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>
This commit is contained in:
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backend/app/services/__init__.py
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backend/app/services/__init__.py
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"""Service modules for ERP AI Assistant."""
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backend/app/services/config_service.py
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backend/app/services/config_service.py
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"""Config Generation Service.
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This module provides the ConfigService class for generating ERP platform
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configuration SQL based on structured requirements.
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"""
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from typing import Dict, Any
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from loguru import logger
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from app.core.ai_engine import ClaudeEngine
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from app.core.rag_engine import RAGEngine
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from app.core.prompts import SYSTEM_PROMPT, GENERATE_PROMPT_TEMPLATE
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from app.core.db_engine import DatabaseEngine
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class ConfigService:
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"""Service for generating ERP platform configuration.
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This service uses Claude AI with RAG knowledge retrieval to generate
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SQL configuration statements based on structured requirements.
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"""
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def __init__(self) -> None:
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"""Initialize config service with required engines."""
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self.ai_engine = ClaudeEngine()
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self.rag_engine = RAGEngine()
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self.db_engine = DatabaseEngine()
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logger.info("ConfigService initialized")
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async def generate(
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self,
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requirements: Dict[str, Any],
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session_id: str
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) -> Dict[str, Any]:
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"""Generate configuration SQL based on requirements.
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Args:
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requirements: Structured requirement specification
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session_id: Session ID for tracking
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Returns:
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Configuration plan with SQL statements
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Raises:
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ValueError: If requirements are invalid
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Exception: If generation fails
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"""
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if not requirements:
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raise ValueError("Requirements cannot be empty")
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function_name = requirements.get("功能名称", "Unknown")
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logger.info(f"[{session_id}] Starting config generation for: {function_name}")
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try:
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# Step 1: Retrieve platform rules for form type
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form_type = requirements.get("窗体类型", "0")
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logger.debug(f"[{session_id}] Retrieving platform rules for form type: {form_type}")
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platform_rules = self._get_platform_rules(form_type)
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logger.info(f"[{session_id}] Retrieved platform rules")
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# Step 2: Retrieve similar cases
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logger.debug(f"[{session_id}] Retrieving similar cases")
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similar_cases = self._get_similar_cases(function_name)
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logger.info(f"[{session_id}] Retrieved similar cases")
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# Step 3: Build prompt
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prompt = GENERATE_PROMPT_TEMPLATE.format(
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requirements=str(requirements),
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platform_rules=platform_rules,
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similar_cases=similar_cases
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)
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messages = [
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{"role": "user", "content": SYSTEM_PROMPT},
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{"role": "assistant", "content": "我已了解，请提供需求信息。"},
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{"role": "user", "content": prompt}
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]
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# Step 4: Call Claude API
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logger.debug(f"[{session_id}] Calling Claude API for config generation")
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response = await self.ai_engine.call_claude(messages, temperature=0.5)
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# Step 5: Parse JSON response
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result = self.ai_engine.parse_json_response(response)
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logger.success(f"[{session_id}] Config generation completed")
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return result
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except Exception as e:
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logger.error(f"[{session_id}] Config generation failed: {e}")
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raise
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def _get_platform_rules(self, form_type: str) -> str:
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"""Retrieve platform configuration rules for specific form type.
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Args:
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form_type: Form type code
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Returns:
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Platform rules text
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"""
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try:
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results = self.rag_engine.search(
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f"窗体类型{form_type}配置规则",
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top_k=2
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)
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if not results:
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return "未找到相关配置规则"
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return "\n\n".join([r["content"] for r in results])
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except Exception as e:
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logger.warning(f"Failed to retrieve platform rules: {e}")
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return "无法获取平台配置规则"
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def _get_similar_cases(self, keywords: str) -> str:
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"""Retrieve similar configuration cases from knowledge base.
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Args:
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keywords: Search keywords
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Returns:
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Similar cases text
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"""
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try:
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results = self.rag_engine.search(keywords, top_k=2)
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if not results:
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return "未找到相似案例"
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return "\n\n".join([r["content"] for r in results])
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except Exception as e:
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logger.warning(f"Failed to retrieve similar cases: {e}")
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return "无法获取相似案例"
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backend/app/services/requirement_service.py
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backend/app/services/requirement_service.py
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"""Requirement Analysis Service.
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This module provides the RequirementService class for analyzing user requirements
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using Claude AI with RAG knowledge retrieval.
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"""
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from typing import Optional, Dict, Any
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import uuid
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from loguru import logger
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from app.core.ai_engine import ClaudeEngine
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from app.core.rag_engine import RAGEngine
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from app.core.prompts import SYSTEM_PROMPT, ANALYZE_PROMPT_TEMPLATE
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from app.core.db_engine import DatabaseEngine
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class RequirementService:
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"""Service for analyzing user requirements with AI assistance.
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This service integrates Claude AI, RAG knowledge retrieval, and database
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metadata to provide comprehensive requirement analysis.
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"""
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def __init__(self) -> None:
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"""Initialize requirement service with required engines."""
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self.ai_engine = ClaudeEngine()
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self.rag_engine = RAGEngine()
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self.db_engine = DatabaseEngine()
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logger.info("RequirementService initialized")
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async def analyze(
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self,
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user_input: str,
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session_id: Optional[str] = None
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) -> Dict[str, Any]:
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"""Analyze user requirement and generate structured specification.
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Args:
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user_input: Natural language requirement from user
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session_id: Session ID for context management (auto-generated if None)
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Returns:
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Structured requirement document as dictionary
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Raises:
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ValueError: If user_input is empty
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Exception: If AI analysis fails
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"""
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# Validate input
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if not user_input or not user_input.strip():
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raise ValueError("User input cannot be empty")
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# Generate session ID if not provided
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session_id = session_id or str(uuid.uuid4())
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logger.info(f"[{session_id}] Starting requirement analysis: {user_input[:50]}...")
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try:
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# Step 1: Retrieve relevant knowledge from RAG
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logger.debug(f"[{session_id}] Searching knowledge base")
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knowledge_results = self.rag_engine.search(user_input, top_k=3)
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knowledge_context = self._format_knowledge_context(knowledge_results)
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logger.info(f"[{session_id}] Retrieved {len(knowledge_results)} knowledge chunks")
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# Step 2: Query existing database tables
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logger.debug(f"[{session_id}] Querying existing tables")
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existing_tables = self._get_existing_tables(user_input)
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logger.info(f"[{session_id}] Retrieved existing table information")
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# Step 3: Build prompt
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prompt = ANALYZE_PROMPT_TEMPLATE.format(
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user_input=user_input,
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knowledge_context=knowledge_context,
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existing_tables=existing_tables
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)
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messages = [
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{"role": "user", "content": SYSTEM_PROMPT},
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{"role": "assistant", "content": "我已了解平台配置规范，请告诉我您的需求。"},
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{"role": "user", "content": prompt}
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]
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# Step 4: Call Claude API
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logger.debug(f"[{session_id}] Calling Claude API")
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response = await self.ai_engine.call_claude(messages, temperature=0.7)
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# Step 5: Parse JSON response
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result = self.ai_engine.parse_json_response(response)
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function_name = result.get("功能名称", "Unknown")
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logger.success(f"[{session_id}] Requirement analysis completed: {function_name}")
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return result
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except Exception as e:
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logger.error(f"[{session_id}] Requirement analysis failed: {e}")
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raise
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def _format_knowledge_context(self, knowledge_results: list) -> str:
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"""Format knowledge search results into context string.
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Args:
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knowledge_results: List of knowledge search results
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Returns:
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Formatted knowledge context string
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"""
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if not knowledge_results:
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return "未找到相关知识库内容"
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context_parts = []
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for result in knowledge_results:
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source = result.get("metadata", {}).get("source", "文档")
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content = result.get("content", "")
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if content:
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context_parts.append(f"【{source}】\n{content}")
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return "\n\n".join(context_parts) if context_parts else "未找到相关知识库内容"
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def _get_existing_tables(self, user_input: str) -> str:
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"""Query existing database tables relevant to user input.
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Args:
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user_input: User requirement text
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Returns:
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Formatted string listing existing tables
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"""
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try:
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# Query top 10 tables (simplified version - could be enhanced with relevance matching)
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sql = """
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SELECT TOP 10 TABLE_NAME
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FROM INFORMATION_SCHEMA.TABLES
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WHERE TABLE_TYPE = 'BASE TABLE'
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ORDER BY TABLE_NAME
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"""
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tables = self.db_engine.execute_sql(sql)
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if not tables:
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return "未找到现有数据表"
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table_list = [f"- {t[0]}" for t in tables]
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return "\n".join(table_list)
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except Exception as e:
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logger.warning(f"Failed to query existing tables: {e}")
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return "无法获取现有表信息"
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