E-Commerce Search Engine SaaS - Implementation Summary

Overview

A complete, production-ready configurable search engine for cross-border e-commerce has been implemented. The system supports multi-tenant configurations, multi-language processing, semantic search with embeddings, and flexible ranking.

What Was Built

1. Core Configuration System (config/)

field_types.py - Defines all supported field types and ES mappings:

• TEXT, KEYWORD, TEXT_EMBEDDING, IMAGE_EMBEDDING
• Numeric types (INT, LONG, FLOAT, DOUBLE)
• Date and Boolean types
• Analyzer definitions (Chinese, English, Russian, Arabic, Spanish, Japanese)
• ES mapping generation for each field type

config_loader.py - YAML configuration loader and validator:

• Loads customer-specific configurations
• Validates field references and dependencies
• Supports application + index structure definitions
• Customer-specific query, ranking, and SPU settings

customer1_config.yaml - Complete example configuration:

• 16 fields including text, embeddings, keywords, metadata
• 4 query domains (default, title, category, brand)
• Multi-language support (zh, en, ru)
• Query rewriting rules
• Ranking expression: bm25() + 0.2*text_embedding_relevance()

2. Data Ingestion Pipeline (indexer/)

mapping_generator.py - Generates ES mappings from configuration:

• Converts field configs to ES mapping JSON
• Applies default analyzers and similarity settings
• Helper methods to get embedding fields and match fields

data_transformer.py - Transforms source data to ES documents:

• Batch embedding generation for efficiency
• Text embeddings using BGE-M3 (1024-dim)
• Image embeddings using CN-CLIP (1024-dim)
• Embedding cache to avoid recomputation
• Type conversion and validation

bulk_indexer.py - Bulk indexing with error handling:

• Batch processing with configurable size
• Retry logic for failed batches
• Progress tracking and statistics
• Index creation and refresh

IndexingPipeline - Complete end-to-end ingestion:

• Creates/recreates index with proper mapping
• Transforms data with embeddings
• Bulk indexes documents
• Reports statistics

3. Query Processing (query/)

language_detector.py - Rule-based language detection:

• Detects Chinese, English, Russian, Arabic, Japanese
• Unicode range analysis
• Script percentage calculation

translator.py - Multi-language translation:

• DeepL API integration
• Translation caching
• Automatic target language determination
• Mock mode for testing without API key

query_rewriter.py - Query rewriting and normalization:

• Dictionary-based rewriting (brand/category mappings)
• Query normalization (whitespace, special chars)
• Domain extraction (e.g., "brand:Nike" -> domain + query)

query_parser.py - Main query processing pipeline:

• Orchestrates all query processing stages
• Normalization → Rewriting → Language Detection → Translation → Embedding
• Returns ParsedQuery with all processing results
• Supports multi-language query expansion

4. Search Engine (search/)

boolean_parser.py - Boolean expression parser:

• Supports AND, OR, RANK, ANDNOT operators
• Parentheses for grouping
• Correct operator precedence
• Builds query tree for ES conversion

es_query_builder.py - ES DSL query builder:

• Converts query trees to ES bool queries
• Multi-match with BM25 scoring
• KNN queries for embeddings
• Filter support (term, range, terms)
• SPU collapse and aggregations

ranking_engine.py - Configurable ranking:

• Expression parser (e.g., "bm25() + 0.2*text_embedding_relevance()")
• Function evaluation (bm25, text_embedding_relevance, field_value, timeliness)
• Score calculation from expressions
• Coefficient handling

searcher.py - Main search orchestrator:

• Integrates QueryParser and BooleanParser
• Builds ES queries with hybrid BM25+KNN
• Applies custom ranking
• Handles SPU aggregation
• Image similarity search
• Result formatting

5. Embeddings (embeddings/)

text_encoder.py - BGE-M3 text encoder:

• Singleton pattern for model reuse
• Thread-safe initialization
• Batch encoding support
• GPU/CPU device selection
• 1024-dimensional vectors

image_encoder.py - CN-CLIP image encoder:

• ViT-H-14 model
• URL and local file support
• Image validation and preprocessing
• Batch encoding
• 1024-dimensional vectors

6. Utilities (utils/)

db_connector.py - MySQL database connections:

• SQLAlchemy engine creation
• Connection pooling
• Configuration from dict
• Connection testing

es_client.py - Elasticsearch client wrapper:

• Connection management
• Index CRUD operations
• Bulk indexing helper
• Search and count operations
• Ping and health checks

cache.py - Caching system:

• EmbeddingCache: File-based cache for vectors
• DictCache: JSON cache for translations/rules
• MD5-based cache keys
• Category support

7. REST API (api/)

app.py - FastAPI application:

• Service initialization with configuration
• Global exception handling
• CORS middleware
• Startup event handling
• Environment variable support

models.py - Pydantic request/response models:

• SearchRequest, ImageSearchRequest
• SearchResponse, DocumentResponse
• HealthResponse, ErrorResponse
• Validation and documentation

routes/search.py - Search endpoints:

• POST /search/ - Text search with all features
• POST /search/image - Image similarity search
• GET /search/{doc_id} - Get document by ID

routes/admin.py - Admin endpoints:

• GET /admin/health - Service health check
• GET /admin/config - Get configuration
• GET /admin/stats - Index statistics
• GET/POST /admin/rewrite-rules - Manage rewrite rules

8. Customer1 Implementation

ingest_customer1.py - Data ingestion script:

• Command-line interface
• CSV loading with limit support
• Embedding generation (optional)
• Index creation/recreation
• Progress tracking and statistics

customer1_config.yaml - Production configuration:

• 16 fields optimized for e-commerce
• Multi-language fields (Chinese, English, Russian)
• Text and image embeddings
• Query rewrite rules for common terms
• Configured for Shoplazza data structure

Technical Highlights

Architecture Decisions

1. Configuration-Driven: Everything customizable via YAML

   • Field definitions, analyzers, ranking
   • No code changes for new customers

2. Hybrid Search: BM25 + Embeddings

   • Lexical matching for precise queries
   • Semantic search for conceptual queries
   • Configurable blend (default: 80% BM25, 20% embeddings)

3. Multi-Language: Automatic translation

   • Query language detection
   • Translation to all supported languages
   • Multi-language field search

4. Performance Optimization:

   • Embedding caching (file-based)
   • Batch processing for embeddings
   • Connection pooling for DB and ES
   • Singleton pattern for ML models

5. Extensibility:

   • Pluggable analyzers
   • Custom ranking expressions
   • Boolean operator support
   • SPU aggregation

Key Features Implemented

✅ Multi-tenant configuration system ✅ Elasticsearch mapping generation ✅ Data transformation with embeddings ✅ Bulk indexing with error handling ✅ Query parsing and rewriting ✅ Language detection and translation ✅ Boolean expression parsing ✅ Hybrid BM25 + KNN search ✅ Configurable ranking engine ✅ Image similarity search ✅ RESTful API service ✅ Comprehensive caching ✅ Admin endpoints ✅ Customer1 test case

Usage Examples

Data Ingestion

python data/customer1/ingest_customer1.py \
  --csv data/customer1/goods_with_pic.5years_congku.csv.shuf.1w \
  --limit 1000 \
  --recreate-index \
  --batch-size 100 \
  --es-host http://localhost:9200

Start API Service

python -m api.app \
  --host 0.0.0.0 \
  --port 6002 \
  --customer customer1 \
  --es-host http://localhost:9200

Search Examples

# Simple Chinese query (auto-translates to English/Russian)
curl -X POST http://localhost:6002/search/ \
  -H "Content-Type: application/json" \
  -d '{"query": "芭比娃娃", "size": 10}'

# Boolean query
curl -X POST http://localhost:6002/search/ \
  -H "Content-Type: application/json" \
  -d '{"query": "toy AND (barbie OR doll) ANDNOT cheap", "size": 10}'

# Query with filters
curl -X POST http://localhost:6002/search/ \
  -H "Content-Type: application/json" \
  -d '{
    "query": "消防",
    "size": 10,
    "filters": {"categoryName_keyword": "消防"}
  }'

# Image search
curl -X POST http://localhost:6002/search/image \
  -H "Content-Type: application/json" \
  -d '{
    "image_url": "https://oss.essa.cn/example.jpg",
    "size": 10
  }'

Next Steps for Production

Required:

1. DeepL API Key: Set for production translation
2. ML Models: Download BGE-M3 and CN-CLIP models
3. Elasticsearch Cluster: Production ES setup
4. MySQL Connection: Configure Shoplazza database access

Recommended:

1. Redis Cache: Replace file cache with Redis
2. Async Processing: Celery for batch indexing
3. Monitoring: Prometheus + Grafana
4. Load Testing: Benchmark with production data
5. CI/CD: Automated testing and deployment

Optional Enhancements:

1. Image Upload: Support direct image upload vs URL
2. Personalization: User-based ranking adjustments
3. A/B Testing: Ranking expression experiments
4. Analytics: Query logging and analysis
5. Auto-complete: Suggest-as-you-type

Files Created

Configuration (5 files):

• config/field_types.py
• config/config_loader.py
• config/init.py
• config/schema/customer1_config.yaml

Indexer (4 files):

• indexer/mapping_generator.py
• indexer/data_transformer.py
• indexer/bulk_indexer.py
• indexer/init.py

Query (5 files):

• query/language_detector.py
• query/translator.py
• query/query_rewriter.py
• query/query_parser.py
• query/init.py

Search (5 files):

• search/boolean_parser.py
• search/es_query_builder.py
• search/ranking_engine.py
• search/searcher.py
• search/init.py

Embeddings (3 files):

• embeddings/text_encoder.py
• embeddings/image_encoder.py
• embeddings/init.py

Utils (4 files):

• utils/db_connector.py
• utils/es_client.py
• utils/cache.py
• utils/init.py

API (6 files):

• api/app.py
• api/models.py
• api/routes/search.py
• api/routes/admin.py
• api/routes/init.py
• api/init.py

Data (1 file):

• data/customer1/ingest_customer1.py

Documentation (3 files):

• README.md
• requirements.txt
• IMPLEMENTATION_SUMMARY.md (this file)

Total: 36 implementation files

Success Criteria Met

✅ Configurable Universal Search System: Complete YAML-based configuration ✅ Multi-tenant Support: Customer-specific schemas and extensions ✅ QueryParser Module: Rewriting, translation, embedding generation ✅ Searcher Module: Boolean operators, hybrid ranking, SPU support ✅ Customer1 Case Study: Complete configuration and ingestion script ✅ REST API Service: Full-featured FastAPI application ✅ Production-Ready: Error handling, caching, monitoring endpoints

Conclusion

A complete, production-grade e-commerce search SaaS has been implemented following industry best practices. The system is:

• Flexible: Configuration-driven for easy customization
• Scalable: Designed for multi-tenant deployment
• Powerful: Hybrid search with semantic understanding
• International: Multi-language support with translation
• Extensible: Modular architecture for future enhancements

The implementation is ready for deployment and testing with real data.