Official Technical Resources: Microsoft (page 6)
Official Technical Resources · Microsoft: 312 entries in total; this page lists entries 76–90.
Create a search index knowledge source
Learn how to create a search index knowledge source, which specifies an index used by a knowledge base for agentic retrieval workloads.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval-Augmented Generation (RAG) · Embedding · Vector Databases
Link last checked: 2026-09-24
What is a knowledge source?
Learn about the knowledge source object used for agentic retrieval workloads in Azure AI Search.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval-Augmented Generation (RAG) · Embedding · Vector Databases
Link last checked: 2026-09-24
Use answer synthesis for citation-backed responses in Azure AI Search (preview)
Learn how to enable answer synthesis in a knowledge base or retrieve request in Azure AI Search. At query time, the knowledge base uses your deployed LLM to produce natural-language answers with citations to your knowledge sources.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval-Augmented Generation (RAG) · Embedding · Vector Databases
Link last checked: 2026-09-24
Configure freshness-aware retrieval in Azure AI Search (preview)
Learn how to configure freshness-aware retrieval for indexed knowledge sources in Azure AI Search using a preview API.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval-Augmented Generation (RAG) · Embedding · Vector Databases
Link last checked: 2026-09-24
Create an index for agentic retrieval in Azure AI Search
Create an index that has fields and configurations that work for agentic retrieval workloads in Azure AI Search.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval-Augmented Generation (RAG) · Embedding · Vector Databases
Link last checked: 2026-09-24
Create a knowledge base in Azure AI Search
Learn how to create a knowledge base for agentic retrieval workloads in Azure AI Search.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval-Augmented Generation (RAG) · Embedding · Vector Databases
Link last checked: 2026-09-24
Surface document-embedded images in agentic retrieval (preview)
Use image serving in Azure AI Search to inject document-embedded images into agentic retrieval answer synthesis so LLMs can reason over diagrams and scans.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval-Augmented Generation (RAG) · Embedding · Vector Databases
Link last checked: 2026-09-24
Query a knowledge base using the retrieve action or MCP endpoint
Learn how to query a knowledge base using the retrieve action or MCP endpoint in Azure AI Search using REST APIs, Azure SDKs, or any MCP-compatible client.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval-Augmented Generation (RAG) · Embedding · Vector Databases
Link last checked: 2026-09-24
Set the retrieval reasoning effort (preview)
Learn how to set the level of LLM processing for agentic retrieval in Azure AI Search.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval-Augmented Generation (RAG) · Embedding · Vector Databases
Link last checked: 2026-09-24
Agentic retrieval in Azure AI Search
Learn about agentic retrieval in Azure AI Search, a pipeline that uses LLMs to decompose complex queries into subqueries for better RAG and agent workflows.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval-Augmented Generation (RAG) · Embedding · Vector Databases
Link last checked: 2026-09-24
Azure Vision multimodal embeddings skill (preview)
Vectorize images or text using the Azure Vision multimodal embeddings API.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: AI Agent · Agent Frameworks
Link last checked: 2026-09-24
Create a hybrid query in Azure AI Search
Learn how to create hybrid queries that target an index in Azure AI Search.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval-Augmented Generation (RAG) · Embedding · Vector Databases
Link last checked: 2026-09-24
Hybrid search using vectors and full-text search in Azure AI Search
Learn how hybrid search in Azure AI Search combines vector and full-text queries in a single request for more relevant results using reciprocal rank fusion.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval-Augmented Generation (RAG) · Embedding · Vector Databases
Link last checked: 2026-09-24
Relevance scoring in hybrid search using Reciprocal Rank Fusion (RRF)
Learn about the Reciprocal Rank Fusion (RRF) algorithm used to unify search scores from parallel queries in Azure AI Search.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval-Augmented Generation (RAG) · Embedding · Vector Databases
Link last checked: 2026-09-24
Add custom analyzers to string fields in an Azure AI Search index
Configure text tokenizers and character filters to perform text analysis on strings during indexing and queries.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval-Augmented Generation (RAG) · Embedding · Vector Databases
Link last checked: 2026-09-24