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#!/usr/bin/env python3
"""
Customer1 data ingestion script.
Loads data from CSV and indexes into Elasticsearch with embeddings.
"""
import sys
import os
import pandas as pd
import argparse
# Add parent directory to path
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from config import ConfigLoader
from utils import ESClient, get_connection_from_config
from indexer import DataTransformer, IndexingPipeline
from embeddings import BgeEncoder, CLIPImageEncoder
def load_csv_data(csv_path: str, limit: Optional[int] = None) -> pd.DataFrame:
"""
Load data from CSV file.
Args:
csv_path: Path to CSV file
limit: Maximum number of rows to load (None for all)
Returns:
DataFrame with data
"""
print(f"[Ingestion] Loading data from: {csv_path}")
df = pd.read_csv(csv_path)
if limit:
df = df.head(limit)
print(f"[Ingestion] Loaded {len(df)} rows")
print(f"[Ingestion] Columns: {df.columns.tolist()}")
return df
def main():
"""Main ingestion function."""
parser = argparse.ArgumentParser(description='Ingest customer1 data into Elasticsearch')
parser.add_argument('--config', default='customer1', help='Customer config name')
parser.add_argument('--csv', default='data/customer1/goods_with_pic.5years_congku.csv.shuf.1w',
help='Path to CSV data file')
parser.add_argument('--limit', type=int, help='Limit number of documents to index')
parser.add_argument('--batch-size', type=int, default=100, help='Batch size for processing')
parser.add_argument('--recreate-index', action='store_true', help='Recreate index if exists')
parser.add_argument('--es-host', default='http://localhost:9200', help='Elasticsearch host')
parser.add_argument('--skip-embeddings', action='store_true', help='Skip embedding generation')
args = parser.parse_args()
print("="*60)
print("Customer1 Data Ingestion")
print("="*60)
# Load configuration
print(f"\n[1/6] Loading configuration: {args.config}")
config_loader = ConfigLoader("config/schema")
config = config_loader.load_customer_config(args.config)
# Validate configuration
errors = config_loader.validate_config(config)
if errors:
print(f"Configuration validation failed:")
for error in errors:
print(f" - {error}")
return 1
print(f"Configuration loaded successfully")
print(f" - Index: {config.es_index_name}")
print(f" - Fields: {len(config.fields)}")
print(f" - Indexes: {len(config.indexes)}")
# Initialize Elasticsearch client
print(f"\n[2/6] Connecting to Elasticsearch: {args.es_host}")
os.environ['ES_HOST'] = args.es_host
es_client = ESClient(hosts=[args.es_host])
if not es_client.ping():
print("Failed to connect to Elasticsearch")
return 1
print("Connected to Elasticsearch successfully")
# Load data
print(f"\n[3/6] Loading data from CSV")
df = load_csv_data(args.csv, limit=args.limit)
# Initialize encoders (if not skipping embeddings)
text_encoder = None
image_encoder = None
if not args.skip_embeddings:
print(f"\n[4/6] Initializing embedding encoders")
print("This may take a few minutes on first run (downloading models)...")
try:
text_encoder = BgeEncoder()
print("Text encoder initialized")
except Exception as e:
print(f"Warning: Failed to initialize text encoder: {e}")
print("Continuing without text embeddings...")
try:
image_encoder = CLIPImageEncoder()
print("Image encoder initialized")
except Exception as e:
print(f"Warning: Failed to initialize image encoder: {e}")
print("Continuing without image embeddings...")
else:
print(f"\n[4/6] Skipping embedding generation (--skip-embeddings)")
# Initialize data transformer
print(f"\n[5/6] Initializing data transformation pipeline")
transformer = DataTransformer(
config=config,
text_encoder=text_encoder,
image_encoder=image_encoder,
use_cache=True
)
# Run indexing pipeline
print(f"\n[6/6] Starting indexing pipeline")
pipeline = IndexingPipeline(
config=config,
es_client=es_client,
data_transformer=transformer,
recreate_index=args.recreate_index
)
results = pipeline.run(df, batch_size=args.batch_size)
# Print summary
print("\n" + "="*60)
print("Ingestion Complete!")
print("="*60)
print(f"Total documents: {results['total']}")
print(f"Successfully indexed: {results['success']}")
print(f"Failed: {results['failed']}")
print(f"Time elapsed: {results['elapsed_time']:.2f}s")
print(f"Throughput: {results['docs_per_second']:.2f} docs/s")
if results['errors']:
print(f"\nFirst few errors:")
for error in results['errors'][:5]:
print(f" - {error}")
# Verify index
print(f"\nVerifying index...")
doc_count = es_client.count(config.es_index_name)
print(f"Documents in index: {doc_count}")
print("\nIngestion completed successfully!")
return 0
if __name__ == "__main__":
sys.exit(main())
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