中文版本

Thinker - A Decision Making Assistant

Thinker provides personalized advice using Anthropic's Claude AI GPT 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 GPT from OpenAI. 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 program follows that pattern:

• The user provides a situation
• LLM generates potential scenarios
• LLM suggests actions for each scenario
• The actions are scored by feasibility via simulation
• By recursively prompting LLM 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:

First, you will need to secure an OpenAI API key at openai.com, as the current Thinker program needs the GPT models to power it up.

Then, under /resources/remote_services/api_key file, here is how you put your api-key:

{
    "openai_api_key":"your api key here",
    "openai_base_url":"put your base url here if you need a proxy",
    "openai_official_api_key":"your api key here"
}

In case if you need to use a proxy to access OpenAI services, you will need to modify commons/components/LLMCores.py as follows:

Change

api_type: str = 'openai'

to

api_type: str = 'proxy'

Finally, use pip install -r requirements.txt to install all the dependencies before using python3 user_interface.py to run the gradio demo.

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!

Buy me a coffe if you like it :))

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.