Hi all,
I am working on a ‘faiss ivf flat’ model. These are my model settings
{
"training_index": self.train_index_name,
"training_field": self.train_vector_name,
"dimension": 24,
"description": "My models description",
"method": {
"name": "ivf",
"space_type": "l2",
"engine": "faiss",
"parameters": {
"nlist": 400,
"encoder": {"name": "flat"},
},
},
}
The training index includes 200,000 vectors. I trained the model several times on this data and each time the trained model gave different results for the same test vector.
At the same time, when I train models with the same parameters through the faiss Python library, I get completely models that give the same results.
Is it possible to train reproducible models with opensearch?
My code for model with python
self.quantiser = faiss.IndexFlatL2(features.shape[1])
self.index = faiss.IndexIVFFlat(
self.quantiser, features.shape[1], self.nlist, faiss.METRIC_L2
)
self.index.train(features.astype(np.float32))
self.index.add(features.astype(np.float32))