1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
|
from r2r import R2RClient
if __name__ == "__main__":
client = R2RClient(base_url="http://localhost:8000")
with open("john.txt", "w") as f:
f.write("John is a person that works at Google.")
with open("paul.txt", "w") as f:
f.write("Paul is a person that works at Microsoft that knows John.")
client.ingest_files(
["john.txt", "paul.txt"],
metadatas=[
{
"title": "KG Document 1",
"user_id": "063edaf8-3e63-4cb9-a4d6-a855f36376c3",
},
{
"title": "KG Document 2",
"user_id": "063edaf8-3e63-4cb9-a4d6-a855f36376c3",
},
],
)
# Get the KG provider
# neo4j_kg = app.providers.kg
# # The expected entities
# entity_names = ["John", "Paul", "Google", "Microsoft"]
# print("\nEntities:")
# for entity in entity_names:
# print(
# f"Locating {entity}:\n", neo4j_kg.get(properties={"name": entity})
# )
# relationships = neo4j_kg.get_triplets(entity_names=entity_names)
# print("\nRelationships:")
# for triplet in relationships:
# source, relation, target = triplet
# print(f"{source} -[{relation.label}]-> {target} ")
# # Search the vector database
# search_results = app.search(query="Who is john")
# print("\nSearch Results:\n", search_results)
# # Semantic search over the knowledge graph
# from r2r.base import VectorStoreQuery
# node_result = neo4j_kg.vector_query(
# VectorStoreQuery(
# query_embedding=app.providers.embedding.get_embedding("A person"),
# )
# )
# print("\nNode Result:", node_result)
# # Structured query
# structured_query = """
# MATCH (p1:person)-[:KNOWS]->(p2:person)
# RETURN p1.name AS Person1, p2.name AS Person2
# ORDER BY p1.name
# LIMIT 10;
# """
# print("Executing query:\n", structured_query)
# structured_result = neo4j_kg.structured_query(structured_query)
# print("Structured Results:\n", structured_result)
|
