Deprecation warning and notebook update

autogen/agentchat/contrib/reasoning_agent.py

@@ -4,6 +4,7 @@
 import math
 import random
 import re
+import warnings
 from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union
 
 from ..agent import Agent
@@ -310,23 +311,50 @@ def __init__(
         Args:
             name: Name of the agent
             llm_config: Configuration for the language model
+            grader_llm_config: Optional separate configuration for the grader model. If not provided, uses llm_config
             max_depth (int): Maximum depth of the reasoning tree
             beam_size (int): DEPRECATED. Number of parallel reasoning paths to maintain
             answer_approach (str): DEPRECATED. Either "pool" or "best" - how to generate final answer
             verbose (bool): Whether to show intermediate steps
-            reason_config (dict): Configuration for the reasoning method, e.g.,
-                {"method": "mcts"} or
-                {"method": "beam_search", "beam_size": 3, "answer_approach": "pool"} or
-                {"method": "lats", "max_iterations": 10, "num_candidates": 5}
+
+            reason_config (dict): Configuration for the reasoning method. Supported parameters:
+                method (str): The search strategy to use. Options:
+                    - "beam_search" (default): Uses beam search with parallel paths
+                    - "mcts": Uses Monte Carlo Tree Search for exploration
+                    - "lats": Uses Language Agent Tree Search with per-step rewards
+                    - "dfs": Uses depth-first search (equivalent to beam_search with beam_size=1)
+
+                Common parameters:
+                    max_depth (int): Maximum depth of reasoning tree (default: 3)
+                    forest_size (int): Number of independent trees to maintain (default: 1)
+                    rating_scale (int): Scale for grading responses, e.g. 1-10 (default: 10)
+
+                Beam Search specific:
+                    beam_size (int): Number of parallel paths to maintain (default: 3)
+                    answer_approach (str): How to select final answer, "pool" or "best" (default: "pool")
+
+                MCTS/LATS specific:
+                    nsim (int): Number of simulations to run (default: 3)
+                    exploration_constant (float): UCT exploration parameter (default: 1.41)
+
+                Example configs:
+                    {"method": "beam_search", "beam_size": 5, "max_depth": 4}
+                    {"method": "mcts", "nsim": 10, "exploration_constant": 2.0}
+                    {"method": "lats", "nsim": 5, "forest_size": 3}
         """
         super().__init__(name=name, llm_config=llm_config, **kwargs)
-        self._max_depth = max_depth
-        self._beam_size = beam_size
         self._verbose = verbose
-        self._answer_approach = answer_approach
         self._llm_config = llm_config
         self._grader_llm_config = grader_llm_config if grader_llm_config else llm_config
 
+        if max_depth != 4 or beam_size != 3 or answer_approach != "pool":
+            # deprecate warning
+            warnings.warn(
+                "The parameters max_depth, beam_size, and answer_approach have been deprecated. "
+                "Please use the reason_config dictionary to configure these settings instead.",
+                DeprecationWarning,
+            )
+
         if reason_config is None:
             reason_config = {}
         self._reason_config = reason_config
@@ -336,13 +364,14 @@ def __init__(
             if self._method == "dfs":
                 self._beam_size = 1
             else:
-                self._beam_size = reason_config.get("beam_size", 3)
-            self._answer_approach = reason_config.get("answer_approach", "pool")
+                self._beam_size = reason_config.get("beam_size", beam_size)
+            self._answer_approach = reason_config.get("answer_approach", answer_approach)
             assert self._answer_approach in ["pool", "best"]
         elif self._method in ["mcts", "lats"]:
             self._nsim = reason_config.get("nsim", 3)
             self._exploration_constant = reason_config.get("exploration_constant", 1.41)
 
+        self._max_depth = reason_config.get("max_depth", max_depth)
         self._forest_size = reason_config.get("forest_size", 1)  # We default use only 1 tree.
         self._rating_scale = reason_config.get("rating_scale", 10)
 
@@ -374,10 +403,12 @@ def generate_forest_response(self, messages, sender, config=None):
 
         forest_answers = []
         for _ in range(self._forest_size):
-            if self._method == "beam_search":
-                success, response = self._beam_reply(prompt, ground_truth)
+            if self._method in ["beam_search", "dfs"]:
+                response = self._beam_reply(prompt, ground_truth)
             elif self._method in ["mcts", "lats"]:
-                success, response = self._mtcs_reply(prompt, ground_truth)
+                response = self._mtcs_reply(prompt, ground_truth)
+            else:
+                raise ValueError("Invalid reasoning method specified.")
 
             forest_answers.append(response)
 
@@ -564,7 +595,7 @@ def _beam_reply(self, prompt, ground_truth=""):
             )
 
         final_answer = self.chat_messages[self][-1]["content"].strip()
-        return True, final_answer
+        return final_answer
 
     def _mtcs_reply(self, prompt, ground_truth=""):
         root = ThinkNode(content=prompt, parent=None)
@@ -622,7 +653,7 @@ def _mtcs_reply(self, prompt, ground_truth=""):
 
         # Best action
         best_ans_node = max(answer_nodes, key=lambda node: node.value)
-        return True, best_ans_node.content
+        return best_ans_node.content
 
     def _expand(self, node: ThinkNode) -> List:
         """