Datasets and batches

Overview

All conversation data in Gaussia is represented using two Pydantic models: Dataset for sessions and Batch for individual interactions.

Dataset

A Dataset represents one complete conversation session between a user and an assistant.

from gaussia.schemas.common import Dataset, Batch

dataset = Dataset(
    session_id="session-001",
    assistant_id="assistant-v2",
    language="english",
    context="Product documentation for the Acme Widget.",
    conversation=[
        Batch(
            qa_id="q1",
            query="How do I install the widget?",
            assistant="Run pip install acme-widget.",
            ground_truth_assistant="Install with: pip install acme-widget",
        ),
    ],
)

Fields

Field	Type	Description
`session_id`	`str`	Unique identifier for the conversation session
`assistant_id`	`str`	Identifier for the AI assistant being evaluated
`language`	`str \| None`	Language of the conversation (default: `"english"`)
`context`	`str`	Background context provided to the assistant
`conversation`	`list[Batch]`	Ordered list of interactions in this session

Batch

A Batch represents a single question–answer interaction.

batch = Batch(
    qa_id="q1",
    query="What is the return policy?",
    assistant="You can return items within 30 days.",
    ground_truth_assistant="Items can be returned within 30 days of purchase.",
    observation="The assistant correctly identified the return window.",
    weight=0.5,
)

Fields

Field	Type	Default	Description
`qa_id`	`str`	required	Unique identifier for this interaction
`query`	`str`	required	The user’s question or input
`assistant`	`str`	required	The assistant’s actual response
`ground_truth_assistant`	`str`	required	The expected or reference response
`observation`	`str \| None`	`None`	Additional notes about the interaction
`weight`	`float \| None`	`None`	Importance weight for aggregation (must be ≥ 0)
`agentic`	`dict \| None`	`{}`	Tool usage metadata (for the Agentic metric)
`ground_truth_agentic`	`dict \| None`	`{}`	Expected tool usage (for the Agentic metric)
`logprobs`	`dict \| None`	`{}`	Token log probabilities

Streamed batch

For stream-based processing (STREAM_BATCHES), individual interactions are wrapped in StreamedBatch:

from gaussia.schemas.common import StreamedBatch, SessionMetadata

streamed = StreamedBatch(
    metadata=SessionMetadata(
        session_id="session-001",
        assistant_id="assistant-v2",
        language="english",
        context="Product documentation.",
    ),
    batch=Batch(
        qa_id="q1",
        query="How do I install?",
        assistant="Run pip install.",
        ground_truth_assistant="Install with pip install.",
    ),
)

Weighting

The weight field on Batch controls how much each interaction contributes to the aggregated score:

No weights set: Equal weight (1/n) for all interactions
All weights set: Must sum to 1.0, otherwise Gaussia falls back to equal weights
Partial weights: Remaining budget is distributed equally among unweighted interactions

conversation = [
    Batch(qa_id="q1", weight=0.6, ...),  # Critical question
    Batch(qa_id="q2", weight=0.4, ...),  # Less important
]

The observation field is used by some metrics (Context, Conversational) as an alternative to ground_truth_assistant. When present, the judge prompt is adjusted to evaluate against the observation rather than the ground truth.

Getting started

Core concepts

Metrics

Integrations

Advanced

Datasets and batches

Overview

Dataset

Fields

Batch

Fields

Streamed batch

Weighting

​Overview

​Dataset

​Fields

​Batch

​Fields

​Streamed batch

​Weighting

Overview

Dataset

Fields

Batch

Fields

Streamed batch

Weighting