  We introduce SymbolicAI, a versatile and modular framework employing a
logic-based approach to concept learning and flow management in generative
processes. SymbolicAI enables the seamless integration of generative models
with a diverse range of solvers by treating large language models (LLMs) as
semantic parsers that execute tasks based on both natural and formal language
instructions, thus bridging the gap between symbolic reasoning and generative
AI. We leverage probabilistic programming principles to tackle complex tasks,
and utilize differentiable and classical programming paradigms with their
respective strengths. The framework introduces a set of polymorphic,
compositional, and self-referential operations for multi-modal data that
connects multi-step generative processes and aligns their outputs with user
objectives in complex workflows. As a result, we can transition between the
capabilities of various foundation models with in-context learning capabilities
and specialized, fine-tuned models or solvers proficient in addressing specific
problems. Through these operations based on in-context learning our framework
enables the creation and evaluation of explainable computational graphs.
Finally, we introduce a quality measure and its empirical score for evaluating
these computational graphs, and propose a benchmark that compares various
state-of-the-art LLMs across a set of complex workflows. We refer to the
empirical score as the "Vector Embedding for Relational Trajectory Evaluation
through Cross-similarity", or VERTEX score for short. The framework codebase
and benchmark are linked below.
