“structured output”的搜索结果

Complete guide for xUnit test output and logging. Use when you need to implement test output, diagnostic logging, or ILogger alternatives in xUnit tests. Covers ITestOutputHelper injection, AbstractLogger pattern, structured output design. Includes XUnitLogger, CompositeLogger, performance test diagnostic tool implementations. Keywords: ITestOutputHelper, ILogger testing, test output xunit, test output, test logging, AbstractLogger, XUnitLogger, CompositeLogger, testOutputHelper.WriteLine, test diagnostics, logger mock, test log, structured output, Received().Log

中文简介正在准备中。当前显示原文说明。

Design AI agents and chatbots in n8n, choosing suitable nodes and connecting models, tools, conversation memory, and structured JSON output. Includes guidance on document retrieval and human approval before tool execution.

中文简介正在准备中。当前显示英语简介。

• Building a chatbot with conversation memory
• Connecting external tools to an AI agent
• Returning data in a defined JSON format

Design AI agents and chatbots in n8n, choosing suitable nodes and connecting models, tools, conversation memory, and structured JSON output. Includes guidance on document retrieval and human approval before tool execution.

中文简介正在准备中。当前显示英语简介。

• Building a chatbot with conversation memory
• Connecting external tools to an AI agent
• Returning data in a defined JSON format

Orchestrator for the eval-output skill suite — evaluate LLM and agent outputs for quality, accuracy, helpfulness, and safety using structured rubrics and LLM-as-judge techniques. Load when the user says "evaluate this output", "score this response", "run an eval", "LLM as judge", "evaluate agent output", "how good is this response", "rate this answer", "eval this", or provides an LLM output that should be assessed for quality. Single entry point for all output evaluation workflows.

中文简介正在准备中。当前显示原文说明。

Guides Python agent development with PydanticAI, covering tool calls and structured outputs checked against defined data types. Explains service injection, provider switching, streaming responses, and tests that run without calling a real language model.

中文简介正在准备中。当前显示英语简介。

• Building agents that return validated structured data
• Connecting customer support agents to order data
• Testing agent logic without real model calls

Build workflows that extract structured data from LLM responses, check field types and values with Pydantic, and retry failed extractions. Includes examples for nested data, classification, and receiving results as they are generated.

中文简介正在准备中。当前显示英语简介。

• Extracting contact and company details from text
• Classifying articles into predefined categories
• Checking review analysis against validation rules

Build workflows that extract structured data from LLM responses, check field types and values with Pydantic, and retry failed extractions. Includes examples for nested data, classification, and receiving results as they are generated.

中文简介正在准备中。当前显示英语简介。

• Extracting contact and company details from text
• Classifying articles into predefined categories
• Checking review analysis against validation rules

Build Python workflows that constrain language model output using text patterns and grammar rules. Generate structured JSON, restrict classifications to allowed labels, extract information, and combine reusable generation steps with loops and tool selection.

中文简介正在准备中。当前显示英语简介。

• Generate JSON with a defined structure
• Constrain date, email, and identifier formats
• Classify text using a fixed set of labels

Set up LLM serving with structured responses, including JSON and outputs restricted to specified patterns. Covers reusing shared prompt calculations, tool calling, and multi-turn conversations through SGLang, with examples for server setup and application integration.

中文简介正在准备中。当前显示英语简介。

• Extract information into JSON with a defined schema
• Build agents that call tools
• Reuse shared prompts across chat requests

Build custom calculation and parsing tools that an n8n AI Agent can call. This skill covers JavaScript or Python inputs, structured arguments, string results, tool descriptions, and fixes for invocation and output errors.

中文简介正在准备中。当前显示英语简介。

• Adding a calculation tool to an AI Agent
• Defining typed inputs for multiple arguments
• Fixing tool output type errors

Build Python workflows that constrain language model output using text patterns and grammar rules. Generate structured JSON, restrict classifications to allowed labels, extract information, and combine reusable generation steps with loops and tool selection.

中文简介正在准备中。当前显示英语简介。

• Generate JSON with a defined structure
• Constrain date, email, and identifier formats
• Classify text using a fixed set of labels

Build LLM serving workflows that produce JSON or other required formats. The skill covers reusing computation for shared prompts, tool calls, and ongoing conversations, with examples for server setup and structured output.

中文简介正在准备中。当前显示英语简介。

• Extracting article details into JSON
• Building agents that call tools
• Reusing shared prompts across conversations

Build custom calculation and parsing tools that an n8n AI Agent can call. This skill covers JavaScript or Python inputs, structured arguments, string results, tool descriptions, and fixes for invocation and output errors.

中文简介正在准备中。当前显示英语简介。

• Adding a calculation tool to an AI Agent
• Defining typed inputs for multiple arguments
• Fixing tool output type errors

Pick an LM output format per (task x consumer x model) rather than by reflex: different formats carry different cognitive load (e.g. code-in-JSON makes the same model write worse code than plain-text+diff, while asking for prose when you need a typed object fails the other way). Use when designing or debugging an LM's output schema, choosing between plain text / diff / JSON / tool-call / grammar-constrained output, or when a model's quality drops after wrapping its output in a structured format.

中文简介正在准备中。当前显示原文说明。

Decide where to enforce structured LM output (constrain at decode time with Outlines vs validate-and-retry with Instructor vs grammar with Guidance) and which failure stance to take (Assert/hard-fail vs Suggest/soft-retry). Use when an LM's output is parsed or typed by downstream code and you must pick one enforcement library plus its failure handling, when malformed output is burning tokens on retries, or when choosing between decode-time vs validation-time constraints for local vs API models.

中文简介正在准备中。当前显示原文说明。

从文本中提取所需信息，整理为固定字段，并用Pydantic验证类型和值。涵盖结构化大语言模型输出、验证失败后的重试，以及逐步接收生成中的结果。

• 从文本中提取联系方式或企业信息
• 将文章归入预设类别
• 统一评论分析的字段

Convert existing scripts, CLI tools, or Crawlee projects into reusable Apify Actors, programs packaged as Docker images. Define JSON inputs and outputs, integrate the SDK, test locally, and deploy to Apify.

中文简介正在准备中。当前显示英语简介。

• Turning an existing script or CLI tool into an Actor
• Migrating a Crawlee project to Apify
• Defining JSON inputs and structured outputs

Design or review command-line tools that AI agents can call. Use 98 proposed rules to define structured output, input validation, error handling, safety controls, and help information, with three levels for gradual adoption.

中文简介正在准备中。当前显示英语简介。

• Design a new command-line tool for AI agents
• Adapt an existing tool for agent use
• Standardize interfaces for automated workflows

Prepare GRPO reinforcement learning configurations for tasks with programmatically checkable answers, such as math, code, and structured output. Design and inspect reward functions, then select training variants when runs show instability or exploit flaws in scoring.

中文简介正在准备中。当前显示英语简介。

• Preparing GRPO training for tasks with automatic correctness checks
• Designing rewards for output format and correctness
• Inspecting reward scores before training

Design workflows that evaluate and refine AI outputs through self-review, separate generation and evaluation roles, tests, or weighted scoring criteria. Includes patterns for structured feedback, iteration limits, and stopping when quality stops improving.

中文简介正在准备中。当前显示英语简介。

• Adding self-review and revision loops to AI outputs
• Separating generation from evaluation
• Refining generated code using test results

Use Outlines to make language models produce data in a defined format. The skill covers extracting information into JSON, restricting classification labels, and applying Pydantic models, which define fields and data types, with local model backends.

中文简介正在准备中。当前显示英语简介。

• Extracting company or profile details into JSON
• Classifying text using a fixed set of labels
• Generating text that matches a specified pattern

Add error handling to n8n workflows with connected error outputs, retries, and failure notifications. Return HTTP status codes that explain what went wrong, distinguish invalid requests from service failures, and keep internal error details out of public responses.

中文简介正在准备中。当前显示英语简介。

• Investigating workflows that fail without notification
• Returning clear errors from webhook APIs
• Retrying temporary network failures

Use Outlines to make language models produce data in a defined format. The skill covers extracting information into JSON, restricting classification labels, and applying Pydantic models, which define fields and data types, with local model backends.

中文简介正在准备中。当前显示英语简介。

• Extracting company or profile details into JSON
• Classifying text using a fixed set of labels
• Generating text that matches a specified pattern

Build failure handling into n8n workflows with separate error paths, retries, alerts, and clear API responses. Use it for webhooks and unattended jobs to make failures visible and return status codes that explain what went wrong.

中文简介正在准备中。当前显示英语简介。

• Add error responses to a webhook API
• Receive alerts when scheduled jobs fail
• Retry temporary external service failures