• Text Generation
  • Connect to Open AI compatible API's i.e. LocalAI
  • Select different prompts
  • Syntax highlighting for code
• Image Generation
  • Connect to stable diffusion
• Authentication
  • Email/Password sign in and registration
  • SSO
• Teams
  • Invite Team Members
  • Manage the teams you belong to
  • Create new teams
  • Switch between teams
  • RBAC
• Document Management
  • Document Upload
  • Allow user to create datasets
  • UI for datasets table
  • Turn documents into 1K batches and generate embeddings
  • OCR for document upload
• Document Pipelines
  • Allow user to upload docs via API to datasets
  • Process documents and create chunks and embeddings
• Retrieval Augmented Generation
  • Parse text out of documents
  • Generate Embeddings and store in pgVector
  • Add Embeddings to the prompt using similarity search
• Prompt Management
  • Create and Edit prompts on a per team basis
  • Associate prompts with datasets
• Model Management
  • Create/update default prompt fo a model
  • Set model location URL.
  • Switchable LLM backends.
  • Associate models with a command i.e. /image
• Guardrails
  • Figure out a strategy
• API
  • Create per team API keys
  • Attach keys to a prompt
  • Revoke keys
• Fine Tuning
  • QLORA adapters
• System Admin
  • Usage Statistics
  • Audit Trail
  • Set API limits
• Deployment
  • Docker compose so people can test quickly.
  • Kubernetes deployment strategy.
  • Kubernetes bionicgpt.yaml
  • Hardware recommendations.

See the open issues for a full list of proposed features (and known issues).

Download our docker-compose.yml file and run docker-compose up access the user interface on http://localhost:7800/auth/sign_up

curl -O https://raw.githubusercontent.com/purton-tech/bionicgpt/main/docker-compose.yml

This has been tested on an AMD 2700x with 16GB of ram. The included llama-2-7b-chat model runs on CPU only.

Warning - The images in this docker-compose are large due to having the model weights pre-loaded for convenience.

BionicGPT is optimized to run on Kubernetes and implements the full pipeline of LLM fine tuning from data acquisition to user interface.