Oracle AI Optimizer and Toolkit

Description

The Oracle AI Optimizer and Toolkit (the AI Optimizer) provides a streamlined environment where developers and data scientists can explore the potential of Generative Artificial Intelligence (GenAI) combined with Retrieval-Augmented Generation (RAG) capabilities. By integrating Oracle AI Database VectorSearch and SelectAI, the Sandbox enables users to enhance existing Large Language Models (LLMs) through RAG.

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AI Optimizer Features

• Configuring Embedding and Chat Models
• Splitting and Embedding Documentation
• Auto-refresh Vector Store from OCI Object Storage buckets
• Modifying System Prompts (Prompt Engineering)
• Experimenting with LLM Parameters
• Testbed for auto-generated or existing Q&A datasets

Getting Started

The AI Optimizer is available to install in your own environment, which may be a developer's desktop, on-premises data center environment, or a cloud provider. It can be run either on bare-metal, within a container, or in a Kubernetes Cluster.

For more information, including more details on Setup and Configuration please visit the documentation.

Prerequisites

• Oracle AI Database incl. Oracle AI Database Free
• Python 3.11 (for running Bare-Metal)
• Container Runtime e.g. docker/podman (for running in a Container)
• Access to an Embedding and Chat Model:
  • API Keys for Third-Party Models
  • On-Premises Models_*

_{*Oracle recommends running On-Premises Models on hardware with GPUs. For more information, please review the Infrastructure documentation.}

Bare-Metal Installation

To run the application on bare-metal; download the source:

1. Create and activate a Python Virtual Environment:

   python3.11 -m venv .venv --copies
   source .venv/bin/activate
   pip3.11 install --upgrade pip wheel setuptools uv

2. Install the Python modules:

   uv pip install -e ".[all]"

3. Configure Settings:

   cp src/.env.example src/.env.dev

   Edit the src/.env.dev file and set your default configuration.
   To utilize database functionality, at a minimum, set the AIO_DB_* parameters.

4. Start Streamlit:

   src/entrypoint.py client

5. Navigate to http://localhost:8501.

6. Configure the AI Optimizer.

Container Installation

To run the application in a container; download the source:

1. Build the all-in-one image.

   Note: MacOS Silicon users may need to specify --arch amd64

   podman build -f src/Dockerfile -t ai-optimizer-aio .

2. Start the Container:

   podman run -p 8501:8501 -it --rm ai-optimizer-aio

3. Navigate to http://localhost:8501.

4. Configure the AI Optimizer.

Got OCI?

The AI Optimizer can be deployed in Oracle Cloud Infrastructure (OCI) using Infrastructure as Code (IaC).

Choose either a light-weight Virtual Machine or robust Oracle Kubernetes Engine deployment, both with an Oracle Autonomous Database:

For more information, please visit the IaC Documentation.

Contributing

This project welcomes contributions from the community. Before submitting a pull request, please review our contribution guide.

Security

Please consult the security guide for our responsible security vulnerability disclosure process.

License

Copyright (c) 2024 Oracle and/or its affiliates. Released under the Universal Permissive License v1.0 as shown at https://oss.oracle.com/licenses/upl/

See LICENSE for more details.