Add DialoguePathSampler class for generating dialogue paths from a graph

dev_packages/chatsky_llm_autoconfig/chatsky_llm_autoconfig/algorithms/dialogue_generation.py

@@ -1,4 +1,5 @@
 import random
+import networkx as nx
 from chatsky_llm_autoconfig.graph import BaseGraph
 from chatsky_llm_autoconfig.algorithms.base import DialogueGenerator
 from chatsky_llm_autoconfig.dialogue import Dialogue
@@ -71,3 +72,49 @@ def invoke(self, graph: BaseGraph, start_node: int = 1, end_node: int = -1, topi
 
     async def ainvoke(self, *args, **kwargs):
         return self.invoke(*args, **kwargs)
+
+
+@AlgorithmRegistry.register(input_type=BaseGraph, output_type=Dialogue)
+class DialoguePathSampler(DialogueGenerator):
+    def invoke(self, graph: BaseGraph, start_node: int = 1, end_node: int = -1, topic="") -> list[Dialogue]:
+        nx_graph = graph.graph
+
+        # Find all nodes with no outgoing edges (end nodes)
+        end_nodes = [node for node in nx_graph.nodes() if nx_graph.out_degree(node) == 0]
+        dialogues = []
+        # If no end nodes found, return empty list
+        if not end_nodes:
+            return []
+
+        all_paths = []
+        # Get paths from start node to each end node
+        for end in end_nodes:
+            paths = list(nx.all_simple_paths(nx_graph, source=start_node, target=end))
+            all_paths.extend(paths)
+
+        for path in all_paths:
+            dialogue_turns = []
+            # Process each node and edge in the path
+            for i in range(len(path)):
+                # Add assistant utterance from current node
+                current_node = path[i]
+                assistant_utterance = random.choice(nx_graph.nodes[current_node]["utterances"])
+                dialogue_turns.append({"text": assistant_utterance, "participant": "assistant"})
+
+                # Add user utterance from edge (if not at last node)
+                if i < len(path) - 1:
+                    next_node = path[i + 1]
+                    edge_data = nx_graph.edges[current_node, next_node]
+                    user_utterance = (
+                        random.choice(edge_data["utterances"])
+                        if isinstance(edge_data["utterances"], list)
+                        else edge_data["utterances"]
+                    )
+                    dialogue_turns.append({"text": user_utterance, "participant": "user"})
+
+            dialogues.append(Dialogue().from_list(dialogue_turns))
+
+        return dialogues
+
+    async def ainvoke(self, *args, **kwargs):
+        return self.invoke(*args, **kwargs)