DonaldFang 方士碩
f810ddc2ea
Features: - FastAPI backend with JWT authentication - MySQL database with SQLAlchemy ORM - KPI workflow: draft → pending → approved → evaluation → completed - Ollama LLM API integration for AI features - Gitea API integration for version control - Complete API endpoints for KPI, dashboard, notifications Tables: KPI_D_* prefix naming convention 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
141 lines
3.0 KiB
Python
141 lines
3.0 KiB
Python
"""
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LLM API
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"""
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from typing import Optional, List
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from fastapi import APIRouter, Depends, HTTPException
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from fastapi.responses import StreamingResponse
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from pydantic import BaseModel
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from app.api.deps import get_current_user
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from app.models.employee import Employee
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from app.services.llm_service import llm_service
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router = APIRouter(prefix="/api/llm", tags=["LLM"])
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class ChatMessage(BaseModel):
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"""聊天訊息"""
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role: str # system, user, assistant
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content: str
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class ChatRequest(BaseModel):
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"""聊天請求"""
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messages: List[ChatMessage]
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model: Optional[str] = None
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temperature: float = 0.7
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stream: bool = False
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class ChatResponse(BaseModel):
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"""聊天回應"""
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content: str
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model: str
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class SimpleAskRequest(BaseModel):
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"""簡單問答請求"""
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question: str
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system_prompt: Optional[str] = "You are a helpful assistant."
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model: Optional[str] = None
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class KPIAnalyzeRequest(BaseModel):
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"""KPI 分析請求"""
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kpi_data: dict
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@router.get("/models")
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def list_models(current_user: Employee = Depends(get_current_user)):
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"""
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列出可用的 LLM 模型
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"""
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models = llm_service.list_models()
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return {"models": models}
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@router.post("/chat", response_model=ChatResponse)
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def chat(
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data: ChatRequest,
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current_user: Employee = Depends(get_current_user),
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):
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"""
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聊天完成請求
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"""
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if data.stream:
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raise HTTPException(
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status_code=400,
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detail="請使用 /chat/stream 端點進行串流請求",
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)
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messages = [{"role": m.role, "content": m.content} for m in data.messages]
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content = llm_service.chat(
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messages=messages,
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model=data.model,
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temperature=data.temperature,
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)
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return ChatResponse(
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content=content,
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model=data.model or llm_service.default_model,
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)
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@router.post("/chat/stream")
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async def chat_stream(
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data: ChatRequest,
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current_user: Employee = Depends(get_current_user),
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):
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"""
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串流聊天請求
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"""
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messages = [{"role": m.role, "content": m.content} for m in data.messages]
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def generate():
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for chunk in llm_service.chat_stream(
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messages=messages,
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model=data.model,
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temperature=data.temperature,
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):
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yield f"data: {chunk}\n\n"
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yield "data: [DONE]\n\n"
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return StreamingResponse(
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generate(),
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media_type="text/event-stream",
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)
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@router.post("/ask")
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def simple_ask(
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data: SimpleAskRequest,
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current_user: Employee = Depends(get_current_user),
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):
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"""
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簡單問答
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"""
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response = llm_service.simple_ask(
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question=data.question,
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system_prompt=data.system_prompt,
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model=data.model,
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)
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return {"answer": response}
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@router.post("/analyze-kpi")
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def analyze_kpi(
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data: KPIAnalyzeRequest,
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current_user: Employee = Depends(get_current_user),
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):
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"""
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AI 分析 KPI 數據
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"""
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analysis = llm_service.analyze_kpi(data.kpi_data)
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return {"analysis": analysis}
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