PromethAI-Memory

Memory management and testing for the AI Applications and RAGs

Dynamic Graph Memory Manager + DB + Rag Test Manager

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

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Production-ready modern data platform

Browsing the database of theresanaiforthat.com, we can observe around 7000 new, mostly semi-finished projects in the field of applied AI. It seems it has never been easier to create a startup, build an app, and go to market… and fail.

Decades of technological advancements have led to small teams being able to do in 2023 what in 2015 required a team of dozens. Yet, the AI apps currently being pushed out still mostly feel and perform like demos. The rise of this new profession is perhaps signaling the need for a solution that is not yet there — a solution that in its essence represents a Large Language Model (LLM) — a powerful general problem solver — available in the palm of your hand 24/7/365.

To address this issue, dlthub and prometh.ai will collaborate on a 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.

Project Structure

Level 1 - OpenAI functions + Pydantic + DLTHub

Scope: Give PDFs to the model and get the output in a structured format 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 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

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

Run the level 3

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

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

Start the docker:

docker compose up promethai_mem

Use the poetry environment:

poetry shell

Make sure to run to initialize DB tables

python scripts/create_database.py

After that, you can run the RAG test manager.

    python rag_test_manager.py \
    --file ".data" \
    --test_set "example_data/test_set.json" \
    --user_id "666" \
    --metadata "example_data/metadata.json" \
    --retriever_type "single_document_context"

Examples of metadata structure and test set are in the folder "example_data"

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