Constrain LLM outputs with regex and grammars for JSON, XML, and code.
npx skills add https://github.com/erfanzar/Xerxes-Agents --skill guidance-erfanzar
Please help me install this Agent Skill. Skill: guidance Source: https://github.com/erfanzar/Xerxes-Agents/tree/main/src/python/xerxes/skills/inference/guidance Command: npx skills add https://github.com/erfanzar/Xerxes-Agents --skill guidance-erfanzar
Constrain LLM outputs and guarantee structured results (JSON/XML/code) using regex constraints and grammars, enabling reliable, auditable generations across complex workflows.
Create a guided JSON object with a validated email and constrained age field.
High-intent search queries and answers about installing and using this skill.
Constrain LLM outputs to generate valid JSON and XML by applying regex constraints and grammar rules during generation. This enforces strict structural formats, ensuring the resulting text is immediately parseable without syntax errors.
Grammar-based generation for structured output is a technique that forces a language model to produce text adhering to specific syntax rules. By defining grammars, you guarantee formats like JSON or code are followed exactly during the generation process.
Yes, you can use regex to validate fields during LLM data extraction. By applying regex constraints, the model is restricted to generating only characters that match your defined pattern, ensuring fields like emails or ages meet exact specifications.
Yes, you need the transformers library along with the core guidance library to use guided generation workflows. These dependencies provide the necessary backend integration to enforce grammar rules and execute constrained multi-step generation.
Token healing is a feature that improves constrained generation by correcting boundary artifacts between forced tokens and model-generated tokens. It ensures coherent outputs by preventing awkward token merges when applying strict grammar or regex constraints.
You build multi-step workflows for template generation by using Pythonic guidance functions to compose advanced prompts. This allows you to chain constrained generation steps together, passing validated structured outputs between stages reliably.
zerwizGenerate grammar-constrained structured outputs validated against Pydantic models and JSON Schema.
peteromalletConstrain LLM outputs with regex and grammars using Guidance.
jason660519Constrain LLM outputs to JSON, XML, and code formats with grammar-based generation.
overviewlabsConstrain LLM outputs with regex and grammar constraints for structured formats.
