NCA-GENM Question 127
Select 3You are building a multimodal generative AI application that processes text input and retrieves semantically similar images from a dataset. Which combination of Python natural language and machine learning tools would be most suitable for this task?
- A
spaCy for text preprocessing and semantic similarity analysis
- B
NumPy for vector operations and similarity calculations
- C
A vector database like FAISS for efficient nearest neighbor search
- D
TensorFlow for training a custom image classification model
- E
spaCy’s built-in image processing module
Show answer and explanation
Correct answers: A, B, C
Explanation
Building a multimodal application that matches text input with semantically similar images requires tools that handle both text preprocessing and vector-based similarity search. spaCy is ideal for text-related tasks, NumPy is critical for vector operations, and a vector database like FAISS ensures efficient retrieval of embeddings. TensorFlow and an image processing feature in spaCy are irrelevant in this scenario.
- A. Correct.
Correct: spaCy is highly effective for text tokenization, preprocessing, and semantic similarity analysis, making it a key tool for handling text input in multimodal tasks.
- B. Correct.
Correct: NumPy provides powerful tools for vector operations, which are essential for representing and calculating similarities between text and image embeddings.
- C. Correct.
Correct: A vector database like FAISS is optimized for storing and retrieving high-dimensional embeddings, which is critical for finding semantically similar images efficiently.
- D. Incorrect.
Incorrect: While TensorFlow is useful for training machine learning models, it is not directly needed for retrieving semantically similar images in this scenario. Pre-trained models and embeddings are more relevant.
- E. Incorrect.
Incorrect: spaCy does not have a built-in image processing module. It is primarily used for text processing tasks.