search

search.py

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+from argparse import ArgumentParser
+from sentence_transformers import SentenceTransformer
+import torch
+import json
+import numpy as np 
+import os
+import gzip
+import faiss
+import time
+import zstandard
+import torch
+from multiprocessing.pool import ThreadPool, Pool
+from itertools import repeat
+import cohere 
+
+cohere_key = os.environ["COHERE_API_KEY"]
+co = cohere.Client(cohere_key)
+
+faiss.omp_set_num_threads(4)
+
+def get_bin_embedding(query, model):
+    query_emb = np.asarray(co.embed(texts=[query], model=model, input_type="search_query").embeddings)
+    query_emb_bin = np.zeros_like(query_emb, dtype=np.int8)
+    query_emb_bin[query_emb > 0] = 1
+    query_emb_bin = np.packbits(query_emb_bin, axis=-1)
+    return query_emb, query_emb_bin
+
+
+def search_cluster(args):
+    #1) Open Centroid
+    with open(os.path.join(args['input_path'], f"emb/{args['cid_folder']}/{args['cid']}.npy"), "rb") as fIn:
+        cluster_emb = np.load(fIn)
+        cluster_index = faiss.IndexBinaryFlat(cluster_emb.shape[1]*8)
+        cluster_index.add(cluster_emb)
+
+    #2) Search
+    cluster_scores, cluster_doc_ids = cluster_index.search(args['query_emb_bin'], args['topk'])
+    
+    return [{'cid': args['cid'], 'doc_idx': doc_idx, 'doc_score': doc_score, 'doc_emb': cluster_emb[doc_idx]} for doc_score, doc_idx in zip(cluster_scores[0], cluster_doc_ids[0])]
+
+def search(args, centroids, query):
+    num_rescore = args.topk*5 
+
+    #Query encoding
+    start_time = time.time()
+    query_emb, query_emb_bin = get_bin_embedding(query, args.model)
+    print(f"Query encoding took {(time.time()-start_time)*1000:.2f}ms")
+
+    start_time = time.time()
+    #Search nprobe closest centroids
+    centroid_scores, centroid_ids = centroids.search(query_emb_bin, args.nprobe)
+    centroid_ids = centroid_ids[0]
+    print(f"Centroid search took {(time.time()-start_time)*1000:.2f}ms")
+
+    start_time = time.time()
+    all_hits = []
+
+    #for cid in centroid_ids:
+    #    global_scores.extend(search_cluster(cid, query_emb_bin))
+
+    pool_args = []
+    for cid in centroid_ids:
+        cid_str = str(cid.item()).zfill(args.ivf['file_len'])
+        cid_folder = cid_str[-args.ivf['folder_len']:]
+        pool_args.append({'cid': cid_str, 'cid_folder': cid_folder, 'input_path': args.input, 'topk': args.topk, 'query_emb_bin': query_emb_bin})
+
+    for result in pool.imap_unordered(search_cluster, pool_args, chunksize=10):
+        all_hits.extend(result)
+ 
+    #Sort global scores
+    all_hits.sort(key=lambda x: x['doc_score'])
+    all_hits = all_hits[0:num_rescore]
+
+    print(f"Searching in clusters took {(time.time()-start_time)*1000:.2f}ms"); start_time = time.time()
+
+    #Dense - Binary Rescoring
+    for hit in all_hits:
+        doc_emb = hit['doc_emb']
+        doc_emb_bin_unpacked = np.unpackbits(doc_emb, axis=-1).astype("int")
+        doc_emb_bin_unpacked = 2*doc_emb_bin_unpacked-1
+        hit['cont_score'] = (query_emb @ doc_emb_bin_unpacked.T).item()
+
+    all_hits.sort(key=lambda x: x['cont_score'], reverse=True)
+    all_hits = all_hits[0:args.topk]
+    print(f"Dense-Binary rescoring took {(time.time()-start_time)*1000:.2f}ms"); start_time = time.time()
+    
+
+    #Fetch documents
+    results = []
+
+    dctx = zstandard.ZstdDecompressor()
+    for hit in all_hits:
+        text_path = os.path.join(args.input, f"text/{hit['cid'][-args.ivf['folder_len']:]}/{hit['cid']}.jsonl.zst")
+        with zstandard.open(text_path, "rt", dctx=dctx) as fIn:
+            for line_idx, line in enumerate(fIn):
+                if line_idx == hit['doc_idx']:
+                    data = json.loads(line)
+                    data['_score'] = hit['cont_score']
+                    data['_path'] = text_path
+                    results.append(data)
+                    break
+
+    print(f"Fetch docs took {(time.time()-start_time)*1000:.2f}ms")
+
+    for hit in results[0:3]:
+        print(hit)
+        print("-------------")
+
+
+def main():
+    parser = ArgumentParser()
+    parser.add_argument("--model", default="embed-english-v3.0")
+    parser.add_argument("--input", required=True, help="IVF Folder")
+    parser.add_argument("--nprobe", type=int, default=100)
+    parser.add_argument("--topk", type=int, default=10)
+    args = parser.parse_args()
+
+    #Load config
+    with open(f"{args.input}/config.json") as fIn:
+        args.ivf = json.load(fIn)
+    
+    
+    #Restore centroid index
+    with open(os.path.join(args.input, "centroid_vecs.npy"), "rb") as fIn:
+        centroid_vec = np.load(fIn)
+        print("Centroids shape:", centroid_vec.shape)
+        centroids = faiss.IndexBinaryFlat(centroid_vec.shape[1]*8)
+        centroids.add(centroid_vec)
+
+
+    while True:
+        query = input("Query: ")
+        search(args, centroids, query)
+        print("\n===========================\n")
+
+
+if __name__ == "__main__":
+    pool = Pool(processes=8)
+    main()