Select AI Concepts

Explores the concepts and terms related to Select AI.

• Actions
  
• AI Profile
  
• AI Provider
  
• Conversations
  
• Database Credentials
  
• Hallucination in LLM
  
• IAM
  
• Iterative Refinement
  
• Large Language Model (LLM)
  
• MapReduce
  
• Metadata
  
• Metadata Clone
  
• Natural Language Prompts
  
• Network Access Control List (ACL)
  
• Retrieval Augmented Generation (RAG)
  
• Semantic Similarity Search
  
• Vector Distance
  
• Vector Index
  
• Vector Store
  

Actions

An action in Select AI is a keyword that instructs Select AI to perform different behavior when acting on the prompt. By specifying an action, users can instruct Select AI to process their natural language prompt to generate SQL code, to respond to a chat prompt, narrate the output, display the SQL statement, or explain the SQL code, leveraging the LLMs to efficiently interact with the data within their database environment.

See Use AI Keyword to Enter Prompts for supported Select AI actions.

AI Profile

An AI profile is a specification that includes the AI provider to use and other details regarding metadata and database objects required for generating responses to natural language prompts. See CREATE_PROFILE Procedure and Profile Attributes.

AI Provider

An AI Provider in Select AI refers to the service provider that supplies the LLM or transformer or both for processing and generating responses to natural language prompts. These providers offer models that can interpret and convert natural language for the use cases highlighted under the LLM concept. See Select your AI Provider and LLMs for the supported providers.

Conversations

Conversations in Select AI represent an interactive exchange between the user and the system, enabling users to query or interact with the database through a series of natural language prompts. Select AI incorporates up to 10 previous prompts into the current request through session-based conversations, creating an augmented prompt sent to the LLM. Select AI supports multiple customizable conversations that can be configured through conversation APIs from the DBMS_CLOUD_AI Package. See Select AI Conversations.

Database Credentials

Database credentials are authentication credentials used to access and interact with databases. They typically consist of a user name and a password, sometimes supplemented by additional authentication factors like security tokens. These credentials are used to establish a secure connection between an application or user and a database, such that only authorized individuals or systems can access and manipulate the data stored within the database.

Hallucination in LLM

Hallucination in the context of Large Language Models refers to a phenomenon where the model generates text that is incorrect, nonsensical, or unrelated to the input prompt. Despite being a result of the model's attempt to generate coherent text, these responses can contain information that is fabricated, misleading, or purely imaginative. Hallucination can occur due to biases in training data, lack of proper context understanding, or limitations in the model's training process.

IAM

Oracle Cloud Infrastructure Identity and Access Management (IAM) lets you control who has access to your cloud resources. You can control what type of access a group of users have and to which specific resources. To learn more, see Overview of Identity and Access Management.

Iterative Refinement

Iterative refinement is a process of gradually improving a solution or a model through repeated cycles of adjustments based on feedback or evaluation. It starts with an initial approximation, refines it step by step, and continues until the desired accuracy or outcome is achieved. Each iteration builds on the previous one, incorporating corrections or optimizations to move closer to the goal.

In text summary generation, iterative refinement can be useful for processing large files or documents. The process splits the text into manageable-sized chunks, for example, that fit within an LLM's token limits, generates a summary for one chunk, and then improves the summary by sequentially incorporating the following chunks.

Use cases for iterative refinement:

• Best suited for situations where contextual accuracy and coherence are critical, such as when summarizing complex or highly interconnected texts where each part builds on the previous.
• Ideal for smaller-scale tasks where sequential processing is acceptable.

See Summarization Techniques.

Large Language Model (LLM)

A Large Language Model (LLM) refers to an advanced type of artificial intelligence model that is trained on massive amounts of text data to support a range of use cases depending on their training data. This includes understanding and generating human-like language as well as software code and database queries. These models are capable of performing a wide range of natural language processing tasks, including text generation, translation, summarization, question answering, sentiment analysis, and more. LLMs are typically based on sophisticated deep learning neural network models that learn patterns, context, and semantics from the input data, enabling them to generate coherent and contextually relevant text.

MapReduce

In general, the MapReduce programming model enables processing large-volume data by dividing tasks into two phases: Map and Reduce.

• Map: Processes input data and transforms it into key-value pairs.
• Reduce: Aggregates and summarizes the mapped data based on keys. MapReduce performs parallel processing of large data sets.

In the case of Select AI Summarize, MapReduce partitions text into multiple chunks and processes them in parallel and independently, generating individual summaries for each chunk. These summaries are then combined to form a cohesive overall summary.

Use cases for map reduce:

• Best suited for large-scale, parallel tasks where speed and scalability are priorities, such as summarizing very large data sets or documents.
• Ideal for situations where chunk independence is acceptable, and the summaries can be aggregated later.

See Summarization Techniques.

Metadata

Database metadata refers to the data that describes the structure, organization, and properties of the database tables and views.

Metadata Clone

A metadata clone or an Autonomous Database clone creates a copy of a metadata defining the database or schema, containing only the structure, not the actual data. This clone includes tables, indexes, views, statistics, procedures, and triggers without any data rows. Developers, testers, or those building database templates find this useful. To learn more, see Clone, Move, or Upgrade an Autonomous Database Instance.

Natural Language Prompts

Natural Language Prompts are human-readable instructions or requests provided to guide generative AI models, such as Large Language Models. Instead of using specific programming languages or commands, users can interact with these models by entering prompts in a more conversational or natural language form. The models then generate output based on the provided prompt.

Network Access Control List (ACL)

A Network Access Control List is a set of rules or permissions that define what network traffic is allowed to pass through a network device, such as a router, firewall, or gateway. ACLs are used to control and filter incoming and outgoing traffic based on various criteria such as IP addresses, port numbers, and protocols. They play a crucial role in network security by enabling administrators to manage and restrict network traffic to prevent unauthorized access, potential attacks, and data breaches.

Retrieval Augmented Generation (RAG)

Retrieval Augmented Generation (RAG) is a technique that involves retrieving relevant information for a user's query and supplying that information to a large language model (LLM) to improve responses and reduce hallucination.

Most commonly, RAG involves vector search, but more generally, includes augmenting a prompt of database content (either manually or automatically) such as schema metadata for SQL generation or database content explicitly queried. Other forms of augmentation can involve technologies such as graph analytics and traditional machine learning.

Semantic Similarity Search

Semantic similarity search identifies and retrieves data points that closely match a given query by comparing feature vectors in a vector store.

Vector Distance

Vector distance measure the similarity or dissimilarity between feature vectors by calculating the distance between them in a multidimensional space.

Vector Index

A vector index organizes and stores vectors to enable efficient similarity search and retrieval of related data.

Vector Store

A vector store includes systems that store, manage, and enable semantic similarity search involving vector embeddings. This includes standalone vector databases and Oracle Database 23ai AI Vector Search.