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7f9dfed | 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 | from __future__ import annotations
import csv
import datetime as dt
import json
import sqlite3
from contextlib import closing
from dataclasses import asdict, dataclass
from pathlib import Path
UTC = getattr(dt, "UTC", dt.timezone.utc) # noqa: UP017
@dataclass
class FieldNote:
"""One human correction record."""
created_at: str
model_id: str
prompt: str
response: str
correction: str
tags: str
image_path: str = ""
video_path: str = ""
use_for_training: bool = True
@classmethod
def create(
cls,
model_id: str,
prompt: str,
response: str,
correction: str,
tags: str,
image_path: str = "",
video_path: str = "",
use_for_training: bool = True,
) -> FieldNote:
return cls(
created_at=dt.datetime.now(UTC).isoformat(),
model_id=model_id,
prompt=prompt,
response=response,
correction=correction,
tags=tags,
image_path=image_path,
video_path=video_path,
use_for_training=use_for_training,
)
@classmethod
def from_row(cls, row: dict[str, str]) -> FieldNote:
use_for_training = str(row.get("use_for_training", "true")).lower() in {
"1",
"true",
"yes",
}
return cls(
created_at=row["created_at"],
model_id=row["model_id"],
prompt=row["prompt"],
response=row["response"],
correction=row["correction"],
tags=row["tags"],
image_path=row.get("image_path", ""),
video_path=row.get("video_path", ""),
use_for_training=use_for_training,
)
def to_dict(self) -> dict[str, object]:
return asdict(self)
class FieldNoteStore:
"""CSV-backed field note storage."""
def __init__(self, path: str | Path = "data/field_notes.csv") -> None:
self.path = Path(path)
def save(self, note: FieldNote) -> Path:
self.path.parent.mkdir(parents=True, exist_ok=True)
is_new = not self.path.exists()
with self.path.open("a", newline="", encoding="utf-8") as f:
writer = csv.DictWriter(f, fieldnames=list(note.to_dict()))
if is_new:
writer.writeheader()
writer.writerow(note.to_dict())
return self.path
def list_notes(
self,
corrected_only: bool = False,
tag: str = "",
training_only: bool = False,
) -> list[FieldNote]:
if not self.path.exists():
return []
with self.path.open(newline="", encoding="utf-8") as f:
rows = list(csv.DictReader(f))
notes = [FieldNote.from_row(row) for row in rows]
if corrected_only:
notes = [note for note in notes if note.correction.strip()]
if tag:
notes = [note for note in notes if tag in _split_tags(note.tags)]
if training_only:
notes = [note for note in notes if note.use_for_training]
return notes
def export_jsonl(
self,
output_path: str | Path = "data/field_notes.jsonl",
corrected_only: bool = True,
training_only: bool = True,
) -> Path:
output = Path(output_path)
output.parent.mkdir(parents=True, exist_ok=True)
notes = self.list_notes(
corrected_only=corrected_only,
training_only=training_only,
)
with output.open("w", encoding="utf-8") as f:
for note in notes:
f.write(json.dumps(note.to_dict(), ensure_ascii=False) + "\n")
return output
def export_hf_dataset(
self,
output_dir: str | Path = "data/hf_field_notes",
corrected_only: bool = True,
training_only: bool = True,
) -> Path:
target = Path(output_dir)
target.mkdir(parents=True, exist_ok=True)
data_file = self.export_jsonl(
target / "data.jsonl",
corrected_only=corrected_only,
training_only=training_only,
)
(target / "README.md").write_text(
"# Field Notes Dataset\n\n"
"Local export generated by OpenBMB Local AI Workbench.\n\n"
f"- Data file: `{data_file.name}`\n"
"- Intended split: `train`\n",
encoding="utf-8",
)
return target
class SQLiteFieldNoteStore:
"""SQLite-backed field note storage for larger correction loops."""
def __init__(self, path: str | Path = "data/field_notes.sqlite") -> None:
self.path = Path(path)
self.path.parent.mkdir(parents=True, exist_ok=True)
self._init_schema()
def _connect(self) -> sqlite3.Connection:
return sqlite3.connect(self.path)
def _init_schema(self) -> None:
with closing(self._connect()) as conn:
conn.execute(
"""
CREATE TABLE IF NOT EXISTS field_notes (
created_at TEXT NOT NULL,
model_id TEXT NOT NULL,
prompt TEXT NOT NULL,
response TEXT NOT NULL,
correction TEXT NOT NULL,
tags TEXT NOT NULL,
image_path TEXT NOT NULL,
video_path TEXT NOT NULL,
use_for_training INTEGER NOT NULL
)
"""
)
conn.commit()
def save(self, note: FieldNote) -> Path:
with closing(self._connect()) as conn:
conn.execute(
"""
INSERT INTO field_notes (
created_at, model_id, prompt, response, correction, tags,
image_path, video_path, use_for_training
)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
""",
(
note.created_at,
note.model_id,
note.prompt,
note.response,
note.correction,
note.tags,
note.image_path,
note.video_path,
int(note.use_for_training),
),
)
conn.commit()
return self.path
def list_notes(
self,
corrected_only: bool = False,
tag: str = "",
training_only: bool = False,
) -> list[FieldNote]:
with closing(self._connect()) as conn:
conn.row_factory = sqlite3.Row
rows = conn.execute(
"""
SELECT created_at, model_id, prompt, response, correction, tags,
image_path, video_path, use_for_training
FROM field_notes
ORDER BY created_at
"""
).fetchall()
notes = [
FieldNote(
created_at=str(row["created_at"]),
model_id=str(row["model_id"]),
prompt=str(row["prompt"]),
response=str(row["response"]),
correction=str(row["correction"]),
tags=str(row["tags"]),
image_path=str(row["image_path"]),
video_path=str(row["video_path"]),
use_for_training=bool(row["use_for_training"]),
)
for row in rows
]
if corrected_only:
notes = [note for note in notes if note.correction.strip()]
if tag:
notes = [note for note in notes if tag in _split_tags(note.tags)]
if training_only:
notes = [note for note in notes if note.use_for_training]
return notes
def _split_tags(tags: str) -> set[str]:
return {tag.strip() for tag in tags.split(",") if tag.strip()}
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