doc: "HaystackDocument") -> "Document":
"""Convert struct from Haystack document format."""
return cls(
text=doc.content, metadata=doc.meta, embedding=doc.embedding, id_=doc.id
)
def to_embedchain_format(self) -> Dict[str, Any]:
"""Convert struct to EmbedChain document format."""
return {
"doc_id": self.id_,
"data": {"content": self.text, "meta_data": self.metadata},
}
@classmethod
def from_embedchain_format(cls, doc: Dict[str, Any]) -> "Document":
"""Convert struct from EmbedChain document format."""
return cls(
text=doc["data"]["content"],
metadata=doc["data"]["meta_data"],
id_=doc["doc_id"],
)
def to_semantic_kernel_format(self) -> "MemoryRecord":
"""Convert struct to Semantic Kernel document format."""
import numpy as np
from semantic_kernel.memory.memory_record import MemoryRecord
return MemoryRecord(
id=self.id_,
text=self.text,
additional_metadata=self.get_metadata_str(),
embedding=np.array(self.embedding) if self.embedding else None,
)
@classmethod
def from_semantic_kernel_format(cls, doc: "MemoryRecord") -> "Document":
"""Convert struct from Semantic Kernel document format."""
return cls(
text=doc._text,
metadata={"additional_metadata": doc._additional_metadata},
embedding=doc._embedding.tolist() if doc._embedding is not None else None,
id_=doc._id,
)
def to_vectorflow(self, client: Any) -> None:
"""Send a document to vectorflow, since they don't have a document object."""
# write document to temp file
import tempfile
with tempfile.NamedTemporaryFile() as f:
f.write(self.text.encode("utf-8"))
f.flush()
client.embed(f.name)
@classmethod
def example(cls) -> "Document":
return Document(
text=SAMPLE_TEXT,
metadata={"filename": "README.md", "category": "codebase"},
)
@classmethod
def class_name(cls) -> str:
return "Document"
class ImageDocument(Document, ImageNode):
"""Data document containing an image."""
@classmethod
def class_name(cls) -> str:
return "ImageDocument"
@dataclass
class QueryBundle(DataClassJsonMixin):
"""
Query bundle.
This dataclass contains the original query string and associated transformations.
Args:
query_str (str): the original user-specified query string.
This is currently used by all non embedding-based queries.
custom_embedding_strs (list[str]): list of strings used for embedding the query.
This is currently used by all embedding-based queries.
embedding (list[float]): the stored embedding for the query.
"""
query_str: str
# using single image path as query input
image_path: Optional[str] = None
custom_embedding_strs: Optional[List[str]] = None
embedding: Optional[List[float]] = None
@property
def embedding_strs(self) -> List[str]:
"""Use custom embedding strs if specified, otherwise use query str."""
if self.custom_embedding_strs is None:
if len(self.query_str) == 0:
return []
return [self.query_str]
else:
return self.custom_embedding_strs
@property
def embedding_image(self) -> List[ImageType]:
"""Use image path for image retrieval."""
if self.image_path is None:
return []
return [self.image_path]
QueryType = Union[str, QueryBundle]