Introduction

LLM-AutoDiff

AdalFlow mainly relys on LLM-AutoDiff to do automatic prompt engineering(APE).

Similar to Auto-differentiation in PyTorch, LLM-AutoDiff works by forming a runtime computation graph of prompts, hyperparameters, intermediate outputs, and losses in the forward pass. In the backward pass, the backward engine LLM we put at each node will work together to identify which prompts are the cause of errors, so that a feedback-driven LM optimizer can leverage it to propose new prompts.

Components

Component is to LM task pipelines what nn.Module is to PyTorch models. A component can recursively contain and register other components, allow easy control of (1) training and inference modes, (2) visualization of the workflow structure, and (3) serialization and deserialization of the component.

We require a component to have (1) a call method, which will be called during the inference time, and (2) a forward method, which will be called during the training time and output a Parameter object which has the output of the component wrapped in the data field.

There are four main types of components in AdalFlow:

  1. Component: the base class of all components. With forward and call method, or `bicall`(handles both in one method). You use it to put together an LM workflow.

  2. GradComponent: a subclass of Component that has a backward method. It defines a unit of computation that are capabalbe of backpropagation. One example is Generator and Retriever.

  3. DataComponent: a subclass of Component that only has call method and does not handle any Parameter object. Examples include Prompt, DataClassParser which only handles the data formatting but rather the transformation.

  4. LossComponent: a subclass of Component that likely takes an evaluation metric function and has a backward method. When it is attached to your LM workflow’s output, the whole training pipeline is capable of backpropagation.

Parameters

Parameter are used to save (1) intermediate forward data and (2) gradients/feedback, and (3) graph information such as predecessors. It also has function like draw_graph to help you visualize the structure of your computation graph.

DataClass and Structured Output

DataClass is used for developers to define a data model. Similar to Pydantic, it has methods like to_yaml_signature, to_json_signature, to_yaml, to_json to help you generate the data model schema and to generate the json/yaml data representation as strings. It can be best used together with DataClassParser for structured output.