Skip to content

Latest commit

 

History

History
219 lines (142 loc) · 7.87 KB

README.md

File metadata and controls

219 lines (142 loc) · 7.87 KB
Raw

Promptimizer – Automated AI-Powered Prompt Optimization Framework

This project implements an automated system for optimizing AI prompts using genetic algorithms and machine learning techniques. It's designed to evolve and improve any LLM prompt. The example in this repo is focused on AI-driven stock screening.

Read more about it here.

Warning/Disclaimer

Optimizing prompts using this framework can be VERY expensive! I accept no liability for any costs incurred. If cost is a consideration, please use local LLMs like Ollama for optimization.

Check out NexusTrade

To see the results of the optimized prompt, check out NexusTrade.io. NexusTrade is an AI-Powered automated trading and investment platform that allows users to create, test, optimize, and deploy algorithmic trading strategies.

With the stock screener, you can ask any of the questions within the groundTruths directory, including:

  • Which semiconductor stocks had the highest net income for the fiscal year 2020?
  • What are the latest EBITDA values for the top 10 pharmaceutical stocks (by market cap)?
  • What is the average free cash flow of the top 10 e-commerce stocks by market cap?

Any question about technical or fundamental data, NexusTrade's AI Stock Screener can answer. Try it today for free!

Features

  • Genetic algorithm-based optimization of AI prompts
  • Population management with crossover and mutation operations
  • Training and validation using separate datasets
  • Automated evaluation of prompt performance
  • Multi-generational evolution of prompts
  • Customizable parameters for population size, generations, and more

Prerequisites

Before you run this project, ensure you have the following prerequisites set up:

  1. Node.js: Make sure you have Node.js installed. You can download it from nodejs.org.

  2. Populate inputs.ts: Create and populate the inputs.ts file with the following format:

const inputs = [
  {
    text: "<Question to ask the model>",
    foldername: "foldername_for_the_input_output",
  },
];

export default inputs;
  1. Populate additionalSystemPrompts.ts: Create and populate the additionalSystemPrompts.ts file with the following format:
const additionalSystemPrompts = [
  "System Prompt 1",
  "System Prompt 2",
  "System Prompt 3",
  "System Prompt 4",
  "System Prompt 5",
];

export default additionalSystemPrompts;
  1. Set Up Environment Variables: Create a .env file in the root directory of the project and add the following environment variables:
ANTHROPIC_API_KEY=your_anthropic_api_key
OPENAI_API_KEY=your_openai_api_key
OLLAMA_SERVICE_URL=http://localhost:11434
GOOGLE_APPLICATION_CREDENTIALS_JSON=path_to_your_google_application_credentials_json
CLOUD_DB=your_cloud_db_connection_string
LOCAL_DB=your_local_db_connection_string
MODEL_NAME=your_model_name

Replace your_anthropic_api_key, your_openai_api_key, path_to_your_google_application_credentials_json, your_cloud_db_connection_string, your_local_db_connection_string, and your_model_name with your actual API keys, the path to your Google application credentials JSON file, your database connection strings, and the model name. You can change the model to an open-source model from Ollama or one from Anthropic by setting the MODEL_NAME environment variable.

For the local db, you can populate it with the following:

LOCAL_DB="mongodb://127.0.0.1:27017/promptoptimizer"

By default, the system will use the local database specified in process.env.LOCAL_DB. If you want to use a cloud database, ensure process.env.CLOUD_DB is populated and modify the code to use it as needed.

Installation

Node.js Dependencies

To install the necessary Node.js dependencies, run the following command in the root directory of the project:

npm install

TypeScript and ts-node

To run TypeScript files directly, you need to install ts-node. You can install it globally using the following command:

npm install -g ts-node

Alternatively, you can add it as a dev dependency to your project:

npm install --save-dev ts-node

Python Dependencies

To install the necessary Python dependencies, ensure you have Python installed and then run the following command in the root directory of the project:

pip install -r requirements.txt

This will install the following Python packages:

  • matplotlib
  • seaborn
  • pandas

MongoDB Installation

To install MongoDB locally, follow these steps:

  1. Download MongoDB: Go to the MongoDB Download Center and download the MongoDB Community Server for your operating system.

  2. Install MongoDB:

  • Windows: Run the downloaded .msi installer and follow the installation instructions.
  • macOS: Use Homebrew to install MongoDB. Open a terminal and run:
brew tap mongodb/brew
brew install [email protected]
  • Linux: Follow the specific instructions for your Linux distribution on the MongoDB website.
  1. Start MongoDB:
  • Windows: MongoDB should start automatically after installation. If not, you can start it manually by running mongod in the command prompt.
  • macOS: Start MongoDB using Homebrew services:
brew services start mongodb/brew/mongodb-community
  • Linux: Start MongoDB by running:
sudo systemctl start mongod
  1. Verify Installation: To verify that MongoDB is running, open a terminal or command prompt and run:
mongo

This should open the MongoDB shell, indicating that MongoDB is installed and running correctly.

Generating Ground Truths

To steer the model towards the desired behavior, you need to know exactly how you want the model to respond to a wide range of inputs. To do this, you will update the file input.ts with filenames and inputs you want the model to understand. Then, you will execute the script populateGroundTruth.ts. This script allows you to create ground truths in a semi-automated way.

Creating a Scoring Heuristic

Using some method (such as a large language model), you need to be able to quantify how close your output is to your desired output. You can do this using the LLM-based "Prompt Evaluator" within the repo. The "Prompt Evaluator" takes the output of the model and the expected output and returns a score.

Setting Up an Alternative Model

To use Ollama as an alternative to OpenAI, follow these steps:

  1. Download Ollama: Go to ollama.com/download and download the appropriate version for your operating system.

  2. Download the Model: Visit ollama.com/library/llama3.1 to download the model you want to use.

  3. Set the Environment Variable: Make sure to set the following environment variable in your .env file:

OLLAMA_SERVICE_URL=http://localhost:11434

If you have an ANTHROPIC_API_KEY, you can alternatively use Anthropic for prompt optimization.

Running the TypeScript Script

To run the TypeScript script, you can use ts-node. Ensure you have ts-node installed as described above, and then run the following command:

ts-node main.ts

Alternatively, you can compile the TypeScript code to JavaScript and then run it using Node.js:

  1. Compile the TypeScript code:
tsc
  1. Run the compiled JavaScript code:
node dist/main.js

Visualizing Performance

To visualize the training and validation fitness over generations, you can use the Python script graph.py. This script generates graphs so you can see how the performance of your prompt changed over time. To run the script, use the following command:

python graph.py

By following these instructions, you'll be able to optimize any arbitrary prompt, quantify its performance, and visualize how it improves over time.