Oracle AI Optimizer and Toolkit Features

Free and open source

Free to use

The project is licensed under the Universal Permissive License v1.0. As the name states, it is a permissive, OSI- and FSF-approved, GPL-compatible license.

Learn from the code and contribute to the project

The code for this project is in the repository. Should you wish, you can read and understand how each feature is implemented and make your contributions.

Easy configuration

Use any LLM for embedding and chat tasks

LLM and embedding models are optimized to achieve different goals. Some are generalists, some are inference focused, some are trained with corpuses that contain information in different languages, some are domain specific. With AI Optimizer and Toolkit, you can use the ones that align with your goals and data. For production, you can use a service provider or a self-hosted model.

Change the model parameters

Every model exposes a set of parameters that modify its creativity, the number of possible continuations that it considers, or restricts the ones that it will consider. You can easily modify these parameters to reduce hallucinations and adjust model behavior to meet your business needs.

Modify the instructions to the LLM

Each request to the LLM includes the user’s prompt, some information about past exchanges, and specific instructions to the LLM on how to behave and respond. You have to flexibility to choose from the predefined options or add your custom instructions to get the most from your model.

Extensible knowledge

Use RAG to access your knowledge stores

Provide foundation models with access to your private data using retrieval-augmented generation. AI Optimizer and Toolkit will help with the setup so your production application has proper access to your data.

Use structured and unstructured data to improve the replies

Store and use unstructured data by breaking it up into chunks and assigning the corresponding embedding. Users can use structured and unstructured data in their interactions with the AI application you create.

Leverage Oracle Database 23ai’s highly scalable vector store

Oracle Database 23ai is capable of semantic search using vector embeddings. Similar items within the database are determined by distance calculations using sophisticated vector indexes.

Use Oracle Database 23ai select AI within your application

Select AI uses natural language to query Oracle Databases and can be used to extend the capabilities of the application you create.

Automatic testing

Autogenerated test Q&A data set

AI Optimizer and Toolkit can generate a set of questions and answers to assess the quality of your chatbot. Use them to automatically test every iteration.

Create a self-evaluating application

Use a second model to automatically assess the quality of the responses from your AI application.