NCA-GENL Question 168
Select 3You are tasked with building a semantic search system for a large collection of documents using Python. The system should identify documents that are contextually similar to a user's query. Which of the following tools or packages would be most appropriate for implementing this system?
- A
spaCy for extracting word embeddings and preprocessing text
- B
NumPy for storing and manipulating dense vector representations
- C
A vector database like Pinecone or Weaviate for indexing and querying vectors
- D
Matplotlib for visualizing the relationships between vectors
- E
Scikit-learn for training a supervised text classification model
Show answer and explanation
Correct answers: A, B, C
Explanation
Semantic search systems rely on transforming text into vector embeddings, storing and indexing these embeddings, and efficiently querying them for similarity. spaCy helps preprocess text and generate embeddings, NumPy supports vector manipulation, and vector databases enable efficient indexing and querying. Tools like Matplotlib and Scikit-learn, while powerful, are not directly suited for this specific use case.
- A. Correct.
Correct: spaCy provides word embeddings and text preprocessing capabilities, which are essential for transforming raw text into meaningful vector representations.
- B. Correct.
Correct: NumPy is widely used for handling and manipulating dense vector representations efficiently, which is critical for semantic search.
- C. Correct.
Correct: Vector databases like Pinecone or Weaviate are designed for storing, indexing, and querying vector embeddings, which is a core requirement for semantic search systems.
- D. Incorrect.
Incorrect: While Matplotlib is useful for visualizations, it is not directly relevant to the implementation of a semantic search system.
- E. Incorrect.
Incorrect: Scikit-learn is commonly used for supervised learning tasks, such as classification, but it is not specifically designed for semantic search or vector similarity tasks.