# Usage Guide

## Load a schema from JSON text

```python
from ie_schema import IESchema

schema = IESchema.loads(
    """
    {
      "entities": ["gene", "disease"],
      "relations": [
        {"associated_with": {"head": "gene", "tail": "disease"}}
      ]
    }
    """
)
```

## Load from a JSON Schema string

The same `IESchema.loads` accepts a **root JSON Schema** object as a string (for example from another tool or hand-written draft). Top-level keys must not match unknown IE ingest fields: native IE JSON rejects unknown top-level keys, so a document with `type` / `properties` is treated as JSON Schema, not as an empty IE schema.

```python
from ie_schema import IESchema

schema = IESchema.loads(
    '{"type":"object","properties":{"title":{"type":"string"},"count":{"type":"integer"}}}'
)
```

## Load from dataclass or Pydantic model

`IESchema.loads` also accepts a stdlib dataclass / Pydantic v2 `BaseModel` class (or instance).
Install the optional dependency first:

```bash
uv add 'ie_schema[model]'
```

```python
from dataclasses import dataclass
from ie_schema import IESchema

@dataclass
class BusinessRecord:
    business_name: str
    score: float

schema = IESchema.loads(BusinessRecord)
```

## Load a schema from disk

```python
schema = IESchema.load("schema.json")
```

## Iterate planned tasks

`IESchema` is iterable and yields task objects for classification, entity extraction, relation extraction, and structure extraction.

```python
for task in schema:
    print(type(task).__name__)
```

## Render prompt debug string

```python
print(schema.prompt())
```