# Project Collection Workspace (`reanimator.collection`)

The `ProjectCollection` class provides a memory-efficient workspace manager that streams documents directly to disk without keeping large collections in RAM.

---

## `ProjectCollection`

```python
class ProjectCollection(
    project_dir: Union[str, Path],
    arxiv_ids: Optional[List[str]] = None,
    dois: Optional[List[str]] = None,
    local_pdfs: Optional[List[Union[str, Path]]] = None,
    topics: Optional[Union[List[str], List[Dict[str, Any]]]] = None,
    name: str = "reanimator_project",
)
```

### Workspace Layout

```text
project_dir/
  ├── manifest.json         # Workspace manifest tracking inputs & status
  ├── pdfs/                 # Raw PDF files
  ├── parsed_docs/          # Parsed Document JSON artifacts
  ├── figures/              # Extracted figure images
  ├── cache/                # Disk-backed VLM response cache
  └── relevance/            # Synthetic judgements & TREC qrels files
```

### Methods

#### `process(pipeline: ReanimatorVLM, email: Optional[str] = None, verbose: bool = True) -> List[str]`
Batch process all collection inputs via VLM pipeline. Saves each parsed `Document` directly to `parsed_docs/{doc_id}.json` on disk and releases RAM immediately.

#### `iter_documents() -> Iterator[Document]`
Generator yielding `Document` instances lazily from disk.

#### `get_document(doc_id: str) -> Optional[Document]`
Load a single `Document` directly from disk by ID.

#### `run_relevance_assessment(evaluator: RelevanceEvaluator, modality: str = "tables", verbose: bool = True) -> List[Judgement]`
Run synthetic relevance assessment across collection documents lazily from disk. Saves `judgements_{modality}.json` and exports TREC `qrels_{modality}.txt`.