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import torch
def create_dense_embeddings(query, model):
dense_emb = model.encode([query]).tolist()
return dense_emb
def create_sparse_embeddings(query, model, tokenizer):
device = "cuda" if torch.cuda.is_available() else "cpu"
inputs = tokenizer(query, return_tensors="pt").to(device)
with torch.no_grad():
logits = model(**inputs).logits
inter = torch.log1p(torch.relu(logits[0]))
token_max = torch.max(inter, dim=0) # sum over input tokens
nz_tokens = torch.where(token_max.values > 0)[0]
nz_weights = token_max.values[nz_tokens]
order = torch.sort(nz_weights, descending=True)
nz_weights = nz_weights[order[1]]
nz_tokens = nz_tokens[order[1]]
return {
"indices": nz_tokens.cpu().numpy().tolist(),
"values": nz_weights.cpu().numpy().tolist(),
}
def hybrid_score_norm(dense, sparse, alpha: float):
"""Hybrid score using a convex combination
alpha * dense + (1 - alpha) * sparse
Args:
dense: Array of floats representing
sparse: a dict of `indices` and `values`
alpha: scale between 0 and 1
"""
if alpha < 0 or alpha > 1:
raise ValueError("Alpha must be between 0 and 1")
hs = {
"indices": sparse["indices"],
"values": [v * (1 - alpha) for v in sparse["values"]],
}
return [v * alpha for v in dense], hs
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