symbolicai
A neurosymbolic perspective on LLMs
Learn to create custom neuro-symbolic operations in SymbolicAI by subclassing the Expression class. Implement forward logic and control result types for powerful AI.
How to Configure API Keys for Different LLM Engines in SymbolicAILearn how to configure API keys for various LLM engines in SymbolicAI. Securely manage credentials using symai.config.json and environment variables for seamless integration.
How to Use the symsh Neuro-Symbolic Shell: A Complete GuideMaster symsh, the neuro-symbolic shell from SymbolicAI. This guide shows you how to blend OS commands with LLM queries for powerful AI-driven workflows.
SymbolicAI CLI Entry Points: A Complete Guide to Command-Line ToolsExplore the seven SymbolicAI CLI entry points symchat symsh sympkg symdev symrun symconfig and symserver Explore interactive chat shell translation package management and server deployment from the command line
External Services Interface Implementations in SymbolicAI: A Complete GuideDiscover SymbolicAI's external services interface implementations. Seamlessly integrate Wolfram Alpha, image generation, search, and speech via configuration. Explore the complete guide.
Where to Find Higher-Level Tools like Conversation and Document Handlers in SymbolicAIDiscover where to find advanced Conversation and document handlers in SymbolicAI. Explore the symai/extended package for powerful chat and document utilities.
PreProcessors and PostProcessors in SymbolicAI: A Complete Guide to LLM Request HandlingMaster LLM request handling with SymbolicAI PreProcessors and PostProcessors. Gain fine-grained control over prompt construction and response parsing using modular pipelines. Explore the complete guide.
How to Use @zero_shot and @few_shot Decorators in SymbolicAI: A Complete GuideUnlock SymbolicAI with zero_shot and few_shot decorators. Effortlessly route prompts and examples to LLM backends like OpenAI, Anthropic, and Gemini for powerful neuro-symbolic AI.
How the @contract Decorator Retry Mechanism Works in SymbolicAIDiscover how SymbolicAI's @contract decorator retries LLM outputs up to 8 times with exponential back-off and remedy LLMs, ensuring robust Pydantic schema validation.
What Is LLMDataModel in SymbolicAI? A Complete Guide to Structured LLM OutputsDiscover LLMDataModel in SymbolicAI. Learn how this Pydantic class transforms Python types into LLM prompts, JSON schemas, and validated outputs for strict code-LLM contracts.
How SymbolicAI Implements Design by Contract for LLMs: A Deep Dive into the @contract DecoratorExplore how SymbolicAI uses the @contract decorator to implement Design by Contract for LLMs. Discover pre-condition validation post-condition verification and self-healing retry logic.
Understanding the @contract Decorator in SymbolicAI: Design-by-Contract for Neuro-Symbolic AIExplore the @contract decorator in SymbolicAI for robust Design-by-Contract. It adds type validation, checks conditions, and uses LLMs for automatic remediation, enhancing neuro-symbolic AI reliability.
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