Applications and reference implementations demonstrating how to build AI-powered solutions with Oracle technologies. These complete, working examples showcase end-to-end implementations of AI ...

Large Language Models (LLMs) are transforming how users approach tasks related to searching, interacting with, and generating new content. These advanced language models have garnered immense ...

RAG is transforming AI apps, and vector databases are the engine behind accurate, real-time responses Choosing the right vector database can make or break performance, scalability, and user experience ...

Traditional RAG systems struggle bridging structured SQL databases and unstructured document collections (a challenge we call the modality gap), leading to incomplete reasoning and hallucinations.

In our earlier article, we demonstrated how to build an AI chatbot with the ChatGPT API and assign a role to personalize it. But what if you want to train the AI on your own data? For example, you may ...

Make sure to have two models deployed, one for generating embeddings (text-embedding-3-small model recommended) and one for handling the chat (gpt-4 turbo recommended). You can use the Azure OpenAI ...

DataStax, an IBM company, provides integrated AI dev platforms that let you harness data in your app without building a complex tool stack. Interacting with databases often requires a level of ...

One decision many enterprises have to make when implementing AI use cases revolves around connecting their data sources to the models they’re using. Different frameworks like LangChain exist to ...

I have encountered challenges when using 7B LLMs for SQL generation tasks, particularly when working with the company’s databases. These models often struggle to generate accurate SQL queries, even ...