Which Python library is used for vectorizing text chunks in the provided example?

The choice of the sentence-transformers library as the correct answer is significant because this library is specifically designed for handling natural language processing tasks, particularly those involving the transformation of sentences or text into dense vector representations. This capability is essential in applications like semantic search, where the goal is to understand the meaning behind text rather than just the literal words.

Sentence-transformers is built on top of popular deep learning frameworks and offers pre-trained models optimized for generating embeddings that capture the semantic relationships between sentences or documents. This allows developers to efficiently convert text chunks into high-dimensional vectors that can then be utilized for various machine learning applications or vector search algorithms.

In contrast, the other libraries listed serve different purposes. The oracledb library is primarily used for interacting with Oracle databases, focusing on database connectivity rather than text vectorization. The oci library is also related to Oracle and is used for Oracle Cloud Infrastructure interfacing, which does not pertain to text processing. Numpy, while a powerful library for numerical computations, does not inherently provide functionalities for text vectorization and is more focused on handling numerical data arrays.

Therefore, the sentence-transformers library is the most suitable choice for vectorizing text chunks due to its specialized functions tailored for transforming sentences into vectors that can