"""Thinking mode configurations."""

from rag.advanced_rag.harness.types import ExecutionStrategy

THINKING_MODES: dict[str, ExecutionStrategy] = {
    "low": ExecutionStrategy(
        label="low",
        execution_strategy="direct_search",
        requires_decomposition=False,
        requires_agent_loop=False,
        requires_sufficiency_judge=False,
        requires_selective_gen=False,
        allows_dynamic_claims=False,
        allows_replan=False,
        max_orchestrator_cycles=1,
        max_agent_cycles=0,
        max_parallel_agents=1,
        available_tools=["hybrid_search"],
        sufficiency_threshold=0.85,
        partial_threshold=0.50,
        fallback_to_direct_llm=False,
    ),
    "medium": ExecutionStrategy(
        label="medium",
        execution_strategy="decompose_and_search",
        requires_decomposition=True,
        requires_agent_loop=False,
        requires_sufficiency_judge=True,
        requires_selective_gen=True,
        allows_dynamic_claims=False,
        allows_replan=False,
        max_orchestrator_cycles=3,
        max_agent_cycles=0,
        max_parallel_agents=1,
        available_tools=["hybrid_search"],
        sufficiency_threshold=0.75,
        partial_threshold=0.40,
        fallback_to_direct_llm=False,
    ),
    "high": ExecutionStrategy(
        label="high",
        execution_strategy="agentic_research",
        requires_decomposition=True,
        requires_agent_loop=True,
        requires_sufficiency_judge=True,
        requires_selective_gen=True,
        allows_dynamic_claims=False,
        allows_replan=False,
        max_orchestrator_cycles=3,
        max_agent_cycles=2,
        max_parallel_agents=2,
        available_tools=[
            "hybrid_search",
            "web_search",
            "catalog_navigate",
            "dataset_navigate",
            "graph_explore",
            "inspector_open_context",
            "inspector_compare",
        ],
        sufficiency_threshold=0.65,
        partial_threshold=0.30,
        fallback_to_direct_llm=False,
    ),
    "ultra": ExecutionStrategy(
        label="ultra",
        execution_strategy="deep_research",
        requires_decomposition=True,
        requires_agent_loop=True,
        requires_sufficiency_judge=True,
        requires_selective_gen=True,
        allows_dynamic_claims=True,
        allows_replan=True,
        max_orchestrator_cycles=4,
        max_agent_cycles=2,
        max_parallel_agents=3,
        available_tools=[
            "hybrid_search",
            "bm25_search",
            "web_search",
            "structured_query",
            "catalog_navigate",
            "dataset_navigate",
            "mindmap_navigate",
            "graph_explore",
            "wiki_query",
            "inspector_open_context",
            "inspector_compare",
            "inspector_grep_within",
            "inspector_request_adjacent",
        ],
        sufficiency_threshold=0.55,
        partial_threshold=0.20,
        fallback_to_direct_llm=True,
    ),
}


def get_mode(label: str) -> ExecutionStrategy:
    mode = THINKING_MODES.get(label)
    if not mode:
        raise ValueError(f"Unknown thinking mode: {label}. Available: {list(THINKING_MODES.keys())}")
    return mode