NebulaGraph Property Graph Index

NebulaGraph is an open-source distributed graph database built for super large-scale graphs with milliseconds of latency.

If you already have an existing graph, please skip to the end of this notebook.

%pip install llama-index llama-index-graph-stores-nebula jupyter-nebulagraph

Docker Setup

To launch NebulaGraph locally, first ensure you have docker installed. Then, you can launch the database with the following docker command.

mkdir nebula-docker-compose
cd nebula-docker-compose
curl --output docker-compose.yaml https://raw.githubusercontent.com/vesoft-inc/nebula-docker-compose/master/docker-compose-lite.yaml
docker compose up

After this, you are ready to create your first property graph!

Other options/details for deploying NebulaGraph can be found in the docs:

• ad-hoc cluster in Google Colab.
• Docker Desktop Extension.

# load NebulaGraph Jupyter extension to enable %ngql magic
%load_ext ngql
# connect to NebulaGraph service
%ngql --address 127.0.0.1 --port 9669 --user root --password nebula
# create a graph space(think of a Database Instance) named: llamaindex_nebula_property_graph
%ngql CREATE SPACE IF NOT EXISTS llamaindex_nebula_property_graph(vid_type=FIXED_STRING(256));

Connection Pool Created

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# use the graph space, which is similar to "use database" in MySQL
# The space was created in async way, so we need to wait for a while before using it, retry it if failed
%ngql USE llamaindex_nebula_property_graph;

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Env Setup

We need just a few environment setups to get started.

import os

os.environ["OPENAI_API_KEY"] = "sk-proj-..."

!mkdir -p 'data/paul_graham/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'

import nest_asyncio

nest_asyncio.apply()

from llama_index.core import SimpleDirectoryReader

documents = SimpleDirectoryReader("./data/paul_graham/").load_data()

We choose using gpt-4o and local embedding model intfloat/multilingual-e5-large . You can change to what you like, by editing the following lines:

%pip install llama-index-embeddings-huggingface

from llama_index.core import Settings
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.embeddings.huggingface import HuggingFaceEmbedding

Settings.llm = OpenAI(model="gpt-4o", temperature=0.3)
Settings.embed_model = HuggingFaceEmbedding(
    model_name="intfloat/multilingual-e5-large"
)
# Settings.embed_model = OpenAIEmbedding(model_name="text-embedding-3-small")

Index Construction

Prepare property graph store

from llama_index.graph_stores.nebula import NebulaPropertyGraphStore

graph_store = NebulaPropertyGraphStore(
    space="llamaindex_nebula_property_graph", overwrite=True
)

And vector store:

from llama_index.core.vector_stores.simple import SimpleVectorStore

vec_store = SimpleVectorStore()

Finally, build the index!

from llama_index.core.indices.property_graph import PropertyGraphIndex
from llama_index.core.storage.storage_context import StorageContext
from llama_index.llms.openai import OpenAI

index = PropertyGraphIndex.from_documents(
    documents,
    property_graph_store=graph_store,
    vector_store=vec_store,
    show_progress=True,
)

index.storage_context.vector_store.persist("./data/nebula_vec_store.json")

/Users/loganmarkewich/Library/Caches/pypoetry/virtualenvs/llama-index-caVs7DDe-py3.11/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm
Parsing nodes: 100%|██████████| 1/1 [00:00<00:00, 20.96it/s]
Extracting paths from text: 100%|██████████| 22/22 [00:19<00:00,  1.15it/s]
Extracting implicit paths: 100%|██████████| 22/22 [00:00<00:00, 25253.06it/s]
Generating embeddings: 100%|██████████| 1/1 [00:01<00:00,  1.06s/it]
Generating embeddings: 100%|██████████| 5/5 [00:02<00:00,  2.50it/s]

Now that the graph is created, we can explore it with jupyter-nebulagraph

%ngql SHOW TAGS

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	Name
0	Chunk__
1	Entity__
2	Node__
3	Props__

%ngql SHOW EDGES

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	Name
0	Relation__
1	__meta__node_label__
2	__meta__rel_label__

%ngql MATCH p=(v:Entity__)-[r]->(t:Entity__) RETURN v.Entity__.name AS src, r.label AS relation, t.Entity__.name AS dest LIMIT 15;

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	src	relation	dest
0	We	Charged	$100 a month for a small store
1	We	Charged	$300 a month for a big store
2	We	Got to work	Build software
3	We	Started	Company
4	We	Start	Investment firm
5	We	Opened for business	January 1996
6	We	Had	One that worked
7	We	Decided to try making	Version of store builder
8	Growth rate	Takes care of	Absolute number
9	Stock	Went up	5x
10	Jessica livingston	In charge of	Marketing at boston investment bank
11	Language	Would be	Dialect of lisp
12	Language	Used	Early version of fortran
13	Arc	Compiled into	Scheme
14	Deal	Became	Model for y combinator's

%ngql MATCH p=(v:Entity__)-[r]->(t:Entity__) RETURN p LIMIT 2;

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	p
0	("We" :Props__{_node_content: __NULL__, _node_type: __NULL__, creation_date: "2024-05-31", doc_id: __NULL__, document_id: __NULL__, file_name: "paul_graham_essay.txt", file_path: "/Users/loganmarkewich/giant_change/llama_index/docs/docs/examples/property_graph/data/paul_graham/paul_graham_essay.txt", file_size: 75042, file_type: "text/plain", last_modified_date: "2024-05-31", ref_doc_id: __NULL__, triplet_source_id: "4145ba08-a096-4ac1-8f7c-f40642c857cc"} :Node__{label: "entity"} :Entity__{name: "We"})-[:Relation__@0{label: "Charged", file_path: "/Users/loganmarkewich/giant_change/llama_index/docs/docs/examples/property_graph/data/paul_graham/paul_graham_essay.txt", file_name: "paul_graham_essay.txt", file_type: "text/plain", file_size: 75042, _node_type: __NULL__, creation_date: "2024-05-31", document_id: __NULL__, last_modified_date: "2024-05-31", doc_id: __NULL__, _node_content: __NULL__, ref_doc_id: __NULL__, triplet_source_id: "0faa4540-57bb-4b94-8bc2-46431d980182"}]->("$100 a month for a small store" :Props__{_node_content: __NULL__, _node_type: __NULL__, creation_date: "2024-05-31", doc_id: __NULL__, document_id: __NULL__, file_name: "paul_graham_essay.txt", file_path: "/Users/loganmarkewich/giant_change/llama_index/docs/docs/examples/property_graph/data/paul_graham/paul_graham_essay.txt", file_size: 75042, file_type: "text/plain", last_modified_date: "2024-05-31", ref_doc_id: __NULL__, triplet_source_id: "0faa4540-57bb-4b94-8bc2-46431d980182"} :Node__{label: "entity"} :Entity__{name: "$100 a month for a small store"})
1	("We" :Props__{_node_content: __NULL__, _node_type: __NULL__, creation_date: "2024-05-31", doc_id: __NULL__, document_id: __NULL__, file_name: "paul_graham_essay.txt", file_path: "/Users/loganmarkewich/giant_change/llama_index/docs/docs/examples/property_graph/data/paul_graham/paul_graham_essay.txt", file_size: 75042, file_type: "text/plain", last_modified_date: "2024-05-31", ref_doc_id: __NULL__, triplet_source_id: "4145ba08-a096-4ac1-8f7c-f40642c857cc"} :Node__{label: "entity"} :Entity__{name: "We"})-[:Relation__@0{label: "Charged", file_path: "/Users/loganmarkewich/giant_change/llama_index/docs/docs/examples/property_graph/data/paul_graham/paul_graham_essay.txt", file_name: "paul_graham_essay.txt", file_type: "text/plain", file_size: 75042, _node_type: __NULL__, creation_date: "2024-05-31", document_id: __NULL__, last_modified_date: "2024-05-31", doc_id: __NULL__, _node_content: __NULL__, ref_doc_id: __NULL__, triplet_source_id: "0faa4540-57bb-4b94-8bc2-46431d980182"}]->("$300 a month for a big store" :Props__{_node_content: __NULL__, _node_type: __NULL__, creation_date: "2024-05-31", doc_id: __NULL__, document_id: __NULL__, file_name: "paul_graham_essay.txt", file_path: "/Users/loganmarkewich/giant_change/llama_index/docs/docs/examples/property_graph/data/paul_graham/paul_graham_essay.txt", file_size: 75042, file_type: "text/plain", last_modified_date: "2024-05-31", ref_doc_id: __NULL__, triplet_source_id: "0faa4540-57bb-4b94-8bc2-46431d980182"} :Node__{label: "entity"} :Entity__{name: "$300 a month for a big store"})

%ng_draw

Querying and Retrieval

retriever = index.as_retriever(
    include_text=False,  # include source text in returned nodes, default True
)

nodes = retriever.retrieve("What happened at Interleaf and Viaweb?")

for node in nodes:
    print(node.text)

Interleaf -> Got a job at -> I
Interleaf -> Crushed -> Moore's law
Interleaf -> Was -> Company
Interleaf -> Built -> Impressive technology
Interleaf -> Added -> Scripting language
Interleaf -> Had -> Smart people
Interleaf -> Made -> Software for creating documents
Viaweb -> Called -> Company
Viaweb -> Worked for -> Dan giffin
Viaweb -> Was -> Application service provider
In viaweb -> Was -> Code editor
Viaweb stock -> Was -> Valuable
Viaweb logo -> Had -> White v on red circle

query_engine = index.as_query_engine(include_text=True)

response = query_engine.query("What happened at Interleaf and Viaweb?")

print(str(response))

Interleaf was a company that built impressive technology and had smart people, but it was ultimately crushed by Moore's Law in the 1990s due to the exponential growth in the power of commodity processors. Despite adding a scripting language and making software for creating documents, it could not keep up with the rapid advancements in hardware.

Viaweb, on the other hand, was an application service provider that created a code editor for users to define their own page styles, which were actually Lisp expressions. The company was eventually bought by Yahoo in the summer of 1998. The Viaweb stock became valuable, and the acquisition marked a significant turning point for its founders. The Viaweb logo featured a white "V" on a red circle, which later inspired the Y Combinator logo.

Loading from an existing Graph

If you have an existing graph, we can connect to and use it!

from llama_index.graph_stores.nebula import NebulaPropertyGraphStore

graph_store = NebulaPropertyGraphStore(
    space="llamaindex_nebula_property_graph"
)

from llama_index.core.vector_stores.simple import SimpleVectorStore

vec_store = SimpleVectorStore.from_persist_path("./data/nebula_vec_store.json")

index = PropertyGraphIndex.from_existing(
    property_graph_store=graph_store,
    vector_store=vec_store,
)

From here, we can still insert more documents!

from llama_index.core import Document

document = Document(text="LlamaIndex is great!")

index.insert(document)

nodes = index.as_retriever(include_text=False).retrieve("LlamaIndex")

print(nodes[0].text)

Llamaindex -> Is -> Great