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.
The following are the supported actions for Select AI:
runsql: Generates the SQL statement for a
natural language prompt and runs the underlying SQL query to return a
rowset. This is the default action and does not require specifying this
parameter.
showsql: Displays the SQL statement for a
natural language prompt.
narrate:
Sends the result of the SQL query run by the database back to the LLM to
generate a natural language description of that result.
When a vector index is specified in the AI profile to enable
RAG, the system uses the specified transformer model to create a vector
embedding from the prompt for semantic similarity search against the
vector
store..
The system then adds the retrieved content from the vector store to the
user prompt and sends it to the LLM to generate a response based on this
information.
chat: Passes the user prompt directly to the
LLM to generate a response, which is provided to the user.
explainsql: Explains the generated SQL from
the prompt into natural language. This option sends the generated SQL to
the AI provider, which then produces a natural language explanation.
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.
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 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, creating an augmented prompt sent to the LLM.
See
Enable Conversations to Enhance User Interaction.
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 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.
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.
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.
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 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.
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) 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.
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.