# Synthetic Relevance Assessment (`reanimator.relevance`)

Domain-independent synthetic 4-level UMBRELA relevance assessment engine.

---

## `RelevanceEvaluator`

```python
class RelevanceEvaluator(
    backend: Optional[Any] = None,
    model_name: str = "synthetic-llm",
    domain_instruction: str = "general information retrieval and document understanding",
    concurrency: int = 10,
)
```

### Methods

#### `evaluate_document(doc: Document, topics: Union[str, List[str], List[Dict]], modality: str = "tables") -> List[Judgement]`
Evaluate all resources extracted from a `Document` against given topics across the specified modality.

---

## `ModalityExtractor`

```python
class ModalityExtractor:
    @staticmethod
    def extract(
        doc: Document,
        modality: str = "tables",
        chunk_size: int = 512,
        chunk_overlap: int = 100,
    ) -> List[Dict[str, Any]]
```

### Supported Modalities

- `'full_document'` / `'full_text'`: Full parsed document including text, HTML tables, figure captions, and formulas.
- `'text_only'`: Body text excluding HTML tables and display math.
- `'tables'`: `Table` objects with content DataFrames, captions, and references.
- `'figures'`: `Figure` objects with captions and references.
- `'formulas'` / `'equations'`: `Formula` objects with surrounding text.
- `'chunks'` / `'passages'`: Text chunks created for RAG.

---

## `export_qrels`

```python
def export_qrels(
    judgements: List[Judgement],
    file_path: Union[str, Path],
    binary: bool = False,
) -> None
```

Export relevance judgements in standard TREC `qrels` format (`<query_id> 0 <resource_id> <score>`).