Feat: neo4j community

examples/basic_modules/neo4j_example.py

@@ -42,7 +42,6 @@ def example_multi_db(db_name: str = "paper"):
         metadata=TreeNodeTextualMemoryMetadata(
             memory_type="LongTermMemory",
             key="Multi-UAV Long-Term Coverage",
-            value="Research topic on distributed multi-agent UAV navigation and coverage",
             hierarchy_level="topic",
             type="fact",
             memory_time="2024-01-01",
@@ -74,7 +73,6 @@ def example_multi_db(db_name: str = "paper"):
             metadata=TreeNodeTextualMemoryMetadata(
                 memory_type="LongTermMemory",
                 key="Reward Function Design",
-                value="Combines coverage, energy efficiency, and overlap penalty",
                 hierarchy_level="concept",
                 type="fact",
                 memory_time="2024-01-01",
@@ -99,7 +97,6 @@ def example_multi_db(db_name: str = "paper"):
             metadata=TreeNodeTextualMemoryMetadata(
                 memory_type="LongTermMemory",
                 key="Energy Model",
-                value="Includes communication and motion energy consumption",
                 hierarchy_level="concept",
                 type="fact",
                 memory_time="2024-01-01",
@@ -122,7 +119,6 @@ def example_multi_db(db_name: str = "paper"):
             metadata=TreeNodeTextualMemoryMetadata(
                 memory_type="LongTermMemory",
                 key="Coverage Metrics",
-                value="CT and FT used for long-term area and fairness evaluation",
                 hierarchy_level="concept",
                 type="fact",
                 memory_time="2024-01-01",
@@ -161,7 +157,6 @@ def example_multi_db(db_name: str = "paper"):
                 metadata=TreeNodeTextualMemoryMetadata(
                     memory_type="WorkingMemory",
                     key="Reward Components",
-                    value="Coverage gain, energy usage penalty, overlap penalty",
                     hierarchy_level="fact",
                     type="fact",
                     memory_time="2024-01-01",
@@ -186,7 +181,6 @@ def example_multi_db(db_name: str = "paper"):
                 metadata=TreeNodeTextualMemoryMetadata(
                     memory_type="LongTermMemory",
                     key="Energy Cost Components",
-                    value="Includes movement and communication energy",
                     hierarchy_level="fact",
                     type="fact",
                     memory_time="2024-01-01",
@@ -211,7 +205,6 @@ def example_multi_db(db_name: str = "paper"):
                 metadata=TreeNodeTextualMemoryMetadata(
                     memory_type="LongTermMemory",
                     key="CT and FT Definition",
-                    value="CT: total coverage duration; FT: fairness index",
                     hierarchy_level="fact",
                     type="fact",
                     memory_time="2024-01-01",
@@ -347,9 +340,202 @@ def example_shared_db(db_name: str = "shared-traval-group"):
         print(graph_alice.get_node(node["id"]))
 
 
+def run_user_session(
+    user_name: str,
+    db_name: str,
+    topic_text: str,
+    concept_texts: list[str],
+    fact_texts: list[str],
+    community: bool = False,
+):
+    print(f"\n=== {user_name} starts building their memory graph ===")
+
+    # Manually initialize correct GraphDB class
+    if community:
+        config = GraphDBConfigFactory(
+            backend="neo4j-community",
+            config={
+                "uri": "bolt://localhost:7687",
+                "user": "neo4j",
+                "password": "12345678",
+                "db_name": db_name,
+                "user_name": user_name,
+                "use_multi_db": False,
+                "auto_create": False,  # Neo4j Community does not allow auto DB creation
+                "embedding_dimension": 768,
+                "vec_config": {
+                    # Pass nested config to initialize external vector DB
+                    # If you use qdrant, please use Server instead of local mode.
+                    "backend": "qdrant",
+                    "config": {
+                        "collection_name": "neo4j_vec_db",
+                        "vector_dimension": 768,
+                        "distance_metric": "cosine",
+                        "host": "localhost",
+                        "port": 6333,
+                    },
+                },
+            },
+        )
+    else:
+        config = GraphDBConfigFactory(
+            backend="neo4j",
+            config={
+                "uri": "bolt://localhost:7687",
+                "user": "neo4j",
+                "password": "12345678",
+                "db_name": db_name,
+                "user_name": user_name,
+                "use_multi_db": False,
+                "auto_create": True,
+                "embedding_dimension": 768,
+            },
+        )
+    graph = GraphStoreFactory.from_config(config)
+
+    # Start with a clean slate for this user
+    graph.clear()
+
+    now = datetime.utcnow().isoformat()
+
+    # === Step 1: Create a root topic node (e.g., user's research focus) ===
+    topic = TextualMemoryItem(
+        memory=topic_text,
+        metadata=TreeNodeTextualMemoryMetadata(
+            memory_type="LongTermMemory",
+            key="Research Topic",
+            hierarchy_level="topic",
+            type="fact",
+            memory_time="2024-01-01",
+            status="activated",
+            visibility="public",
+            updated_at=now,
+            embedding=embed_memory_item(topic_text),
+        ),
+    )
+    graph.add_node(topic.id, topic.memory, topic.metadata.model_dump(exclude_none=True))
+
+    # === Step 2: Create two concept nodes linked to the topic ===
+    concept_items = []
+    for i, text in enumerate(concept_texts):
+        concept = TextualMemoryItem(
+            memory=text,
+            metadata=TreeNodeTextualMemoryMetadata(
+                memory_type="LongTermMemory",
+                key=f"Concept {i + 1}",
+                hierarchy_level="concept",
+                type="fact",
+                memory_time="2024-01-01",
+                status="activated",
+                visibility="public",
+                updated_at=now,
+                embedding=embed_memory_item(text),
+                tags=["concept"],
+                confidence=90 + i,
+            ),
+        )
+        graph.add_node(concept.id, concept.memory, concept.metadata.model_dump(exclude_none=True))
+        graph.add_edge(topic.id, concept.id, type="PARENT")
+        concept_items.append(concept)
+
+    # === Step 3: Create supporting facts under each concept ===
+    for i, text in enumerate(fact_texts):
+        fact = TextualMemoryItem(
+            memory=text,
+            metadata=TreeNodeTextualMemoryMetadata(
+                memory_type="WorkingMemory",
+                key=f"Fact {i + 1}",
+                hierarchy_level="fact",
+                type="fact",
+                memory_time="2024-01-01",
+                status="activated",
+                visibility="public",
+                updated_at=now,
+                embedding=embed_memory_item(text),
+                confidence=85.0,
+                tags=["fact"],
+            ),
+        )
+        graph.add_node(fact.id, fact.memory, fact.metadata.model_dump(exclude_none=True))
+        graph.add_edge(concept_items[i % len(concept_items)].id, fact.id, type="PARENT")
+
+    # === Step 4: Retrieve memory using semantic search ===
+    vector = embed_memory_item("How is memory retrieved?")
+    search_result = graph.search_by_embedding(vector, top_k=2)
+    for r in search_result:
+        node = graph.get_node(r["id"])
+        print("🔍 Search result:", node["memory"])
+
+    # === Step 5: Tag-based neighborhood discovery ===
+    neighbors = graph.get_neighbors_by_tag(["concept"], exclude_ids=[], top_k=2)
+    print("📎 Tag-related nodes:", [neighbor["memory"] for neighbor in neighbors])
+
+    # === Step 6: Retrieve children (facts) of first concept ===
+    children = graph.get_children_with_embeddings(concept_items[0].id)
+    print("📍 Children of concept:", [child["memory"] for child in children])
+
+    # === Step 7: Export a local subgraph and grouped statistics ===
+    subgraph = graph.get_subgraph(topic.id, depth=2)
+    print("📌 Subgraph node count:", len(subgraph["neighbors"]))
+
+    stats = graph.get_grouped_counts(["memory_type", "status"])
+    print("📊 Grouped counts:", stats)
+
+    # === Step 8: Demonstrate updates and cleanup ===
+    graph.update_node(concept_items[0].id, {"confidence": 99.0})
+    graph.remove_oldest_memory("WorkingMemory", keep_latest=1)
+    graph.delete_edge(topic.id, concept_items[0].id, type="PARENT")
+    graph.delete_node(concept_items[1].id)
+
+    # === Step 9: Export and re-import the entire graph structure ===
+    exported = graph.export_graph()
+    graph.import_graph(exported)
+    print("📦 Graph exported and re-imported, total nodes:", len(exported["nodes"]))
+
+
+def example_complex_shared_db(db_name: str = "shared-traval-group-complex", community=False):
+    # User 1: Alice explores structured memory for LLMs
+    run_user_session(
+        user_name="alice",
+        db_name=db_name,
+        topic_text="Alice studies structured memory and long-term memory optimization in LLMs.",
+        concept_texts=[
+            "Short-term memory can be simulated using WorkingMemory blocks.",
+            "A structured memory graph improves retrieval precision for agents.",
+        ],
+        fact_texts=[
+            "Embedding search is used to find semantically similar memory items.",
+            "User memories are stored as node-edge structures that support hierarchical reasoning.",
+        ],
+        community=community,
+    )
+
+    # User 2: Bob focuses on GNN-based reasoning
+    run_user_session(
+        user_name="bob",
+        db_name=db_name,
+        topic_text="Bob investigates how graph neural networks can support knowledge reasoning.",
+        concept_texts=[
+            "GNNs can learn high-order relations among entities.",
+            "Attention mechanisms in graphs improve inference precision.",
+        ],
+        fact_texts=[
+            "GAT outperforms GCN in graph classification tasks.",
+            "Multi-hop reasoning helps answer complex queries.",
+        ],
+        community=community,
+    )
+
+
 if __name__ == "__main__":
     print("\n=== Example: Multi-DB ===")
     example_multi_db(db_name="paper")
 
     print("\n=== Example: Single-DB ===")
-    example_shared_db(db_name="shared-traval-group11")
+    example_shared_db(db_name="shared-traval-group")
+
+    print("\n=== Example: Single-DB-Complex ===")
+    example_complex_shared_db(db_name="shared-traval-group-complex-new")
+
+    print("\n=== Example: Single-Community-DB-Complex ===")
+    example_complex_shared_db(db_name="paper", community=True)

examples/data/config/tree_config_community.json

@@ -0,0 +1,50 @@
+{
+  "extractor_llm": {
+    "backend": "ollama",
+    "config": {
+      "model_name_or_path": "qwen3:0.6b",
+      "temperature": 0.0,
+      "remove_think_prefix": true,
+      "max_tokens": 8192
+    }
+  },
+  "dispatcher_llm": {
+    "backend": "ollama",
+    "config": {
+      "model_name_or_path": "qwen3:0.6b",
+      "temperature": 0.0,
+      "remove_think_prefix": true,
+      "max_tokens": 8192
+    }
+  },
+  "embedder": {
+    "backend": "ollama",
+    "config": {
+      "model_name_or_path": "nomic-embed-text:latest"
+    }
+  },
+  "graph_db": {
+    "backend": "neo4j-community",
+    "config": {
+      "uri": "bolt://localhost:7687",
+      "user": "neo4j",
+      "password": "12345678",
+      "db_name": "neo4j",
+      "user_name": "alice",
+      "use_multi_db": false,
+      "auto_create": false,
+      "embedding_dimension": 768,
+      "vec_config": {
+        "backend": "qdrant",
+        "config": {
+          "collection_name": "neo4j_vec_db",
+          "vector_dimension": 768,
+          "distance_metric": "cosine",
+          "host": "localhost",
+          "port": 6333
+        }
+      }
+    }
+  },
+  "reorganize": true
+}