faiss-index/README.md

# ANN Search Operator: Faiss

*author: shiyu*

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## Desription


Only for local test. If you want to use a vector database in a production environment, you can use Milvus(https://github.com/milvus-io/milvus).

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## Code Example

> please insert data into faiss first.

### Example

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

```python
from towhee import pipe, ops

p = pipe.input('vec')  \
    .flat_map('vec', 'rows', ops.ann_search.faiss('./data_dir', 5))  \
    .map('rows', ('id', 'score'), lambda x: (x[0], x[1]))  \
    .output('id', 'score')

p(<your-vector>)
```

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## Factory Constructor

Create the operator via the following factory method:

***ops.ann_search.faiss_index('./data_dir', 5)***

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**Parameters:**

***data_dir:*** *str*

The path to faiss index and meta data.


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## Interface

**Parameters:**

***query:*** *ndarray*

Query embedding in Faiss

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**Returns:** *Entity*

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


# More Resources

- [Zilliz Triumphed in Billion-Scale ANN Search Challenge of NeurIPS 2021 - Zilliz Newsroom; Zilliz Triumphed in Billion-Scale ANN Search Challenge of NeurIPS 2021](https://zilliz.com/news/zilliz-triumphed-Neurips-2021): Zilliz team has won the first place in the Disk-based ANN Search track in NeurIPS 2021. The performance of BBAnn developed by Zilliz research team peaked during the search in the SimSearchNet++ dataset.
- [What is Faiss (Facebook AI Similarity Search)? - Zilliz blog](https://zilliz.com/learn/faiss): Faiss (Facebook AI similarity search) is an open-source library for efficient similarity search of unstructured data and clustering of dense vectors.
- [What is approximate nearest neighbor search (ANNS)?](https://zilliz.com/glossary/anns): Learn how to use Approximate nearest neighbor search (ANNS) for efficient nearest-neighbor search in large datasets.
- [Setting Up With Facebook AI Similarity Search (FAISS) - Zilliz blog](https://zilliz.com/blog/set-up-with-facebook-ai-similarity-search-faiss): Here's your FAISS tutorial that helps you set up FAISS, get it up and running, and demonstrate its power through a sample search program.