Text Search Engine

In this tutorial, you will learn how to use Milvus, the open-source vector database, to build a text search engine.

• Open Jupyter notebook

The ML model and third-party software used include:

• BERT
• MySQL
• Towhee

One major application of Milvus in the field of natural language processing (NLP) is text search engine. It is a great tool that can help users find the information they are looking for. It can even surface information that is hard to find. Text search engines compare the keywords or semantics users input against a database of texts, and then return the results that meet certain criteria.

In this tutorial, you will learn how to build a text search engine. This tutorial uses BERT to convert texts into fixed-length vectors. Milvus is used as a vector database for storage and vector similarity search. Then use MySQL to map the vector IDs generated by Milvus to the text data.

text_search_engine text_search_engine