模型推理思想树：Tree-of-thoughts

# Comprehensive Documentation and Changelog This document provides a comprehensive overview of the changes made to the TreeofThoughts class and its methods to improve readability and understandability. The changes include updating variable names to be more meaningful and descriptive, as well as modifying the structure of the code for better readability. ## Changelog 1. TreeofThoughts Class Updated the class definition to include a more descriptive docstring. 2. __init__ Method No changes were made to the __init__ method. 3. solve Method Updated variable names: x -> initial_prompt k -> num_thoughts T -> max_steps b -> max_states vth -> value_threshold 4. tot_bfs Method Updated variable names: x -> initial_prompt k -> num_thoughts T -> max_steps b -> max_states S0 -> current_states S0_t -> generated_states Vt -> state_values St -> selected_states 5. tot_dfs Method Updated variable names: x -> initial_prompt k -> num_thoughts T -> max_steps vth -> value_threshold s -> state t -> step s_prime -> next_state child -> child_state ### Added optional parameters for better control over the search process: pruning_threshold confidence_threshold max_iterations convergence_threshold convergence_count 6. save_tree_to_json Method No changes were made to the save_tree_to_json method. 7. print_tree Method No changes were made to the print_tree method. # Documentation TreeofThoughts Class The TreeofThoughts class is designed to solve problems using a tree-based search algorithm. It takes a model and a search algorithm (either 'BFS' or 'DFS') as input and provides methods to solve problems using the chosen algorithm. ## Initialization The __init__ method initializes the TreeofThoughts class with the given model and search algorithm. It also initializes an empty tree structure to store the search results. ## Solve Method The solve method is the main entry point for solving problems using the TreeofThoughts class. It takes the following parameters: initial_prompt: The initial problem or prompt to be solved. num_thoughts: The number of thoughts to generate at each step. max_steps: The maximum number of steps to perform in the search. max_states: The maximum number of states to consider at each step (for BFS). value_threshold: The threshold value for pruning states (for DFS). timeout: The maximum time allowed for the search process. confidence_threshold: The confidence threshold for stopping the search. max_iterations: The maximum number of iterations allowed for the search. convergence_threshold: The threshold for determining convergence. convergence_count: The number of consecutive convergences required to stop the search. Based on the chosen search algorithm, the solve method calls either the tot_bfs or tot_dfs method to perform the search. ## tot_bfs Method The tot_bfs method performs a breadth-first search to solve the problem. It takes the following parameters: initial_prompt: The initial problem or prompt to be solved. num_thoughts: The number of thoughts to generate at each step. max_steps: The maximum number of steps to perform in the search. max_states: The maximum number of states to consider at each step. pruning_threshold: The threshold value for pruning states. The method generates and evaluates states at each step, selecting the best states based on their values. The search continues until the maximum number of steps is reached, and the best state is returned. ## tot_dfs Method The tot_dfs method performs a depth-first search to solve the problem. It takes the following parameters: initial_prompt: The initial problem or prompt to be solved. num_thoughts: The number of thoughts to generate at each step. max_steps: The maximum number of steps to perform in the search. value_threshold: The threshold value for pruning states. pruning_threshold: The threshold value for pruning states based on their values. confidence_threshold: The confidence threshold for stopping the search. max_iterations: The maximum number of iterations allowed for the search. convergence_threshold: The threshold for determining convergence. convergence_count: The number of consecutive convergences required to stop the search. The method uses a recursive depth-first search approach to explore the state space. It generates and evaluates states at each step, and if a state's value is above the value_threshold and pruning_threshold, it continues the search with the new state. The search stops when the maximum number of steps is reached, the confidence threshold is met, or the convergence criteria are satisfied. The best state is then returned. ## save_tree_to_json Method The save_tree_to_json method saves the current tree structure and metrics to a JSON file. It takes the following parameter: file_name: The name of the JSON file to save the tree structure and metrics. This method is useful for logging the search process and analyzing the results later. ## print_tree Method The print_tree method prints the tree structure in a human-readable format. It takes the following parameters: node: The current node in the tree. depth: The depth of the current node in the tree (default is 0). This method is useful for visualizing the tree structure and understanding the search process. ## Usage To use the TreeofThoughts class, follow these steps: Initialize the class with a model and a search algorithm (either 'BFS' or 'DFS'). Call the solve method with the required parameters to perform the search and obtain the best state. (Optional) Use the save_tree_to_json method to save the tree structure and metrics to a JSON file. (Optional) Use the print_tree method to visualize the tree structure. Here's an example of how to use the TreeofThoughts class: # V2 with Monte Carlo, A* Search Algorithm, BFS, Best First Search ### 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 met