Error message: Storage Error: The VID must be a 64-bit integer or a string fitting space vertex id length limit.
import os
from IPython.display import Markdown, display
from llama_index.core import KnowledgeGraphIndex, ServiceContext
from llama_index.core import Settings
from llama_index.core import StorageContext
from llama_index.core.data_structs.data_structs import KG
from llama_index.core.indices.knowledge_graph import KGTableRetriever, KnowledgeGraphRAGRetriever
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.graph_stores.nebula import NebulaGraphStore
from llama_index.llms.ollama import Ollama
from llama_index.embeddings.ollama import OllamaEmbedding
import torch.backends
embed_model = OllamaEmbedding(
model_name=“nn200433/text2vec-bge-large-chinese”,
base_url=“http://localhost:11434”,
ollama_additional_kwargs={“mirostat”: 0},
)
llm = Ollama(model=“deepseek-r1:1.5b”, request_timeout=30.0)
#service_context = ServiceContext.from_defaults(llm=llm,embed_model=Settings.embed_model )
Settings.llm = llm
Settings.embed_model = embed_model
print(llm.complete(“Hi”))
os.environ[“NEBULA_USER”] = “root”
os.environ[“NEBULA_PASSWORD”] = “nebula”
os.environ[“NEBULA_ADDRESS”] = “192.168.10.14:9669”
space_name = “RealEstateRegistry”
edge_types, rel_prop_names = [“MortgageRelation”], [“”] # default, could be omit if create from an empty kg
tags = [“Mortgage”,“Property”] # default, could be omit if create from an empty kg
tag_prop_names=[“mortgageId,mortgagee”,“propertyId,address”]
graph_store = NebulaGraphStore(
space_name=space_name,
edge_types=edge_types,
rel_prop_names=rel_prop_names,
tags=tags,
tag_prop_names=tag_prop_names)
storage_context = StorageContext.from_defaults(graph_store=graph_store)
graph_store.query(“SHOW HOSTS”)
#Settings.llm = llm
#Settings.embed_model = embed_model
Settings.chunk_size = 512
#service_context = ServiceContext.from_defaults(
embed_model=embed_model, llm=llm)
from llama_index.core.query_engine import RetrieverQueryEngine, KnowledgeGraphQueryEngine
graph_rag_retriever = KnowledgeGraphRAGRetriever(
storage_context=storage_context,
llm=llm,
verbose=True,
with_nl2graphquery=True
)
query_engine = RetrieverQueryEngine.from_args(
graph_rag_retriever,
llm=llm,
)
response = query_engine.query(“What’s the relationship between PROP_0001 and MORT_0001”)
print(response)…
ddl如下
Create Space
CREATE SPACE RealEstateRegistry (partition_num = 100, replica_factor = 1, charset = utf8, collate = utf8_bin, vid_type = FIXED_STRING(36));
:sleep 20;
USE RealEstateRegistry;