Corrado De Bari
Developer Evangelist, Microservices & AI, Oracle Database
Fiumicino, Italy
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Developer Evangelist in MAA Platform Engineering team at Oracle, with more than 25 years of experience in Information Technology, he is currently focused on AI Microservices platform development with the Oracle Database. In Oracle Italy since 2010, he joined Technology Sales Consulting team, working on Italian large and medium accounts on OCI, AI/ML, DataLakes and BPM/SOA platforms. Formerly Sales Engineer at Sun Microsystems, as well as Java & Software Ambassador for Italy, starting his career as architect in Telecom Italia Mobile. He holds the CEFRIEL Master in Information Technology and a bachelor's degree in Computer Science.
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The Oracle AI Microservices Sandbox for RAG rapid prototyping
Although we are inundated with articles and tutorials promising GenAI-based chatbots in just five minutes or so, the reality of developing a reliable agent is much more challenging. Some predict that 30% of projects will be abandoned due to the difficulty of translating proofs of concept and substantial investments into tangible benefits. To help prevent this, an open-source rapid prototyping platform called "Oracle AI Microservices Sandbox" has been released. This platform enables zero-code development of knowledge bases using Oracle DB 23c AI and its Similarity Search capabilities, allowing users to select any LLM for vector embedding and chat completion, whether on-premises or in the cloud, starting from template-based prompt engineering. The hyperparameters related to the LLM and the knowledge base processing can be tested either individually or in bulk, facilitated by the generation of Q&A test datasets to determine accuracy rates. Successfully tested configurations, along with the generated knowledge base, can be published and exported as AI microservices using Spring AI or LangChain, accessible through OpenAI API-compatible REST endpoints.
Accepted @ Analytics and Data Summit 2025
Text-to-SQL: chat with a DB exploiting the Generative AI
The endeavor to translate natural language queries into SQL has been a long-standing objective within the realm of computational linguistics and database management. Recent advancements in Generative Artificial Intelligence mark a pivotal moment in this journey. Current benchmarks have showcased that Large Language Models (LLMs) extend their utility beyond mere code generation to embrace specialized tasks such as Text-to-SQL conversion. This seminar aims to explore the cutting-edge developments in this field, highlighting effective methodologies based on frameworks that transcend the traditional tools and languages commonly associated with data science, like Spring Boot and the innovative "Spring AI" APIs, showing how these modern frameworks can facilitate equally the development of a bridge between natural language processing and database interaction.
Accepted @ Spring I/O 2024
Java meets Generative AI: build a knowledge management system with Spring AI
Generative AI and its applications are no longer just a prerogative of data scientists using Python, but with Java and SpringBoot skills it could be approached through "Spring AI" API.
We will see step-by-step how we can implement a Spring Boot application based on Retrieval Augmented Generation (RAG) with: cloud, hybrid or on-premises LLM models, using a knowledge base stored in a VectorDB, showing tips&trics in ingestion, choosing the LLMs models and prompt engineering. The transition phase to production, through a Backend platform, will not be overlooked.
Accepted @ MakeIT 2024 / JCON OpenBlend Slovenia 2024
Analytics and Data Summit 2025 Sessionize Event
AnDOUC TechCasts User group Sessionize Event
Corrado De Bari
Developer Evangelist, Microservices & AI, Oracle Database
Fiumicino, Italy
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