Search Index

Data Cloud uses a search index to manage structured and unstructured content in a search-optimized way. Create a search index configuration to chunk and vectorize yout content. Chunking breaks the text into smaller units, reflecting passages of the original content, such as sentences or paragraphs. Vectorization converts chunks into numeric representations of the text that capture semantic similarities.

In Data Cloud, you can create vector or hybrid search indexes depending on data and query needs.

Use vector search when your data source and search queries benefit more from semantically aware matches within a large dataset. For example, if you send a query such as “What is Acme famous for?”, the LLM retrieves chunks that have the highest vector search score that relates to the closest semantic match with the search query.

When your data source and search queries benefit from both semantically aware vector search and the precision of a keyword search, use hybrid search. For example, in the query “Does Acme motor XYZ123 use hydraulic pumps?” the addition of keyword search promotes higher-ranking positions for more relevant content, thus providing the results with better grounding.

Learn more about the Data Cloud search index.

• Vector Search
• Hybrid Search
• Search Index Reference
• Manage Search Indexes