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
|
"""Abstractions for documents and their extractions."""
import base64
import json
import logging
import uuid
from datetime import datetime
from enum import Enum
from typing import Optional, Union
from pydantic import BaseModel, Field
logger = logging.getLogger(__name__)
DataType = Union[str, bytes]
class DocumentType(str, Enum):
"""Types of documents that can be stored."""
CSV = "csv"
DOCX = "docx"
HTML = "html"
JSON = "json"
MD = "md"
PDF = "pdf"
PPTX = "pptx"
TXT = "txt"
XLSX = "xlsx"
GIF = "gif"
PNG = "png"
JPG = "jpg"
JPEG = "jpeg"
SVG = "svg"
MP3 = "mp3"
MP4 = "mp4"
class Document(BaseModel):
id: uuid.UUID = Field(default_factory=uuid.uuid4)
type: DocumentType
data: Union[str, bytes]
metadata: dict
def __init__(self, *args, **kwargs):
data = kwargs.get("data")
if data and isinstance(data, str):
try:
# Try to decode if it's already base64 encoded
kwargs["data"] = base64.b64decode(data)
except:
# If it's not base64, encode it to bytes
kwargs["data"] = data.encode("utf-8")
doc_type = kwargs.get("type")
if isinstance(doc_type, str):
kwargs["type"] = DocumentType(doc_type)
# Generate UUID based on the hash of the data
if "id" not in kwargs:
if isinstance(kwargs["data"], bytes):
data_hash = uuid.uuid5(
uuid.NAMESPACE_DNS, kwargs["data"].decode("utf-8")
)
else:
data_hash = uuid.uuid5(uuid.NAMESPACE_DNS, kwargs["data"])
kwargs["id"] = data_hash # Set the id based on the data hash
super().__init__(*args, **kwargs)
class Config:
arbitrary_types_allowed = True
json_encoders = {
uuid.UUID: str,
bytes: lambda v: base64.b64encode(v).decode("utf-8"),
}
class DocumentStatus(str, Enum):
"""Status of document processing."""
PROCESSING = "processing"
# TODO - Extend support for `partial-failure`
# PARTIAL_FAILURE = "partial-failure"
FAILURE = "failure"
SUCCESS = "success"
class DocumentInfo(BaseModel):
"""Base class for document information handling."""
document_id: uuid.UUID
version: str
size_in_bytes: int
metadata: dict
status: DocumentStatus = DocumentStatus.PROCESSING
user_id: Optional[uuid.UUID] = None
title: Optional[str] = None
created_at: Optional[datetime] = None
updated_at: Optional[datetime] = None
def convert_to_db_entry(self):
"""Prepare the document info for database entry, extracting certain fields from metadata."""
now = datetime.now()
metadata = self.metadata
if "user_id" in metadata:
metadata["user_id"] = str(metadata["user_id"])
metadata["title"] = metadata.get("title", "N/A")
return {
"document_id": str(self.document_id),
"title": metadata.get("title", "N/A"),
"user_id": metadata.get("user_id", None),
"version": self.version,
"size_in_bytes": self.size_in_bytes,
"metadata": json.dumps(self.metadata),
"created_at": self.created_at or now,
"updated_at": self.updated_at or now,
"status": self.status,
}
class ExtractionType(Enum):
"""Types of extractions that can be performed."""
TXT = "txt"
IMG = "img"
MOV = "mov"
class Extraction(BaseModel):
"""An extraction from a document."""
id: uuid.UUID
type: ExtractionType = ExtractionType.TXT
data: DataType
metadata: dict
document_id: uuid.UUID
class FragmentType(Enum):
"""A type of fragment that can be extracted from a document."""
TEXT = "text"
IMAGE = "image"
class Fragment(BaseModel):
"""A fragment extracted from a document."""
id: uuid.UUID
type: FragmentType
data: DataType
metadata: dict
document_id: uuid.UUID
extraction_id: uuid.UUID
class Entity(BaseModel):
"""An entity extracted from a document."""
category: str
subcategory: Optional[str] = None
value: str
def __str__(self):
return (
f"{self.category}:{self.subcategory}:{self.value}"
if self.subcategory
else f"{self.category}:{self.value}"
)
class Triple(BaseModel):
"""A triple extracted from a document."""
subject: str
predicate: str
object: str
def extract_entities(llm_payload: list[str]) -> dict[str, Entity]:
entities = {}
for entry in llm_payload:
try:
if "], " in entry: # Check if the entry is an entity
entry_val = entry.split("], ")[0] + "]"
entry = entry.split("], ")[1]
colon_count = entry.count(":")
if colon_count == 1:
category, value = entry.split(":")
subcategory = None
elif colon_count >= 2:
parts = entry.split(":", 2)
category, subcategory, value = (
parts[0],
parts[1],
parts[2],
)
else:
raise ValueError("Unexpected entry format")
entities[entry_val] = Entity(
category=category, subcategory=subcategory, value=value
)
except Exception as e:
logger.error(f"Error processing entity {entry}: {e}")
continue
return entities
def extract_triples(
llm_payload: list[str], entities: dict[str, Entity]
) -> list[Triple]:
triples = []
for entry in llm_payload:
try:
if "], " not in entry: # Check if the entry is an entity
elements = entry.split(" ")
subject = elements[0]
predicate = elements[1]
object = " ".join(elements[2:])
subject = entities[subject].value # Use entity.value
if "[" in object and "]" in object:
object = entities[object].value # Use entity.value
triples.append(
Triple(subject=subject, predicate=predicate, object=object)
)
except Exception as e:
logger.error(f"Error processing triplet {entry}: {e}")
continue
return triples
class KGExtraction(BaseModel):
"""An extraction from a document that is part of a knowledge graph."""
entities: dict[str, Entity]
triples: list[Triple]
|
