PromethAI

We took all the work we did with PromethAI into our new product, cognee -> check it out here

What is it

PromethAI is a Python-based AGI project that recommends choices based on a user's goals and preferences and can modify its recommendations based on user feedback.

Our focus is currently on food, but the system is extendible to any area.

💡 Features

• Optimized for Autonomous Agents
• Personalized for each user
• Introduces decision trees to help user navigate and decide on a solution
• Runs asynchronusly
• For App builds, check out this repo promethAI-GUI
• Supports automating tasks and executing decisions
• Multiple Vector DBs supported trough Langchain
• Low latency
• Easy to use
• Easy to deploy

💻 Demo

🛣 Architecture

🛣 Roadmap

⚙️ Setting up

1. Download the repo using git clone https://github.com/topoteretes/PromethAI-Backend-Backend.git in your terminal or directly from github page in zip format.
2. Navigate to the directory using cd PromethAI-Backend and create a copy of .env.template and name it .env.
3. Enter your unique OpenAI API Key, Google key, Custom search engine ID without any quotes or spaces in .env file. Follow the links below to get your keys:

4. Ensure that Docker and Docker Compose are installed in your system, if not, Install it from here.
5. Once you have Docker Desktop running, run command : docker-compose up promethai --build in promethai directory. Open your browser and go to localhost:3000 to see promethAI running.

Resources

Papers like "Generative Agents: Interactive Simulacra of Human Behavior"

Quick start

Make sure to add your credentions in the .env file.Launch the app with:

docker-compose build promethai && docker-compose up promethai

How it Works

Here is what happens everytime the AI is queried by the user:

1. AI vectorizes the query and stores it in a Pinecone Vector Database
2. AI looks inside its memory and finds memories and past queries that are relevant to the current query
3. AI thinks about what action to take
4. AI stores the thought from Step 3
5. Based on the thought from Step 3 and relevant memories from Step 2, AI generates an output
6. AI stores the current query and its answer in its Pinecone vector database memory

How to use

docker-compose build  promethai

6. Access the API by doing CURL requests, example:

curl -X POST "http://0.0.0.0:8000/data-request" -H "Content-Type: application/json" --data-raw 

Example of available endpoint

The available endpoint:

POST request to '/recipe-request' endpoint that takes a JSON payload containing 'user_id', 'session_id', 'factors' keys, and returns a JSON response with a 'response' key.

All endpoints receive a payload in JSON format and return a response in JSON format.

Example of curl requests

curl --location --request POST 'http://0.0.0.0:8000/recipe-request' \
--header 'Content-Type: application/json' \
--data-raw '{
  "payload": {
    "user_id": "659",
    "session_id": "459",
    "model_speed":"slow",
    "prompt":"I would like a healthy chicken meal over 125$"
    
  }
}'

🔰 Notice

PromethAI is a work in progress, delivered to you without any guarantees, whether explicit or implied. By choosing to use this application, you consent to take on any associated risks, including data loss, system failure, or any other complications that may arise.

The creators and contributors of PromethAI disclaim any responsibility or liability for any potential losses, damages,