清理旧代码，具体如下：

1. 删除 IndexingPipeline 类文件：indexer/bulk_indexer.py 删除：IndexingPipeline 类（第201-259行）删除：不再需要的 load_mapping 导入 2. 删除 main.py 中的旧代码删除：cmd_ingest() 函数（整个函数）删除：ingest 子命令定义删除：main() 中对 ingest 命令的处理删除：不再需要的 pandas 导入更新：文档字符串，移除 ingest 命令说明 3. 删除旧的数据导入脚本删除：data/customer1/ingest_customer1.py（依赖已废弃的 DataTransformer 和 IndexingPipeline）

清理旧代码，具体如下：
1. 删除 IndexingPipeline 类文件：indexer/bulk_indexer.py 删除：IndexingPipeline 类（第201-259行）删除：不再需要的 load_mapping 导入 2. 删除 main.py 中的旧代码删除：cmd_ingest() 函数（整个函数）删除：ingest 子命令定义删除：main() 中对 ingest 命令的处理删除：不再需要的 pandas 导入更新：文档字符串，移除 ingest 命令说明 3. 删除旧的数据导入脚本删除：data/customer1/ingest_customer1.py（依赖已废弃的 DataTransformer 和 IndexingPipeline）
tangwang
1 parent 5ac64fc7
Showing 5 changed files with 6 additions and 324 deletions Show diff stats
CLAUDE.md
data/customer1/ingest_customer1.py
docs/常用查询 - ES.md
indexer/bulk_indexer.py
main.py
@@ -132,7 +132,7 @@ python main.py search &quot;query&quot; --tenant-id 1 --size 10
 2. **Indexing Layer** (`indexer/`):
    - Reads from MySQL, applies transformations with embeddings
-   - Uses `DataTransformer` and `IndexingPipeline` for batch processing
+   - Uses `SPUTransformer`, `BulkIndexingService`, and `IncrementalIndexerService` for batch processing
    - Supports both full and incremental indexing with embedding caching
 3. **Query Processing Layer** (`query/`):
@@ -1,198 +0,0 @@
-#!/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
-from typing import Optional
-
-# Add parent directory to path (go up 3 levels: customer1 -> data -> SearchEngine -> root)
-project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-sys.path.insert(0, project_root)
-
-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('--es-username', default=None, help='Elasticsearch username (or set ES_USERNAME env var)')
-    parser.add_argument('--es-password', default=None, help='Elasticsearch password (or set ES_PASSWORD env var)')
-    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}")
-    
-    # Get credentials: prioritize command-line args, then environment variables, then .env file
-    es_username = args.es_username
-    es_password = args.es_password
-    
-    # If not provided via args, try to load from .env file via env_config
-    if not es_username or not es_password:
-        try:
-            from config.env_config import get_es_config
-            es_config = get_es_config()
-            es_username = es_username or es_config.get('username')
-            es_password = es_password or es_config.get('password')
-        except Exception:
-            # Fallback to environment variables
-            es_username = es_username or os.getenv('ES_USERNAME')
-            es_password = es_password or os.getenv('ES_PASSWORD')
-    
-    # Create ES client with credentials if available
-    if es_username and es_password:
-        print(f"  Using authentication: {es_username}")
-        es_client = ESClient(hosts=[args.es_host], username=es_username, password=es_password)
-    else:
-        print(f"  Warning: No authentication credentials found")
-        print(f"  Attempting connection without authentication (will fail if ES requires auth)")
-        es_client = ESClient(hosts=[args.es_host])
-
-    if not es_client.ping():
-        print("Failed to connect to Elasticsearch")
-        print("\nTroubleshooting:")
-        print("  1. Check if Elasticsearch is running: curl http://localhost:9200")
-        print("  2. If ES requires authentication, provide credentials:")
-        print("     - Use --es-username and --es-password arguments, or")
-        print("     - Set ES_USERNAME and ES_PASSWORD environment variables")
-        print("  3. Verify the host URL is correct: --es-host")
-        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())
@@ -233,7 +233,7 @@ curl -u &#39;essa:4hOaLaf41y2VuI8y&#39;   -X GET &#39;http://localhost:9200/search_products/
 curl -u 'essa:4hOaLaf41y2VuI8y' -X GET 'http://localhost:9200/search_products/_count?pretty' -H 'Content-Type: application/json' -d '{
   "query": {
     "term": {
-      "tenant_id": "162"
+      "tenant_id": "170"
     }
   }
 }'
@@ -7,7 +7,7 @@ Handles batch indexing of documents with progress tracking and error handling.
 from typing import List, Dict, Any, Optional
 from elasticsearch.helpers import bulk, BulkIndexError
 from utils.es_client import ESClient
-from indexer.mapping_generator import load_mapping, DEFAULT_INDEX_NAME
+from indexer.mapping_generator import DEFAULT_INDEX_NAME
 import time
@@ -196,64 +196,3 @@ class BulkIndexer:
         except Exception as e:
             print(f"[BulkIndexer] Update by query failed: {e}")
             return 0
-
-
-class IndexingPipeline:
-    """Complete indexing pipeline from source data to ES."""
-
-    def __init__(
-        self,
-        es_client: ESClient,
-        data_transformer,
-        index_name: str = None,
-        recreate_index: bool = False
-    ):
-        """
-        Initialize indexing pipeline.
-
-        Args:
-            es_client: Elasticsearch client
-            data_transformer: Data transformer instance
-            index_name: Index name (defaults to DEFAULT_INDEX_NAME)
-            recreate_index: Whether to recreate index if exists
-        """
-        self.es_client = es_client
-        self.transformer = data_transformer
-        self.index_name = index_name or DEFAULT_INDEX_NAME
-        self.recreate_index = recreate_index
-
-    def run(self, df, batch_size: int = 100) -> Dict[str, Any]:
-        """
-        Run complete indexing pipeline.
-
-        Args:
-            df: Source dataframe
-            batch_size: Batch size for processing
-
-        Returns:
-            Indexing statistics
-        """
-        # Load and create index
-        mapping = load_mapping()
-
-        if self.recreate_index:
-            if self.es_client.index_exists(self.index_name):
-                print(f"[IndexingPipeline] Deleting existing index: {self.index_name}")
-                self.es_client.delete_index(self.index_name)
-
-        if not self.es_client.index_exists(self.index_name):
-            print(f"[IndexingPipeline] Creating index: {self.index_name}")
-            self.es_client.create_index(self.index_name, mapping)
-        else:
-            print(f"[IndexingPipeline] Using existing index: {self.index_name}")
-
-        # Transform data
-        print(f"[IndexingPipeline] Transforming {len(df)} documents...")
-        documents = self.transformer.transform_batch(df, batch_size=batch_size)
-        print(f"[IndexingPipeline] Transformed {len(documents)} documents")
-
-        # Bulk index
-        indexer = BulkIndexer(self.es_client, self.index_name, batch_size=500)
-        results = indexer.index_documents(documents, id_field="skuId")
-
-        return results
@@ -3,8 +3,8 @@
 Main entry point for SearchEngine operations.
 Provides a unified CLI for common operations:
-- ingest: Ingest data into Elasticsearch
-- serve: Start API service
+- serve: Start API service (search + admin routes)
+- serve-indexer: Start dedicated Indexer API service
 - search: Test search from command line
 """
@@ -12,7 +12,6 @@ import sys
 import os
 import argparse
 import json
-import pandas as pd
 import uvicorn
 # Add parent directory to path
@@ -23,53 +22,6 @@ from utils import ESClient
 from search import Searcher
-def cmd_ingest(args):
-    """Run data ingestion."""
-    # Local imports to avoid hard dependency at module import time
-    import pandas as pd
-    from embeddings import BgeEncoder, CLIPImageEncoder
-    from indexer.bulk_indexer import IndexingPipeline
-    # NOTE: DataTransformer was referenced historically, but the concrete
-    # implementation is now provided via customer-specific scripts
-    # (e.g. data/customer1/ingest_customer1.py). If you still need a generic
-    # ingestion pipeline here, you can wire your own transformer.
-    from indexer.spu_transformer import SPUTransformer as DataTransformer
-    print("Starting data ingestion")
-
-    # Load config
-    config_loader = ConfigLoader("config/config.yaml")
-    config = config_loader.load_config()
-
-    # Initialize ES
-    es_client = ESClient(hosts=[args.es_host])
-    if not es_client.ping():
-        print(f"ERROR: Cannot connect to Elasticsearch at {args.es_host}")
-        return 1
-
-    # Load data
-    df = pd.read_csv(args.csv_file)
-    if args.limit:
-        df = df.head(args.limit)
-    print(f"Loaded {len(df)} documents")
-
-    # Initialize encoders
-    text_encoder = None if args.skip_embeddings else BgeEncoder()
-    image_encoder = None if args.skip_embeddings else CLIPImageEncoder()
-
-    # Transform and index
-    transformer = DataTransformer(config, text_encoder, image_encoder, use_cache=True)
-    pipeline = IndexingPipeline(config, es_client, transformer, recreate_index=args.recreate)
-
-    results = pipeline.run(df, batch_size=args.batch_size)
-
-    print(f"\nIngestion complete:")
-    print(f"  Success: {results['success']}")
-    print(f"  Failed: {results['failed']}")
-    print(f"  Time: {results['elapsed_time']:.2f}s")
-
-    return 0
-
-
 def cmd_serve(args):
     """Start API service."""
     os.environ['ES_HOST'] = args.es_host
@@ -154,15 +106,6 @@ def main():
     subparsers = parser.add_subparsers(dest='command', help='Command to execute')
-    # Ingest command
-    ingest_parser = subparsers.add_parser('ingest', help='Ingest data into Elasticsearch')
-    ingest_parser.add_argument('csv_file', help='Path to CSV data file')
-    ingest_parser.add_argument('--es-host', default='http://localhost:9200', help='Elasticsearch host')
-    ingest_parser.add_argument('--limit', type=int, help='Limit number of documents')
-    ingest_parser.add_argument('--batch-size', type=int, default=100, help='Batch size')
-    ingest_parser.add_argument('--recreate', action='store_true', help='Recreate index')
-    ingest_parser.add_argument('--skip-embeddings', action='store_true', help='Skip embeddings')
-
     # Serve command
     serve_parser = subparsers.add_parser('serve', help='Start API service (multi-tenant)')
     serve_parser.add_argument('--host', default='0.0.0.0', help='Host to bind to')
@@ -197,9 +140,7 @@ def main():
         return 1
     # Execute command
-    if args.command == 'ingest':
-        return cmd_ingest(args)
-    elif args.command == 'serve':
+    if args.command == 'serve':
         return cmd_serve(args)
     elif args.command == 'serve-indexer':
         return cmd_serve_indexer(args)