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import pandas as pd
import faiss
import torch
from open_clip import create_model_from_pretrained, get_tokenizer
class SearchSigLIP():
def __init__(self, index_path, metadata_path):
# 1. Initialise Index
print(f'Loading index from PATH={index_path}')
self.index_path = index_path
self.init_index()
print('[DONE]')
# 2. Initialise Metadata
print(f'Loading metadata from PATH={metadata_path}')
self.metadata_path = metadata_path
self.metadata_df = pd.read_parquet(self.metadata_path)
print('[DONE]')
# 3. Initialise Text Encoder
self.init_model()
def init_index(self):
self.cpu_index = faiss.read_index(self.index_path)
res = faiss.StandardGpuResources()
cloner_options = faiss.GpuClonerOptions()
cloner_options.useFloat16LookupTables = True
self.gpu_index = faiss.index_cpu_to_gpu(res, 0, self.cpu_index, cloner_options)
self.gpu_index.nprobe = 32 # Higher = more accurate, slower
def init_model(self):
self.model, self.preprocess = create_model_from_pretrained('hf-hub:timm/ViT-SO400M-14-SigLIP-384')
self.model.eval()
self.tokenizer = get_tokenizer('hf-hub:timm/ViT-SO400M-14-SigLIP')
def encode_text(self, text, device='cuda'):
self.model.to(device)
with torch.no_grad():
text = self.tokenizer([text], context_length=self.model.context_length)
return self.model.encode_text(text.to(device))
def search_with_grid(self, query_vec, k=5):
# Prepare query
if isinstance(query_vec, torch.Tensor):
query_vec = query_vec.cpu().squeeze().numpy()
query_vec = query_vec.reshape(1, -1).astype('float32')
faiss.normalize_L2(query_vec)
# Search
distances, indices = self.gpu_index.search(query_vec, k)
# Flatten results
ids = indices[0]
scores = distances[0]
results = []
# Batch lookup in pandas (Faster than looping)
# We ignore -1 (which happens if k > total vectors, unlikely here)
valid_mask = ids != -1
valid_ids = ids[valid_mask]
valid_scores = scores[valid_mask]
if len(valid_ids) > 0:
# MAGIC LINE: Direct lookup by integer index
matches = self.metadata_df.iloc[valid_ids].copy()
matches['score'] = valid_scores
# Convert to list of dicts for easy usage
results = matches.to_dict(orient='records')
return results
def faiss(self, text, k=1): # k - number of neighbours
# 1. Compute query
q = self.encode_text(text)
# 2. Find Hits
results = self.search_with_grid(q, k=k)
return results |