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>

backend/services/qa_service.py

@@ -0,0 +1,85 @@
+"""QA Service — Agentic-RAG wrapper."""
+from __future__ import annotations
+
+import time
+import uuid
+from datetime import datetime, timezone
+
+from storage import file_store as fs
+
+
+def run_query(question: str, history: list[dict]) -> dict:
+    from pipeline.qa_agent import run_qa
+
+    nodes = fs.load_kg_nodes()
+    edges = fs.load_kg_edges()
+
+    if not nodes:
+        raise ValueError("KG_EMPTY")
+
+    start = time.time()
+    result = run_qa(question, history, nodes, edges)
+    elapsed = round(time.time() - start, 2)
+
+    query_id = f"q_{uuid.uuid4().hex[:10]}"
+    now = datetime.now(timezone.utc).isoformat()
+
+    record = {
+        "id": query_id,
+        "question": question,
+        "answer": result["answer"],
+        "tool_calls": result["tool_calls"],
+        "cited_nodes": result["cited_nodes"],
+        "duration_seconds": elapsed,
+        "timestamp": now,
+    }
+    fs.append_query_history(record)
+    return record
+
+
+def get_history(page: int = 1, page_size: int = 20) -> dict:
+    all_records = fs.load_query_history()
+    total = len(all_records)
+    start = (page - 1) * page_size
+    return {
+        "total": total,
+        "page": page,
+        "page_size": page_size,
+        "items": all_records[start: start + page_size],
+    }
+
+
+def start_batch(questions: list[str]) -> dict:
+    import threading
+
+    batch_id = f"batch_{uuid.uuid4().hex[:10]}"
+    now = datetime.now(timezone.utc).isoformat()
+    meta = {
+        "batch_id": batch_id,
+        "total": len(questions),
+        "completed": 0,
+        "failed": 0,
+        "status": "submitted",
+        "created_at": now,
+        "results": [],
+    }
+    fs.save_batch_meta(batch_id, meta)
+
+    def _run():
+        for q in questions:
+            try:
+                res = run_query(q, [])
+                meta["results"].append(res)
+                meta["completed"] += 1
+            except Exception as e:
+                meta["failed"] += 1
+                meta["results"].append({"question": q, "error": str(e)})
+        meta["status"] = "done"
+        fs.save_batch_meta(batch_id, meta)
+
+    threading.Thread(target=_run, daemon=True).start()
+    return {"batch_id": batch_id, "total": len(questions), "status": "submitted", "created_at": now}
+
+
+def get_batch_result(batch_id: str) -> dict | None:
+    return fs.load_batch_meta(batch_id)