guardrails-ai-setup

# guardrails-ai-setup Configure Guardrails AI validation framework to ensure LLM outputs meet quality, safety, and structural requirements. Implement validators for input sanitization, output format enforcement, and safety constraints. ## Overview Guardrails AI provides: - Input validation before LLM calls - Output validation after LLM responses - Structured output enforcement (JSON, XML, etc.) - Pre-built validators from Guardrails Hub - Custom validator creation - Automatic retry and correction mechanisms ## Capabilities ### Input Validation - Sanitize user inputs - Detect prompt injection attempts - Validate input formats and lengths - Check for PII before processing ### Output Validation - Enforce structured output schemas - Validate content accuracy - Check for harmful content - Verify factual consistency ### Safety Constraints - Content moderation - Toxicity detection - Bias checking - Hallucination detection ### Integration Features - LangChain integration - Streaming support - Automatic retries - Correction strategies ## Usage ### Basic Setup ```python from guardrails import Guard from guardrails.hub import ValidJson, ToxicLanguage, DetectPII # Create guard with validators guard = Guard().use_many( ValidJson(), ToxicLanguage(on_fail="fix"), DetectPII(on_fail="fix") ) # Use with LLM from langchain_openai import ChatOpenAI llm = ChatOpenAI(model="gpt-4") result = guard( llm, prompt="Generate a product description for a laptop", max...