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', '$clusterTime': {'clusterTime': Timestamp(1716546450, 2), 'signature': {'hash': b'xf4B1Nxc2xffGx9d$J}xeaxadxfexdfzx83Cxx80', '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.