Oracle AI Optimizer and Toolkit

Simplify and accelerate development of AI-powered applications using retrieval-augmented generation and agentic use cases through rapid experimentation and evaluation.

Introducing Oracle AI Optimizer and Toolkit (2:41)
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免费参加 6 月 17 日的线上活动，了解如何利用 Oracle 的数据库和云技术创新成果来推进您的 AI 和多云战略。

Why Oracle AI Optimizer and Toolkit?

Organizations are trying to balance potentially significant development costs against the business value they can derive from generative AI initiatives.

Oracle AI Optimizer and Toolkit can help you rapidly develop proofs of concept. It speeds experimentation and evaluation, by integrating your data with any large language model (LLM) and any embedding model, providing full control over the model parameters, chunking strategy, and semantic search.

Oracle AI Optimizer and Toolkit allows you to easily explore retrieval-augmented generation (RAG) and agents in a no-code environment, with the full power of Oracle AI Vector Search to optimize the accuracy and safety of your AI project. Use the built-in evaluation framework to find the right models and parameters for your specific use case.

Time to go into production? Turn your experiment into a highly scalable service with the API server included or easily generate microservices code in Java (Spring AI) or Python (LangChain) to deploy as independent microservices. Integration with Oracle Database 23ai provides the ability to manage millions of chunks and embedding vectors, giving you the scalability to meet your demands.

Download and try AI Optimizer and Toolkit

Benefits of Oracle AI Optimizer and Toolkit

  • Enjoy a zero-code chatbot

    Without writing a line of code, you can explore the feasibility of creating a chatbot that uses RAG to access your knowledge base. Experiment from within the user interface and check the effects as you go.

  • Use any LLM

    Use the latest foundation LLMs for embedding and text generation to create your GenAI chatbot. Keep your data within your network and bring LLMs into your private network rather than use public LLMs, which could put your data at risk.

  • Generate and run automatic tests

    No need to incur the cost of creating large test data sets to check your chatbot. Safely generate testing data sets using your knowledge bases, refining them to evaluate different configurations as you go.

  • Easily release your application into production

    Leverage the high performance, OpenAI-compliant API server to release your tested chatbot in minutes. Or export it as a Spring AI microservice and run on a Kubernetes platform, such as Oracle Backend for Microservices and AI.

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