Accuracy in vector databases contributes to the effectiveness of Large Language Models (LLMs) by preserving...

Comprehensive and Detailed In-Depth Explanation:

Vector databases store embeddings that preserve semantic relationships (e.g., similarity between "dog" and "puppy") via their positions in high-dimensional space. This accuracy enables LLMs to retrieve contextually relevant data, improving understanding and generation, making Option B correct. Option A (linear) is too vague and unrelated. Option C (hierarchical) applies more to relational databases. Option D (temporal) isn’t the focus—semantics drives LLM performance. Semantic accuracy is vital for meaningful outputs.

OCI 2025 Generative AI documentation likely discusses vector database accuracy under embeddings and RAG.