GraphRAG Studio — initial commit: multimodal RAG system with KG visualization
Full-stack application for document-to-knowledge-graph pipeline: - Backend: FastAPI + LangGraph ReAct agent + DeepSeek + MinerU parsing - Frontend: React 19 + Vite + D3.js + shadcn/ui - Pipeline: MinerU parsing → LangExtract entity extraction → KG building Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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171
backend/routers/system.py
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171
backend/routers/system.py
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"""F 组：系统（4 个端点）"""
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import os
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import time
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from pathlib import Path
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from fastapi import APIRouter
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from models.schemas import APIResponse
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from storage import file_store as fs
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router = APIRouter(tags=["System"])
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_START_TIME = time.time()
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@router.get("/health")
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async def health_check():
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env_path = Path(__file__).parent.parent / ".env"
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from dotenv import load_dotenv
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load_dotenv(env_path, override=False)
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mineru_python = Path(os.getenv("MINERU_PYTHON", "F:/GraphRAGAgent/mineru_mvp/.venv/Scripts/python.exe"))
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backend_python = Path(__file__).parent.parent / ".venv" / "Scripts" / "python.exe"
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deepseek_key = os.getenv("DEEPSEEK_API_KEY", "")
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deepseek_url = os.getenv("DEEPSEEK_BASE_URL", "https://api.deepseek.com")
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# Check if langextract is importable from backend's venv
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try:
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import subprocess
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result = subprocess.run(
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[str(backend_python), "-c", "import langextract; print('ok')"],
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capture_output=True, text=True, timeout=10
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)
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langextract_ok = result.returncode == 0 and "ok" in result.stdout
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except Exception:
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langextract_ok = False
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components = {
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"mineru_venv": {
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"status": "ok" if mineru_python.exists() else "error",
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"path": str(mineru_python),
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"exists": mineru_python.exists(),
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},
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"langextract_venv": {
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"status": "ok" if langextract_ok else "error",
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"path": str(backend_python),
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"exists": backend_python.exists(),
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},
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"deepseek_api": {
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"status": "ok" if deepseek_key else "error",
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"base_url": deepseek_url,
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"key_configured": bool(deepseek_key),
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},
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"storage": {
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"status": "ok",
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"kg_nodes_exists": fs.kg_nodes_path().exists(),
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"kg_edges_exists": fs.kg_edges_path().exists(),
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"uploads_dir_exists": fs.UPLOADS_DIR.exists(),
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},
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}
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overall = "healthy" if all(c["status"] == "ok" for c in components.values()) else "degraded"
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return APIResponse.ok({
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"status": overall,
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"version": "1.0.0",
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"uptime_seconds": round(time.time() - _START_TIME, 1),
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"components": components,
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})
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@router.get("/system/stats")
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async def system_stats():
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from services import indexing_service as idx_svc
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docs = list(fs.load_docs_index().values())
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nodes = fs.load_kg_nodes()
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edges = fs.load_kg_edges()
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history = fs.load_query_history()
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type_dist: dict[str, int] = {}
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for n in nodes:
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t = n.get("type", "UNKNOWN")
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type_dist[t] = type_dist.get(t, 0) + 1
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return APIResponse.ok({
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"total_documents": len(docs),
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"indexed_documents": sum(1 for d in docs if d.get("status") == "indexed"),
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"failed_documents": sum(1 for d in docs if d.get("status") == "failed"),
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"total_nodes": len(nodes),
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"total_edges": len(edges),
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"type_distribution": type_dist,
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"total_queries": len(history),
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"active_jobs": idx_svc.count_active_jobs(),
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"storage_used_mb": fs.storage_used_mb(),
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})
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@router.get("/system/formats")
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async def list_formats():
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return APIResponse.ok({
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"formats": [
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{"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},
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{"ext": "docx", "description": "Microsoft Word（新版）", "max_size_mb": 200, "max_pages": 600, "requires_ocr": False},
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{"ext": "doc", "description": "Microsoft Word（旧版）", "max_size_mb": 200, "max_pages": 600, "requires_ocr": False},
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{"ext": "pptx", "description": "PowerPoint（新版）", "max_size_mb": 200, "max_pages": 600, "requires_ocr": False},
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{"ext": "ppt", "description": "PowerPoint（旧版）", "max_size_mb": 200, "max_pages": 600, "requires_ocr": False},
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{"ext": "png", "description": "PNG 图片（单页）", "max_size_mb": 200, "max_pages": 1, "requires_ocr": True},
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{"ext": "jpg", "description": "JPEG 图片（单页）", "max_size_mb": 200, "max_pages": 1, "requires_ocr": True},
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{"ext": "jpeg", "description": "JPEG 图片（单页）", "max_size_mb": 200, "max_pages": 1, "requires_ocr": True},
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{"ext": "html", "description": "HTML 文件", "max_size_mb": 200, "max_pages": 600, "requires_ocr": False},
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],
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"ocr_languages": [
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{"code": "ch", "name": "中文（默认）"},
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{"code": "en", "name": "英文"},
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{"code": "japan", "name": "日文"},
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{"code": "korean", "name": "韩文"},
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{"code": "french", "name": "法文"},
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{"code": "german", "name": "德文"},
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],
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"notes": [
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"language 参数默认值为 'ch'（非 'zh'），遵循 PaddleOCR v3 语言代码规范",
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"上传时不需要携带 Content-Type，服务端自动识别",
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"PNG/JPG/JPEG 单次最多处理 1 页",
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],
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})
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@router.get("/system/demo")
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async def get_demo_data():
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# Try backend KG first, then fall back to graphrag_pipeline/output
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nodes = fs.load_kg_nodes()
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edges = fs.load_kg_edges()
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if not nodes:
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# Fallback: load from existing graphrag_pipeline output
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legacy_nodes_path = Path("F:/GraphRAGAgent/graphrag_pipeline/output/kg_nodes.json")
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legacy_edges_path = Path("F:/GraphRAGAgent/graphrag_pipeline/output/kg_edges.json")
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if legacy_nodes_path.exists():
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import json
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nodes = json.loads(legacy_nodes_path.read_text(encoding="utf-8"))
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edges = json.loads(legacy_edges_path.read_text(encoding="utf-8")) if legacy_edges_path.exists() else []
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else:
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from fastapi.responses import JSONResponse
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return JSONResponse(
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status_code=400,
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content=APIResponse.err(3002, "No demo data available. Index a document first.").model_dump(),
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)
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type_counts: dict[str, int] = {}
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for n in nodes:
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t = n.get("type", "UNKNOWN")
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type_counts[t] = type_counts.get(t, 0) + 1
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import networkx as nx
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G = nx.Graph()
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for n in nodes:
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G.add_node(n["id"])
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for e in edges:
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G.add_edge(e["source"], e["target"])
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return APIResponse.ok({
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"nodes": nodes,
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"edges": edges,
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"stats": {
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"nodes": len(nodes),
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"edges": len(edges),
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"type_counts": type_counts,
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"density": round(nx.density(G), 4) if G.number_of_nodes() > 1 else 0.0,
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},
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})
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