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You have a Microsoft Foundry project that contains an agent. The agent uses Azure AI Search for Retrieval Augmented Generation (RAG). You plan to ingest and index PDF product manuals. You need to build a solution that supports semantic similarity matching. The solution must ensure that the agent retrieves relevant data when user questions use different wording than the product manuals.
Use vector search because it performs similarity matching based on semantic meaning rather than requiring literal keyword overlap. During ingestion, the product manuals should be divided into appropriately sized chunks. An embedding model converts each chunk into a numerical vector, which Azure AI Search stores in a vector field. At query time, the user's question is also converted into an embedding, and the service retrieves chunks whose vectors are closest to the query vector. Consequently, wording such as ''reset the device to factory settings'' can match manual content such as ''restore the unit's original configuration,'' despite minimal lexical overlap. Azure AI Search explicitly defines vector fields as enabling similarity searches over mathematically represented content and supports this retrieval pattern for RAG applications.
Semantic ranking improves the ordering of an existing result set but is not the indexing mechanism that stores embeddings and performs nearest-neighbor similarity matching. Suggesters provide search-as-you-type functionality, while analyzers control tokenization and lexical processing for text fields.
This aligns with the AI-103 Study Guide objectives for implementing RAG, configuring Azure AI Search grounding, generating embeddings, and selecting vector retrieval techniques for agentic solutions.
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