> ## Documentation Index
> Fetch the complete documentation index at: https://phidatainc-studio-tools-doc.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Classification and span labeling

> Assign one label, a set of labels, a taxonomy path, or marked spans.

Classification is extraction where the schema is a closed set. Use a `Literal` for one label, a `List[Literal]` for many.

```python theme={null}
from typing import Literal

from agno.agent import Agent
from agno.models.google import Gemini
from pydantic import BaseModel, Field


class Classification(BaseModel):
    label: Literal["positive", "negative", "neutral"] = Field(
        ..., description="The assigned sentiment label"
    )


agent = Agent(
    model=Gemini(id="gemini-3.5-flash"),
    instructions="You classify product reviews by sentiment.",
    output_schema=Classification,
)

result = agent.run("It works as described, nothing special.").content
# Classification(label='neutral')
```

The `Literal` constrains the model to the closed set. There is no invalid label to clean up downstream.

## Multi-label

A review can touch several aspects. Return any subset.

```python theme={null}
from typing import List, Literal

from pydantic import BaseModel, Field

Aspect = Literal["food", "service", "value", "atmosphere", "cleanliness"]


class Tagging(BaseModel):
    tags: List[Aspect] = Field(
        ..., description="Every aspect the reviewer commented on; empty if none"
    )
```

## Hierarchical

For a taxonomy, return the path instead of a flat label.

```python theme={null}
from typing import List

from pydantic import BaseModel, Field


class HierarchicalTag(BaseModel):
    parent: str = Field(..., description="Top-level category")
    child: str = Field(..., description="Sub-category under the parent")


class Tagging(BaseModel):
    tags: List[HierarchicalTag]
```

## Span labeling

Asking the model to count characters is unreliable. Have it return the exact substring and locate offsets in Python.

```python theme={null}
from typing import List, Literal

from agno.agent import Agent
from agno.models.google import Gemini
from pydantic import BaseModel, Field


class Entity(BaseModel):
    text: str = Field(..., description="Exact substring from the input")
    label: Literal["PERSON", "ORG", "LOCATION", "DATE"]


class Entities(BaseModel):
    entities: List[Entity]


agent = Agent(
    model=Gemini(id="gemini-3.5-flash"),
    instructions=(
        "Extract all named entities. Return the exact substring as it "
        "appears, with its label. Do not paraphrase or normalize."
    ),
    output_schema=Entities,
)

text = "On March 3rd, Sarah Johnson left Acme Corp to join Lumen Labs."
result = agent.run(text).content

for e in result.entities:
    start = text.find(e.text)
    print(e.label, e.text, start, start + len(e.text))
```

The same shape drives PII redaction: detect the spans, then replace each with its tag in post-processing.

## Choosing the shape

| You need             | Schema                                           |
| -------------------- | ------------------------------------------------ |
| Exactly one label    | `Literal[...]`                                   |
| Any subset of labels | `List[Literal[...]]`                             |
| A taxonomy path      | A model with `parent` / `child`                  |
| Marked substrings    | A model with `text` + `label`, offsets in Python |

## Other modalities

Image, audio, video, and document classification follow the same schema pattern with a different input argument. See [Multimodal inputs](/use-cases/data-labeling/multimodal-inputs).

| Modality | Cookbook                                                                                                                  |
| -------- | ------------------------------------------------------------------------------------------------------------------------- |
| Image    | [image\_classification](https://github.com/agno-agi/agno/tree/main/cookbook/data_labeling/_06_image_classification)       |
| Audio    | [audio\_classification](https://github.com/agno-agi/agno/tree/main/cookbook/data_labeling/_10_audio_classification)       |
| Video    | [video\_classification](https://github.com/agno-agi/agno/tree/main/cookbook/data_labeling/_13_video_classification)       |
| Document | [document\_classification](https://github.com/agno-agi/agno/tree/main/cookbook/data_labeling/_15_document_classification) |

## Next steps

| Task                             | Guide                                                                   |
| -------------------------------- | ----------------------------------------------------------------------- |
| Extract fields instead of labels | [Structured extraction](/use-cases/data-labeling/structured-extraction) |
| Score outputs against a rubric   | [LLM as judge](/use-cases/data-labeling/llm-as-judge)                   |
| Add reviewer agreement           | [Quality pipeline](/use-cases/data-labeling/quality-pipeline)           |

## Developer Resources

* [Text classification cookbook](https://github.com/agno-agi/agno/tree/main/cookbook/data_labeling/_01_text_classification)
* [Span labeling cookbook](https://github.com/agno-agi/agno/tree/main/cookbook/data_labeling/_04_text_span_labeling)