The Tree of Thoughts (ToT) algorithm is a groundbreaking approach to enhancing model reasoning, increasing efficiency by up to 70%. In this article, we’ll guide you through installing the algorithm, setting it up, and running your first example—all while keeping things as user-friendly as possible!
Getting Started with Tree of Thoughts
Before diving into the installation and implementation, let’s break it down into simple steps:
1. Installation
- Open your terminal.
- Install the Tree of Thoughts library by executing this command:
bash
$ pip3 install -U tree-of-thoughts
2. Set Up Your Workspace
To store your metadata, you will need to create a workspace directory:
bash
export WORKSPACE_DIR=artifacts
3. Example Implementation
Now that the installation is complete, let’s take a look at an example implementation. This code snippet demonstrates how you can solve a problem using the Tree of Thoughts algorithm:
python
from tree_of_thoughts import TotAgent, ToTDFSAgent
from dotenv import load_dotenv
# Load environment variables
load_dotenv()
# Create instances of the TotAgent and ToTDFSAgent classes
tot_agent = TotAgent()
dfs_agent = ToTDFSAgent(
agent=tot_agent,
threshold=0.8,
max_loops=1,
prune_threshold=0.5,
number_of_agents=4,
)
# Define the initial state
initial_state = "Your task: is to use 4 numbers and basic arithmetic operations (+-*) to obtain 24 in 1 equation, return only the math"
# Run the DFS algorithm to solve the problem
final_thought = dfs_agent.run(initial_state)
# Print the final thought
print(final_thought)
With this snippet, we are creating a team of intelligent agents that collectively reason through a series of steps to arrive at the correct mathematical operation that results in the number 24.
Analogical Understanding of the Code
Imagine you’re assembling a complex puzzle, one where each piece corresponds to a number and an operation. The TotAgent acts like your brain, which recognizes potential pieces and where they might fit. Meanwhile, the ToTDFSAgent is akin to a group of friends who take turns trying to fit pieces into the puzzle. Each friend (agent) has the responsibility to test different combinations within a limit (max_loops), ensuring that only the best-fitting pieces (branches) are kept—similar to pruning less promising options.
Basic Prompts for Usage
To explore the collaborative thought process further, you can set up dialogues among virtual experts. They will refine their thoughts based on what each presents, continuously striving for accuracy. Think of this dynamic as three chefs working on the same recipe, adjusting their ingredients step-by-step based on the feedback from each other until they create the perfect dish.
Troubleshooting
- If you encounter issues during installation, ensure Python and pip are updated to the latest versions.
- Verify that your terminal has access permissions to create the directory specified in WORKSPACE_DIR.
- When running the code, if you see errors related to imports, check the installation of the required libraries.
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Next Steps
After implementing the basic structure, consider these tasks:
- Finish implementing the depth or max_loops feature in the DFS class.
- Develop a new BFS search algorithm.
- Implement a Monte Carlo search algorithm.
- Create a function that visualizes the tree of thoughts from a JSON input.
Acknowledgements
Special thanks to Shunyu Yao (Princeton University), Dian Yu & Jeffrey Zhao (Google DeepMind), and the other contributors for their incredible work! Moreover, cheers to Phil Wang and Lucidrains for inspiring the open-source AI revolution.
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.