Lead DevelopmentFACT · 4/5••Via DEV Community
Building Semantic Search with GPT-5 and Microsoft Foundry
This technical tutorial demonstrates building a production-grade semantic search pipeline using GPT-5 for query planning and Microsoft Foundry's Foundry IQ (an agentic retrieval layer on Azure AI Search) for retrieval. The guide walks through creating a knowledge source (backed by blob storage and automatically indexed by Azure AI Search), configuring a knowledge base that uses an LLM deployment (example: gpt-5-mini) for query planning and answer synthesis, wiring the knowledge base into a Foundry agent via an MCP endpoint, and tuning retrieval_reasoning_effort to balance cost, latency, and answer quality. The article emphasizes multi-hop queries, iterative planning, and the trade-offs between extractive and synthesized answers.
- •Microsoft Foundry's agentic retrieval layer is called Foundry IQ and is built on Azure AI Search.
- •The tutorial uses GPT-5 (example deployment 'gpt-5-mini') for query planning and answer synthesis within a Foundry knowledge base.
- •Creating a knowledge source (e.g., pointing at a Blob Storage container) triggers Azure AI Search to generate the index, skillset, and indexer to chunk and vectorize content automatically.