"""F 组：系统（4 个端点）""" import os import time from pathlib import Path from fastapi import APIRouter from models.schemas import APIResponse from storage import file_store as fs router = APIRouter(tags=["System"]) _START_TIME = time.time() @router.get("/health") async def health_check(): env_path = Path(__file__).parent.parent / ".env" from dotenv import load_dotenv load_dotenv(env_path, override=False) mineru_python = Path(os.getenv("MINERU_PYTHON", "F:/GraphRAGAgent/mineru_mvp/.venv/Scripts/python.exe")) backend_python = Path(__file__).parent.parent / ".venv" / "Scripts" / "python.exe" deepseek_key = os.getenv("DEEPSEEK_API_KEY", "") deepseek_url = os.getenv("DEEPSEEK_BASE_URL", "https://api.deepseek.com") # Check if langextract is importable from backend's venv try: import subprocess result = subprocess.run( [str(backend_python), "-c", "import langextract; print('ok')"], capture_output=True, text=True, timeout=10 ) langextract_ok = result.returncode == 0 and "ok" in result.stdout except Exception: langextract_ok = False components = { "mineru_venv": { "status": "ok" if mineru_python.exists() else "error", "path": str(mineru_python), "exists": mineru_python.exists(), }, "langextract_venv": { "status": "ok" if langextract_ok else "error", "path": str(backend_python), "exists": backend_python.exists(), }, "deepseek_api": { "status": "ok" if deepseek_key else "error", "base_url": deepseek_url, "key_configured": bool(deepseek_key), }, "storage": { "status": "ok", "kg_nodes_exists": fs.kg_nodes_path().exists(), "kg_edges_exists": fs.kg_edges_path().exists(), "uploads_dir_exists": fs.UPLOADS_DIR.exists(), }, } overall = "healthy" if all(c["status"] == "ok" for c in components.values()) else "degraded" return APIResponse.ok({ "status": overall, "version": "1.0.0", "uptime_seconds": round(time.time() - _START_TIME, 1), "components": components, }) @router.get("/system/stats") async def system_stats(): from services import indexing_service as idx_svc docs = list(fs.load_docs_index().values()) nodes = fs.load_kg_nodes() edges = fs.load_kg_edges() history = fs.load_query_history() type_dist: dict[str, int] = {} for n in nodes: t = n.get("type", "UNKNOWN") type_dist[t] = type_dist.get(t, 0) + 1 return APIResponse.ok({ "total_documents": len(docs), "indexed_documents": sum(1 for d in docs if d.get("status") == "indexed"), "failed_documents": sum(1 for d in docs if d.get("status") == "failed"), "total_nodes": len(nodes), "total_edges": len(edges), "type_distribution": type_dist, "total_queries": len(history), "active_jobs": idx_svc.count_active_jobs(), "storage_used_mb": fs.storage_used_mb(), }) @router.get("/system/formats") async def list_formats(): return APIResponse.ok({ "formats": [ {"ext": "pdf", "description": "PDF 文档（文本型/扫描型/混合型）", "max_size_mb": 200, "max_pages": 600, "requires_ocr": False}, {"ext": "docx", "description": "Microsoft Word（新版）", "max_size_mb": 200, "max_pages": 600, "requires_ocr": False}, {"ext": "doc", "description": "Microsoft Word（旧版）", "max_size_mb": 200, "max_pages": 600, "requires_ocr": False}, {"ext": "pptx", "description": "PowerPoint（新版）", "max_size_mb": 200, "max_pages": 600, "requires_ocr": False}, {"ext": "ppt", "description": "PowerPoint（旧版）", "max_size_mb": 200, "max_pages": 600, "requires_ocr": False}, {"ext": "png", "description": "PNG 图片（单页）", "max_size_mb": 200, "max_pages": 1, "requires_ocr": True}, {"ext": "jpg", "description": "JPEG 图片（单页）", "max_size_mb": 200, "max_pages": 1, "requires_ocr": True}, {"ext": "jpeg", "description": "JPEG 图片（单页）", "max_size_mb": 200, "max_pages": 1, "requires_ocr": True}, {"ext": "html", "description": "HTML 文件", "max_size_mb": 200, "max_pages": 600, "requires_ocr": False}, ], "ocr_languages": [ {"code": "ch", "name": "中文（默认）"}, {"code": "en", "name": "英文"}, {"code": "japan", "name": "日文"}, {"code": "korean", "name": "韩文"}, {"code": "french", "name": "法文"}, {"code": "german", "name": "德文"}, ], "notes": [ "language 参数默认值为 'ch'（非 'zh'），遵循 PaddleOCR v3 语言代码规范", "上传时不需要携带 Content-Type，服务端自动识别", "PNG/JPG/JPEG 单次最多处理 1 页", ], }) @router.get("/system/demo") async def get_demo_data(): # Try backend KG first, then fall back to graphrag_pipeline/output nodes = fs.load_kg_nodes() edges = fs.load_kg_edges() if not nodes: # Fallback: load from existing graphrag_pipeline output legacy_nodes_path = Path("F:/GraphRAGAgent/graphrag_pipeline/output/kg_nodes.json") legacy_edges_path = Path("F:/GraphRAGAgent/graphrag_pipeline/output/kg_edges.json") if legacy_nodes_path.exists(): import json nodes = json.loads(legacy_nodes_path.read_text(encoding="utf-8")) edges = json.loads(legacy_edges_path.read_text(encoding="utf-8")) if legacy_edges_path.exists() else [] else: from fastapi.responses import JSONResponse return JSONResponse( status_code=400, content=APIResponse.err(3002, "No demo data available. Index a document first.").model_dump(), ) type_counts: dict[str, int] = {} for n in nodes: t = n.get("type", "UNKNOWN") type_counts[t] = type_counts.get(t, 0) + 1 import networkx as nx G = nx.Graph() for n in nodes: G.add_node(n["id"]) for e in edges: G.add_edge(e["source"], e["target"]) return APIResponse.ok({ "nodes": nodes, "edges": edges, "stats": { "nodes": len(nodes), "edges": len(edges), "type_counts": type_counts, "density": round(nx.density(G), 4) if G.number_of_nodes() > 1 else 0.0, }, })