guidancelisted

# Guidance: Constrained LLM Generation ## When to Use This Skill Use Guidance when you need to: - **Control LLM output syntax** with regex or grammars - **Guarantee valid JSON/XML/code** generation - **Reduce latency** vs traditional prompting approaches - **Enforce structured formats** (dates, emails, IDs, etc.) - **Build multi-step workflows** with Pythonic control flow - **Prevent invalid outputs** through grammatical constraints **GitHub Stars**: 18,000+ | **From**: Microsoft Research ## Installation ```bash # Base installation pip install guidance # With specific backends pip install guidance[transformers] # Hugging Face models pip install guidance[llama_cpp] # llama.cpp models ``` ## Quick Start ### Basic Example: Structured Generation ```python from guidance import models, gen # Load model (supports OpenAI, Transformers, llama.cpp) lm = models.OpenAI("gpt-4") # Generate with constraints result = lm + "The capital of France is " + gen("capital", max_tokens=5) print(result["capital"]) # "Paris" ``` ### With Anthropic Claude ```python from guidance import models, gen, system, user, assistant # Configure Claude lm = models.Anthropic("claude-sonnet-4-5-20250929") # Use context managers for chat format with system(): lm += "You are a helpful assistant." with user(): lm += "What is the capital of France?" with assistant(): lm += gen(max_tokens=20) ``` ## Core Concepts ### 1. Context Managers Guidance uses Pythonic context managers for ch