AGI-in-a-box
Exactly what it sounds like: an LLM based collection of agent workflows that performs some of the tasks an AGI might need. The title is pretentious, but the work is unglamorous.
Those tasks include things like:
- self-configuration (change Ansible variable values on the fly and reconfigured the install)
- determine if exterior cloud-based agents need to run to accomplish a task, and execute those agents
- commit existing request history to a git repository automatically to provide a complete history of use
- backup existing repository history past a specific threshold to low cost cloud backup target
- backup existing model history, if desired, to a low cost cloud backup target for reuse
- determine if new models should be downloaded and used in place of existing models, and obtain them
- write LLM chat output to specific files for reuse, as needed, to reconfigure the main application
- maintain a dynamic list of data sources and files to ingest via RAG for given tasks
- maintain a prompt library for easy retrieval and reuse via Ansible variables and version control
- methods for transferring the application to new hardware for upgrade or expansion
Why build this??
- why not?
- hardware configuration and orchestration keeps AGI practical and yours
- as you use AI, the knowledge grows but so does the data and history
- This can act as a model for other large-scale open source AI apps
Prerequisites:
- install poetry to build an isolated environment for dependencies
- run "poetry init" to install those dependencies
- to run any of the agent workflows, type "poetry run python {name of script}"
Right now, these workflows in here are just tests so I can understand how crew.ai works.