Best Practices and Tips
- Get familiar with the data : Before using Faiss, you need to spend a little time understanding the data. You can ask yourself some questions, such as: How big is this data set? Is the data information complete? Familiarity with the data will help in choosing the correct Faiss index type and determining the best way to handle the data.
- Data preprocessing : Data preprocessing will greatly affect the use of Faiss. For text data, consider smarter ways to convert words to numbers, such as models like TF-IDF or Word2Vec. For image data, you can try to use convolutional neural network (CNN) to process.
- Choose the most suitable index type : Faiss provides a variety of index types, each of which has different applicable scenarios. Some indexes can efficiently handle high-dimensional data, some indexes are suitable for processing binary vectors, and some indexes are designed to handle large amounts of data. Therefore, you can choose the most suitable index type according to your needs and actual situation.
- Batch query : If there are multiple queries that need to be run at the same time, Faiss can be used to process them together. It is more efficient to run batch queries at one time, and Faiss is optimized for batch processing.
- Adjustment parameters : Faiss supports flexible adjustment of parameters, for example, the number of data clusters and the number of queries (nprobe) can be adjusted when building an index. The default value does not necessarily give full play to the maximum performance of an index. Therefore, you can try to adjust the parameter values to find the most suitable parameter settings.