PlanExecutor error caused by embedding index not indexed as knnVector

I have an ML model written in torch which produces an embedding of size 512.

I wrote a custom script utilizing the model and sending the embedding to the collection (which is empty).

class FaceEmbedding(Model):    def __init__(self, MONGO_CONNECTION_STRING:str=MONGO_CONNECTION_STRING) -> None:        super().__init__()        self.FaceEmbeddingModel = torch.load('Model/model2.pth')        self.device = next(self.FaceEmbeddingModel.parameters()).device        self.T = v2.Compose([            v2.ToImage(),            v2.ToDtype(torch.float32),            v2.Resize(256),            v2.CenterCrop(224),            v2.Normalize(mean = [0.48235, 0.45882, 0.40784],                          std=[0.00392156862745098, 0.00392156862745098, 0.00392156862745098])        ])        self.dbName = "FaceSimilarity"        self.collectionName = "Embeddings"        self.client = MongoClient(MONGO_CONNECTION_STRING)        self.collection = self.client[self.dbName][self.collectionName]    def __makeEucEmbeddings(self, img:np.ndarray)->np.ndarray:        img_t = self.T(img)        img_t = torch.unsqueeze(img_t, dim=0)        embedding = self.FaceEmbeddingModel.pos(img_t.to(self.device))        del img_t        torch.cuda.empty_cache()        return embedding.squeeze().cpu().detach().numpy()    def makeEmbeddings(self, img:np.ndarray, k:int):        face_locations = fr.face_locations(img)        sorted(face_locations, key = lambda rect: abs(rect[2]-rect[0])*abs(rect[1]-rect[3]))        face_locations = face_locations[:k][::-1]        EucEmb = []        FREmb = []        for face in face_locations:            top, right, bottom, left = face            face_img = img[top:bottom, left:right]            FREmb.append(fr.face_encodings(img, [face])[0])            Euc = self.__makeEucEmbeddings(face_img)            EucEmb.append(Euc.tolist())        return EucEmb, FREmb, face_locations    def __make_pipeline(self, EucEmb):         pipeline = [{"$vectorSearch": {"index": "vector_index","path": "EuclidianEmbedding","queryVector":EucEmb,"numCandidates":200,"limit":10            }        }]        return pipeline    def saveEmbedding(self, embeddings)->None:        data = []        for EucEmb, FREmb in embeddings:            data.append({"EuclidianEmbedding":EucEmb,"FREmbedding":FREmb            })        self.collection.insert_many(data)    def __vectorSearch(self, img, k):        EucEmb, FREmb, face_locations = self.makeEmbeddings(img, k)        ResEmb = []        if isinstance(EucEmb, list):            for emb in EucEmb:                ResEmb.append(self.collection.aggregate(self.__make_pipeline(emb)))        RecFace = []        NotRecFace = []        for emb in range(len(FREmb)):            match = fr.compare_faces([i['FREmbedding'] for i in ResEmb], FREmb[emb])[0]            if True in match:                idx = match.index(True)                RecFace.append(ResEmb[idx])            else:                idx = FREmb.index(emb)                NotRecFace.append([                    EucEmb[emb],                    FREmb[emb]                ])        return RecFace, NotRecFace, face_locations    def vectorSearch(self, img, k, SaveNotRecFace=False):        RecFace, NotRecFace, face_locations = self.__vectorSearch(img, k)                if SaveNotRecFace:            for embedding in NotRecFace:                self.saveEmbedding(embedding)        return RecFace, NotRecFace, face_locations

I am getting this error upon running the script on various images with one face common among them.

OperationFailure: PlanExecutor error during aggregation :: caused by :: EuclidianEmbedding_1 is not indexed as knnVector, full error: {'ok': 0.0, 'errmsg': 'PlanExecutor error during aggregation :: caused by :: EuclidianEmbedding_1 is not indexed as knnVector', 'code': 8, 'codeName': 'UnknownError', '$clusterTime': {'clusterTime': Timestamp(1716546450, 2), 'signature': {'hash': b'\xf4B1N\xc2\xffG\x9d$J}\xea\xad\xfe\xdfz\x83Cx\x80', 'keyId': 7345064297216606213}}, 'operationTime': Timestamp(1716546450, 2)}

Defined Indexes in the Mongo Compass for the collection

In Atlas Vector Search, I defined this JSON

{"fields": [    {"numDimensions": 512,"path": "EuclideanEmbedding","similarity": "euclidean","type": "vector"    }  ]}

I searched this up on the docs of MongoDB and came across the knnVector type index in Atlas Vector Search but, changing the type in the JSON gives error again.