dazhuang
acd73431ae
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>
89 lines
2.4 KiB
Python
89 lines
2.4 KiB
Python
"""Request models for ERP AI Assistant API.
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This module defines Pydantic models for API request validation.
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"""
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from typing import Optional
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from pydantic import BaseModel, Field
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class AnalyzeRequest(BaseModel):
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"""Request model for requirement analysis.
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Attributes:
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input_type: Type of input - 'natural_language' or 'structured'
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content: Requirement content text
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session_id: Optional session ID for context continuity
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"""
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input_type: str = Field(
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...,
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description="输入类型: natural_language | structured"
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)
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content: str = Field(..., description="需求内容")
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session_id: Optional[str] = Field(None, description="会话ID")
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model_config = {
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"json_schema_extra": {
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"examples": [
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{
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"input_type": "natural_language",
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"content": "创建一个销售订单管理页面",
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"session_id": "session-123"
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}
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]
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}
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}
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class GenerateRequest(BaseModel):
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"""Request model for config generation.
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Attributes:
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session_id: Session ID from previous analysis
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requirements: Structured requirements from analysis
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"""
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session_id: str = Field(..., description="会话ID")
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requirements: dict = Field(..., description="结构化需求")
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model_config = {
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"json_schema_extra": {
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"examples": [
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{
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"session_id": "session-123",
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"requirements": {
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"功能名称": "销售订单管理",
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"功能类型": "列表页面"
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}
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}
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]
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}
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}
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class ExecuteRequest(BaseModel):
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"""Request model for config execution.
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Attributes:
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session_id: Session ID for tracking
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confirmed: User confirmation flag
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backup_enabled: Whether to create backup before execution
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"""
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session_id: str = Field(..., description="会话ID")
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confirmed: bool = Field(False, description="用户确认标识")
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backup_enabled: bool = Field(True, description="是否启用备份")
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model_config = {
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"json_schema_extra": {
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"examples": [
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{
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"session_id": "session-123",
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"confirmed": True,
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"backup_enabled": True
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}
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]
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}
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} |