In the context of retriever and search indexes, what best describes the data preparation process in Data Cloud?
A. Data preparation focuses on real-time data ingestion and dynamic indexing to generate dynamic grounding reference data without preprocessing steps.
B. Data preparation entails aggregating, normalizing, and encoding structured datasets to ensure compliance with data governance and security protocols.
C. Data preparation Involves loading, chunking, vectorizing, and storing content in a search-optimized manner to support retrieval from the vector database.
C. Data preparation Involves loading, chunking, vectorizing, and storing content in a search-optimized manner to support retrieval from the vector database.
Explanation:
In Data Cloud, preparing data for retrievers and search indexes follows a structured pipeline to enable semantic search and AI grounding. Here’s the breakdown:
Key Steps in Data Preparation
Loading: Ingest raw data (e.g., CRM records, PDFs) into Data Cloud.
Chunking: Split large documents/text into smaller, meaningful segments (e.g., paragraphs).
Vectorization: Convert text into numerical embeddings (vectors) using AI models to capture semantic meaning.
Storage: Index vectors in a vector database for fast similarity searches.
Why Not the Other Options?
A. "Real-time ingestion without preprocessing":
Incorrect. Data must be preprocessed (chunked, vectorized) to work with retrievers. Real-time updates still require these steps.
B. "Aggregating/normalizing for governance":
While governance is critical, this describes data unification, not the vector search pipeline.
Example Workflow:
A product manual PDF is chunked into sections, vectorized, and stored.
When a user asks, "How do I troubleshoot Error 404?", the retriever finds the closest-matching vectorized chunk.