# CLAUDE.md
  

  This file provides comprehensive guidance for Claude Code (claude.ai/code) when working with this Search Engine codebase.

  
  ## Project Overview
  

  This is a **production-ready Multi-Tenant E-Commerce Search SaaS** platform specifically designed for Shoplazza (店匠) independent sites. It's a sophisticated search system that combines traditional keyword-based search with modern AI/ML capabilities, serving multiple tenants from a unified infrastructure.
  
  **Core Architecture Philosophy:**
  - **Unified Multi-Tenant Design**: Single Elasticsearch index with tenant isolation via `tenant_id`
  - **Hybrid Search Engine**: BM25 text relevance + Dense vector similarity (BGE-M3)
  - **SPU-Centric Indexing**: Product-level indexing with nested SKU structures
  - **Production-Grade**: Comprehensive error handling, monitoring, and operational features

  
  **Tech Stack:**

  - **Search Backend**: Elasticsearch 8.x with custom BM25 similarity (b=0.0, k1=0.0)
  - **Data Source**: MySQL (Shoplazza database) with custom data transformers
  - **Backend Framework**: FastAPI with async support and comprehensive middleware
  - **ML/AI Models**: BGE-M3 for text embeddings (1024-dim), CN-CLIP for image embeddings (1024-dim)
  - **Language Processing**: Multi-language support (Chinese, English, Russian) with DeepL API
  - **API Layer**: RESTful FastAPI service on port 6002 with auto-generated documentation
  - **Frontend**: Debugging UI on port 6003 with real-time search capabilities

  ## Development Environment

  **Required Environment Setup:** Default to the project venv via root `activate.sh` (activates `./.venv` and loads `.env`). Do not default to system `python3`/`pip3` for repo work.

  ```bash

  source activate.sh

  ```

  See `docs/QUICKSTART.md` §1.4–1.8 for first-time env creation and production credentials (`./scripts/create_venv.sh` for the main venv).
  
  **Environment Resolution Rules:**
  - Main app, backend, indexer, frontend, generic scripts, and most tests: `./.venv`
  - Translator service runtime and local translation model tooling: `./.venv-translator`
  - Embedding service runtime: `./.venv-embedding`
  - Reranker service runtime: `./.venv-reranker`
  - CN-CLIP service runtime: `./.venv-cnclip`
  - Never assume the system interpreter reflects project dependencies; prefer `source activate.sh` or invoke the exact venv binary directly.
  
  **Operational Rule For Commands:**
  - For repo-wide `pytest`, ad hoc Python scripts, and lightweight development commands, use the matching venv interpreter first.
  - For isolated services, prefer the service scripts (`./scripts/start_translator.sh`, `./scripts/start_embedding_service.sh`, `./scripts/start_reranker.sh`, `./scripts/start_cnclip_service.sh`) because they already select the correct environment.
  - If a dependency appears “missing”, check whether the command was run under the wrong venv before proposing installs.

  
  **Database Configuration:**

  ```yaml

  host: 120.79.247.228
  port: 3316
  database: saas
  username: saas
  password: P89cZHS5d7dFyc9R
  ```
  

  **Service Endpoints:**
  - **Backend API**: http://localhost:6002 (FastAPI)
  - **Frontend UI**: http://localhost:6003 (Debug interface)

  - **Search evaluation UI**: http://localhost:6010 (`eval-web`, `scripts/evaluation/`; start via `./scripts/service_ctl.sh start eval-web` or included in `./run.sh all`)

  - **Elasticsearch**: http://localhost:9200
  - **API Documentation**: http://localhost:6002/docs
  

  ## Common Development Commands

  ### Environment Setup
  ```bash

  # Activate main project environment (canonical)

  source activate.sh

  # First-time / new machine: create env and install deps

  ./setup.sh
  # or:
  ./scripts/create_venv.sh
  source activate.sh

  ```

  ### Data Management
  ```bash
  # Generate test data (Tenant1 Mock + Tenant2 CSV)
  ./scripts/mock_data.sh

  # Ingest data to Elasticsearch
  ./scripts/ingest.sh <tenant_id> [recreate]  # e.g., ./scripts/ingest.sh 1 true
  python main.py ingest data.csv --limit 1000 --batch-size 50
  ```

  ### Running Services
  ```bash
  # Start all services (production)
  ./run.sh

  # Start development server with auto-reload
  ./scripts/start_backend.sh
  python main.py serve --host 0.0.0.0 --port 6002 --reload

  # Start frontend debugging UI
  ./scripts/start_frontend.sh
  ```

  ### Testing
  ```bash
  # Run all tests

  pytest tests/

  # Run specific test types

  pytest tests/unit/          # Unit tests
  pytest tests/integration/   # Integration tests
  pytest -m "api"             # API tests only

  # Test search from command line
  python main.py search "query" --tenant-id 1 --size 10
  ```

  ### Development Utilities
  ```bash
  # Stop all services
  ./scripts/stop.sh

  # Test environment (for CI/development)
  ./scripts/start_test_environment.sh
  ./scripts/stop_test_environment.sh

  # Install server dependencies
  ./scripts/install_server_deps.sh
  ```

  ## Architecture Overview
  
  ### Core Components
  ```

  /data/tw/saas-search/

  ├── api/              # FastAPI REST API service (port 6002)
  ├── config/           # Configuration management system
  ├── indexer/          # MySQL → Elasticsearch data pipeline
  ├── search/           # Search engine and ranking logic
  ├── query/            # Query parsing, translation, rewriting
  ├── embeddings/       # ML models (BGE-M3, CN-CLIP)
  ├── scripts/          # Automation and utility scripts
  ├── utils/            # Shared utilities (ES client, etc.)
  ├── frontend/         # Simple debugging UI
  ├── mappings/         # Elasticsearch index mappings
  └── tests/            # Unit and integration tests
  ```
  
  ### Data Flow Architecture
  **Pipeline**: MySQL → Indexer → Elasticsearch → API → Frontend
  
  1. **Data Source Layer**:
     - Shoplazza MySQL database with `shoplazza_product_sku` and `shoplazza_product_spu` tables
     - Tenant-specific extension tables for custom attributes and multi-language fields
  
  2. **Indexing Layer** (`indexer/`):
     - Reads from MySQL, applies transformations with embeddings

     - Uses `SPUTransformer`, `BulkIndexingService`, and `IncrementalIndexerService` for batch processing

     - Supports both full and incremental indexing with embedding caching
  
  3. **Query Processing Layer** (`query/`):
     - `QueryParser`: Handles query rewriting, translation, and text embedding conversion
     - Multi-language support with automatic detection and translation
     - Boolean logic parsing with operator precedence: `()` > `ANDNOT` > `AND` > `OR` > `RANK`
  
  4. **Search Engine Layer** (`search/`):
     - `Searcher`: Executes hybrid searches combining BM25 and dense vectors
     - Configurable ranking expressions with function_score support
     - Multi-tenant isolation via `tenant_id` field
  
  5. **API Layer** (`api/`):
     - FastAPI service on port 6002 with multi-tenant support
     - Text search: `POST /search/`
     - Image search: `POST /image-search/`
     - Tenant identification via `X-Tenant-ID` header

  ### Multi-Tenant Configuration System

  The system uses centralized configuration through `config/config.yaml`:

  1. **Field Configuration** (`config/field_types.py`):
     - Defines field types: TEXT, KEYWORD, EMBEDDING, INT, DOUBLE, etc.
     - Specifies analyzers: Chinese (ansj), English, Arabic, Spanish, Russian, Japanese
     - Required fields and preprocessing rules

  2. **Index Configuration** (`mappings/search_products.json`):
     - Unified index structure shared by all tenants
     - Elasticsearch field mappings and analyzer configurations
     - BM25 similarity with modified parameters (`b=0.0, k1=0.0`)

  3. **Query Configuration** (`search/query_config.py`):
     - Query domain definitions (default, category_name, title, brand_name, etc.)
     - Ranking expressions and function_score configurations
     - Translation and embedding settings
  
  ### Embedding Models
  
  **Text Embedding** (`embeddings/bge_encoder.py`):
  - Uses BGE-M3 model (`Xorbits/bge-m3`)
  - Singleton pattern with thread-safe initialization
  - Generates 1024-dimensional vectors with GPU/CUDA support
  - Configurable caching to avoid recomputation

  **Image Embedding** (`embeddings/clip_encoder.py`):

  - Uses a configurable CN-CLIP model (default managed in `embeddings/config.py`)

  - Downloads and preprocesses images from URLs
  - Supports both local and remote image processing
  - Generates 1024-dimensional vectors
  
  ### Search and Ranking

  **Hybrid Search Approach**:
  - Combines traditional BM25 text relevance with dense vector similarity
  - Supports text embeddings (BGE-M3) and image embeddings (CN-CLIP)
  - Configurable ranking expressions like: `static_bm25() + 0.2*text_embedding_relevance() + general_score*2 + timeliness(end_time)`
  
  **Boolean Search Support**:
  - Full boolean logic with AND, OR, ANDNOT, RANK operators
  - Parentheses for complex query structures
  - Configurable operator precedence
  
  **Faceted Search**:
  - Terms and range faceting support
  - Multi-dimensional filtering capabilities
  - Configurable facet fields and aggregations
  
  ## Testing Infrastructure
  
  **Test Framework**: pytest with async support
  
  **Test Structure**:
  - `tests/conftest.py`: Comprehensive test fixtures and configuration
  - `tests/unit/`: Unit tests for individual components
  - `tests/integration/`: Integration tests for system workflows
  - Test markers: `@pytest.mark.unit`, `@pytest.mark.integration`, `@pytest.mark.api`
  
  **Test Data**:
  - Tenant1: Mock data with 10,000 product records
  - Tenant2: CSV-based test dataset
  - Automated test data generation via `scripts/mock_data.sh`
  
  **Key Test Fixtures** (from `conftest.py`):
  - `sample_search_config`: Complete configuration for testing
  - `mock_es_client`: Mocked Elasticsearch client
  - `test_searcher`: Searcher instance with mock dependencies
  - `temp_config_file`: Temporary YAML configuration for tests
  
  ## API Endpoints
  
  **Main API** (FastAPI on port 6002):
  - `POST /search/` - Text search with multi-language support
  - `POST /image-search/` - Image search using CN-CLIP embeddings
  - Health check and management endpoints
  - Multi-tenant support via `X-Tenant-ID` header
  
  **API Features**:
  - Hybrid search combining text and vector similarity
  - Configurable ranking and filtering
  - Faceted search with aggregations
  - Multi-language query processing and translation
  - Real-time search with configurable result sizes
  

  ## Core System Architecture & Design
  
  ### Unified Multi-Tenant Index Structure
  
  **Index Design Philosophy**: Single `search_products` index shared by all tenants with data isolation via `tenant_id` field.
  
  **Key Benefits**:
  - Resource efficiency and cost optimization
  - Simplified maintenance and operations
  - Better query performance through optimized sharding
  - Easier cross-tenant analytics and monitoring
  
  ### Index Structure (from mappings/search_products.json)
  
  **Core Document Structure** (SPU-level):
  ```json
  {
    "tenant_id": "keyword",           // Multi-tenant isolation
    "spu_id": "keyword",              // Product identifier

    "title.zh/en": "text",            // Multi-language titles
    "brief.zh/en": "text",            // Short descriptions
    "description.zh/en": "text",      // Detailed descriptions
    "vendor.zh/en": "text",           // Supplier/brand with keyword subfield
    "category_path.zh/en": "text",    // Hierarchical category paths
    "category_name_text.zh/en": "text",    // Category names for search

    "category1/2/3_name": "keyword",  // Multi-level category filtering
    "tags": "keyword",                // Product tags
    "specifications": "nested",       // Product variants (color, size, etc.)
    "skus": "nested",                 // Detailed SKU information
    "min_price/max_price": "float",   // Price range calculations
    "title_embedding": "dense_vector", // 1024-dim semantic vectors
    "image_embedding": "nested",      // Image vectors for visual search
    "total_inventory": "long"         // Aggregate inventory
  }
  ```
  
  **Analyzers Configuration**:
  - **Chinese fields**: `hanlp_index` (indexing) / `hanlp_standard` (searching)
  - **English fields**: `english` analyzer
  - **BM25 Similarity**: Custom parameters (b=0.0, k1=0.0) for optimized scoring
  
  ### Data Source Architecture (from 索引字段说明v2-参考表结构.md)
  
  **Primary MySQL Tables**:
  
  **shoplazza_product_spu** (Product Level):
  ```sql
  - id, shop_id, shoplazza_id, handle
  - title, brief, description, vendor
  - category, category_id, category_level, category_path
  - image_src, image_width, image_height
  - tags, fake_sales, published
  - inventory_policy, inventory_quantity
  - seo_title, seo_description, seo_keywords
  - tenant_id, create_time, update_time
  ```
  
  **shoplazza_product_sku** (Variant Level):
  ```sql
  - id, spu_id, shop_id, shoplazza_id
  - title, sku, barcode
  - price, compare_at_price, cost_price
  - option1, option2, option3 (variant values)
  - inventory_quantity, weight, weight_unit
  - image_src, wholesale_price, extend
  - tenant_id, create_time, update_time
  ```
  
  **Data Transformation Pipeline**:
  1. **SPU-Centric Aggregation**: Group SKUs under parent SPU
  2. **Multi-Language Field Mapping**: MySQL → ES bilingual fields
  3. **Category Path Parsing**: Extract hierarchical categories
  4. **Specifications Building**: Create nested variant structures
  5. **Price Range Calculation**: min/max across all SKUs
  6. **Vector Generation**: BGE-M3 embeddings for titles
  
  ### Advanced Search Configuration (from config/config.yaml)
  
  **Field Boost Configuration**:
  ```yaml
  field_boosts:

    title.zh/en: 3.0              # Highest priority
    brief.zh/en: 1.5              # Medium priority
    description.zh/en: 1.0        # Lower priority
    vendor.zh/en: 1.5             # Brand emphasis
    category_path.zh/en: 1.5      # Category relevance

    tags: 1.0                     # Tag matching
  ```
  
  **Search Domains**:
  - **default**: Comprehensive search across all text fields
  - **title**: Title-focused search (boost: 2.0)
  - **vendor**: Brand-specific search (boost: 1.5)
  - **category**: Category-focused search (boost: 1.5)
  - **tags**: Tag-based search (boost: 1.0)
  
  **Query Processing Features**:
  ```yaml
  query_config:
    supported_languages: ["zh", "en", "ru"]
    enable_translation: true        # DeepL API integration
    enable_text_embedding: true     # BGE-M3 vector search
    enable_query_rewrite: true      # Dictionary-based expansion

  ```
  
  **Ranking Formula**:
  ```
  bm25() + 0.2*text_embedding_relevance()
  ```
  
  ### Sophisticated Query Processing Pipeline
  
  **Multi-Language Search Architecture**:
  1. **Query Normalization**: Clean and standardize input
  2. **Language Detection**: Automatic identification (zh/en/ru)
  3. **Query Rewriting**: Dictionary-based expansion and synonyms
  4. **Translation Service**: DeepL API for cross-language search
  5. **Vector Generation**: BGE-M3 embeddings for semantic search
  6. **Boolean Parsing**: Complex expression evaluation
  
  **Boolean Expression Support**:
  - **Operators**: AND, OR, ANDNOT, RANK, parentheses
  - **Precedence**: `()` > `ANDNOT` > `AND` > `OR` > `RANK`
  - **Example**: `玩具 AND (乐高 OR 芭比) ANDNOT 电动`
  
  ### E-Commerce Specialized Features
  
  **Specifications System** (Product Variants):
  ```json
  "specifications": [
    {
      "sku_id": "sku_123",
      "name": "color",
      "value": "white"
    },
    {
      "sku_id": "sku_123",
      "name": "size",
      "value": "256GB"
    }
  ]
  ```
  
  **Advanced Filtering Logic**:
  - **Different dimensions** (different `name`): AND relationship
  - **Same dimension** (same `name`): OR relationship
  - **Example**: `(color=white OR color=black) AND size=256GB`
  
  **Faceted Search Capabilities**:
  - **Category Faceting**: Multi-level category aggregations
  - **Specifications Faceting**: Nested aggregations by variant name
  - **Range Faceting**: Price ranges, date ranges
  - **Multi-Select Support**: Disjunctive faceting for filters

  **SKU Filtering System**:
  - **Dimension-based Grouping**: Filter by `option1/2/3` or specification names
  - **Application Layer**: Performance-optimized filtering outside ES
  - **Use Case**: Display one SKU per variant combination (e.g., one per color)

  ### API Architecture & Usage（见 `docs/搜索API对接指南-00-总览与快速开始.md` 及分册 `-01`…`-10`）

  **Core API Endpoints**:
  ```
  POST /search/                    # Main text search
  POST /search/image              # Image search (CN-CLIP)
  GET /search/{doc_id}            # Document retrieval
  GET /admin/health              # Health check
  GET /admin/config              # Configuration info
  GET /admin/stats               # Index statistics
  ```
  
  **Request Structure**:
  ```json
  {
    "query": "string (required)",
    "size": 10, "from": 0,
    "language": "zh|en",
    "filters": {}, "range_filters": {},
    "facets": [],
    "sort_by": "price|sales|create_time",
    "sort_order": "asc|desc",
    "sku_filter_dimension": ["color", "size"],
    "min_score": 0.0,
    "debug": false
  }
  ```
  
  **Advanced Filter Examples**:
  
  **Specifications Filtering**:
  ```json
  {
    "filters": {
      "specifications": {
        "name": "color",
        "value": "white"
      }
    }
  }
  ```
  
  **Multi-Dimension Specifications**:
  ```json
  {
    "filters": {
      "specifications": [
        {"name": "color", "value": "white"},
        {"name": "size", "value": "256GB"}
      ]
    }
  }
  ```
  
  **Range Filtering**:
  ```json
  {
    "range_filters": {
      "min_price": {"gte": 50, "lte": 200},
      "create_time": {"gte": "2024-01-01T00:00:00Z"}
    }
  }
  ```
  
  **Faceted Search Configuration**:
  ```json
  {
    "facets": [
      {"field": "category1_name", "size": 15, "type": "terms"},
      {"field": "specifications.color", "size": 20, "type": "terms"},
      {"field": "min_price", "type": "range", "ranges": [...]}
    ]
  }
  ```
  
  ### Multi-Select Faceting (NEW FEATURE)
  

  **Standard Mode** (`disjunctive: false`):

  - Behavior: Selected value becomes the only option shown
  - Use Case: Hierarchical category navigation
  - Example: Toys → Dolls → Barbie
  

  **Multi-Select Mode** (`disjunctive: true`) ⭐:

  - Behavior: All options remain visible after selection
  - Use Case: Colors, brands, sizes (switchable attributes)
  - Example: Select "red" but still see "blue", "green", etc.
  
  **Recommended Configuration**:
  | Facet Type | Multi-Select | Reason |
  |-----------|-------------|---------|
  | Color | `true` | Users need to switch colors |
  | Brand | `true` | Users need to compare brands |
  | Size | `true` | Users need to check other sizes |
  | Category | `false` | Hierarchical navigation |
  | Price Range | `false` | Mutually exclusive ranges |
  
  ### Production Features & Operations
  
  **Comprehensive Error Handling**:
  - Graceful degradation for model failures
  - Fallback mechanisms for service unavailability
  - Detailed error logging and context tracking
  
  **Performance Optimizations**:
  - Embedding caching to avoid redundant computations
  - Adaptive vector search (disabled for short queries)
  - Batch processing for data indexing
  - Connection pooling for database operations
  
  **Security & Multi-Tenancy**:
  - Strict tenant isolation via `tenant_id` filtering
  - Rate limiting with SlowAPI integration
  - CORS and security headers middleware
  - Request context logging for auditing
  
  **Monitoring & Observability**:
  - Structured logging with request tracing
  - Health check endpoints for all dependencies
  - Performance metrics and timing information
  - Index statistics and document counts
  
  ### Data Model Insights
  
  **Key Design Decisions**:
  
  1. **SPU over SKU Indexing**: Each ES document represents a product (SPU) with nested SKUs
     - Reduces index size and improves search performance
     - Maintains variant information through nested structures
  
  2. **Bilingual Field Strategy**: Separate `*_zh` and `*_en` fields
     - Enables language-specific analyzer configuration
     - Provides fallback mechanisms for missing translations
  
  3. **Nested vs Flat Design**: Strategic use of nested vs flattened fields
     - `specifications` and `skus`: Nested for complex queries
     - `min_price`, `total_inventory`: Flattened for filtering/sorting
  
  4. **Vector Field Isolation**: Embedding fields only used for search
     - Not returned in API responses (index: false where appropriate)
     - Reduces network payload and improves response times
  
  ### AI/ML Integration Details
  
  **Text Embedding Pipeline**:
  - **Model**: BGE-M3 (`Xorbits/bge-m3`)
  - **Dimensions**: 1024-dimensional vectors
  - **Hardware**: GPU/CUDA acceleration with CPU fallback
  - **Caching**: Redis-based caching for common queries
  - **Usage**: Semantic search combined with BM25 relevance
  
  **Image Search Pipeline**:

  - **Model**: CN-CLIP (configured in `embeddings/config.py`)

  - **Processing**: URL download → preprocessing → vectorization
  - **Storage**: Nested structure with vector + original URL
  - **Application**: Visual similarity search for products
  
  **Translation Integration**:
  - **Service**: DeepL API with configurable auth
  - **Languages**: Chinese ↔ English ↔ Russian support
  - **Caching**: Translation result caching
  - **Fallback**: Mock mode returns original text if API unavailable
  
  ## Development & Deployment
  
  **Environment Configuration**:
  ```bash
  # Core Services
  ./run.sh                    # Start all services
  ./scripts/start_backend.sh  # Backend only (port 6002)
  ./scripts/start_frontend.sh # Frontend UI (port 6003)
  
  # Data Operations
  ./scripts/ingest.sh <tenant_id> [recreate]  # Index data
  ./scripts/mock_data.sh                    # Generate test data
  
  # Testing
  python -m pytest tests/    # Full test suite
  python main.py search "query" --tenant-id 1  # Quick search test
  ```
  
  **Key Files for Configuration**:
  - `config/config.yaml`: Search behavior configuration
  - `mappings/search_products.json`: ES index structure
  - `.env`: Environment variables and secrets
  - `api/models.py`: Pydantic request/response models
  
  **Common Development Tasks**:
  1. **Modifying Search Behavior**: Edit `config/config.yaml`
  2. **Changing Index Structure**: Update `mappings/search_products.json`
  3. **Adding New Filters**: Extend `api/models.py` with new Pydantic models

  4. **Testing Queries**: Use frontend UI at http://localhost:6003

  
  ## Key Implementation Details

  1. **Environment Variables**: All sensitive configuration in `.env` (template: `.env.example`)
  2. **Configuration Management**: Centralized YAML config with validation
  3. **Error Handling**: Comprehensive exception handling with proper HTTP status codes
  4. **Performance**: Batch processing, embedding caching, connection pooling
  5. **Logging**: Structured logging with request tracing and context
  6. **Security**: Tenant isolation, rate limiting, CORS, security headers
  7. **API Documentation**: Auto-generated FastAPI docs at `/docs`
  8. **Multi-tenant Architecture**: Single index with `tenant_id` isolation
  9. **Hybrid Search**: BM25 + vector similarity with configurable weighting
  10. **Production Ready**: Health checks, monitoring, graceful degradation