Building applications semantic kernel von veyson eira (9 Ergebnisse)

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Paperback. Zustand: new. Paperback. Ship production ready AI apps in .NET with Semantic Kernel, agents, RAG, and Microsoft.Extensions.AI.Many teams can prototype an LLM feature, fewer can ship one that is observable, cost aware, and easy to maintain. Model choices, tool calling, retrieval, safety, and deployment details often derail real projects.This book gives .NET developers a clear path from first chat to production. You will use provider neutral abstractions, add structured tools, integrate retrieval and memory, and deploy with confidence on Azure or containers.Set up clean architecture with Semantic Kernel, Microsoft.Extensions.AI, and Azure AI Agent ServiceBootstrap a kernel with dependency injection, then stream chat responses with minimal latencySwitch providers at runtime, OpenAI, Azure OpenAI, GitHub Models, Azure AI Inference, and local OllamaDefine tools and argument schemas, validate inputs, and return structured outputs with typed resultsAdopt reliable planning, native function calling by default, plus Handlebars and Stepwise when they fitBuild agents with the SK Agent Framework for single agent and multi agent collaboration with tool routingAdd safety gates, Azure AI Content Safety, prompt shields, and failure handling in request pipelinesUse the Process Framework for deterministic workflows, events, steps, retries, and idempotencyDesign retrieval with Microsoft.Extensions.VectorData, schemas, metadata, filters, and hybrid searchStand up Azure AI Search vector indexes, ingestion, scoring profiles, and grounded RAG with citationsTune performance, top k, context sizing, freshness, and token budgets for predictable costImplement OpenTelemetry traces, logs, and metrics, plus usage tracking, alerts, and dashboardsRun continuous evaluation in CI with quality gates for prompts, tools, and retrieval changesShip with .NET Aspire or containers, manage environment config, secrets, and private networkingHandle rate limits with backoff, batching, streaming design, and per tenant quotasPrepare incident playbooks, degraded modes, kill switches, and safe rollback plansFollow end to end case studies, a chat assistant with tools, a multi agent workflow, and a RAG serviceThis is a code heavy guide with working C#, Bash, YAML, JSON, SQL, and configuration snippets that map directly to real services.Grab your copy today and ship AI features your team can operate with confidence. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

Paperback. Zustand: new. Paperback. Ship production ready AI apps in .NET with Semantic Kernel, agents, RAG, and Microsoft.Extensions.AI.Many teams can prototype an LLM feature, fewer can ship one that is observable, cost aware, and easy to maintain. Model choices, tool calling, retrieval, safety, and deployment details often derail real projects.This book gives .NET developers a clear path from first chat to production. You will use provider neutral abstractions, add structured tools, integrate retrieval and memory, and deploy with confidence on Azure or containers.Set up clean architecture with Semantic Kernel, Microsoft.Extensions.AI, and Azure AI Agent ServiceBootstrap a kernel with dependency injection, then stream chat responses with minimal latencySwitch providers at runtime, OpenAI, Azure OpenAI, GitHub Models, Azure AI Inference, and local OllamaDefine tools and argument schemas, validate inputs, and return structured outputs with typed resultsAdopt reliable planning, native function calling by default, plus Handlebars and Stepwise when they fitBuild agents with the SK Agent Framework for single agent and multi agent collaboration with tool routingAdd safety gates, Azure AI Content Safety, prompt shields, and failure handling in request pipelinesUse the Process Framework for deterministic workflows, events, steps, retries, and idempotencyDesign retrieval with Microsoft.Extensions.VectorData, schemas, metadata, filters, and hybrid searchStand up Azure AI Search vector indexes, ingestion, scoring profiles, and grounded RAG with citationsTune performance, top k, context sizing, freshness, and token budgets for predictable costImplement OpenTelemetry traces, logs, and metrics, plus usage tracking, alerts, and dashboardsRun continuous evaluation in CI with quality gates for prompts, tools, and retrieval changesShip with .NET Aspire or containers, manage environment config, secrets, and private networkingHandle rate limits with backoff, batching, streaming design, and per tenant quotasPrepare incident playbooks, degraded modes, kill switches, and safe rollback plansFollow end to end case studies, a chat assistant with tools, a multi agent workflow, and a RAG serviceThis is a code heavy guide with working C#, Bash, YAML, JSON, SQL, and configuration snippets that map directly to real services.Grab your copy today and ship AI features your team can operate with confidence. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …