# Operator: ANN Search: Faiss

*author: shiyu*

<br />



## Desription

Search embedding in [Faiss](https://github.com/facebookresearch/faiss), **please make sure you have inserted data to Faiss before search**.

<br />



## Code Example

- Insert data into Faiss first

```python
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:*

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

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

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

<img src="./result.png" height="100px"/>

<br />



## 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.


<br />



## Interface

**Parameters:**

***query:*** *list*

Query embeddings in Faiss



**Returns:** *Entity*

Return the results in Faiss with `key` and `score`.