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README.md

PromethAI-Memory

AI Applications and RAGs - Cognitive Architecture, Testability, Production Ready Apps

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Open-source framework for building and testing RAGs and Cognitive Architectures, designed for accuracy, transparency, and control.

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This repo is built to test and evolve RAG architecture, inspired by human cognitive processes, using Python. It's aims to be production ready, testable, but give great visibility in how we build RAG applications.

This project is a part of the PromethAI ecosystem.

It runs in iterations, with each iteration building on the previous one.

Keep Ithaka always in your mind. Arriving there is what you’re destined for. But don’t hurry the journey at all. Better if it lasts for years

Installation

To get started with PromethAI Memory, start with the latest iteration, and follow the instructions in the README.md file

Current Focus

Cogntive Architecture, RAGs, and production-ready applications

Image

Project Structure

Level 1 - OpenAI functions + Pydantic + DLTHub

Scope: Give PDFs to the model and get the output in a structured format Blog post: Link We introduce the following concepts:

  • Structured output with Pydantic
  • CMD script to process custom PDFs

Level 2 - Memory Manager + Metadata management

Scope: Give PDFs to the model and consolidate with the previous user activity and more Blog post: Link We introduce the following concepts:

  • Long Term Memory -> store and format the data
  • Episodic Buffer -> isolate the working memory
  • Attention Modulators -> improve semantic search
  • Docker
  • API

Level 3 - Dynamic Graph Memory Manager + DB + Rag Test Manager

Scope: Store the data in N-related stores and test the retrieval with the Rag Test Manager Blog post: Link

  • Dynamic Memory Manager -> store the data in N hierarchical stores
  • Auto-generation of tests
  • Multiple file formats supported
  • Postgres DB to store metadata
  • Docker
  • API
  • Superset to visualize the results

Level 4 - Dynamic Graph Memory Manager + DB + Rag Test Manager

Scope: Use Neo4j to map the user queries into a knowledge graph based on cognitive architecture Blog post: Soon!

  • Dynamic Memory Manager -> store the data in N hierarchical stores
  • Dynamic Graph -> map the user queries into a knowledge graph
  • Postgres DB to store metadata - soon
  • Docker
  • API - soon

Run the level 4

Make sure you have Docker, Poetry, and Python 3.11 installed and postgres installed.

Copy the .env.example to .env and fill in the variables

poetry shell

docker compose up

Run

python main.py