Thinker - A Decision Making Assistant

Thinker provides personalized advice using Anthropic's Claude AI assistant based on your unique context.

This Python script implements a decision making assistant using the Tree of Thoughts prompting technique with the Claude API from Anthropic. It allows the user to iteratively explore potential actions by simulating and discussing interactive advice.

FIND ME ON DISCORD! I am pretty sure that guys like you who find this project are talented to make it BETTER! My discord: https://discord.gg/7bmgzFkn

Background

The Tree of Thoughts is a prompting approach that starts broad and gradually narrows down on useful specific ideas through an iterative cycle of generation, simulation, and ranking.

This script follows that pattern:

The user provides a situation
Claude generates potential scenarios
Claude suggests actions for each scenario
The actions are scored by feasibility via simulation
Top ranked suggestions are discussed interactively
By recursively prompting Claude to simulate and build on its own ideas, the assistant can rapidly explore the space of options and zoom in on targeted, relevant advice.

Here is a flowchart that explains how the second generation of Thinker works:

A flowchart for the underneath design of Thinker Gen.2

Features

Saves conversation history for contextual awareness
Uses NLP embedding similarity to find relevant past situations
Generates multi-step Tree of Thoughts:
Situational summary
Potential scenarios
Suggested actions
Simulated evaluations
Interactive discussion
Ranks suggestions by running Claude simulations
Extracts representative suggestions via clustering
Allows interactive elaboration on top advice

Usage

To run locally:

Sign up for Claude API key at anthropic.com
Clone repo and install requirements
Set ANTHROPIC_API_KEY environment variable
Run python claude_decision_maker.py
Enter your situation and thought process when prompted
Interactively discuss top recommendations
The core prompting cycle is implemented in get_scenario_responses, parse_suggestions, add_scores, interactive_advice and other functions defined in the script.

Roadmap

Ideas and improvements welcome! Some possibilities:

Integrate OpenAI models, or other capable LLMs, as the underneath engine.
Alternative simulation scoring methods
Front-end UI for better experience
Integration with user calendar and task tracking
Containerization for easy deployment
More refined clustering and ranking approaches
The core prompting framework could also be extended to other use cases like creative ideation, strategic planning, and more. Please open issues or PRs if you build on this project!

License

This project is licensed under the MIT license. Feel free join my efforts in making this app more useful and helpful to people! Just do not forget to reference this project when needed.

yhyu13 / Thinker_DecisionMakingAssistant