GenAI Factory

Demo an end to end LLM agent solution with modular architecture, persistent storage and front-end UI that can work with various LLM models and storage solutions.

the configuration is specified in a YAML file, which indicate the model, embeddings, storage to use, and various parameters. the user can point to the configuration file by setting the AGENT_CONFIG_PATH environment variable.

environment variables and credentials can be loaded from a .env file in the root directory. or an alternate path set by the AGENT_ENV_PATH environment variable. data can be stored in local files or remote SQL and Vector databases. the local file storage path can be set by the AGENT_DATA_PATH environment variable (defaults to ./data/).

Getting it to work

In order to deploy the GenAI Factory locally, we need to update the docker desktop software and to enable host networking. For more information, please refer to the following link: https://docs.docker.com/network/drivers/host/#docker-desktop

Deploy the controller

This command will start the API controller server into a local docker container.

make controller

Initialize the database:

The database is Initialized when building the controller. In order to erase and start fresh, we can simply use the controller's command line interface.

python -m controller.src.main initdb

To start the application's API:

uvicorn pipeline:app

To start UI:

Future work will include a UI command to run the UI.

make ui

CLI usage

To ingest data into the vector database:

python -m controller.src.main ingest -l web https://milvus.io/docs/overview.md

To ask a question:

python -m controller.src.main query "What is a vector?" 

Full CLI:

python -m controller.src.main

Usage: python -m controller.src.main [OPTIONS] COMMAND [ARGS]...

Options:
  --help  Show this message and exit.

Commands:
  config  Print the config as a yaml file
  ingest  Ingest documents into the vector database
  initdb  Initialize the database (delete old tables)
  list    List the different objects in the database (by category)
  query   Run a chat query on the vector database collection