feat: implement ERP AI Assistant Phase 1

Backend (FastAPI + SQLAlchemy + Claude API + RAG):
- Config management with Pydantic v2
- Database engine with connection pooling and SQL injection prevention
- AI engine with Claude API integration (support custom base URL)
- RAG engine with ChromaDB and sentence-transformers
- Requirement analysis service
- Config generation service
- Executor engine with SQL validation
- REST API endpoints: /analyze, /generate, /execute

Frontend (Vue 3 + Element Plus + Pinia):
- Complete 3-step workflow: analyze → generate → execute
- Step indicator with progress visualization
- Analysis result display with field table
- SQL preview with monospace font
- Execute confirmation dialog with safety warning
- Execution result display
- State management with Pinia
- API service integration

Security:
- SQL injection prevention with parameterized queries
- Dangerous SQL operation blocking
- Database password URL encoding
- Transaction auto-rollback
- Pydantic config validation

Features:
- Natural language requirement analysis
- Automated SQL configuration generation
- Safe execution with human review
- LAN access support
- Custom Claude API endpoint support

Documentation:
- README with quick start guide
- Quick start guide
- LAN access configuration
- Dependency fixes guide
- Claude API configuration
- Git operation guide
- Implementation report

Dependencies fixed:
- numpy<2.0.0 for chromadb compatibility
- sentence-transformers==2.7.0 for huggingface_hub compatibility

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

backend/app/core/db_engine.py

@@ -0,0 +1,78 @@
+from typing import Optional
+from sqlalchemy import create_engine, text
+from sqlalchemy.orm import sessionmaker
+from contextlib import contextmanager
+from loguru import logger
+from app.config import get_settings
+
+
+class DatabaseEngine:
+    """数据库操作引擎"""
+
+    def __init__(self):
+        settings = get_settings()
+        self.engine = create_engine(
+            settings.DATABASE_URL,
+            pool_size=20,
+            max_overflow=10,
+            pool_pre_ping=True,
+            echo=settings.DEBUG
+        )
+        self.Session = sessionmaker(bind=self.engine)
+
+    @contextmanager
+    def get_session(self):
+        """获取数据库会话（上下文管理器）"""
+        session = self.Session()
+        try:
+            yield session
+            session.commit()
+        except Exception as e:
+            session.rollback()
+            logger.error(f"数据库操作失败：{e}")
+            raise
+        finally:
+            session.close()
+
+    def execute_sql(self, sql: str, params: Optional[dict] = None) -> list:
+        """执行单条 SQL"""
+        with self.get_session() as session:
+            result = session.execute(text(sql), params or {})
+            return result.fetchall()
+
+    def execute_transaction(self, sql_list: list, params_list: Optional[list] = None) -> bool:
+        """执行事务（多条 SQL）"""
+        params_list = params_list or [None] * len(sql_list)
+        with self.get_session() as session:
+            for sql, params in zip(sql_list, params_list):
+                session.execute(text(sql), params or {})
+        return True
+
+    def get_table_structure(self, table_name: str):
+        """获取表结构（安全参数化查询）"""
+        sql = """
+        SELECT
+            COLUMN_NAME,
+            DATA_TYPE,
+            CHARACTER_MAXIMUM_LENGTH,
+            IS_NULLABLE,
+            COLUMN_DEFAULT
+        FROM INFORMATION_SCHEMA.COLUMNS
+        WHERE TABLE_NAME = :table_name
+        ORDER BY ORDINAL_POSITION
+        """
+        return self.execute_sql(sql, {"table_name": table_name})
+
+    def table_exists(self, table_name: str) -> bool:
+        """检查表是否存在（安全参数化查询）"""
+        sql = """
+        SELECT COUNT(*)
+        FROM INFORMATION_SCHEMA.TABLES
+        WHERE TABLE_NAME = :table_name
+        """
+        result = self.execute_sql(sql, {"table_name": table_name})
+        return result[0][0] > 0
+
+    def dispose(self):
+        """关闭连接池，释放资源"""
+        self.engine.dispose()