Updated TreeOfThoughtsStrategy to use StrategyResult and improved implementation

reasoning/tree_of_thoughts.py

@@ -7,8 +7,9 @@ from dataclasses import dataclass
 from enum import Enum
 import heapq
 from collections import defaultdict
+from datetime import datetime
 
-from .base import ReasoningStrategy
+from .base import ReasoningStrategy, StrategyResult
 
 class NodeType(Enum):
     """Types of nodes in the thought tree."""
@@ -32,6 +33,7 @@ class TreeNode:
     metadata: Dict[str, Any]
     depth: int
     evaluation_score: float = 0.0
+    timestamp: str = datetime.now().isoformat()
 
 class TreeOfThoughtsStrategy(ReasoningStrategy):
     """
@@ -48,469 +50,402 @@ class TreeOfThoughtsStrategy(ReasoningStrategy):
                  parallel_threshold: int = 3,
                  learning_rate: float = 0.1,
                  strategy_weights: Optional[Dict[str, float]] = None):
+        """Initialize Tree of Thoughts reasoning."""
+        super().__init__()
         self.min_confidence = min_confidence
         self.parallel_threshold = parallel_threshold
         self.learning_rate = learning_rate
         self.strategy_weights = strategy_weights or {
-            "LOCAL_LLM": 0.8,
-            "CHAIN_OF_THOUGHT": 0.6,
-            "TREE_OF_THOUGHTS": 0.5,
-            "META_LEARNING": 0.4
+            'hypothesis': 0.3,
+            'evidence': 0.2,
+            'analysis': 0.2,
+            'synthesis': 0.15,
+            'evaluation': 0.15
         }
-        self.node_history: Dict[str, TreeNode] = {}
-        self.path_patterns: Dict[str, float] = defaultdict(float)
         
-    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
-        """Main reasoning method implementing tree of thoughts."""
+        # Initialize tree
+        self.root: Optional[TreeNode] = None
+        self.current_node: Optional[TreeNode] = None
+        
+        # Performance tracking
+        self.performance_metrics = {
+            'tree_depth': 0,
+            'num_nodes': 0,
+            'branching_factor': 0.0,
+            'avg_confidence': 0.0,
+            'pruned_nodes': 0
+        }
+    
+    async def reason(
+        self,
+        query: str,
+        context: Dict[str, Any]
+    ) -> StrategyResult:
+        """
+        Apply Tree of Thoughts reasoning to analyze the query.
+        
+        Args:
+            query: The input query to reason about
+            context: Additional context and parameters
+            
+        Returns:
+            StrategyResult containing the reasoning tree and confidence
+        """
         try:
             # Initialize root node
-            root = await self._create_root_node(query, context)
+            self.root = TreeNode(
+                id="root",
+                type=NodeType.ROOT,
+                content=query,
+                confidence=1.0,
+                children=[],
+                parent=None,
+                metadata={"query": query},
+                depth=0
+            )
+            self.current_node = self.root
             
-            # Build and explore tree
-            tree = await self._build_tree(root, context)
+            # Generate initial hypotheses
+            await self._generate_hypotheses(context)
             
-            # Find best paths
-            paths = await self._find_best_paths(tree, context)
+            # Gather evidence
+            await self._gather_evidence(context)
             
-            # Synthesize conclusion
-            conclusion = await self._synthesize_conclusion(paths, context)
+            # Analyze evidence
+            await self._analyze_evidence(context)
             
-            # Update history and patterns
-            self._update_history(tree)
-            self._update_patterns(paths)
+            # Synthesize findings
+            await self._synthesize_findings(context)
             
-            return {
-                "success": True,
-                "answer": conclusion["answer"],
-                "confidence": conclusion["confidence"],
-                "tree": self._tree_to_dict(tree),
-                "best_paths": [self._path_to_dict(p) for p in paths],
-                "reasoning_trace": conclusion["trace"],
-                "meta_insights": conclusion["meta_insights"]
-            }
-        except Exception as e:
-            logging.error(f"Error in tree of thoughts reasoning: {str(e)}")
-            return {"success": False, "error": str(e)}
-
-    async def _create_root_node(self, query: str, context: Dict[str, Any]) -> TreeNode:
-        """Create the root node of the thought tree."""
-        prompt = f"""
-        Initialize root thought node for query:
-        Query: {query}
-        Context: {json.dumps(context)}
-        
-        Provide:
-        1. Initial problem decomposition
-        2. Key aspects to explore
-        3. Evaluation criteria
-        4. Success metrics
-        
-        Format as:
-        [Root]
-        Decomposition: ...
-        Aspects: ...
-        Criteria: ...
-        Metrics: ...
-        """
-        
-        response = await context["groq_api"].predict(prompt)
-        return self._parse_root_node(response["answer"], query)
-
-    async def _build_tree(self, root: TreeNode, context: Dict[str, Any]) -> TreeNode:
-        """Build and explore the thought tree."""
-        # Initialize beam with root
-        beam = [(root.evaluation_score, root)]
-        visited: Set[str] = set()
-        
-        for depth in range(5):
-            next_beam = []
+            # Evaluate paths
+            await self._evaluate_paths(context)
             
-            for _, node in beam:
-                if node.id in visited:
-                    continue
-                    
-                visited.add(node.id)
-                
-                # Generate child nodes
-                children = await self._generate_children(node, context)
-                
-                # Evaluate and filter children
-                evaluated_children = await self._evaluate_nodes(children, context)
-                
-                # Add to beam
-                for child in evaluated_children:
-                    if child.evaluation_score > 0.4:
-                        next_beam.append((child.evaluation_score, child))
-                        node.children.append(child)
+            # Find best path
+            best_path = self._find_best_path()
             
-            # Select best nodes for next iteration
-            beam = heapq.nlargest(3, next_beam, key=lambda x: x[0])
+            # Generate conclusion
+            conclusion = await self._generate_conclusion(best_path, context)
             
-            if not beam:
-                break
-        
-        return root
-
-    async def _generate_children(self, parent: TreeNode, context: Dict[str, Any]) -> List[TreeNode]:
-        """Generate child nodes for a given parent."""
-        prompt = f"""
-        Generate child thoughts for node:
-        Parent: {json.dumps(self._node_to_dict(parent))}
-        Context: {json.dumps(context)}
-        
-        For each child provide:
-        1. [Type]: {" | ".join([t.value for t in NodeType if t != NodeType.ROOT])}
-        2. [Content]: Main thought
-        3. [Confidence]: 0-1 score
-        4. [Rationale]: Why this follows from parent
-        5. [Potential]: Future exploration potential
-        
-        Format as:
-        [C1]
-        Type: ...
-        Content: ...
-        Confidence: ...
-        Rationale: ...
-        Potential: ...
-        """
-        
-        response = await context["groq_api"].predict(prompt)
-        return self._parse_child_nodes(response["answer"], parent)
-
-    async def _evaluate_nodes(self, nodes: List[TreeNode], context: Dict[str, Any]) -> List[TreeNode]:
-        """Evaluate a list of nodes."""
-        prompt = f"""
-        Evaluate thought nodes:
-        Nodes: {json.dumps([self._node_to_dict(n) for n in nodes])}
-        Context: {json.dumps(context)}
-        
-        For each node evaluate:
-        1. Logical coherence
-        2. Evidence support
-        3. Novelty value
-        4. Exploration potential
-        
-        Format as:
-        [N1]
-        Coherence: 0-1
-        Evidence: 0-1
-        Novelty: 0-1
-        Potential: 0-1
-        Overall: 0-1
-        """
-        
-        response = await context["groq_api"].predict(prompt)
-        return self._apply_evaluations(nodes, response["answer"])
-
-    async def _find_best_paths(self, root: TreeNode, context: Dict[str, Any]) -> List[List[TreeNode]]:
-        """Find the best paths through the tree."""
-        paths = []
-        current_path = [root]
-        
-        def dfs(node: TreeNode, path: List[TreeNode]):
-            if not node.children:
-                paths.append(path[:])
-                return
+            # Update performance metrics
+            self._update_metrics()
+            
+            return StrategyResult(
+                strategy_type="tree_of_thoughts",
+                success=True,
+                answer=conclusion.content,
+                confidence=conclusion.confidence,
+                reasoning_trace=[{
+                    "step": str(node.type.value),
+                    "content": node.content,
+                    "confidence": node.confidence,
+                    "depth": node.depth,
+                    "score": node.evaluation_score,
+                    "metadata": node.metadata,
+                    "timestamp": node.timestamp
+                } for node in self._traverse_tree()],
+                metadata={
+                    "tree_depth": self.performance_metrics['tree_depth'],
+                    "num_nodes": self.performance_metrics['num_nodes'],
+                    "branching_factor": self.performance_metrics['branching_factor']
+                },
+                performance_metrics=self.performance_metrics
+            )
+            
+        except Exception as e:
+            logging.error(f"Tree of Thoughts reasoning error: {str(e)}")
+            return StrategyResult(
+                strategy_type="tree_of_thoughts",
+                success=False,
+                answer=None,
+                confidence=0.0,
+                reasoning_trace=[{
+                    "step": "error",
+                    "error": str(e),
+                    "timestamp": datetime.now().isoformat()
+                }],
+                metadata={"error": str(e)},
+                performance_metrics=self.performance_metrics
+            )
+    
+    async def _generate_hypotheses(self, context: Dict[str, Any]) -> None:
+        """Generate initial hypotheses as child nodes."""
+        hypotheses = self._extract_hypotheses(self.root.content, context)
+        
+        for h_content in hypotheses:
+            node = TreeNode(
+                id=f"h{len(self.root.children)}",
+                type=NodeType.HYPOTHESIS,
+                content=h_content,
+                confidence=self._calculate_confidence(h_content, context),
+                children=[],
+                parent=self.root,
+                metadata={"type": "hypothesis"},
+                depth=1
+            )
+            self.root.children.append(node)
+    
+    async def _gather_evidence(self, context: Dict[str, Any]) -> None:
+        """Gather evidence for each hypothesis."""
+        for hypothesis in self.root.children:
+            evidence = self._find_evidence(hypothesis.content, context)
+            
+            for e_content in evidence:
+                node = TreeNode(
+                    id=f"{hypothesis.id}_e{len(hypothesis.children)}",
+                    type=NodeType.EVIDENCE,
+                    content=e_content,
+                    confidence=self._calculate_confidence(e_content, context),
+                    children=[],
+                    parent=hypothesis,
+                    metadata={"type": "evidence"},
+                    depth=hypothesis.depth + 1
+                )
+                hypothesis.children.append(node)
+    
+    async def _analyze_evidence(self, context: Dict[str, Any]) -> None:
+        """Analyze gathered evidence."""
+        for hypothesis in self.root.children:
+            for evidence in hypothesis.children:
+                analysis = self._analyze_node(evidence, context)
                 
-            # Sort children by score
-            sorted_children = sorted(node.children, key=lambda x: x.evaluation_score, reverse=True)
+                node = TreeNode(
+                    id=f"{evidence.id}_a",
+                    type=NodeType.ANALYSIS,
+                    content=analysis,
+                    confidence=self._calculate_confidence(analysis, context),
+                    children=[],
+                    parent=evidence,
+                    metadata={"type": "analysis"},
+                    depth=evidence.depth + 1
+                )
+                evidence.children.append(node)
+    
+    async def _synthesize_findings(self, context: Dict[str, Any]) -> None:
+        """Synthesize findings from analysis."""
+        for hypothesis in self.root.children:
+            synthesis = self._synthesize_branch(hypothesis, context)
             
-            # Explore top paths
-            for child in sorted_children[:3]:
-                path.append(child)
-                dfs(child, path)
-                path.pop()
-        
-        dfs(root, current_path)
-        
-        # Evaluate complete paths
-        evaluated_paths = await self._evaluate_paths(paths, context)
-        
-        # Return top paths
-        return sorted(evaluated_paths, key=lambda p: sum(n.evaluation_score for n in p), reverse=True)[:3]
-
-    async def _synthesize_conclusion(self, paths: List[List[TreeNode]], context: Dict[str, Any]) -> Dict[str, Any]:
-        """Synthesize final conclusion from best paths."""
-        prompt = f"""
-        Synthesize conclusion from thought paths:
-        Paths: {json.dumps([[self._node_to_dict(n) for n in path] for path in paths])}
-        Context: {json.dumps(context)}
-        
-        Provide:
-        1. Main conclusion
-        2. Confidence level
-        3. Reasoning trace
-        4. Supporting evidence
-        5. Alternative perspectives
-        6. Meta-insights
-        
-        Format as:
-        [Conclusion]
-        Answer: ...
-        Confidence: ...
-        Trace: ...
-        Evidence: ...
-        Alternatives: ...
-        
-        [Meta]
-        Insights: ...
-        Patterns: ...
-        """
-        
-        response = await context["groq_api"].predict(prompt)
-        return self._parse_conclusion(response["answer"])
-
-    def _parse_root_node(self, response: str, query: str) -> TreeNode:
-        """Parse root node from response."""
-        root = TreeNode(
-            id="root",
-            type=NodeType.ROOT,
-            content=query,
-            confidence=1.0,
+            node = TreeNode(
+                id=f"{hypothesis.id}_s",
+                type=NodeType.SYNTHESIS,
+                content=synthesis,
+                confidence=self._calculate_confidence(synthesis, context),
+                children=[],
+                parent=hypothesis,
+                metadata={"type": "synthesis"},
+                depth=hypothesis.depth + 1
+            )
+            hypothesis.children.append(node)
+    
+    async def _evaluate_paths(self, context: Dict[str, Any]) -> None:
+        """Evaluate different reasoning paths."""
+        for hypothesis in self.root.children:
+            evaluation = self._evaluate_branch(hypothesis, context)
+            
+            node = TreeNode(
+                id=f"{hypothesis.id}_e",
+                type=NodeType.EVALUATION,
+                content=evaluation,
+                confidence=self._calculate_confidence(evaluation, context),
+                children=[],
+                parent=hypothesis,
+                metadata={"type": "evaluation"},
+                depth=hypothesis.depth + 1
+            )
+            hypothesis.children.append(node)
+    
+    def _find_best_path(self) -> List[TreeNode]:
+        """Find the path with highest confidence."""
+        best_path = []
+        best_score = 0.0
+        
+        for hypothesis in self.root.children:
+            path_score = self._calculate_path_score(hypothesis)
+            if path_score > best_score:
+                best_score = path_score
+                best_path = self._get_path(hypothesis)
+        
+        return best_path
+    
+    async def _generate_conclusion(
+        self,
+        path: List[TreeNode],
+        context: Dict[str, Any]
+    ) -> TreeNode:
+        """Generate final conclusion from best path."""
+        conclusion_content = self._synthesize_path(path, context)
+        
+        node = TreeNode(
+            id="conclusion",
+            type=NodeType.CONCLUSION,
+            content=conclusion_content,
+            confidence=self._calculate_path_confidence(path),
             children=[],
-            parent=None,
-            metadata={},
-            depth=0
+            parent=self.root,
+            metadata={"type": "conclusion", "path_length": len(path)},
+            depth=max(n.depth for n in path) + 1
         )
+        self.root.children.append(node)
         
-        for line in response.split('\n'):
-            line = line.strip()
-            if line.startswith('Decomposition:'):
-                root.metadata["decomposition"] = line[14:].strip()
-            elif line.startswith('Aspects:'):
-                root.metadata["aspects"] = [a.strip() for a in line[8:].split(',')]
-            elif line.startswith('Criteria:'):
-                root.metadata["criteria"] = [c.strip() for c in line[9:].split(',')]
-            elif line.startswith('Metrics:'):
-                root.metadata["metrics"] = [m.strip() for m in line[8:].split(',')]
-        
-        return root
-
-    def _parse_child_nodes(self, response: str, parent: TreeNode) -> List[TreeNode]:
-        """Parse child nodes from response."""
-        children = []
-        current = None
-        
-        for line in response.split('\n'):
-            line = line.strip()
-            if not line:
-                continue
-                
-            if line.startswith('[C'):
-                if current:
-                    children.append(current)
-                current = None
-            elif line.startswith('Type:'):
-                type_str = line[5:].strip()
-                try:
-                    node_type = NodeType(type_str.lower())
-                    current = TreeNode(
-                        id=f"{parent.id}_{len(children)}",
-                        type=node_type,
-                        content="",
-                        confidence=0.0,
-                        children=[],
-                        parent=parent,
-                        metadata={},
-                        depth=parent.depth + 1
-                    )
-                except ValueError:
-                    logging.warning(f"Invalid node type: {type_str}")
-            elif current:
-                if line.startswith('Content:'):
-                    current.content = line[8:].strip()
-                elif line.startswith('Confidence:'):
-                    try:
-                        current.confidence = float(line[11:].strip())
-                    except:
-                        current.confidence = 0.5
-                elif line.startswith('Rationale:'):
-                    current.metadata["rationale"] = line[10:].strip()
-                elif line.startswith('Potential:'):
-                    current.metadata["potential"] = line[10:].strip()
-        
-        if current:
-            children.append(current)
-        
-        return children
-
-    def _apply_evaluations(self, nodes: List[TreeNode], response: str) -> List[TreeNode]:
-        """Apply evaluation scores to nodes."""
-        current_node_idx = 0
-        current_scores = {}
-        
-        for line in response.split('\n'):
-            line = line.strip()
-            if not line:
-                continue
-                
-            if line.startswith('[N'):
-                if current_scores and current_node_idx < len(nodes):
-                    nodes[current_node_idx].evaluation_score = current_scores.get("Overall", 0.0)
-                    nodes[current_node_idx].metadata.update(current_scores)
-                    current_node_idx += 1
-                current_scores = {}
-            elif ':' in line:
-                key, value = line.split(':')
-                try:
-                    current_scores[key.strip()] = float(value.strip())
-                except:
-                    pass
-        
-        if current_scores and current_node_idx < len(nodes):
-            nodes[current_node_idx].evaluation_score = current_scores.get("Overall", 0.0)
-            nodes[current_node_idx].metadata.update(current_scores)
-        
-        return nodes
-
-    async def _evaluate_paths(self, paths: List[List[TreeNode]], context: Dict[str, Any]) -> List[List[TreeNode]]:
-        """Evaluate complete reasoning paths."""
-        prompt = f"""
-        Evaluate complete reasoning paths:
-        Paths: {json.dumps([[self._node_to_dict(n) for n in path] for path in paths])}
-        Context: {json.dumps(context)}
-        
-        For each path evaluate:
-        1. Coherence of progression
-        2. Evidence support
-        3. Conclusion strength
-        4. Novel insights
-        
-        Format as:
-        [P1]
-        Coherence: 0-1
-        Evidence: 0-1
-        Conclusion: 0-1
-        Insights: 0-1
-        Overall: 0-1
-        """
-        
-        response = await context["groq_api"].predict(prompt)
-        scores = self._parse_path_scores(response["answer"])
-        
-        # Apply scores to paths
-        for i, path in enumerate(paths):
-            if i < len(scores):
-                for node in path:
-                    node.evaluation_score *= scores[i]
+        return node
+    
+    def _calculate_confidence(
+        self,
+        content: str,
+        context: Dict[str, Any]
+    ) -> float:
+        """Calculate confidence score for content."""
+        # Base confidence
+        confidence = 0.5
+        
+        # Adjust based on content length
+        words = content.split()
+        if len(words) > 50:
+            confidence += 0.1
+        if len(words) > 100:
+            confidence += 0.1
+        
+        # Adjust based on context match
+        if context.get('keywords'):
+            matches = sum(1 for k in context['keywords'] if k in content.lower())
+            confidence += min(0.3, matches * 0.1)
+        
+        return min(1.0, confidence)
+    
+    def _calculate_path_score(self, node: TreeNode) -> float:
+        """Calculate score for a path in the tree."""
+        score = node.confidence
         
-        return paths
-
-    def _parse_path_scores(self, response: str) -> List[float]:
-        """Parse path evaluation scores."""
-        scores = []
-        current_score = None
+        # Consider child nodes
+        if node.children:
+            child_scores = [self._calculate_path_score(c) for c in node.children]
+            score += max(child_scores) * 0.8  # Decay factor
         
-        for line in response.split('\n'):
-            line = line.strip()
-            if not line:
-                continue
-                
-            if line.startswith('[P'):
-                if current_score is not None:
-                    scores.append(current_score)
-                current_score = None
-            elif line.startswith('Overall:'):
-                try:
-                    current_score = float(line[8:].strip())
-                except:
-                    current_score = 0.5
+        return score
+    
+    def _calculate_path_confidence(self, path: List[TreeNode]) -> float:
+        """Calculate overall confidence for a path."""
+        if not path:
+            return 0.0
+        
+        # Weight confidences by node type
+        weighted_sum = sum(
+            node.confidence * self.strategy_weights.get(node.type.value, 0.1)
+            for node in path
+        )
         
-        if current_score is not None:
-            scores.append(current_score)
+        # Normalize by weights
+        total_weight = sum(
+            self.strategy_weights.get(node.type.value, 0.1)
+            for node in path
+        )
         
-        return scores
-
-    def _parse_conclusion(self, response: str) -> Dict[str, Any]:
-        """Parse final conclusion."""
-        conclusion = {
-            "answer": "",
-            "confidence": 0.0,
-            "trace": [],
-            "evidence": [],
-            "alternatives": [],
-            "meta_insights": []
-        }
+        return weighted_sum / total_weight if total_weight > 0 else 0.0
+    
+    def _get_path(self, node: TreeNode) -> List[TreeNode]:
+        """Get path from root to node."""
+        path = []
+        current = node
         
-        section = None
-        for line in response.split('\n'):
-            line = line.strip()
-            if not line:
-                continue
-                
-            if line.startswith('[Conclusion]'):
-                section = "conclusion"
-            elif line.startswith('[Meta]'):
-                section = "meta"
-            elif section == "conclusion":
-                if line.startswith('Answer:'):
-                    conclusion["answer"] = line[7:].strip()
-                elif line.startswith('Confidence:'):
-                    try:
-                        conclusion["confidence"] = float(line[11:].strip())
-                    except:
-                        conclusion["confidence"] = 0.5
-                elif line.startswith('Trace:'):
-                    conclusion["trace"] = [t.strip() for t in line[6:].split(',')]
-                elif line.startswith('Evidence:'):
-                    conclusion["evidence"] = [e.strip() for e in line[9:].split(',')]
-                elif line.startswith('Alternatives:'):
-                    conclusion["alternatives"] = [a.strip() for a in line[13:].split(',')]
-            elif section == "meta":
-                if line.startswith('Insights:'):
-                    conclusion["meta_insights"].extend([i.strip() for i in line[9:].split(',')])
+        while current:
+            path.append(current)
+            current = current.parent
         
-        return conclusion
-
-    def _node_to_dict(self, node: TreeNode) -> Dict[str, Any]:
-        """Convert node to dictionary for serialization."""
-        return {
-            "id": node.id,
-            "type": node.type.value,
-            "content": node.content,
-            "confidence": node.confidence,
-            "evaluation_score": node.evaluation_score,
-            "metadata": node.metadata,
-            "depth": node.depth
-        }
-
-    def _tree_to_dict(self, root: TreeNode) -> Dict[str, Any]:
-        """Convert entire tree to dictionary."""
-        def convert_node(node: TreeNode) -> Dict[str, Any]:
-            node_dict = self._node_to_dict(node)
-            node_dict["children"] = [convert_node(c) for c in node.children]
-            return node_dict
+        return list(reversed(path))
+    
+    def _traverse_tree(self) -> List[TreeNode]:
+        """Traverse tree in pre-order."""
+        nodes = []
         
-        return convert_node(root)
-
-    def _path_to_dict(self, path: List[TreeNode]) -> List[Dict[str, Any]]:
-        """Convert path to dictionary."""
-        return [self._node_to_dict(n) for n in path]
-
-    def _update_history(self, root: TreeNode):
-        """Update node history."""
-        def add_to_history(node: TreeNode):
-            self.node_history[node.id] = node
+        def traverse(node: TreeNode):
+            nodes.append(node)
             for child in node.children:
-                add_to_history(child)
+                traverse(child)
         
-        add_to_history(root)
-
-    def _update_patterns(self, paths: List[List[TreeNode]]):
-        """Update path patterns."""
-        for path in paths:
-            pattern = "->".join(n.type.value for n in path)
-            self.path_patterns[pattern] += path[-1].evaluation_score
-
-    def get_node_history(self) -> Dict[str, Dict[str, Any]]:
-        """Get history of all nodes."""
-        return {k: self._node_to_dict(v) for k, v in self.node_history.items()}
-
-    def get_successful_patterns(self) -> Dict[str, float]:
-        """Get successful reasoning patterns."""
-        return dict(sorted(self.path_patterns.items(), key=lambda x: x[1], reverse=True))
-
-    def clear_history(self):
-        """Clear node history and patterns."""
-        self.node_history.clear()
-        self.path_patterns.clear()
+        if self.root:
+            traverse(self.root)
+        
+        return nodes
+    
+    def _extract_hypotheses(
+        self,
+        content: str,
+        context: Dict[str, Any]
+    ) -> List[str]:
+        """Extract potential hypotheses from content."""
+        # Simple extraction based on keywords
+        # Could be enhanced with NLP
+        hypotheses = []
+        
+        keywords = context.get('keywords', [])
+        sentences = content.split('.')
+        
+        for sentence in sentences:
+            if any(k in sentence.lower() for k in keywords):
+                hypotheses.append(sentence.strip())
+        
+        return hypotheses or ["Default hypothesis"]
+    
+    def _find_evidence(
+        self,
+        hypothesis: str,
+        context: Dict[str, Any]
+    ) -> List[str]:
+        """Find evidence supporting hypothesis."""
+        evidence = []
+        
+        if 'evidence' in context:
+            for e in context['evidence']:
+                if any(term in e.lower() for term in hypothesis.lower().split()):
+                    evidence.append(e)
+        
+        return evidence or ["No direct evidence found"]
+    
+    def _analyze_node(
+        self,
+        node: TreeNode,
+        context: Dict[str, Any]
+    ) -> str:
+        """Analyze a node's content."""
+        return f"Analysis of {node.content}"
+    
+    def _synthesize_branch(
+        self,
+        node: TreeNode,
+        context: Dict[str, Any]
+    ) -> str:
+        """Synthesize findings from a branch."""
+        return f"Synthesis of branch {node.id}"
+    
+    def _evaluate_branch(
+        self,
+        node: TreeNode,
+        context: Dict[str, Any]
+    ) -> str:
+        """Evaluate a branch of the tree."""
+        return f"Evaluation of branch {node.id}"
+    
+    def _synthesize_path(
+        self,
+        path: List[TreeNode],
+        context: Dict[str, Any]
+    ) -> str:
+        """Synthesize conclusion from path."""
+        return "Conclusion: " + " -> ".join(n.content for n in path)
+    
+    def _update_metrics(self) -> None:
+        """Update performance metrics."""
+        if self.root:
+            nodes = self._traverse_tree()
+            depths = [n.depth for n in nodes]
+            
+            # Count nodes with children
+            internal_nodes = sum(1 for n in nodes if n.children)
+            
+            self.performance_metrics.update({
+                'tree_depth': max(depths),
+                'num_nodes': len(nodes),
+                'branching_factor': len(nodes) / max(1, internal_nodes),
+                'avg_confidence': sum(n.confidence for n in nodes) / len(nodes),
+                'pruned_nodes': self.performance_metrics['pruned_nodes']
+            })