# infospace-bench Cost Estimator Migration

`infospace-bench` can replace its local rate table and coarse word-count
budget math with the primitives added in `LLM-WP-0005`.

## Rate Table

- Drop `src/infospace_bench/model_rates.yaml` after the dependency is bumped.
- Load `ModelRateRegistry.default()` from `llm-connect`.
- Keep the workspace-level `model-rates.yaml` override and merge it with
  `default().merged_with(ModelRateRegistry.from_yaml(path))`.
- Preserve `--cost-per-1k` as an explicit blended-rate override. When supplied,
  it should win over the registry and report `cost_source="cost_per_1k_blended"`.

## Plan Summary Sketch

```python
from llm_connect import (
    CostEstimate,
    ModelRateRegistry,
    ProblemClassRegistry,
    estimate_cost,
)


def plan_generation_summary(...):
    problem_classes = ProblemClassRegistry.default()
    rates = ModelRateRegistry.default()
    workspace_rates = _workspace_rate_path(root_path)
    if workspace_rates.exists():
        rates = rates.merged_with(ModelRateRegistry.from_yaml(workspace_rates))

    total_prompt_tokens = 0
    total_completion_tokens = 0
    per_stage = []
    for workflow_id in workflow_ids:
        class_name, dimensions = _problem_class_for_workflow(
            workflow_id,
            selected_chunks=selected,
            template_words=template_words,
            entities_per_chunk=entities_per_chunk,
        )
        estimate = problem_classes.get(class_name).estimate(dimensions)
        calls = _calls_for_workflow(workflow_id, selected, entities_per_chunk)
        prompt_tokens = estimate.prompt_tokens * calls
        completion_tokens = estimate.completion_tokens * calls
        total_prompt_tokens += prompt_tokens
        total_completion_tokens += completion_tokens
        per_stage.append(
            {
                "workflow_id": workflow_id,
                "problem_class": class_name,
                "calls": calls,
                "prompt_tokens_estimate": prompt_tokens,
                "completion_tokens_estimate": completion_tokens,
                "confidence": estimate.confidence,
            }
        )

    if cost_per_1k_tokens > 0:
        total_tokens = total_prompt_tokens + total_completion_tokens
        cost = (total_tokens / 1000.0) * cost_per_1k_tokens
        cost_source = "cost_per_1k_blended"
    elif model:
        cost_estimate = estimate_cost(
            model,
            total_prompt_tokens,
            total_completion_tokens,
            registry=rates,
        )
        cost = cost_estimate.cost_usd
        cost_source = cost_estimate.cost_source
    else:
        cost = None
        cost_source = None

    return {
        "per_workflow": per_stage,
        "total_prompt_tokens_estimate": total_prompt_tokens,
        "estimated_completion_tokens": total_completion_tokens,
        "estimated_cost_usd": round(cost, 6) if cost is not None else None,
        "cost_source": cost_source,
        ...
    }
```

## Workflow Mapping

Initial mapping can stay intentionally thin:

| infospace-bench workflow | llm-connect problem class |
|---|---|
| `summarize-source` | `chunk-summarization` |
| entity extraction workflows | `entity-extraction` |
| relation extraction workflows | `relation-extraction` |
| `generic-source-evaluations` | `judge-eval` |
| final report or rollup synthesis | `report-synthesis` |

The consumer still owns structure-specific dimensions such as selected chunk
counts, profile template word counts, and expected entities per chunk.