Semantic Reranking for RAG in .NET: A Second-Stage Retrieval Model
See how semantic reranking for RAG in .NET applies Azure AI Search semantic ranking after retrieval, with configuration, scores, captions, and limits.
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AI & .NET
Practical guidance on building AI-powered .NET apps, covering Model Context Protocol servers, Microsoft Agent Framework, and GitHub Copilot SDK integrations.
Articles
Showing 13–18 of 78 articles
Hybrid Search for RAG in .NET: BM25, Vectors, and Reciprocal Rank Fusion
Learn how hybrid search RAG .NET combines lexical BM25 and dense vector candidates in one Azure AI Search request with reciprocal rank fusion.
Vector Search in .NET: The Azure AI Search Query Model
Learn Azure AI Search vector search .NET query mechanics, including vector fields, dimension alignment, top-k results, filters, scores, and stable C# APIs.
Embedding Pipelines in .NET with Microsoft.Extensions.AI
Learn how Microsoft.Extensions.AI embeddings .NET pipelines use batching, vectors, caching, telemetry, and decorators to keep retrieval work observable.
RAG and Embeddings in .NET: A Stable Architecture Guide
Learn a stable architecture for RAG and embeddings in .NET, connecting ingestion, retrieval, citations, authorization, evaluation, observability, and deletion.
Testing and Debugging Your C# MCP Server with MCP Inspector
Use MCP Inspector to test and debug C# MCP server .NET projects over stdio and HTTP, inspect JSON-RPC logs, verify tools, and avoid host integration guesswork.