bulk importer did not work using docling

This commit is contained in:
stephen 2026-06-13 12:41:59 +10:00
parent 31de93dad3
commit 1def894c48
4 changed files with 1979 additions and 51 deletions

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@ -1,78 +1,89 @@
# core/bulk_importer.py
import re
from database.connection import get_connection
from docling.document_converter import DocumentConverter
class BulkImporter:
def __init__(self):
# Matches typical patterns: "Spanish word", "English translation", "Unit metadata"
self.row_regex = re.compile(r'"([^"]+)"\s*,\s*"([^"]+)"\s*,\s*"([^"]+)"')
# Converter engine for handling multi-column documents
self.converter = DocumentConverter()
# Matches textbook unit codes (e.g., U5_3D, U7 4A, UG_11B)
self.unit_regex = re.compile(r'U([0-9]+)')
def clean_text(self, text: str) -> str:
"""Strips newlines and extra spaces from extracted data fields."""
return text.replace('\n', ' ').strip()
"""Cleans syntax breaks, structural commas, and quotes from strings."""
if not text:
return ""
return text.replace('\n', ' ').replace('"', '').replace("'", "").strip()
def parse_unit(self, context_str: str) -> int:
"""
Extracts the unit integer from codes like 'U5_3D', 'U7 4A', or 'UG_11B'.
Returns None if it's a general marker like 'UT LEX'.
"""
match = re.search(r'U([0-9])', context_str)
"""Extracts the exact Unit integer from codes like U5_3D or U2 1A."""
match = self.unit_regex.search(context_str)
if match:
return int(match.group(1))
return None
return None # General reference markers like 'UT LEX' or 'UG'
def import_pdf_glossary(self, file_path: str, textbook_name: str):
"""Converts multi-column PDF via Docling and maps items to SQLite."""
print(f"🔄 Docling is analyzing structural layout for '{file_path}'...")
try:
# Render layout-aware structural conversion
result = self.converter.convert(file_path)
document_text = result.document.export_to_markdown()
except Exception as e:
print(f"❌ Docling processing failed: {e}")
return 0
def import_glossary_file(self, file_path: str, textbook_name: str):
"""Reads the structural text lines and inserts them into the translation database."""
conn = get_connection()
cursor = conn.cursor()
print(f"📖 Starting bulk ingestion for '{file_path}'...")
print("📥 Parsing text structures and writing to database...")
count = 0
# Regex targeted to catch both standard blocks and tabular lines safely
# Matches patterns like: "Spanish Word", "English Translation", "Unit Code"
pattern = re.compile(r'([^,\n"\[]+?)\s*,\s*([^,\n"\[]+?)\s*,\s*(U[G|T|0-9][^\n,]+)')
matches = pattern.findall(document_text)
try:
with open(file_path, 'r', encoding='utf-8') as f:
content = f.read()
# Find all matching row structures inside the text
matches = self.row_regex.findall(content)
for es_raw, en_raw, ctx_raw in matches:
spanish_text = self.clean_text(es_raw)
english_text = self.clean_text(en_raw)
context_tag = self.clean_text(ctx_raw)
unit_number = self.parse_unit(context_tag)
# Skip header rows or structural markers
if spanish_text.lower() in ["alphabetical glossary", "spanish", "word"]:
# Avoid table headers or non-lexical entries
if spanish_text.lower() in ["spanish", "word", "alphabetical glossary"] or len(spanish_text) <= 1:
continue
# 1. Insert Spanish Term
# 1. Insert Spanish Term Entry
cursor.execute("""
INSERT INTO phrases (text, language, textbook, unit, source_context)
VALUES (?, 'es', ?, ?, ?)
""", (spanish_text, textbook_name, unit_number, context_tag))
es_id = cursor.lastrowid
# 2. Insert English Term
# 2. Insert English Translation Entry
cursor.execute("""
INSERT INTO phrases (text, language, textbook, unit, source_context)
VALUES (?, 'en', ?, ?, ?)
""", (english_text, textbook_name, unit_number, context_tag))
en_id = cursor.lastrowid
# 3. Create Bidirectional Bridges
# 3. Create Bidirectional Cross-Reference Records
cursor.execute("INSERT INTO translations (source_phrase_id, target_phrase_id) VALUES (?, ?)", (es_id, en_id))
cursor.execute("INSERT INTO translations (source_phrase_id, target_phrase_id) VALUES (?, ?)", (en_id, es_id))
count += 1
conn.commit()
print(f"🎉 Successfully imported {count} linked glossary pairs into SQLite!")
print(f"🎉 Bulk ingestion complete! Registered {count} matched glossary items.")
return count
except Exception as e:
conn.rollback()
print(f"❌ Error during bulk data ingestion: {e}")
print(f"❌ Error during database transaction: {e}")
return 0
finally:
conn.close()

33
main.py
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@ -1,33 +1,20 @@
# main.py
import sys
import asyncio
from database.connection import init_db
from core.phrase_manager import PhraseManager
from core.bulk_importer import BulkImporter
async def test_pipeline():
print("🚀 Booting Castilian Voice Trainer Ingestion Engine...")
# Ensure database is present
async def main():
# Keep database schemas active and verified
init_db()
# Initialize the phrase controller manager
manager = PhraseManager()
# Initialize our ingestion utility
importer = BulkImporter()
# Test Entry: Let's log an authentic textbook phrase from Aula Internacional
print("\n📥 Processing sample entry...")
success = await manager.add_translation_pair(
spanish_text="¿Cómo se pronuncia esta palabra?",
english_text="How do you pronounce this word?",
textbook="Aula Internacional 1 Plus",
unit=2,
context="Glossary / Lesson Terms",
voice_gender="female" # Let's verify Elvira's voice on this one!
# Process the file sitting right in your root directory
importer.import_pdf_glossary(
file_path="aula_int_plus_1_glos_en_alfa.pdf",
textbook_name="Aula Internacional 1 Plus"
)
if success:
print("\n🎉 Verification Phase 1 Pipeline Test complete!")
else:
print("\n⚠️ Pipeline processing failed.")
if __name__ == "__main__":
asyncio.run(test_pipeline())
asyncio.run(main())

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@ -5,6 +5,7 @@ description = "Add your description here"
readme = "README.md"
requires-python = ">=3.13"
dependencies = [
"docling>=2.102.1",
"edge-tts>=7.2.8",
"fastdtw>=0.3.4",
"genanki>=0.13.1",

1929
uv.lock

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