7.2 Data Science Agent Concepts

Here is a list of key concepts and terms commonly used in Data Science Agent.

AI Credential

An AI credential stores authentication details that the database uses to access the selected AI provider or related cloud resources. Depending on the provider, it may contain an API key, OCI signing key details, or other provider-specific authentication fields. The credential comprises the following information:

• user_ocid: This is the unique identifier of the OCI user.
• tenancy_ocid: This is the unique identifier of the OCI tenancy in your cloud account.
• private_key: The private key associated with the OCI user. It is required for secure authentication.
• fingerprint: The fingerprint of the public key linked to the OCI user.

AI Profile

An AI Profile is a named configuration object that specifies how the database connects to an LLM — including the provider, (for example, openai, oci), credential, model, and optional parameters such as temperature and max_tokens and so on.

For more information, see Manage AI Profiles.

Conversation

The interaction with Data Science Agent takes the form of a conversation, each made up of alternating turns. Each turn begins with a user prompt, followed by the agent’s response. The conversation retains the context throughout, allowing you to refer back to previous answers in later questions. For example, you might ask, "filter the dataset you just profiled," or "train a model using the training dataset we prepared".

Conversation history

Conversation history is a persistent record of past conversations with the agent. It allows you to browse through conversation history, review previous results, and continue past sessions without losing the context. This ensures continuity over time, allows multiple workloads in separate chats, supports reproducibility of analyses, and provides an auditable trail on how insights were derived.

Conversation Objects Catalog

Data Science Agent operates on and produces three types of database objects while handling requests. If you associate these objects to your conversation, the agent can inspect, analyze, transform, and model from those objects directly. This will thereby enhance the quality of the agent's response. If you do not associate any object, the agent will automatically scan the database for relevant objects based on your prompt.

The objects are available in the Conversation Object Catalog:

• Tables: Source data and persisted modeling results (created by the agent)
• Views: Views may be pre-existing data sources or derived datasets created by the agent. They are used for analysis, modeling, or general data transformation. Views created by the agent use the prefix DSAGENT$ and may include a unique suffix.
• Mining models: The Oracle Machine Learning (OML) models trained by the agent.

For more information on how to associate these objects to your conversation, see Add Objects to Data Science Agent Conversation.

Prompt

A prompt is your input or message that initiates an interaction. It can be a question, command, statement, or request that Data Science Agent processes in order to generate an appropriate response. Essentially, the prompt guides the agent on what information or action you are seeking.

Prompt library

A prompt library is a curated set of system, task, and tool-specific prompts that defines how the agent interprets your prompts, interprets results, and calls various tools. The prompts are designed to encode domain knowledge and ensure consistent, reliable behavior.

Service Levels

In Oracle Machine Learning (OML) on Autonomous AI Database, service levels refer to the predefined configurations for resource allocation and workload management. Essentially, it determines how many ECPUs, and memory are allocated to a session. There are three types of service levels - Low, Medium, and High.

These service levels help manage and prioritize workloads running on the database, ensuring appropriate performance based on the use case.

For more information how to change the service levels of your conversation, see Use Data Science Agent Chat Interface.

Tools

Tools are modular components that enable the agent to perform specific tasks such as profiling a data object, computing feature correlations, or training a model. Each tool has clearly defined inputs, outputs, and constraints. In short, tools serve as the building blocks of Data Science Agent’s functionality. Although end users do not interact with these tools directly, it determines the user experience of the agent.