chainy 0.1.0

Declarative prompt chaining

Chainy

Overview

Chainy is a Python package for declarative prompt chaining. It allows users to define a chain of prompts to be run by a Large Language Model (LLM). These chains are defined using a YAML configuration file and can include dependencies, which are handled automatically.

Installation

To install chainy, you can use a package manager like pip.

pip install chainy

You can find the package repository here.

Usage

Configuration

Chainy uses YAML configuration files to define chains of prompts. Here's an example of what these configuration files look like:

inputs:
  - input_1
  - input_2
prompts:
  prompt_1:
    model: my_model
    template: tmpl01.md
    substitute:
      var_1: input_1
      var_2: input_2
  prompt_2:
    model: my_model
    template: tmpl02.md
    substitute:
      res_1: prompt_1

In this example, prompt_1 and prompt_2 are the prompts to be run. Each prompt includes a template file and a dictionary of variables to be substituted into the template. The dependencies between prompts are defined in the substitute section: prompt_2 depends on prompt_1.

Expected Directory Structure

|- yourproject/
|--- prompts/
|----- tmpl01.md
|----- tmpl02.md
|--- chains/
|----- example-1.yml
|--- entrypoint.py

Bring Your Own Model (BYOM)

Each prompt must specify the alias of the model that will be used to generate the response. Models must be added to a chain with Chain.add_model(name, model) before the chain is started.

Models are user defined classes that adhere to the LanguageModelProtocol or ChatModelProtocol, which can be found in the llm module. Essentially, the model needs to expose the appropriate generate() method, then chainy will take it from there.

Running a Chain

To run a chain, call the Chain.start() method with the required input values:

from chainy.model import Chain

# STEP 1: Load your chain from configuration
chain = Chain.from_config("chains/example-1.yml")

# STEP 2: Add your model(s)
model = ...  # instantiate your model here!
chain.add_model("my_model", model)

# STEP 3: Start the chain
chain.start("hey", "bud")

Testing

Tests are located in the tests/ directory. To run them, use your preferred test runner.

Contributing

We welcome contributions! Please open an issue or submit a pull request if you have something to add.

License

MIT