diff --git a/README.md b/README.md index 079eddac9..d8bfb5211 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,6 @@ # PromethAI-Memory +Memory management and testing for the AI Applications and RAGs @@ -72,48 +73,69 @@ ![Infographic Image](https://github.com/topoteretes/PromethAI-Memory/blob/main/infographic_final.png) -## The Motivation -Browsing the database of theresanaiforthat.com, we can observe around [7000 new, mostly semi-finished projects](https://theresanaiforthat.com/) in the field of applied AI, whose development is fueled by new improvements in foundation models and open-source community contributions. +## Production-ready modern data platform + +Browsing the database of theresanaiforthat.com, we can observe around [7000 new, mostly semi-finished projects](https://theresanaiforthat.com/) 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. -AI apps currently being pushed out still mostly feel and perform like demos. +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](https://lilianweng.github.io/posts/2023-06-23-agent/?fbclid=IwAR1p0W-Mg_4WtjOCeE8E6s7pJZlTDCDLmcXqHYVIrEVisz_D_S8LfN6Vv20) — available in the palm of your hand 24/7/365. -To address this issue, [dlthub](https://dlthub.com/) and [prometh.ai](http://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. +To address this issue, [dlthub](https://dlthub.com/) and [prometh.ai](http://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. -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](http://prometh.ai/promethai-memory-blog-post-one) - -#### Or check this [Princeton paper](https://arxiv.org/abs/2309.02427) released after our demo +## Read more on our blog post [prometh.ai](http://prometh.ai/promethai-memory-blog-post-on) -## 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 +## 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: - 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 +- Long Term Memory -> store and format the data +- Episodic Buffer -> isolate the working memory +- Attention Modulators -> improve semantic search +- Docker +- API + +### Level 3 - Dynamic Memory Manager + DB + Rag Test Manager +Scope: Store the data in N stores and test the retrieval with the Rag Test Manager +- Dynamic Memory Manager -> store the data in N stores +- Auto-generation of tests +- Multiple file formats supported +- Postgres DB to manage state +- Docker +- API + + +## Run the level 3 + +```docker compose up promethai_mem ``` + +``` poetry shell ``` + +Make sure to run + +``` python scripts/create_database.py ``` + +After that, you can run: + +``` + python rag_test_manager.py \ + --url "https://www.ibiblio.org/ebooks/London/Call%20of%20Wild.pdf" \ + --test_set "example_data/test_set.json" \ + --user_id "666" \ + --metadata "example_data/metadata.json" + +``` + -## How to use -Each of the folders contains a README to get started. diff --git a/level_2/poetry.lock b/level_2/poetry.lock index 0c641e46b..c0559040f 100644 --- a/level_2/poetry.lock +++ b/level_2/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. 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