Build neuro-symbolic LLM applications with Synalinks framework. Use when working with DataModel, Program, Generator, Module, training LLM pipelines, in-context learning, structured output, JSON operators, Branch/Decision control flow, FunctionCallingAgent, RAG/KAG, or Keras-like LLM workflows.
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The Synalinks framework is an open-source, Keras-inspired toolkit designed to simplify the creation of neuro-symbolic language model applications. By leveraging the power of in-context reinforcement learning, Synalinks enables developers to create more efficient and flexible systems that handle structured input and output data, making it ideal for applications that require complex decision-making and reasoning. It helps bridge the gap between traditional machine learning and symbolic reasoning, offering a hybrid approach to natural language processing tasks.
You can integrate your custom language model by passing it to the 'language_model' parameter when creating a 'Generator' or 'Program'. The 'Generator' uses the language model to process inputs and generate outputs.
Synalinks uses Pydantic-style DataModel classes to define structured input and output data. This approach allows users to define fields and their types with strong validation capabilities.
Synalinks is primarily designed to work with LLMs and integrates well with tools that support Python-based machine learning workflows. However, it is not directly tied to other frameworks like TensorFlow or PyTorch.
Yes, Synalinks allows you to build programs with conditional logic using its 'Program' class, where you can define branches, decisions, and custom control flow to adapt your application to different input scenarios.
Synalinks is a Python-based framework, and you will need Python 3.7 or newer to run it. Additionally, make sure you have the required dependencies, such as Pydantic and any machine learning libraries for your chosen language model.
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SynaLinks/synalinks-skills