Feat/reorg dev (#202)

Author: Nyakult

…al/tree_text_memory/organize/conflict.py …ual/tree_text_memory/organize/handler.pysrc/memos/memories/textual/tree_text_memory/organize/conflict.py renamed to src/memos/memories/textual/tree_text_memory/organize/handler.py src/memos/memories/textual/tree_text_memory/organize/conflict.py renamed to src/memos/memories/textual/tree_text_memory/organize/handler.py

@@ -1,6 +1,5 @@
 import json
 import re
-
 from datetime import datetime
 
 from dateutil import parser
@@ -11,82 +10,68 @@
 from memos.log import get_logger
 from memos.memories.textual.item import TextualMemoryItem, TreeNodeTextualMemoryMetadata
 from memos.templates.tree_reorganize_prompts import (
-    CONFLICT_DETECTOR_PROMPT,
-    CONFLICT_RESOLVER_PROMPT,
+    MEMORY_RELATION_DETECTOR_PROMPT,
+    MEMORY_RELATION_RESOLVER_PROMPT,
 )
 
-
 logger = get_logger(__name__)
 
 
-class ConflictHandler:
+class NodeHandler:
     EMBEDDING_THRESHOLD: float = 0.8  # Threshold for embedding similarity to consider conflict
 
     def __init__(self, graph_store: Neo4jGraphDB, llm: BaseLLM, embedder: BaseEmbedder):
         self.graph_store = graph_store
         self.llm = llm
         self.embedder = embedder
 
-    def detect(
-        self, memory: TextualMemoryItem, top_k: int = 5, scope: str | None = None
-    ) -> list[tuple[TextualMemoryItem, TextualMemoryItem]]:
-        """
-        Detect conflicts by finding the most similar items in the graph database based on embedding, then use LLM to judge conflict.
-        Args:
-            memory: The memory item (should have an embedding attribute or field).
-            top_k: Number of top similar nodes to retrieve.
-            scope: Optional memory type filter.
-        Returns:
-            List of conflict pairs (each pair is a tuple: (memory, candidate)).
-        """
+    def detect(self, memory, top_k: int = 5, scope=None):
         # 1. Search for similar memories based on embedding
         embedding = memory.metadata.embedding
         embedding_candidates_info = self.graph_store.search_by_embedding(
-            embedding, top_k=top_k, scope=scope
+            embedding, top_k=top_k, scope=scope, threshold=self.EMBEDDING_THRESHOLD
         )
         # 2. Filter based on similarity threshold
         embedding_candidates_ids = [
-            info["id"]
-            for info in embedding_candidates_info
-            if info["score"] >= self.EMBEDDING_THRESHOLD and info["id"] != memory.id
+            info["id"] for info in embedding_candidates_info if info["id"] != memory.id
         ]
         # 3. Judge conflicts using LLM
         embedding_candidates = self.graph_store.get_nodes(embedding_candidates_ids)
-        conflict_pairs = []
+        detected_relationships = []
         for embedding_candidate in embedding_candidates:
             embedding_candidate = TextualMemoryItem.from_dict(embedding_candidate)
             prompt = [
-                {
-                    "role": "system",
-                    "content": "You are a conflict detector for memory items.",
-                },
                 {
                     "role": "user",
-                    "content": CONFLICT_DETECTOR_PROMPT.format(
-                        statement_1=memory.memory,
-                        statement_2=embedding_candidate.memory,
+                    "content": MEMORY_RELATION_DETECTOR_PROMPT.format(
+                        statement_1=memory.memory, statement_2=embedding_candidate.memory
                     ),
-                },
+                }
             ]
             result = self.llm.generate(prompt).strip()
-            if "yes" in result.lower():
-                conflict_pairs.append([memory, embedding_candidate])
-        if len(conflict_pairs):
-            conflict_text = "\n".join(
-                f'"{pair[0].memory!s}" <==CONFLICT==> "{pair[1].memory!s}"'
-                for pair in conflict_pairs
-            )
-            logger.warning(
-                f"Detected {len(conflict_pairs)} conflicts for memory {memory.id}\n {conflict_text}"
-            )
-        return conflict_pairs
+            if result == "contradictory":
+                logger.warning(
+                    f'detected "{memory.memory}" <==CONFLICT==> "{embedding_candidate.memory}"'
+                )
+                detected_relationships.append([memory, embedding_candidate, "contradictory"])
+            elif result == "redundant":
+                logger.warning(
+                    f'detected "{memory.memory}" <==REDUNDANT==> "{embedding_candidate.memory}"'
+                )
+                detected_relationships.append([memory, embedding_candidate, "redundant"])
+            elif result == "independent":
+                pass
+            else:
+                pass
+        return detected_relationships
 
-    def resolve(self, memory_a: TextualMemoryItem, memory_b: TextualMemoryItem) -> None:
+    def resolve(self, memory_a: TextualMemoryItem, memory_b: TextualMemoryItem, relation) -> None:
         """
         Resolve detected conflicts between two memory items using LLM fusion.
         Args:
             memory_a: The first conflicting memory item.
             memory_b: The second conflicting memory item.
+            relation: relation
         Returns:
             A fused TextualMemoryItem representing the resolved memory.
         """
@@ -96,13 +81,10 @@ def resolve(self, memory_a: TextualMemoryItem, memory_b: TextualMemoryItem) -> N
         metadata_1 = memory_a.metadata.model_dump_json(include=metadata_for_resolve)
         metadata_2 = memory_b.metadata.model_dump_json(include=metadata_for_resolve)
         prompt = [
-            {
-                "role": "system",
-                "content": "",
-            },
             {
                 "role": "user",
-                "content": CONFLICT_RESOLVER_PROMPT.format(
+                "content": MEMORY_RELATION_RESOLVER_PROMPT.format(
+                    relation=relation,
                     statement_1=memory_a.memory,
                     metadata_1=metadata_1,
                     statement_2=memory_b.memory,
@@ -119,7 +101,7 @@ def resolve(self, memory_a: TextualMemoryItem, memory_b: TextualMemoryItem) -> N
             # —————— 2.1 Can't resolve conflict, hard update by comparing timestamp ————
             if len(answer) <= 10 and "no" in answer.lower():
                 logger.warning(
-                    f"Conflict between {memory_a.id} and {memory_b.id} could not be resolved. "
+                    f"{relation} between {memory_a.id} and {memory_b.id} could not be resolved. "
                 )
                 self._hard_update(memory_a, memory_b)
             # —————— 2.2 Conflict resolved, update metadata and memory ————

src/memos/memories/textual/tree_text_memory/organize/manager.py

@@ -158,106 +158,18 @@ def _add_to_graph_memory(self, memory: TextualMemoryItem, memory_type: str):
         - topic_summary_prefix: summary node id prefix if applicable
         - enable_summary_link: whether to auto-link to a summary node
         """
-        embedding = memory.metadata.embedding
-
-        # Step 1: Find similar nodes for possible merging
-        similar_nodes = self.graph_store.search_by_embedding(
-            vector=embedding,
-            top_k=3,
-            scope=memory_type,
-            threshold=self._threshold,
-            status="activated",
-        )
-
-        if similar_nodes and similar_nodes[0]["score"] > self._merged_threshold:
-            return self._merge(memory, similar_nodes)
-        else:
-            node_id = str(uuid.uuid4())
-            # Step 2: Add new node to graph
-            self.graph_store.add_node(
-                node_id, memory.memory, memory.metadata.model_dump(exclude_none=True)
-            )
-            self.reorganizer.add_message(
-                QueueMessage(
-                    op="add",
-                    after_node=[node_id],
-                )
-            )
-            return node_id
-
-    def _merge(self, source_node: TextualMemoryItem, similar_nodes: list[dict]) -> str:
-        """
-        TODO: Add node traceability support by optionally preserving source nodes and linking them with MERGED_FROM edges.
-
-        Merge the source memory into the most similar existing node (only one),
-        and establish a MERGED_FROM edge in the graph.
-
-        Parameters:
-            source_node: The new memory item (not yet in the graph)
-            similar_nodes: A list of dicts returned by search_by_embedding(), ordered by similarity
-        """
-        original_node = similar_nodes[0]
-        original_id = original_node["id"]
-        original_data = self.graph_store.get_node(original_id)
-
-        target_text = original_data.get("memory", "")
-        merged_text = f"{target_text}\n⟵MERGED⟶\n{source_node.memory}"
-
-        original_meta = TreeNodeTextualMemoryMetadata(**original_data["metadata"])
-        source_meta = source_node.metadata
-
-        merged_key = source_meta.key or original_meta.key
-        merged_tags = list(set((original_meta.tags or []) + (source_meta.tags or [])))
-        merged_sources = list(set((original_meta.sources or []) + (source_meta.sources or [])))
-        merged_background = f"{original_meta.background}\n⟵MERGED⟶\n{source_meta.background}"
-        merged_embedding = self.embedder.embed([merged_text])[0]
-
-        original_conf = original_meta.confidence or 0.0
-        source_conf = source_meta.confidence or 0.0
-        merged_confidence = float((original_conf + source_conf) / 2)
-        merged_usage = list(set((original_meta.usage or []) + (source_meta.usage or [])))
-
-        # Create new merged node
-        merged_id = str(uuid.uuid4())
-        merged_metadata = source_meta.model_copy(
-            update={
-                "embedding": merged_embedding,
-                "updated_at": datetime.now().isoformat(),
-                "key": merged_key,
-                "tags": merged_tags,
-                "sources": merged_sources,
-                "background": merged_background,
-                "confidence": merged_confidence,
-                "usage": merged_usage,
-            }
-        )
-
+        node_id = str(uuid.uuid4())
+        # Step 2: Add new node to graph
         self.graph_store.add_node(
-            merged_id, merged_text, merged_metadata.model_dump(exclude_none=True)
+            node_id, memory.memory, memory.metadata.model_dump(exclude_none=True)
         )
-
-        # Add traceability edges: both original and new point to merged node
-        self.graph_store.add_edge(original_id, merged_id, type="MERGED_TO")
-        self.graph_store.update_node(original_id, {"status": "archived"})
-        source_id = str(uuid.uuid4())
-        source_metadata = source_node.metadata.model_copy(update={"status": "archived"})
-        self.graph_store.add_node(source_id, source_node.memory, source_metadata.model_dump())
-        self.graph_store.add_edge(source_id, merged_id, type="MERGED_TO")
-        # After creating merged node and tracing lineage
-        self._inherit_edges(original_id, merged_id)
-
-        # log to reorganizer before updating the graph
         self.reorganizer.add_message(
             QueueMessage(
-                op="merge",
-                before_node=[
-                    original_id,
-                    source_node.id,
-                ],
-                after_node=[merged_id],
+                op="add",
+                after_node=[node_id],
             )
         )
-        return merged_id
+        return node_id
 
     def _inherit_edges(self, from_id: str, to_id: str) -> None:
         """