Datasets:
File size: 5,699 Bytes
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import argparse
import json
import sys
from dataclasses import asdict, dataclass
from pathlib import Path
BACKEND_DIR = Path(__file__).resolve().parents[1]
PROJECT_ROOT = BACKEND_DIR.parent
CHATBOT_SERVICE_DIR = PROJECT_ROOT / 'chatbot_service'
if str(CHATBOT_SERVICE_DIR) not in sys.path:
sys.path.insert(0, str(CHATBOT_SERVICE_DIR))
from rag.document_loader import LoadedDocument, load_documents # noqa: E402
from rag.embeddings import normalize_text # noqa: E402
DEFAULT_PERSIST_DIR = BACKEND_DIR / 'data' / 'chroma_db'
@dataclass(slots=True)
class IndexedChunk:
chunk_id: str
source: str
title: str
category: str
content: str
def _default_source_dirs() -> list[Path]:
return [
BACKEND_DIR / 'datasets' / 'legal',
CHATBOT_SERVICE_DIR / 'data' / 'legal',
CHATBOT_SERVICE_DIR / 'data' / 'medical',
]
def _origin_label(path: Path) -> str:
try:
return str(path.resolve().relative_to(PROJECT_ROOT)).replace('\\', '/')
except ValueError:
return str(path.resolve()).replace('\\', '/')
def _merged_category(origin: Path, document: LoadedDocument) -> str:
base_category = origin.name.lower() or 'general'
if document.category and document.category != 'general':
return f'{base_category}/{document.category}'
return base_category
def _chunk_document(document: LoadedDocument, *, origin_label: str, category: str) -> list[IndexedChunk]:
paragraphs = [normalize_text(item) for item in document.text.split('\n') if normalize_text(item)]
if not paragraphs:
paragraphs = [document.text]
chunks: list[IndexedChunk] = []
current: list[str] = []
current_length = 0
chunk_index = 1
source = f'{origin_label}/{document.source}'
for paragraph in paragraphs:
if current and current_length + len(paragraph) > 900:
chunks.append(
IndexedChunk(
chunk_id=f'{source}:{chunk_index}',
source=source,
title=document.title,
category=category,
content='\n'.join(current),
)
)
chunk_index += 1
current = []
current_length = 0
current.append(paragraph)
current_length += len(paragraph)
if current:
chunks.append(
IndexedChunk(
chunk_id=f'{source}:{chunk_index}',
source=source,
title=document.title,
category=category,
content='\n'.join(current),
)
)
return chunks
def _collect_chunks(source_dirs: list[Path]) -> tuple[list[IndexedChunk], dict[str, int]]:
chunks: list[IndexedChunk] = []
source_counts: dict[str, int] = {}
for source_dir in source_dirs:
if not source_dir.exists():
continue
origin_label = _origin_label(source_dir)
documents = load_documents(source_dir)
source_counts[origin_label] = len(documents)
for document in documents:
category = _merged_category(source_dir, document)
chunks.extend(_chunk_document(document, origin_label=origin_label, category=category))
return chunks, source_counts
def _write_index(persist_dir: Path, chunks: list[IndexedChunk], source_counts: dict[str, int]) -> None:
persist_dir.mkdir(parents=True, exist_ok=True)
index_path = persist_dir / 'simple_index.json'
manifest_path = persist_dir / 'manifest.json'
index_path.write_text(
json.dumps([asdict(chunk) for chunk in chunks], ensure_ascii=False, indent=2),
encoding='utf-8',
)
manifest_path.write_text(
json.dumps(
{
'chunk_count': len(chunks),
'category_count': len({chunk.category for chunk in chunks}),
'sources': source_counts,
},
ensure_ascii=False,
indent=2,
),
encoding='utf-8',
)
def main() -> None:
parser = argparse.ArgumentParser(
description='Build a lightweight local document index for backend legal and medical datasets.',
)
parser.add_argument(
'--source-dir',
action='append',
type=Path,
help='Optional source directory. Repeat to include multiple directories. Defaults to backend legal plus chatbot legal/medical data.',
)
parser.add_argument(
'--persist-dir',
type=Path,
default=DEFAULT_PERSIST_DIR,
help=f'Directory for the generated JSON index. Defaults to {DEFAULT_PERSIST_DIR}',
)
parser.add_argument(
'--mirror-chatbot-index',
action='store_true',
help='Also copy the generated index into chatbot_service/data/chroma_db.',
)
args = parser.parse_args()
source_dirs = args.source_dir or _default_source_dirs()
chunks, source_counts = _collect_chunks(source_dirs)
if not chunks:
raise SystemExit(
'No supported documents were found. Add PDFs, CSVs, JSON, TXT, or MD files to one of: '
+ ', '.join(str(path) for path in source_dirs)
)
_write_index(args.persist_dir, chunks, source_counts)
print(
f'Indexed {len(chunks)} chunks from {sum(source_counts.values())} documents '
f'into {args.persist_dir / "simple_index.json"}'
)
if args.mirror_chatbot_index:
mirror_dir = CHATBOT_SERVICE_DIR / 'data' / 'chroma_db'
_write_index(mirror_dir, chunks, source_counts)
print(f'Mirrored the index to {mirror_dir / "simple_index.json"}')
if __name__ == '__main__':
main()
|