diagraph 0.4.1

DAGs for LLM interactions

Diagraph

Diagraph represents Large Language Model (LLM) interactions as a graph, which makes it easy to build, edit, and re-execute a chain of structured dialogues.

Key features:

• Code Faster & Save Money: Diagraph's ability to cache and replay specific parts of your code saves on execution time and reduces prompt costs.
• Quick Iteration: Edit interactions on the fly. Rewrite the LLM's results as well as the functions.
• Easy Refactoring: Specify a function's dependencies as parameters for clean, readable, and refactorable code.
• Bring Your Own Code: Use any LLM or set of tools you want. Diagraph operates on top of plain Python functions.
• Visualize The System: Get a straightforward view of your graph with a built-in Jupyter visualization tool.
• Concurrency: Interactions that can be run in parallel, should be run in parallel. Get this behavior for free.

Requirements

Python 3.10+

Installation

pip install diagraph

Quickstart

from diagraph import Diagraph, Depends, prompt, llm

openai.api_key = 'sk-<OPENAI_TOKEN>'

@prompt
def tell_me_a_joke():
  return 'Computer! Tell me a joke about tomatoes.'

@prompt
def explanation(joke: str = Depends(tell_me_a_joke)) -> str:
  return f'Explain why the joke "{joke}" is funny.'

dg = Diagraph(explanation).run()
print(dg.result) # 'The joke is a play on words and concepts. There are two main ideas that make it humorous...
dg

Usage

See the usage guide.

License

MIT