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Merge pull request #14 from topoteretes/added_backend_pg_and_dynamic_memory_classes 
Added backend pg and dynamic memory classes
2023-10-08 21:33:23 +02:00
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README.md

PromethAI-Memory

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Open-source framework that manages memory for AI Agents and LLM apps

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Infographic Image

The Motivation

Browsing the database of theresanaiforthat.com, we can observe around 7000 new, mostly semi-finished projects in the field of applied AI, whose development is fueled by new improvements in foundation models and open-source community contributions.

It seems it has never been easier to create a startup, build an app, and go to market… and fail.

AI apps currently being pushed out still mostly feel and perform like demos.

To address this issue, dlthub and prometh.ai will collaborate on productionizing a common use-case, progressing step by step. We will utilize the LLMs, frameworks, and services, refining the code until we attain a clearer understanding of what a modern LLM architecture stack might entail.

We go on a journey and propose a new way to reason about Language Architecture for Agents based on cognitive sciences.

Read more on our blog post prometh.ai

Or check this Princeton paper released after our demo

PromethAI-Memory Repo Structure

The repository contains a set of folders that represent the steps in the evolution of the modern data stack from POC to production

Level 1 - CMD script to process PDFs

We introduce the following concepts:

  1. Structured output with Pydantic
  2. CMD script to process custom PDFs

Level 2 - Memory Manager implemented in Python

We introduce the following concepts:

  1. Long Term Memory
  2. Short Term Memory
  3. Episodic Buffer
  4. Attention Modulators

The code at this level contains:

  1. Simple PDF ingestion
  2. FastAPI
  3. Docker Image
  4. Memory manager
  5. Langchain-based Agent Simulator
  6. Data schema

How to use

Each of the folders contains a README to get started.