According to the AgentForce Data Cloud Search Indexing Guide and RAG Optimization Framework, a hybrid search index combines both keyword-based (lexical) and vector-based (semantic) search capabilities. This dual-mode retrieval enables AgentForce to interpret user intent while still honoring exact keyword matches.
In many enterprise scenarios, queries contain a mixture of specific terms (e.g., “contract ID 54321”) and semantic intent (e.g., “renew my subscription”). A purely vector search might overlook exact keywords, while a keyword-only search might miss semantically relevant results. Hybrid indexing ensures that both types of retrieval are available simultaneously — providing the best balance of precision and contextual understanding.
Option A is incorrect because hybrid search still uses embeddings; it doesn’t eliminate them. Option B partially describes the hybrid search process but oversimplifies its purpose — the primary goal isn’t just prefiltering for performance, but combining semantic recall and exact matching for more relevant, balanced results.
Thus, per AgentForce documentation, hybrid search indexes are preferred when organizations need both literal keyword matching and semantic understanding for complex, natural-language queries.
[Reference: AgentForce Data Cloud Documentation — “Hybrid Search Index: Combining Keyword and Semantic Retrieval.”, , , , , ]
