# PromethAI-Memory

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



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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](https://prometh.ai/) 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](https://github.com/topoteretes/PromethAI-Memory/blob/main/level_4/User_graph.png)

### 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](https://prometh.ai/promethai-memory-blog-post-one)
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](https://www.notion.so/topoteretes/Going-beyond-Langchain-Weaviate-Level-2-towards-Production-98ad7b915139478992c4c4386b5e5886?pvs=4)
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](https://topoteretes.notion.site/Going-beyond-Langchain-Weaviate-Level-3-towards-production-e62946c272bf412584b12fbbf92d35b0?pvs=4)
- 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 ``` 








