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4f45746 c4e128a 4f45746 c4e128a 4f45746 c4e128a 4f45746 c4e128a 4f45746 c4e128a 4f45746 c4e128a 4f45746 c4e128a 4f45746 c4e128a 4f45746 c4e128a 4f45746 c4e128a 4f45746 c4e128a 4f45746 c4e128a 4f45746 c4e128a 4f45746 | 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 | """kdc ingest command — full PDF ingestion pipeline."""
from __future__ import annotations
from pathlib import Path
import typer
from rich.console import Console
from rich.panel import Panel
from rich.progress import (
BarColumn,
MofNCompleteColumn,
Progress,
SpinnerColumn,
TextColumn,
TimeElapsedColumn,
)
from rich.table import Table
from app.collectors.local import LocalCollector
from app.collectors.pipeline import IngestionPipeline, IngestionStats
from app.models.document import DocumentMeta
from app.utils.container import Container
from app.utils.file import human_size
app = typer.Typer(help="Ingest PDFs, calculate SHA256, deduplicate, store, and build metadata.")
console = Console()
@app.command()
def run(
ctx: typer.Context,
source: Path = typer.Option(
Path("pdf"),
"--source",
"-s",
"--pdf-dir",
help="Input directory containing PDFs.",
),
pdf_dir: Path = typer.Option(
Path("pdf"),
"--pdf-dir-out",
help="Output directory to store organized corpus PDFs.",
),
meta_dir: Path = typer.Option(
Path("metadata"), "--meta-dir", help="Metadata JSON output directory."
),
render_previews: bool = typer.Option(
True, "--preview/--no-preview", help="Render preview images."
),
export_dataset: bool = typer.Option(
True, "--export/--no-export", help="Export Parquet/JSONL datasets."
),
) -> None:
"""Ingest PDFs from SOURCE folder, calculate SHA256, assign UUIDs, store PDFs, build metadata, skip duplicates."""
container: Container = ctx.obj
source_dir = source.resolve()
pdf_dir_out = pdf_dir.resolve()
meta_dir_out = meta_dir.resolve()
meta_dir_out.mkdir(parents=True, exist_ok=True)
pdf_dir_out.mkdir(parents=True, exist_ok=True)
collector = LocalCollector(pdf_dir=source_dir)
collected_files = list(collector.collect())
if not collected_files:
console.print(f"[yellow]No PDF files found in {source_dir}[/yellow]")
raise typer.Exit()
pipeline = IngestionPipeline(
extractor=container.extractor,
schema_validator=container.schema_validator,
content_validator=container.content_validator,
dedup_registry=container.dedup_registry,
preview_renderer=container.renderer,
parquet_exporter=container.parquet_exporter,
jsonl_exporter=container.jsonl_exporter,
)
stats = IngestionStats(total_found=len(collected_files))
valid_docs: list[DocumentMeta] = []
console.print(
Panel.fit(
f"[bold cyan]Ingesting {stats.total_found} PDF(s) from [yellow]{source_dir}[/yellow]"
)
)
with Progress(
SpinnerColumn(),
TextColumn("[progress.description]{task.description}"),
BarColumn(),
MofNCompleteColumn(),
TimeElapsedColumn(),
console=console,
) as progress:
task = progress.add_task("Processing PDFs...", total=stats.total_found)
for item in collected_files:
progress.update(task, description=f"[cyan]Ingesting {item.path.name[:30]}[/cyan]")
doc_meta, status = pipeline.process_file(
pdf_path=item.path,
category_hint=item.category_hint,
target_pdf_dir=pdf_dir_out,
target_meta_dir=meta_dir_out,
render_preview=render_previews,
)
if status == "success" and doc_meta is not None:
valid_docs.append(doc_meta)
stats.ingested += 1
stats.total_pages += doc_meta.pages
stats.total_bytes += doc_meta.file_size_bytes
elif status == "duplicate":
stats.duplicates_skipped += 1
elif status == "invalid":
stats.invalid_skipped += 1
else:
stats.errors += 1
progress.advance(task)
if container.dedup_registry:
container.dedup_registry.save()
# Rich summary table
table = Table(
title="Ingestion Pipeline Summary",
show_header=True,
header_style="bold green",
)
table.add_column("Metric", style="cyan")
table.add_column("Value", justify="right", style="bold white")
table.add_row("Total Files Discovered", str(stats.total_found))
table.add_row("Successfully Ingested & Stored", f"[green]{stats.ingested}[/green]")
table.add_row("Duplicates Skipped (SHA256)", f"[yellow]{stats.duplicates_skipped}[/yellow]")
table.add_row("Invalid Files Skipped", f"[red]{stats.invalid_skipped}[/red]")
table.add_row("Extraction Errors", f"[red]{stats.errors}[/red]")
table.add_row("Total Pages Extracted", str(stats.total_pages))
table.add_row("Total Corpus Size", human_size(stats.total_bytes))
console.print()
console.print(table)
# Export datasets
if export_dataset and valid_docs:
if container.parquet_exporter:
p_path = container.parquet_exporter.export(valid_docs)
console.print(f"[green]✓ Parquet dataset exported to {p_path}[/green]")
if container.jsonl_exporter:
j_path = container.jsonl_exporter.export(valid_docs)
console.print(f"[green]✓ JSONL dataset exported to {j_path}[/green]")
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