haive.games.hold_em.aug_llms
============================

.. py:module:: haive.games.hold_em.aug_llms

.. autoapi-nested-parse::

   Texas Hold'em specialized augmented LLM configurations.

   This module provides specialized augmented LLM configurations for Texas Hold'em poker,
   with customized prompts, output schemas, and model configurations for different
   aspects of poker gameplay:
       - Hand evaluation and analysis
       - Opponent modeling and profiling
       - Betting strategy and decision-making
       - Position-based play adaptation
       - Pot odds and equity calculations

   These specialized configurations build on the base engines in engines.py but provide
   more targeted capabilities for specific poker reasoning tasks.

   .. rubric:: Examples

   >>> from haive.games.hold_em.aug_llms import get_hand_analyzer, get_bluff_detector
   >>> from haive.core.engine.aug_llm import AugLLMConfig
   >>>
   >>> # Get a specialized hand analyzer
   >>> hand_analyzer = get_hand_analyzer("advanced")
   >>> result = hand_analyzer.invoke({
   >>>     "hole_cards": ["Ah", "Kh"],
   >>>     "community_cards": ["Qh", "Jh", "2s", "7c", "9d"],
   >>> })



Functions
---------

.. autoapisummary::

   haive.games.hold_em.aug_llms.get_betting_strategist
   haive.games.hold_em.aug_llms.get_bluff_detector
   haive.games.hold_em.aug_llms.get_complete_llm_suite
   haive.games.hold_em.aug_llms.get_hand_analyzer
   haive.games.hold_em.aug_llms.get_opponent_profiler
   haive.games.hold_em.aug_llms.get_situation_analyzer
   haive.games.hold_em.aug_llms.get_table_dynamics_analyzer


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

.. py:function:: get_betting_strategist(style = 'gto')

   Get a specialized betting strategy configuration.

   This function returns an augmented LLM configuration specialized for making
   betting decisions with different strategic approaches:
   - gto: Game Theory Optimal balanced approach
   - exploitative: Adjusts to exploit opponent tendencies
   - aggressive: Higher variance, aggressive betting strategy
   - conservative: Lower variance, tighter betting strategy

   :param style: Strategic style ("gto", "exploitative", "aggressive", or "conservative")

   :returns: Configured betting strategist
   :rtype: AugLLMConfig


.. py:function:: get_bluff_detector(sensitivity = 'balanced')

   Get a specialized bluff detection configuration.

   This function returns an augmented LLM configuration specialized for detecting
   opponent bluffs with different sensitivity levels:
   - conservative: Lower false positive rate, only identifies clear bluffs
   - balanced: Moderate sensitivity to bluffing signals
   - aggressive: Higher sensitivity, may have more false positives

   :param sensitivity: Bluff detection sensitivity ("conservative", "balanced", or "aggressive")

   :returns: Configured bluff detector
   :rtype: AugLLMConfig


.. py:function:: get_complete_llm_suite(player_style = 'balanced')

   Get a complete suite of specialized LLMs for a poker player.

   This function creates a coordinated set of specialized LLM configurations
   that work well together based on a player's overall style.

   :param player_style: Overall player style ("tight", "loose", "aggressive",
                        "passive", "balanced", or "tricky")

   :returns: Dictionary of specialized LLM configurations
   :rtype: Dict[str, AugLLMConfig]


.. py:function:: get_hand_analyzer(level = 'standard')

   Get a specialized hand analyzer configuration.

   This function returns an augmented LLM configuration specialized for analyzing
   poker hands with different levels of sophistication:
   - basic: Simple hand strength evaluation
   - standard: Balanced analysis considering draws and relative strength
   - advanced: Sophisticated analysis with equity calculations and range analysis

   :param level: Complexity level of the analyzer ("basic", "standard", or "advanced")

   :returns: Configured hand analyzer
   :rtype: AugLLMConfig


.. py:function:: get_opponent_profiler(tracking_depth = 'standard')

   Get a specialized opponent profiling configuration.

   This function returns an augmented LLM configuration specialized for building
   opponent models with different levels of detail:
   - basic: Simple tracking of betting patterns
   - standard: Balanced profiling of play style and tendencies
   - deep: Sophisticated profiling with psychological modeling

   :param tracking_depth: Depth of opponent tracking ("basic", "standard", or "deep")

   :returns: Configured opponent profiler
   :rtype: AugLLMConfig


.. py:function:: get_situation_analyzer(focus = 'general')

   Get a specialized situation analyzer configuration.

   This function returns an augmented LLM configuration specialized for analyzing
   poker game situations with different focus areas:
   - general: Balanced analysis of the overall situation
   - positional: Focus on positional dynamics and advantages
   - tournament: Specialized for tournament situations with ICM considerations
   - cash_game: Specialized for cash game dynamics

   :param focus: Analysis focus ("general", "positional", "tournament", or "cash_game")

   :returns: Configured situation analyzer
   :rtype: AugLLMConfig


.. py:function:: get_table_dynamics_analyzer()

   Get a specialized table dynamics analyzer configuration.

   This function returns an augmented LLM configuration specialized for analyzing
   overall poker table dynamics, player interactions, and meta-game considerations.

   :returns: Configured table dynamics analyzer
   :rtype: AugLLMConfig