https://github.com/LachlanGray/lmql-tree-of-thoughts

LMQL implementation of tree of thoughts
https://github.com/LachlanGray/lmql-tree-of-thoughts

Last synced: 2 months ago
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LMQL implementation of tree of thoughts

• Host: GitHub
• URL: https://github.com/LachlanGray/lmql-tree-of-thoughts
• Owner: LachlanGray
• Created: 2023-05-29T16:11:32.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-01-31T15:45:20.000Z (12 months ago)
• Last Synced: 2024-08-03T19:08:56.477Z (6 months ago)
• Language: Python
• Size: 41 KB
• Stars: 32
• Watchers: 3
• Forks: 6
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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README

        
# Tree of thoughts

A LMQL implementation of something like tree of thoughts. Applies a natural selection process to steer reasoning and constrain the results.

Many many improvements to be made.

## Features

I meant this to be as "engineerable" as possible. Each tree instance is configured to solve a specific problem, and be used as a function. It can apply a callback function to the result so it doesn't necessarily have to return text.

Some of the main features:

- Asynchronous

- Configurable

- Prompt-based and programmatic result validation

Some planned features:

- Multiple arguments and argument types

- Feature weighting: option to assign relative importance to selection criteria

- Dynamic width: method for determining how many branches should stem from each thought

## How it works

Each iteration consists of a review phase, a generation phase, an evaluation phase.

- **Selection:** The top-k scoring lines of thought are selected

- **Review:** Selected lines of thought are checked to see if they contain an answer.

- **Generation:**  A fixed number of branching thoughts are generate from selected leaf thoughts. If a selected leaf contains an answer, a conclusion is generated instead.

- **Evaluation:** New thoughts are scored against defined criteria to determine the relative strength of the threads. If any conclusions were generated, they are validated and returned if they pass. 

## Usage

For now see the `examples` folder to get a sense of it. In a nutshell there's three configurations: one for the initial prompt, one that governs the reasoning dynamics (evaluation, answer recognition), and one that describes how answer attempts are handled (conclusion generation, callbacks, validation).