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0d3f7cc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 | """Tree-of-Thoughts reasoning pattern implementation."""
from __future__ import annotations
import logging
from typing import Any
from hermes.core.types import AgentStrategy
logger = logging.getLogger(__name__)
class TreeNode:
"""A node in the thought tree."""
def __init__(
self,
thought: str,
state: str = "",
score: float = 0.0,
parent: TreeNode | None = None,
) -> None:
self.thought = thought
self.state = state
self.score = score
self.parent = parent
self.children: list[TreeNode] = []
self.depth = (parent.depth + 1) if parent else 0
def add_child(self, child: TreeNode) -> None:
"""Add a child node."""
child.parent = self
child.depth = self.depth + 1
self.children.append(child)
def get_path(self) -> list[str]:
"""Get the path from root to this node."""
path = []
node: TreeNode | None = self
while node:
path.append(node.thought)
node = node.parent
return list(reversed(path))
class TreeOfThoughtsReasoner:
"""Implements the Tree-of-Thoughts reasoning pattern."""
def __init__(self, max_depth: int = 3, max_branches: int = 3) -> None:
self.strategy = AgentStrategy.TREE_OF_THOUGHTS
self.max_depth = max_depth
self.max_branches = max_branches
def create_generation_prompt(self, task: str, state: str = "", num_thoughts: int = 3) -> str:
"""Create prompt for generating thought candidates."""
state_section = f"\nCurrent state:\n{state}\n" if state else ""
return f"""You are an AI agent exploring multiple reasoning paths for a task.
Task: {task}
{state_section}
Generate {num_thoughts} different possible next thoughts/reasoning steps.
Each thought should take a different approach or consider different aspects.
Format your response as:
Thought 1: [First possible reasoning step]
Approach: [brief description of the approach taken]
Thought 2: [Second possible reasoning step]
Approach: [brief description]
Thought 3: [Third possible reasoning step]
Approach: [brief description]
Important:
- Each thought should be distinct and explore a different angle
- Consider both promising and unconventional approaches
- Be specific about what each approach entails"""
def create_evaluation_prompt(self, task: str, thoughts: list[str]) -> str:
"""Create prompt for evaluating thought candidates."""
thoughts_text = "\n".join(
f"Thought {i + 1}: {t}" for i, t in enumerate(thoughts)
)
return f"""Evaluate the following reasoning thoughts for a task.
Task: {task}
Thoughts to evaluate:
{thoughts_text}
Rate each thought on a scale of 1-10 based on:
1. Relevance to the task
2. Potential to lead to a solution
3. Logical soundness
4. Completeness
Format your response as:
Evaluation 1: [thought number]
Score: [1-10]
Reasoning: [why this score]
Evaluation 2: [thought number]
Score: [1-10]
Reasoning: [why this score]
...
Best Thought: [number of the best thought]"""
def create_state_prompt(self, task: str, path: list[str]) -> str:
"""Create prompt to generate state from a thought path."""
path_text = "\n".join(f"Step {i + 1}: {p}" for i, p in enumerate(path))
return f"""Based on the following reasoning steps, summarize the current state.
Task: {task}
Reasoning steps taken:
{path_text}
Provide a concise summary of:
1. What has been determined so far
2. What information has been gathered
3. What remains to be resolved
Format:
Current State: [concise summary]
Key Findings: [list of important findings]
Open Questions: [what still needs to be addressed]"""
def parse_generation_response(self, response: str) -> list[str]:
"""Parse thought generation response."""
thoughts = []
lines = response.strip().split("\n")
current_thought: list[str] = []
for line in lines:
stripped = line.strip()
if stripped.startswith("Thought ") and ":" in stripped:
if current_thought:
thoughts.append(" ".join(current_thought))
current_thought = [stripped[stripped.index(":") + 1 :].strip()]
elif stripped.startswith("Approach:"):
if current_thought:
current_thought.append(f"({stripped})")
elif current_thought and stripped:
current_thought.append(stripped)
if current_thought:
thoughts.append(" ".join(current_thought))
return thoughts[: self.max_branches]
def parse_evaluation_response(self, response: str) -> list[dict[str, Any]]:
"""Parse evaluation response."""
evaluations = []
lines = response.strip().split("\n")
current_eval: dict[str, Any] = {}
for line in lines:
stripped = line.strip()
if stripped.startswith("Evaluation ") and ":" in stripped:
if current_eval:
evaluations.append(current_eval)
current_eval = {"thought_number": stripped}
elif stripped.startswith("Score:"):
if current_eval:
try:
current_eval["score"] = float(stripped.split(":")[1].strip())
except ValueError:
current_eval["score"] = 5.0
elif stripped.startswith("Reasoning:"):
if current_eval:
current_eval["reasoning"] = stripped[len("Reasoning:"):].strip()
if current_eval:
evaluations.append(current_eval)
return evaluations
def select_best(self, evaluations: list[dict[str, Any]]) -> int:
"""Select the best thought index based on evaluations."""
if not evaluations:
return 0
best_idx = 0
best_score = -1
for i, eval_data in enumerate(evaluations):
score = eval_data.get("score", 0)
if score > best_score:
best_score = score
best_idx = i
return best_idx
def build_tree(
self, task: str, generations: list[list[str]], evaluations: list[list[dict[str, Any]]]
) -> TreeNode:
"""Build a thought tree from generations and evaluations."""
root = TreeNode(thought=task, state="Initial task")
current_nodes = [root]
for _gen_idx, (gen, evals) in enumerate(zip(generations, evaluations, strict=False)):
next_nodes: list[TreeNode] = []
for node in current_nodes:
scores = {e.get("thought_number", ""): e.get("score", 5.0) for e in evals}
for i, thought in enumerate(gen):
thought_key = f"Thought {i + 1}"
score = scores.get(thought_key, 5.0)
child = TreeNode(thought=thought, score=score, parent=node)
node.add_child(child)
next_nodes.append(child)
current_nodes = next_nodes
return root
def get_best_path(self, root: TreeNode) -> list[str]:
"""Get the best path through the tree."""
best_leaf = root
best_score = float("-inf")
def _find_best(node: TreeNode) -> None:
nonlocal best_leaf, best_score
if not node.children and node.score > best_score:
best_score = node.score
best_leaf = node
for child in node.children:
_find_best(child)
_find_best(root)
return best_leaf.get_path()
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