From 4a69b6a82f7dbdf38f5f91959b259de3c40c8c54 Mon Sep 17 00:00:00 2001
From: AkshatMekol <83553564+AkshatMekol@users.noreply.github.com>
Date: Sun, 23 Nov 2025 10:25:28 +0530
Subject: [PATCH] Create gpu_server.py

---
 gpu_server.py | 76 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 76 insertions(+)
 create mode 100644 gpu_server.py

diff --git a/gpu_server.py b/gpu_server.py
new file mode 100644
index 0000000..5a36cf8
--- /dev/null
+++ b/gpu_server.py
@@ -0,0 +1,76 @@
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from queue import Queue
+import threading
+import gc
+from utils.embedding_utils import embed_batch
+from utils.mongo_utils import store_embeddings_in_db, vector_collection
+import torch
+
+embedding_queue = Queue(maxsize=20000)
+STOP_SIGNAL = object()
+
+app = FastAPI(title="GPU Embedding Server")
+
+def gpu_worker():
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    print(f"🚀 GPU worker started on: {device}")
+
+    while True:
+        task = embedding_queue.get()
+        if task is STOP_SIGNAL:
+            print("🛑 GPU worker stopping (STOP signal)")
+            break
+
+        chunks, document_name, tender_id, is_last_batch = task
+        for c in chunks:
+            c["tender_id"] = tender_id
+            c["document_name"] = document_name
+
+        try:
+            embeddings = embed_batch(chunks)
+            print(f"[{document_name}] 🔹 Vectorized {len(chunks)} chunks")
+
+            store_embeddings_in_db(embeddings, document_name, tender_id)
+            print(f"[{document_name}] 💾 Stored in MongoDB")
+
+            if is_last_batch:
+                vector_collection.update_one(
+                    {"tender_id": tender_id, "document_name": document_name},
+                    {"$set": {"document_complete": True}},
+                    upsert=True
+                )
+                print(f"[{document_name}] 🎉 Document marked COMPLETE")
+
+        except Exception as e:
+            print(f"[GPU WORKER] ❌ Error: {document_name}: {e}")
+
+        gc.collect()
+        embedding_queue.task_done()
+
+@app.on_event("startup")
+def start_worker():
+    thread = threading.Thread(target=gpu_worker, daemon=True)
+    thread.start()
+    print("✅ GPU worker thread started")
+
+@app.on_event("shutdown")
+def stop_worker():
+    embedding_queue.put(STOP_SIGNAL)
+    print("🛑 Stop signal sent to GPU worker")
+
+class EmbedRequest(BaseModel):
+    chunks: list
+    document_name: str
+    tender_id: str
+    is_last_batch: bool
+
+@app.post("/enqueue")
+def enqueue_embedding(req: EmbedRequest):
+    try:
+        embedding_queue.put(
+            (req.chunks, req.document_name, req.tender_id, req.is_last_batch)
+        )
+        return {"status": "queued", "document_name": req.document_name}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))