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f7b6133 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | """Optional dense ANN baseline helpers.
These deliberately accept PRECOMPUTED embeddings. They never run a transformer.
Install with: pip install 'geomretrieval[ann]'
"""
from __future__ import annotations
import time
import numpy as np
def faiss_flat_ip(corpus: np.ndarray, queries: np.ndarray, k: int = 100):
import faiss
xb = np.ascontiguousarray(corpus.astype(np.float32))
xq = np.ascontiguousarray(queries.astype(np.float32))
index = faiss.IndexFlatIP(xb.shape[1])
index.add(xb)
t0 = time.perf_counter()
D, I = index.search(xq, k)
ms = (time.perf_counter() - t0) * 1000.0 / len(xq)
return I, D, ms
def faiss_hnsw_ip(corpus: np.ndarray, queries: np.ndarray, k: int = 100, M: int = 32, ef_search: int = 128):
import faiss
xb = np.ascontiguousarray(corpus.astype(np.float32))
xq = np.ascontiguousarray(queries.astype(np.float32))
index = faiss.IndexHNSWFlat(xb.shape[1], M, faiss.METRIC_INNER_PRODUCT)
index.hnsw.efSearch = ef_search
index.add(xb)
t0 = time.perf_counter()
D, I = index.search(xq, k)
ms = (time.perf_counter() - t0) * 1000.0 / len(xq)
return I, D, ms
def faiss_ivf_flat_ip(corpus: np.ndarray, queries: np.ndarray, k: int = 100, nlist: int = 4096, nprobe: int = 64):
import faiss
xb = np.ascontiguousarray(corpus.astype(np.float32))
xq = np.ascontiguousarray(queries.astype(np.float32))
quant = faiss.IndexFlatIP(xb.shape[1])
index = faiss.IndexIVFFlat(quant, xb.shape[1], nlist, faiss.METRIC_INNER_PRODUCT)
index.train(xb)
index.add(xb)
index.nprobe = nprobe
t0 = time.perf_counter()
D, I = index.search(xq, k)
ms = (time.perf_counter() - t0) * 1000.0 / len(xq)
return I, D, ms
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