International Journal For Multidisciplinary Research

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Low-Code, High Privacy: Leveraging Open-Source LLMs for On-Premises AI in Oracle APEX

Author(s) Ashraf Syed
Country United States
Abstract The rapid evolution of artificial intelligence (AI) has prompted enterprises to seek secure, cost-effective ways to incorporate generative capabilities into their applications. Oracle Application Express (APEX), a low-code platform for building database-centric web applications, has introduced native support for generative AI in version 24.1, primarily through integrations with commercial providers like OpenAI, Oracle Cloud Infrastructure (OCI) Generative AI, and Cohere. However, for organizations prioritizing data privacy, regulatory compliance, and cost control, open-source large language models (LLMs) offer a compelling alternative when deployed on-premises. This article explores the integration of Oracle APEX with five prominent open-source LLMs: LLaMA 3, Mixtral 8x7B, Falcon 40B, BLOOM 176B, and Gemma 7B to enable secure, local AI functionalities. The article details methodological steps for deployment and integration using tools like Ollama for OpenAI-compatible APIs, and discuss performance implications, challenges, and benefits. Through architectural diagrams, workflows, and empirical comparisons, it demonstrates how these integrations facilitate on-premises AI without external dependencies, reducing latency and exposure risks. Findings indicate that while initial setup requires infrastructure investment, the approach yields enhanced security and customization, with future trends pointing toward hybrid models and edge computing. This work provides a blueprint for developers to harness open-source AI in APEX, fostering innovation in privacy-sensitive environments.
Keywords Oracle APEX, open-source LLMs, on-premises AI, LLaMA 3, Mixtral 8x7B, Falcon 40B, BLOOM 176B, Gemma 7B, generative AI integration, low-code platforms, secure deployment, REST APIs, Ollama, data privacy, AI workflows.
Field Engineering
Published In Volume 6, Issue 5, September-October 2024
Published On 2024-10-05
DOI https://doi.org/10.36948/ijfmr.2024.v06i05.57940
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E-ISSN 2582-2160
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