Add visualization methods

README.md

@@ -22,6 +22,7 @@ This repository hosts the code of LightRAG. The structure of this code is based
 </div>
 
 ## 🎉 News
+- [x] [2024.10.20]🎯🎯📢📢We add two methods to visualize the graph.
 - [x] [2024.10.18]🎯🎯📢📢We’ve added a link to a [LightRAG Introduction Video](https://youtu.be/oageL-1I0GE). Thanks to the author!
 - [x] [2024.10.17]🎯🎯📢📢We have created a [Discord channel](https://discord.gg/mvsfu2Tg)! Welcome to join for sharing and discussions! 🎉🎉
 - [x] [2024.10.16]🎯🎯📢📢LightRAG now supports [Ollama models](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)!
@@ -221,7 +222,11 @@ with open("./newText.txt") as f:
 
 ### Graph Visualization
 
-* Generate html file
+<details>
+<summary> Graph visualization with html </summary>
+
+* The following code can be found in `examples/graph_visual_with_html.py`
+
 ```python
 import networkx as nx
 from pyvis.network import Network
@@ -238,6 +243,137 @@ net.from_nx(G)
 # Save and display the network
 net.show('knowledge_graph.html')
 ```
+
+</details>
+
+<details>
+<summary> Graph visualization with Neo4j </summary>
+
+* The following code can be found in `examples/graph_visual_with_neo4j.py`
+
+```python
+import os
+import json
+from lightrag.utils import xml_to_json
+from neo4j import GraphDatabase
+
+# Constants
+WORKING_DIR = "./dickens"
+BATCH_SIZE_NODES = 500
+BATCH_SIZE_EDGES = 100
+
+# Neo4j connection credentials
+NEO4J_URI = "bolt://localhost:7687"
+NEO4J_USERNAME = "neo4j"
+NEO4J_PASSWORD = "your_password"
+
+def convert_xml_to_json(xml_path, output_path):
+    """Converts XML file to JSON and saves the output."""
+    if not os.path.exists(xml_path):
+        print(f"Error: File not found - {xml_path}")
+        return None
+
+    json_data = xml_to_json(xml_path)
+    if json_data:
+        with open(output_path, 'w', encoding='utf-8') as f:
+            json.dump(json_data, f, ensure_ascii=False, indent=2)
+        print(f"JSON file created: {output_path}")
+        return json_data
+    else:
+        print("Failed to create JSON data")
+        return None
+
+def process_in_batches(tx, query, data, batch_size):
+    """Process data in batches and execute the given query."""
+    for i in range(0, len(data), batch_size):
+        batch = data[i:i + batch_size]
+        tx.run(query, {"nodes": batch} if "nodes" in query else {"edges": batch})
+
+def main():
+    # Paths
+    xml_file = os.path.join(WORKING_DIR, 'graph_chunk_entity_relation.graphml')
+    json_file = os.path.join(WORKING_DIR, 'graph_data.json')
+
+    # Convert XML to JSON
+    json_data = convert_xml_to_json(xml_file, json_file)
+    if json_data is None:
+        return
+
+    # Load nodes and edges
+    nodes = json_data.get('nodes', [])
+    edges = json_data.get('edges', [])
+
+    # Neo4j queries
+    create_nodes_query = """
+    UNWIND $nodes AS node
+    MERGE (e:Entity {id: node.id})
+    SET e.entity_type = node.entity_type,
+        e.description = node.description,
+        e.source_id = node.source_id,
+        e.displayName = node.id  
+    REMOVE e:Entity  
+    WITH e, node
+    CALL apoc.create.addLabels(e, [node.entity_type]) YIELD node AS labeledNode
+    RETURN count(*)
+    """
+
+    create_edges_query = """
+    UNWIND $edges AS edge
+    MATCH (source {id: edge.source})
+    MATCH (target {id: edge.target})
+    WITH source, target, edge,
+         CASE
+            WHEN edge.keywords CONTAINS 'lead' THEN 'lead'
+            WHEN edge.keywords CONTAINS 'participate' THEN 'participate'
+            WHEN edge.keywords CONTAINS 'uses' THEN 'uses'
+            WHEN edge.keywords CONTAINS 'located' THEN 'located'
+            WHEN edge.keywords CONTAINS 'occurs' THEN 'occurs'
+           ELSE REPLACE(SPLIT(edge.keywords, ',')[0], '\"', '')
+         END AS relType
+    CALL apoc.create.relationship(source, relType, {
+      weight: edge.weight,
+      description: edge.description,
+      keywords: edge.keywords,
+      source_id: edge.source_id
+    }, target) YIELD rel
+    RETURN count(*)
+    """
+
+    set_displayname_and_labels_query = """
+    MATCH (n)
+    SET n.displayName = n.id
+    WITH n
+    CALL apoc.create.setLabels(n, [n.entity_type]) YIELD node
+    RETURN count(*)
+    """
+
+    # Create a Neo4j driver
+    driver = GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USERNAME, NEO4J_PASSWORD))
+
+    try:
+        # Execute queries in batches
+        with driver.session() as session:
+            # Insert nodes in batches
+            session.execute_write(process_in_batches, create_nodes_query, nodes, BATCH_SIZE_NODES)
+
+            # Insert edges in batches
+            session.execute_write(process_in_batches, create_edges_query, edges, BATCH_SIZE_EDGES)
+
+            # Set displayName and labels
+            session.run(set_displayname_and_labels_query)
+
+    except Exception as e:
+        print(f"Error occurred: {e}")
+    
+    finally:
+        driver.close()
+
+if __name__ == "__main__":
+    main()
+```
+
+</details>
+
 ## Evaluation
 ### Dataset
 The dataset used in LightRAG can be downloaded from [TommyChien/UltraDomain](https://huggingface.co/datasets/TommyChien/UltraDomain).
@@ -484,8 +620,9 @@ def extract_queries(file_path):
 .
 ├── examples
 │   ├── batch_eval.py
+│   ├── graph_visual_with_html.py
+│   ├── graph_visual_with_neo4j.py
 │   ├── generate_query.py
-│   ├── graph_visual.py
 │   ├── lightrag_azure_openai_demo.py
 │   ├── lightrag_bedrock_demo.py
 │   ├── lightrag_hf_demo.py