# Quick Start Guide
  
  ## Prerequisites
  
  1. **Python 3.8+**
  2. **Elasticsearch 8.x** (running on localhost:9200 or remote)
  3. **Optional**: CUDA-enabled GPU for faster embeddings
  
  ## Installation
  
  ### 1. Install Dependencies
  
  ```bash
  cd /data/tw/SearchEngine
  pip install -r requirements.txt
  ```
  
  ### 2. Set Environment Variables (Optional)
  
  ```bash
  # Elasticsearch
  export ES_HOST="http://localhost:9200"
  
  # DeepL API (for translation)
  export DEEPL_API_KEY="your-api-key-here"
  

  # Tenant ID
  export TENANT_ID="tenant1"

  ```
  
  ## Running the System
  
  ### Option 1: Quick Test (Without Full Data)
  
  ```bash
  # 1. Start Elasticsearch (if not running)
  docker run -d -p 9200:9200 -e "discovery.type=single-node" elasticsearch:8.11.0
  
  # 2. Ingest sample data (100 documents for quick test)

  cd data/tenant1
  python ingest_tenant1.py \

    --limit 100 \
    --recreate-index \
    --es-host http://localhost:9200 \
    --skip-embeddings
  
  # 3. Start API service
  cd ../..

  python -m api.app --host 0.0.0.0 --port 6002

  
  # 4. Test search

  curl -X POST http://localhost:6002/search/ \

    -H "Content-Type: application/json" \
    -d '{"query": "消防", "size": 5}'
  ```
  
  ### Option 2: Full System with Embeddings
  
  ```bash
  # 1. Start Elasticsearch
  docker run -d -p 9200:9200 -e "discovery.type=single-node" -e "ES_JAVA_OPTS=-Xms4g -Xmx4g" elasticsearch:8.11.0
  
  # 2. Ingest full dataset with embeddings (requires GPU, takes ~10-30 min)

  cd data/tenant1
  python ingest_tenant1.py \

    --csv goods_with_pic.5years_congku.csv.shuf.1w \
    --recreate-index \
    --batch-size 100 \
    --es-host http://localhost:9200
  
  # 3. Start API service
  cd ../..
  python -m api.app \
    --host 0.0.0.0 \

    --port 6002 \

    --tenant tenant1 \

    --es-host http://localhost:9200
  
  # 4. Test various searches
  # Simple search

  curl -X POST http://localhost:6002/search/ \

    -H "Content-Type: application/json" \
    -d '{"query": "芭比娃娃", "size": 10}'
  
  # Boolean search

  curl -X POST http://localhost:6002/search/ \

    -H "Content-Type: application/json" \
    -d '{"query": "toy AND (barbie OR doll)", "size": 10}'
  
  # With filters

  curl -X POST http://localhost:6002/search/ \

    -H "Content-Type: application/json" \
    -d '{"query": "娃娃", "size": 10, "filters": {"categoryName_keyword": "芭比"}}'
  ```
  
  ## API Documentation
  
  Once the service is running, visit:

  - **Swagger UI**: http://localhost:6002/docs
  - **ReDoc**: http://localhost:6002/redoc

  
  ## Common Issues
  
  ### Issue: Elasticsearch connection failed
  **Solution**: Ensure Elasticsearch is running and accessible
  ```bash
  curl http://localhost:9200
  ```
  
  ### Issue: Model download fails
  **Solution**: Check internet connection, models are downloaded from Hugging Face/ModelScope
  ```bash
  # Pre-download models (optional)
  python -c "from embeddings import BgeEncoder; BgeEncoder()"
  python -c "from embeddings import CLIPImageEncoder; CLIPImageEncoder()"
  ```
  
  ### Issue: Out of memory during embedding generation
  **Solution**: Reduce batch size or skip embeddings initially
  ```bash

  python ingest_tenant1.py --skip-embeddings --limit 1000

  ```
  
  ### Issue: Translation not working
  **Solution**: Set DeepL API key or translations will use mock mode (returns original text)
  ```bash
  export DEEPL_API_KEY="your-key"
  ```
  
  ## Testing
  
  ### Test Health
  ```bash

  curl http://localhost:6002/admin/health

  ```
  
  ### Test Configuration
  ```bash

  curl http://localhost:6002/admin/config

  ```
  
  ### Test Index Stats
  ```bash

  curl http://localhost:6002/admin/stats

  ```
  
  ### Test Search
  ```bash
  # Chinese query (auto-translates to English/Russian)

  curl -X POST http://localhost:6002/search/ \

    -H "Content-Type: application/json" \
    -d '{"query": "消防套", "size": 5}'
  
  # English query

  curl -X POST http://localhost:6002/search/ \

    -H "Content-Type: application/json" \
    -d '{"query": "fire control set", "size": 5}'
  
  # Russian query

  curl -X POST http://localhost:6002/search/ \

    -H "Content-Type: application/json" \
    -d '{"query": "Наборы для пожаротушения", "size": 5}'
  ```
  
  ## What's Next?
  

  1. **Customize Configuration**: Edit `config/schema/tenant1_config.yaml`

  2. **Add More Data**: Ingest your own product data
  3. **Tune Ranking**: Adjust ranking expression in config
  4. **Add Rewrite Rules**: Update via API `/admin/rewrite-rules`
  5. **Monitor Performance**: Check `/admin/stats` endpoint
  6. **Scale**: Deploy to production with proper ES cluster
  
  ## Architecture Quick Reference
  
  ```
  Query Flow:
  User Query → QueryParser (normalize, rewrite, translate, embed)
           → Searcher (boolean parse, build ES query)
           → Elasticsearch (BM25 + KNN)
           → RankingEngine (custom scoring)
           → Results
  
  Indexing Flow:
  CSV Data → DataTransformer (field mapping, embeddings)
          → BulkIndexer (batch processing)
          → Elasticsearch
  ```
  
  ## Support
  
  For issues or questions, refer to:
  - **README.md**: Comprehensive documentation
  - **IMPLEMENTATION_SUMMARY.md**: Technical details
  - **CLAUDE.md**: Development guidelines

  - **API Docs**: http://localhost:6002/docs