| .data | ||
| .github | ||
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| bin | ||
| cognitive_architecture | ||
| iterations | ||
| level_2/tests/__pycache__ | ||
| .env.template | ||
| .gitignore | ||
| __init__.py | ||
| api.py | ||
| CONTRIBUTING.md | ||
| docker-compose.yml | ||
| Dockerfile | ||
| entrypoint.sh | ||
| LICENSE | ||
| main.py | ||
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| README.md | ||
PromethAI-Memory
AI Applications and RAGs - Cognitive Architecture, Testability, Production Ready Apps
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.
The project run in iterations, from POC towards production ready code. The iterations are numbered from 0 to 7, with 0 being the simplest iteration and 7 being the most complex one. To run a specific iteration, navigate to the iteration's folder and follow the instructions in the README file. To read more about the approach and details on cognitive architecture, see the blog post: AI Applications and RAGs - Cognitive Architecture, Testability, Production Ready Apps
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
Current Focus
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
- Classification -> classify the user queries and choose relevant graph nodes
- Context manager -> generate context for LLM to process containing Semantic, Episodic and Vector store data
- Postgres DB to store metadata
- Docker
- API
Installation
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
If you are running natively, change ENVIRONMENT to local in the .env file If you are running in docker, change ENVIRONMENT to postgres in the .env file
Run
python main.py
Or run
``` docker compose up promethai-mem ```
And send API requests add-memory, user-query-to-graph, document-to-graph-db, user-query-processor to the locahost:8000