Observed Signal · Jul 19, 2026 · Technical Release · Source: DEV Community · Impact: 4/5 · Sentiment: Positive
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's Foundry IQ and GPT-5 integration represents a major platform-level capability for agentic, multi-hop semantic retrieval on Azure; this can materially change enterprise search, knowledge-driven automation, and how MarTech/AdTech systems perform document-level reasoning and synthesis, with implications for cost/latency and deployment patterns.
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Key Takeaways & Evidence Grounding
- 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.
- Each knowledge base exposes a standalone MCP endpoint that MCP-compatible clients (including Foundry Agent Service and GitHub Copilot) can call using the knowledge_base_retrieve tool.
- The knowledge base setting retrieval_reasoning_effort (Minimal, Medium, High) controls LLM-driven planning, affecting latency, token consumption, and the ability to perform multi-hop queries and iterative retrieval.
Connected Companies & Entities
3 Entities mapped“Microsoft Foundry's answer to this is Foundry IQ: an agentic retrieval layer built on Azure AI Search that treats retrieval as a reasoning t...”
“Any MCP-compatible client — including Foundry Agent Service, but also GitHub Copilot or other MCP clients — can call its `knowledge_base_ret...”
“KnowledgeBaseAzureOpenAIModel(azure_open_ai_parameters=AzureOpenAIVectorizerParameters(resource_url="https://<your-foundry-resource>.openai....”
Ontology Mapping & Concepts
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