"""
adapters/llm/anthropic.py
Anthropic Claude adapter — Phase 2 stub.

TODO (Phase 2):
  - Implement complete() using the anthropic SDK (anthropic.Anthropic client).
  - Implement resolve_model() by reading config/team.yaml capability_map.
  - Handle streaming responses, rate-limit retries, and token counting.
  - Support system-prompt injection via context["system_prompt"].
  - Map capability → model using the provider's capability_map config.
"""
from __future__ import annotations

from adapters.base.llm import LLMAdapter


class AnthropicAdapter(LLMAdapter):
    """
    LLM adapter for Anthropic Claude models.

    Reads model configuration from config/team.yaml:
        models.provider: anthropic
        models.capability_map.reasoning-heavy.anthropic: claude-opus-4-6
        models.capability_map.capable.anthropic: claude-sonnet-4-6
        models.capability_map.fast-cheap.anthropic: claude-haiku-3-5
    """

    def __init__(self, config: dict) -> None:
        # TODO (Phase 2): Accept loaded team.yaml config dict.
        # Extract API key from environment (ANTHROPIC_API_KEY).
        # Initialise the anthropic.Anthropic() client.
        raise NotImplementedError("AnthropicAdapter.__init__ is not yet implemented.")

    def complete(self, prompt: str, capability: str, context: dict) -> str:
        # TODO (Phase 2): Call anthropic client messages.create().
        # Use resolve_model(capability) to pick the model.
        # Support context keys: system_prompt, max_tokens, temperature.
        # Return response text as a plain string.
        raise NotImplementedError("AnthropicAdapter.complete is not yet implemented.")

    def resolve_model(self, capability: str) -> str:
        # TODO (Phase 2): Look up capability in team.yaml capability_map.
        # Fall back to "capable" tier model if capability is unknown.
        raise NotImplementedError("AnthropicAdapter.resolve_model is not yet implemented.")