Master Knowledge Search with Generative AI

Master Knowledge Search with Generative AI

Table of Contents

1. Introduction
2. What is Artificial Intelligence?
3. Understanding Generative AI
4. Oracle Generative AI Services
   1. Oracle Generative AI in the AI Stack
   2. Oracle Generative AI for Machine Learning
   3. Oracle Generative AI for Speech
   4. Oracle Generative AI for Language
   5. Oracle Generative AI for Vision
   6. Oracle Generative AI for Document Understanding
5. The Evolution of Generative AI
6. Oracle Generative AI and Coare Partnership
7. Enterprise Focus and Security in Oracle Generative AI
8. How to Create a Rag with Oracle Generative AI
   1. Embedding and Chunking Data
   2. Setting Up Prompts and Guard Rails
9. Use Cases for Oracle Generative AI
   1. Chat Summaries for Customer Support
   2. Retrieval Augmented Generation (RAG)
   3. Generating Content with Oracle Generative AI
   4. Question Answering and Data Summarization
   5. Semantic Search and Data Extraction
10. Combining Oracle Digital Assistant and Oracle Generative AI
    1. Connecting Unstructured Documents and Websites
    2. Integrating with Structured Data
    3. Conversational AI on Various Channels
    4. Analytics and Insights with Oracle Digital Assistant
11. FAQ
12. Conclusion

Introduction

In this article, we will explore Oracle Generative AI and its applications in the field of artificial intelligence. We will discuss the concept of generative AI and how it differs from traditional AI models. Additionally, we will Delve into the various services offered by Oracle Generative AI and examine their use cases.

What is Artificial Intelligence?

Before delving into Oracle Generative AI, let's first understand the fundamentals of artificial intelligence. Artificial intelligence refers to the ability of machines to learn from past data, make decisions, and perform tasks that mimic human intelligence. This includes tasks such as natural language processing, image recognition, and problem-solving.

Understanding Generative AI

Generative AI is a branch of artificial intelligence that focuses on machines' ability to generate new, original content Based on existing data. Unlike traditional AI models, which rely on classification and discrimination, generative AI goes beyond the simple classification of data and can create new outputs based on Patterns and trends in the input data. This technology has the potential to revolutionize the way machines learn and Interact with humans.

Oracle Generative AI Services

Oracle offers a suite of generative AI services within its AI stack. These services cover a wide range of AI applications, including machine learning, speech recognition, language processing, vision analysis, and document understanding. Each service is designed to address specific challenges and provide tailored solutions for different business needs.

Oracle Generative AI in the AI Stack

Oracle Generative AI is a key component of Oracle's comprehensive AI stack. It works alongside other AI services, such as machine learning, speech AI, language AI, vision AI, and document understanding. This integration allows users to leverage various AI capabilities to develop advanced solutions for their businesses.

Oracle Generative AI for Machine Learning

One of the major areas where Oracle Generative AI excels is machine learning. It offers deep learning capabilities, allowing machines to learn from large datasets and generate accurate predictions. This technology has evolved from simple classification models to complex generative models that can create new data based on existing patterns.

Oracle Generative AI for Speech

Speech recognition is another vital application of Oracle Generative AI. By leveraging the power of natural language processing and machine learning, the service can convert speech into text, enabling users to interact with machines using voice commands. This technology is widely used in voice assistants and customer service applications.

Oracle Generative AI for Language

Oracle Generative AI also offers a language processing service that can analyze and understand human language. This technology enables machines to comprehend and generate human-like text, making it ideal for applications such as chatbots, content generation, and text summarization. Its ability to analyze and generate text in multiple languages makes it a versatile tool for businesses operating globally.

Oracle Generative AI for Vision

Vision analysis is a crucial aspect of AI, and Oracle Generative AI provides powerful vision processing capabilities. By leveraging deep learning models, this service can analyze images and videos to extract valuable information. It can recognize objects, identify patterns, and categorize visual data, making it beneficial for applications such as image recognition, video surveillance, and autonomous vehicles.

Oracle Generative AI for Document Understanding

The ability to understand and extract information from documents is essential for many businesses. Oracle Generative AI offers document understanding services that can automatically extract data from various document formats, such as PDFs, Word documents, and HTML pages. This technology streamlines processes like data entry, document classification, and information extraction.

The Evolution of Generative AI

Generative AI has come a long way since its inception. In the early days, AI models focused on discrimination and classification tasks. However, with advancements in deep learning and neural networks, the concept of generative AI emerged. Instead of simply classifying data, modern generative AI models can generate new outputs based on patterns and trends in the input data.

Oracle has been at the forefront of generative AI, continuously pushing the boundaries of what machines can create. With Oracle Generative AI, users can tap into the power of generative models and unlock new possibilities for innovation and problem-solving.

Oracle Generative AI and Coare Partnership

Oracle has partnered with Coare, a leading research organization, to enhance the capabilities of Oracle Generative AI. Coare's command model, backed by extensive research conducted at Stanford University, offers enhanced information retrieval capabilities and improved performance in various scenarios. By leveraging Coare's expertise, Oracle ensures that its Generative AI delivers optimal results and maintains a competitive edge in the market.

Enterprise Focus and Security in Oracle Generative AI

One of Oracle's key objectives with Oracle Generative AI is to cater to enterprises' needs and prioritize data security. Unlike other AI models that focus on generating generic content or poems, Oracle's primary focus is on providing enterprise-grade solutions and ensuring the security of sensitive data.

Oracle Generative AI seamlessly integrates with various Oracle products, such as Oracle Digital Assistant, to provide a secure and efficient AI ecosystem. This not only enables businesses to generate valuable insights but also ensures that their data remains protected.

How to Create a Rag with Oracle Generative AI

Creating a RAG (Retrieval Augmented Generation) model with Oracle Generative AI involves a few essential steps. First, the data needs to be prepared by chunking it into smaller pieces and converting it into embeddings using an embedding model. Once the data is prepared, prompts and guard rails are set up to guide the model's responses.

Prompts play a crucial role in defining the role, Context, task, and expectations for the model. By providing clear instructions and constraints, users can control the model's output and ensure that it stays within the predefined boundaries.

Oracle Generative AI also offers the ability to do semantic searches on the data to retrieve Relevant information based on specific queries. This enables users to extract precise and valuable insights without manually sifting through vast amounts of data.

Use Cases for Oracle Generative AI

Oracle Generative AI has a wide range of applications across various industries. Some of the key use cases include:

1. Chat Summaries for Customer Support: Oracle Generative AI can automatically generate summaries of chat conversations, making it easier to review and analyze customer interactions.

2. Retrieval Augmented Generation (RAG): By combining retrieval-based and generation-based techniques, RAG models can retrieve relevant information and generate accurate responses based on the context.

3. Generating Content with Oracle Generative AI: This technology can automatically generate written content, such as blog posts, articles, and reports, based on specific prompts and guidelines.

4. Question Answering and Data Summarization: By analyzing large datasets, Oracle Generative AI can answer complex questions and summarize data in a concise and informative manner.

5. Semantic Search and Data Extraction: Oracle Generative AI can perform semantic searches on large amounts of unstructured data, allowing users to extract specific information and insights.

These are just a few examples of how Oracle Generative AI can be utilized to streamline processes, improve decision-making, and enhance customer experiences.

Combining Oracle Digital Assistant and Oracle Generative AI

Oracle Generative AI can be seamlessly integrated with Oracle Digital Assistant to create a powerful conversational AI solution. By combining both technologies, users can connect with unstructured documents, websites, and structured data, enabling natural language interactions and automated processes.

Oracle Digital Assistant provides a low-code platform for building conversational interfaces that can be deployed across various channels, such as mobile apps, websites, and messaging platforms. It also offers analytics and insights to monitor and improve the performance of AI models.

When combined with Oracle Generative AI, Oracle Digital Assistant becomes a comprehensive AI assistant that can understand and respond to users' queries, automate tasks, and provide personalized assistance based on the context.

FAQ

Q: Can Oracle Generative AI connect to a database to retrieve data? A: Yes, Oracle Generative AI can connect to databases and retrieve data using appropriate APIs or data extraction methods.

Q: Is there a size limit on the llm prompt in Oracle Generative AI? A: As of now, there is a limit of 4,000 tokens for the llm prompt. However, this limit may increase in the future.

Q: Can large objects (lobs) be used as input for rag models in Oracle Generative AI? A: Oracle Generative AI supports the use of large objects (lobs) as input. However, it is important to ensure that the data is chunked appropriately and that the models can handle the size of the input.

Q: Can Oracle Generative AI be incorporated into an Oracle APEX application? A: Yes, Oracle Generative AI can be incorporated into an Oracle Apex application by leveraging APIs or other integration methods.

Q: Is there an iPhone version available for Oracle Generative AI? A: Currently, there is no specific iPhone version available for Oracle Generative AI. However, Oracle Generative AI can be accessed through web browsers on a smartphone.

Conclusion

Oracle Generative AI is a powerful tool that offers a range of services for AI application development. By leveraging generative models, businesses can generate new content, answer complex questions, and extract valuable insights from various data sources. With its integration with Oracle Digital Assistant, Oracle Generative AI provides a comprehensive conversational AI solution that combines the power of natural language processing and generative AI. Whether it's generating content, answering customer queries, or automating processes, Oracle Generative AI offers endless possibilities for businesses to stay at the forefront of AI innovation.