Tree of Thoughts (ToT) is an all-new powerful and flexible algorithm that advances model reasoning by a whopping 70%. This is an plug in and play verision, connect your own models and enjoy superintelligence!
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This implementation of Tree of Thoughts is brought to you by Agora, Agora advances Humanity with open source SOTA Multi-Modality AI research! We plan on combating Humanity's grandest root problems like food insecurity, planetary insecurity, and disease, and hopefully death itself.
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Clone this repository with git clone https://github.com/kyegomez/tree-of-thoughts
or:
pip install tree-of-thoughts
Navigate to the repository folder: cd tree-of-thoughts
pip install openai
Create a Python script (e.g., example.py) and import the necessary classes:
from tree_of_thoughts.treeofthoughts import OpenAILanguageModel, CustomLanguageModel, TreeofThoughts, OptimizedOpenAILanguageModel, OptimizedTreeofThoughts
use_v2 = False
api_key=""
api_base= "" # leave it blank if you simply use default openai api url
if not use_v2:
#v1
model = OpenAILanguageModel(api_key=api_key, api_base=api_base)
else:
#v2 parallel execution, caching, adaptive temperature
model = OptimizedOpenAILanguageModel(api_key=api_key, api_base=api_base)
#choose search algorithm('BFS' or 'DFS')
search_algorithm = "BFS"
#cot or propose
strategy="cot"
# value or vote
evaluation_strategy = "value"
if not use_v2:
#create an instance of the tree of thoughts class v1
tree_of_thoughts = TreeofThoughts(model, search_algorithm)
else:
#or v2 -> dynamic beam width -< adjust the beam width [b] dynamically based on the search depth quality of the generated thoughts
tree_of_thoughts= OptimizedTreeofThoughts(model, search_algorithm)
input_problem = "use 4 numbers and basic arithmetic operations (+-*/) to obtain 24"
k = 5
T = 3
b = 5
vth = 0.5
timeout = 10
confidence = 1.0 #cmodel is confident on performance
max_iterations = 40 #tree branh nodes
convergence_threshold = 0.01
convergence_count = 5
solution = tree_of_thoughts.solve(input_problem, k, T, b, vth, timeout, confidence_threshold=confidence, max_iterations=max_iterations, convergence_threshold=convergence_threshold, convergence_count=convergence_count)
#use the solution in your production environment
print(f'solution {solution}')
Or Integrate your own custom language model:
class CustomLanguageModel(AbstractLanguageModel):
def __init__(self, model):
self.model = model
def generate_thoughts(self, state, k):
#implement the thought generation logic using self.model
pass
def evaluate_states(self, states):
#implement state evaluation logic using self.model
passRun the example script
- General problem-solving framework for language models
- Supports both breadth-first search (BFS) and depth-first search (DFS) algorithms
- Easy integration with popular language models like OpenAI and Hugging Face
- Extensible and adaptable to different problem properties and resource constraints
- Define the thought decomposition based on the problem properties.
- Create a thought generator function G(pΞΈ, s, k) with two strategies: a. Sample i.i.d. thoughts from a CoT prompt. b. Propose thoughts sequentially using a "propose prompt".
- Create a state evaluator function V(pΞΈ, S) with two strategies: a. Value each state independently. b. Vote across states.
- Choose a search algorithm (BFS or DFS) based on the tree structure.
- Implement the chosen search algorithm.
- Execute the chosen search algorithm with the input problem, thought generator, state evaluator, and other required parameters.
class TreeofThoughts:
def __init__(self, model, search_algorithm):
self.model = model
self.search_algorithm = search_algorithm
def solve(self, x, k, T, b, vth):
if self.search_algorithm == 'BFS':
return self.tot_bfs(x, k, T, b)
elif self.search_algorithm == 'DFS':
return self.tot_dfs(x, k, T, vth)
else:
raise ValueError("Invalid search algorithm. Choose 'BFS' or 'DFS'.")
def tot_bfs(self, x, k, T, b):
S0 = {x}
for t in range(1, T + 1):
S0_t = {(*s, z) for s in S0 for z in self.model.generate_thoughts(s, k)}
Vt = self.model.evaluate_states(S0_t)
St = sorted(S0_t, key=lambda s: Vt[s], reverse=True)[:b]
S0 = set(St)
return self.model.generate_thoughts(max(St, key=lambda s: Vt[s]), 1)
def tot_dfs(self, x, k, T, vth):
output = []
def dfs(s, t):
if t > T:
output.append(self.model.generate_thoughts(s, 1))
return
for s_prime in sorted(self.model.generate_thoughts(s, k)):
if self.model.evaluate_states({s_prime})[s_prime] > vth:
dfs((*s, s_prime), t + 1)
dfs(x, 1)
return output
To use Tree of Thoughts with OpenAI's API, create a custom model class that inherits from AbstractLanguageModel and implements the required methods using OpenAI's API. Then, create an instance of the TreeOfThoughts class with the custom model and the desired search algorithm ('BFS' or 'DFS').
To use Tree of Thoughts with Hugging Face Transformers, create a custom model class that inherits from AbstractLanguageModel and implements the required methods using Hugging Face Transformers. Then, create an instance of the TreeOfThoughts class with the custom model and the desired search algorithm ('BFS' or 'DFS').
This algorithm is still infant yet it's potential remains unimaginable, let's advance the reasoning of AI's together under this banner.
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now: Generate suite of evaluations used in the paper testing AI agents with other reasoning methods like COT and self consistency and run them in parallel to conduct evaluation experiments.
Implement a more sophisticated prompt engineering strategy to guide the model's reasoning process more effectively.
Make TreeofThoughts class completely customizable with a config yml file with params like chatbot: type: "openai" max_context_length: 8000 include_chat_history_in_query: false openai: model: <model_name> api_key: <your_open_ai_api_key>
Script that generates an dataset based on a topic input, -> set of questions are asked, then multiple trees of thoughts are run concurrently to generate the decision making rich dataset
Introduce a reinforcement learning, distillment, and finetuning scripts to finely tune the model based on feedback from the Tree of Thoughts algorithm.
Integrate heuristics that autonomously determine the search algorithm based on indicators
Integrate heuristics that autonomously determine the strategy cos or propose
Integrate heuristics that autonomously set the input params:
k = T = b = vth =
multi-modality tree of thoughts
multi-modality forest of thoughts
multi-modality world of thoughts
The next big advancement for the Tree of Thoughts algorithm is to extend it to multi-modality, enabling it to handle not only text but also images, audio, and other data types. This will bring us closer to multi-modal superintelligence.
- Research and identify suitable multi-modal pre-trained models that can handle various data types (e.g., text, images, audio).
- Adapt the thought decomposition, thought generator, and state evaluator functions to handle multi-modal data.
- Develop a method for combining different modalities in the search tree, allowing the algorithm to reason across different data types.
- Implement and test the multi-modal Tree of Thoughts algorithm with various problems and datasets.
- Optimize the algorithm for performance and resource usage, ensuring it scales well with large multi-modal datasets.
- Publish the results and gather feedback from the community to further improve the multi-modal Tree of Thoughts algorithm.
Join us on this exciting journey to advance the Tree of Thoughts algorithm to multi-modality superintelligence! π
Utilizing Tree of Thoughts for optimal program synthesis
Welcome to The Compiler, a novel child project under the Tree of Thoughts (ToT) paradigm. This project is crafted with the intent of making autonomous programming not just a reality, but an effortless task for you.
In essence, The Compiler allows you to "grow" any program you can dream of. By providing a high-level specification of the product you would like, you can sit back and let The Compiler do the heavy lifting.
The Compiler leverages the ToT framework and large language models (LLMs) to handle the programming process, from abstract specifications to a working program.
Here's a basic breakdown of the workflow:
- Input: You provide an abstract specification for the product you would like.
- Unit Tests Generation: We use an LLM on ToT to produce a suite of unit tests for the code.
- Run ToT: We run the Tree of Thoughts LLM on the given specification, using the generated unit tests as the evaluation score.
- Output: Ready to use program!
The Compiler, leveraging the Tree of Thoughts paradigm, consists of several primary components, including the Specification Parser, Thought Decomposer, Thought Generator, State Evaluator, and the Search Algorithm.
-
Specification Parser: This interprets your high-level input specifications and translates them into a format that the Thought Decomposer can understand and work with.
-
Thought Decomposer: This component breaks down the programming problem into manageable "thoughts" or steps.
-
Thought Generator: It generates potential thoughts or steps from the current state using two strategies, either sampling thoughts independently or proposing thoughts sequentially.
-
State Evaluator: It evaluates the progress of different states towards solving the programming problem, acting as a heuristic for the Search Algorithm.
-
Search Algorithm: This module determines which states to keep exploring and in which order. It employs either Breadth-First Search (BFS) or Depth-First Search (DFS), depending on the nature of the problem.
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Let's revolutionize the world of programming together with The Compiler!
Thanks to: Shunyu Yao Princeton University, Dian Yu Google DeepMind, Jeffrey Zhao, Google DeepMind, Izhak Shafran Google DeepMind, Thomas L. Griffiths, Princeton University, Yuan Cao Google DeepMind, Karthik Narasimha, Princeton University for sharing this amazing work with the world!
And, thanks to Phil Wang or Lucidrains for inspiring me to devote myself to open source AI Research