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@kyegomez kyegomez released this 04 Jun 23:08
· 79 commits to main since this release
E26
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Tree of Thought Search Classes Documentation

Class: TreeofThoughts

This class represents the base class for the Tree of Thoughts search algorithm. It contains the following methods:

  • __init__(self, model): Initializes the TreeofThoughts object with the given model.
  • save_tree_to_json(self, file_name): Saves the tree to a JSON file with the given file name.
  • logNewState(self, state, evaluation): Logs a new state and its evaluation to the tree.
  • adjust_pruning_threshold_precentile(self, evaluated_thoughts, percentile): Adjusts the pruning threshold based on the percentile of evaluated thoughts.
  • adjust_pruning_threshold_moving_average(self, evaluated_thoughts, window_size): Adjusts the pruning threshold based on the moving average of evaluated thoughts.

Class: TreeofThoughtsBFS

This class represents the Breadth-First Search (BFS) variant of the Tree of Thoughts search algorithm. It inherits from the TreeofThoughts class and contains the following method:

  • solve(self, initial_prompt, num_thoughts, max_steps, max_states, value_threshold, pruning_threshold=0.5): Solves the problem using BFS with the given parameters.

Class: TreeofThoughtsDFS

This class represents the Depth-First Search (DFS) variant of the Tree of Thoughts search algorithm. It inherits from the TreeofThoughts class and contains the following method:

  • solve(self, initial_prompt, num_thoughts, max_steps, value_threshold, pruning_threshold=0.5): Solves the problem using DFS with the given parameters.

Class: TreeofThoughtsBEST

This class represents the Best-First Search variant of the Tree of Thoughts search algorithm. It contains the following methods:

  • __init__(self, model): Initializes the TreeofThoughtsBEST object with the given model.
  • save_tree_to_json(self, file_name): Saves the tree to a JSON file with the given file name.
  • log_new_state(self, state, evaluation): Logs a new state and its evaluation to the tree.
  • solve(self, initial_prompt, num_thoughts, max_steps, pruning_threshold): Solves the problem using Best-First Search with the given parameters.

Class: TreeofThoughtsASearch

This class represents the A* Search variant of the Tree of Thoughts search algorithm. It contains the following methods:

  • __init__(self, model): Initializes the TreeofThoughtsASearch object with the given model.
  • solve(self, initial_prompt, num_thoughts=5, max_steps=30, pruning_threshold=0.4): Solves the problem using A* Search with the given parameters.
  • is_goal(self, state, score): Determines if the given state is a goal state based on its score.
  • reconstruct_path(self, came_from, current_state, initial_prompt): Reconstructs the path from the initial state to the current state using the came_from dictionary.

Class: MonteCarloTreeofThoughts

This class represents the Monte Carlo Tree Search variant of the Tree of Thoughts search algorithm. It inherits from the TreeofThoughts class and contains the following methods:

  • __init__(self, model, objective="balance"): Initializes the MonteCarloTreeofThoughts object with the given model and objective.
  • optimize_params(self, num_thoughts, max_steps, max_states): Optimizes the search parameters based on the objective.
  • solve(self, initial_prompt, num_thoughts, max_steps, max_states, pruning_threshold): Solves the problem using

Monte Carlo Tree Search with the given parameters.

  • monte_carlo_search(self, initial_prompt, num_thoughts, max_steps, max_states, pruning_threshold): Performs the Monte Carlo Tree Search with the given parameters.

Class: OptimizedTreeofThoughts

This class represents an optimized version of the Tree of Thoughts search algorithm. It inherits from the TreeofThoughts class and contains the following method:

  • solve(self, x, k=None, T=None, b=None, vth=None, timeout=None, confidence_threshold=None, max_iterations=None, convergence_threshold=None, convergence_count=None): Solves the problem using an optimized search algorithm with the given parameters.
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