Tensorflow Recommender - ScaNN passing embedding as query - tensorflow2.0

Question

I want to pass a query embedding to ScaNN instead of a model, what data type should I use for this?
My query would look like this [1, 0.3, 0.4]
My candidate embedding would be something like:
[[0.2, 1, .4],
[0.3,0.1,0.56]]
All the examples I see are passing an query model, not the embedding itself.
I tried passing a numpy array but it didn't work

Answer 1

Embeddings are just lists of vectors which your model produces. In this case using the tf.keras.layers.Embedding layer.
self._embeddings = {}
# Compute embeddings for string features
for feature_name in str_features:
vocabulary = vocabularies[feature_name]
self._embeddings[feature_name] = tf.keras.Sequential([
tf.keras.layers.StringLookup(
vocabulary=vocabulary, mask_token=None),
tf.keras.layers.Embedding(len(vocabulary) + 1,
self.embedding_dimension)
])
You can also use another model such as a Sentence Transformer to create embeddings.
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
embeddings = model.encode(sentences)
print(embeddings)
You do not need to pass the model to ScaNN, you can pass it the embeddings directly as well as mentioned in the documentation here
Here is a sample code snippet on how to pass embeddings directly to scann
import pandas as pd
from sklearn import preprocessing, metrics
df = pd.read_csv("./data/mydata.csv")
# normalization
df_np = preprocessing.normalize(df.iloc[:,1:], norm=norm)
num_neighbors = 100
# creating searcher
k = int(np.sqrt(df_np.shape[0]))
searcher = scann.scann_ops_pybind.builder(df_np, num_neighbors, "dot_product").tree(
num_leaves=k,
num_leaves_to_search=int(k/20),
training_sample_size=2500).score_brute_force(2).reorder(7).build()
Here is a blog post on using ScaNN
ScaNN optimization and configuration