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d0a567e | 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 | import os
import sqlite3
import lancedb
import PyPDF2
from sentence_transformers import SentenceTransformer
from lancedb.pydantic import LanceModel, Vector
import warnings
# Suppress warnings
warnings.filterwarnings("ignore")
# CONFIGURATION
DATA_DIR = "data"
DB_NAME = "epstein.db"
VECTOR_DB_DIR = "data/lancedb"
print("Initializing models and databases...")
# 1. Setup SQLite (For Keyword Search)
conn = sqlite3.connect(DB_NAME)
cursor = conn.cursor()
# Create main table and FTS (Full Text Search) virtual table
cursor.execute("""
CREATE TABLE IF NOT EXISTS pages (
id INTEGER PRIMARY KEY AUTOINCREMENT,
filename TEXT,
filepath TEXT,
page_number INTEGER,
text_content TEXT
)
""")
cursor.execute("""
CREATE VIRTUAL TABLE IF NOT EXISTS pages_fts USING fts5(
filename,
text_content,
content='pages',
content_rowid='id'
)
""")
cursor.execute("""
CREATE TRIGGER IF NOT EXISTS pages_ai AFTER INSERT ON pages BEGIN
INSERT INTO pages_fts(rowid, filename, text_content) VALUES (new.id, new.filename, new.text_content);
END;
""")
conn.commit()
# 2. Setup LanceDB (For Vector/AI Search)
model = SentenceTransformer('all-MiniLM-L6-v2')
ldb = lancedb.connect(VECTOR_DB_DIR)
# --- THE FIX: Use Pydantic to define the Schema ---
class PageSchema(LanceModel):
vector: Vector(384) # 384 is the dimension of all-MiniLM-L6-v2
text: str
filename: str
page_number: int
filepath: str
# Create or Open the table using the Class Schema
try:
tbl = ldb.open_table("pages")
except:
tbl = ldb.create_table("pages", schema=PageSchema)
# --------------------------------------------------
def chunk_text(text, chunk_size=500):
"""Split long page text into smaller chunks for better vector search"""
words = text.split()
chunks = []
current_chunk = []
current_length = 0
for word in words:
current_length += len(word) + 1
current_chunk.append(word)
if current_length >= chunk_size:
chunks.append(" ".join(current_chunk))
current_chunk = []
current_length = 0
if current_chunk:
chunks.append(" ".join(current_chunk))
return chunks
def process_pdf(filepath):
filename = os.path.basename(filepath)
print(f"Processing: {filename}...")
try:
with open(filepath, 'rb') as f:
reader = PyPDF2.PdfReader(f)
num_pages = len(reader.pages)
for i in range(num_pages):
try:
page = reader.pages[i]
text = page.extract_text()
# Junk Filter: Skip pages with too little text
if not text or len(text.strip()) < 50:
continue
clean_text = text.replace('\x00', '') # Remove null bytes
# A. Insert into SQLite (Keyword Search)
cursor.execute(
"INSERT INTO pages (filename, filepath, page_number, text_content) VALUES (?, ?, ?, ?)",
(filename, filepath, i + 1, clean_text)
)
# B. Insert into LanceDB (Vector Search)
chunks = chunk_text(clean_text)
vectors = model.encode(chunks)
data_to_add = []
for chunk, vector in zip(chunks, vectors):
data_to_add.append({
"vector": vector,
"text": chunk,
"filename": filename,
"page_number": i + 1,
"filepath": filepath
})
if data_to_add:
tbl.add(data_to_add)
except Exception as e:
print(f" Error on page {i+1}: {e}")
conn.commit()
except Exception as e:
print(f"Failed to read {filename}: {e}")
def main():
print(f"Scanning directory: {DATA_DIR}")
pdf_count = 0
for root, dirs, files in os.walk(DATA_DIR):
for file in files:
if file.lower().endswith('.pdf'):
full_path = os.path.join(root, file)
process_pdf(full_path)
pdf_count += 1
print(f"Done! Processed {pdf_count} PDF files.")
print("Run 'python app.py' next to start the server.")
if __name__ == "__main__":
main() |