> ## Documentation Index
> Fetch the complete documentation index at: https://docs.perceptron.inc/llms.txt
> Use this file to discover all available pages before exploring further.

# Perceive basics

> Learn the Perceptron Perceive decorator, core nodes, and the patterns for authoring your first multimodal prompt.

Perceive turns regular Python functions into prompt templates. You describe the sequence of messages, call the function, and receive a structured result.

```python theme={null}
from perceptron import perceive, image, text

@perceive()
def describe(image_path):
    return image(image_path) + text("Describe the primary object.")

print(describe("drone.png").text)
```

The SDK assembles the request payload, calls the API, and returns a `PerceiveResult` that contains text, structured points, parsed segments, and semantic warnings.

## Building blocks

```python theme={null}
from perceptron import system, text, agent, image, point, box, polygon, block
```

| Node                     | What it adds                     | Example                                      |
| ------------------------ | -------------------------------- | -------------------------------------------- |
| `system(text)`           | Global instruction.              | `system("You are an inspection assistant.")` |
| `text(str)`              | User text content.               | `text("List defects")`                       |
| `image(value)`           | Register an image input.         | `img = image("frame.png")`                   |
| `agent(text)`            | Assistant history.               | `agent(previous_answer)`                     |
| `point/box/polygon(...)` | Spatial anchor tied to an image. | `box(40, 60, 160, 140, image=img)`           |
| `block(*nodes)`          | Reusable group of nodes.         | `block(system_prompt, text_hint)`            |

Compose nodes with `+` to keep ordering explicit.

## Expect a specific structure

```python theme={null}
from perceptron import perceive, image, text

@perceive(expects="point")
def find_center(frame):
    return image(frame) + text("Return one point marking the defect center.")

result = find_center("part.jpg")
print(result.points)  # [SinglePoint(...)]
print(result.errors)  # semantic warnings, if any
```

Setting `expects` adds deterministic guidance, validates the result, and filters `result.points` to that type. Turn on `allow_multiple=True` when the model should return more than one tag.

## Multiple images

```python theme={null}
from perceptron import perceive, image, text, box

@perceive()
def compare(day, night):
    day_img = image(day)
    night_img = image(night)
    return (
        text("Compare the highlighted region in both frames.")
        + night_img
        + box(40, 60, 160, 140, mention="focus", image=night_img)
        + day_img
    )
```

When more than one image is present, always pass `image=` to the anchors so the SDK binds coordinates to the correct image. With `strict=True`, missing anchors raise `AnchorError`; otherwise the SDK records a warning in `result.errors`.

## Validate and debug

* Review `result.errors` for semantic issues before trusting structured outputs.
* Inspect `result.parsed` to see text and tags in order.
* Call `inspect_task(your_function, ...)` to view the compiled Task without sending a request.

Need more answers? The [`Python SDK FAQs`](/isaac-0.1/guides/python-sdk/faqs) cover common troubleshooting steps and roadmap questions.