Operator: ANN Search: Faiss

author: shiyu

Desription

Search embedding in Faiss, please make sure you have inserted data to Faiss before search.

Code Example

Insert data into Faiss first

import numpy as np
import towhee

vec = np.random.random((10, 100)).astype('float32')
ids = list(i for i in range(10))

x = towhee.dc['id'](ids) \
    .runas_op['id', 'vec'](func=lambda x: vec[x]) \
    .to_faiss['id', 'vec'](findex='index.bin')

Example

Write the pipeline in simplified style:

query = vec[0:2]
towhee.dc(query) \
    .ann_search.faiss(findex='index.bin')

Write a same pipeline with explicit inputs/outputs name specifications:

query = vec[0:2]
towhee.dc['vec'](query) \
    .ann_search.faiss['vec', 'results'](findex='index.bin') \
    .show()

Factory Constructor

Create the operator via the following factory method:

ann_search.faiss(findex)

Parameters:

findex: str or faiss.INDEX

The path to faiss index file or faiss index.

Interface

Parameters:

query: list

Query embeddings in Faiss

Returns: Entity

Return the results in Faiss with key and score.