haive.games.tic_tac_toe.configurable_config
===========================================

.. py:module:: haive.games.tic_tac_toe.configurable_config

.. autoapi-nested-parse::

   Configurable Tic-Tac-Toe configuration using the generic player agent system.

   This module provides configurable Tic-Tac-Toe game configurations that replace hardcoded
   LLM settings with dynamic, configurable player agents.



Classes
-------

.. autoapisummary::

   haive.games.tic_tac_toe.configurable_config.ConfigurableTicTacToeConfig


Functions
---------

.. autoapisummary::

   haive.games.tic_tac_toe.configurable_config.create_budget_ttt_config
   haive.games.tic_tac_toe.configurable_config.create_experimental_ttt_config
   haive.games.tic_tac_toe.configurable_config.create_quick_ttt_config
   haive.games.tic_tac_toe.configurable_config.create_ttt_config
   haive.games.tic_tac_toe.configurable_config.create_ttt_config_from_example
   haive.games.tic_tac_toe.configurable_config.create_ttt_config_from_player_configs
   haive.games.tic_tac_toe.configurable_config.get_example_config
   haive.games.tic_tac_toe.configurable_config.list_example_configurations


Module Contents
---------------

.. py:class:: ConfigurableTicTacToeConfig

   Bases: :py:obj:`haive.games.tic_tac_toe.config.TicTacToeConfig`


   Configurable Tic-Tac-Toe configuration with dynamic LLM selection.

   This configuration allows users to specify different LLMs for different
   roles in the Tic-Tac-Toe game, providing flexibility and avoiding hardcoded models.

   .. attribute:: x_model

      Model for X player (can be string or LLMConfig)

   .. attribute:: o_model

      Model for O player (can be string or LLMConfig)

   .. attribute:: x_player_name

      Name for the X player

   .. attribute:: o_player_name

      Name for the O player

   .. attribute:: example_config

      Optional example configuration name

   .. attribute:: player_configs

      Optional detailed player configurations

   .. attribute:: temperature

      Temperature for LLM generation

   .. attribute:: max_moves

      Maximum number of moves before draw

   .. attribute:: enable_analysis

      Whether to enable position analysis

   .. attribute:: recursion_limit

      Python recursion limit for game execution


   .. py:method:: model_post_init(__context)

      Initialize engines after model creation.



.. py:function:: create_budget_ttt_config(**kwargs)

   Create a budget-friendly Tic-Tac-Toe configuration.


.. py:function:: create_experimental_ttt_config(**kwargs)

   Create an experimental Tic-Tac-Toe configuration with mixed providers.


.. py:function:: create_quick_ttt_config(**kwargs)

   Create a quick Tic-Tac-Toe configuration with fast models.


.. py:function:: create_ttt_config(x_model = 'gpt-4o', o_model = 'claude-3-5-sonnet-20240620', **kwargs)

   Create a configurable Tic-Tac-Toe configuration with simple model specifications.

   :param x_model: Model for X player and analyzer
   :param o_model: Model for O player and analyzer
   :param \*\*kwargs: Additional configuration parameters

   :returns: Configured Tic-Tac-Toe game
   :rtype: ConfigurableTicTacToeConfig

   .. rubric:: Examples

   >>> config = create_ttt_config("gpt-4o", "claude-3-opus", temperature=0.5)
   >>> config = create_ttt_config(
   ...     "openai:gpt-4o",
   ...     "anthropic:claude-3-5-sonnet-20240620",
   ...     max_moves=9
   ... )


.. py:function:: create_ttt_config_from_example(example_name, **kwargs)

   Create a configurable Tic-Tac-Toe configuration from a predefined example.

   :param example_name: Name of the example configuration
   :param \*\*kwargs: Additional configuration parameters to override

   :returns: Configured Tic-Tac-Toe game
   :rtype: ConfigurableTicTacToeConfig

   Available examples:
       - "gpt_vs_claude": GPT-4 vs Claude
       - "gpt_only": GPT-4 for both players
       - "claude_only": Claude for both players
       - "budget": Cost-effective models
       - "mixed": Different provider per role

   .. rubric:: Examples

   >>> config = create_ttt_config_from_example("budget", max_moves=9)
   >>> config = create_ttt_config_from_example("gpt_vs_claude", enable_analysis=False)


.. py:function:: create_ttt_config_from_player_configs(player_configs, **kwargs)

   Create a configurable Tic-Tac-Toe configuration from detailed player.
   configurations.

   :param player_configs: Dictionary mapping role names to player configurations
   :param \*\*kwargs: Additional configuration parameters

   :returns: Configured Tic-Tac-Toe game
   :rtype: ConfigurableTicTacToeConfig

   Expected roles:
       - "X_player": X player configuration
       - "O_player": O player configuration
       - "X_analyzer": X analyzer configuration
       - "O_analyzer": O analyzer configuration

   .. rubric:: Examples

   >>> player_configs = {
   ...     "X_player": PlayerAgentConfig(
   ...         llm_config="gpt-4o",
   ...         temperature=0.7,
   ...         player_name="Strategic X"
   ...     ),
   ...     "O_player": PlayerAgentConfig(
   ...         llm_config="claude-3-opus",
   ...         temperature=0.3,
   ...         player_name="Tactical O"
   ...     ),
   ...     "X_analyzer": PlayerAgentConfig(
   ...         llm_config="gpt-4o",
   ...         temperature=0.2,
   ...         player_name="X Analyst"
   ...     ),
   ...     "O_analyzer": PlayerAgentConfig(
   ...         llm_config="claude-3-opus",
   ...         temperature=0.2,
   ...         player_name="O Analyst"
   ...     ),
   ... }
   >>> config = create_ttt_config_from_player_configs(player_configs)


.. py:function:: get_example_config(name)

   Get a predefined example configuration by name.

   :param name: Name of the example configuration

   :returns: The example configuration
   :rtype: ConfigurableTicTacToeConfig

   :raises ValueError: If the example name is not found


.. py:function:: list_example_configurations()

   List all available example configurations.

   :returns: Mapping of configuration names to descriptions
   :rtype: Dict[str, str]