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LlamaIndex Python Documentation
TypeScript

Nebula Graph Store 

%pip install llama-index-llms-openai
%pip install llama-index-embeddings-openai
%pip install llama-index-graph-stores-nebula
%pip install llama-index-llms-azure-openai
# For OpenAI


import os


os.environ["OPENAI_API_KEY"] = "INSERT OPENAI KEY"


import logging
import sys
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings


logging.basicConfig(stream=sys.stdout, level=logging.INFO)


# define LLM
# NOTE: at the time of demo, text-davinci-002 did not have rate-limit errors
llm = OpenAI(temperature=0, model="gpt-3.5-turbo")


Settings.llm = llm
Settings.chunk_size = 512
# For Azure OpenAI
import os
import json
import openai
from llama_index.llms.azure_openai import AzureOpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core import (
    VectorStoreIndex,
    SimpleDirectoryReader,
    KnowledgeGraphIndex,
)


from llama_index.core import StorageContext
from llama_index.graph_stores.nebula import NebulaGraphStore


import logging
import sys


from IPython.display import Markdown, display


logging.basicConfig(
    stream=sys.stdout, level=logging.INFO
)  # logging.DEBUG for more verbose output
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))


openai.api_type = "azure"
openai.api_base = "https://<foo-bar>.openai.azure.com"
openai.api_version = "2022-12-01"
os.environ["OPENAI_API_KEY"] = "<your-openai-key>"
openai.api_key = os.getenv("OPENAI_API_KEY")


llm = AzureOpenAI(
    model="<foo-bar-model>",
    engine="<foo-bar-deployment>",
    temperature=0,
    api_key=openai.api_key,
    api_type=openai.api_type,
    api_base=openai.api_base,
    api_version=openai.api_version,
)


# You need to deploy your own embedding model as well as your own chat completion model
embedding_model = OpenAIEmbedding(
    model="text-embedding-ada-002",
    deployment_name="<foo-bar-deployment>",
    api_key=openai.api_key,
    api_base=openai.api_base,
    api_type=openai.api_type,
    api_version=openai.api_version,
)


Settings.llm = llm
Settings.chunk_size = chunk_size
Settings.embed_model = embedding_model

Using Knowledge Graph with NebulaGraphStore

Section titled “Using Knowledge Graph with NebulaGraphStore”

Building the Knowledge Graph

Section titled “Building the Knowledge Graph”
from llama_index.core import KnowledgeGraphIndex, SimpleDirectoryReader
from llama_index.core import StorageContext
from llama_index.graph_stores.nebula import NebulaGraphStore




from llama_index.llms.openai import OpenAI
from IPython.display import Markdown, display
documents = SimpleDirectoryReader(
    "../../../../examples/paul_graham_essay/data"
).load_data()

Prepare for NebulaGraph

Section titled “Prepare for NebulaGraph”
%pip install nebula3-python


os.environ["NEBULA_USER"] = "root"
os.environ[
    "NEBULA_PASSWORD"
] = "<password>"  # replace with your password, by default it is "nebula"
os.environ[
    "NEBULA_ADDRESS"
] = "127.0.0.1:9669"  # assumed we have NebulaGraph 3.5.0 or newer installed locally


# Assume that the graph has already been created
# Create a NebulaGraph cluster with:
# Option 0: `curl -fsSL nebula-up.siwei.io/install.sh | bash`
# Option 1: NebulaGraph Docker Extension https://hub.docker.com/extensions/weygu/nebulagraph-dd-ext
# and that the graph space is called "paul_graham_essay"
# If not, create it with the following commands from NebulaGraph's console:
# CREATE SPACE paul_graham_essay(vid_type=FIXED_STRING(256), partition_num=1, replica_factor=1);
# :sleep 10;
# USE paul_graham_essay;
# CREATE TAG entity(name string);
# CREATE EDGE relationship(relationship string);
# CREATE TAG INDEX entity_index ON entity(name(256));


space_name = "paul_graham_essay"
edge_types, rel_prop_names = ["relationship"], [
    "relationship"
]  # default, could be omit if create from an empty kg
tags = ["entity"]  # default, could be omit if create from an empty kg

Instantiate GPTNebulaGraph KG Indexes

Section titled “Instantiate GPTNebulaGraph KG Indexes”
graph_store = NebulaGraphStore(
    space_name=space_name,
    edge_types=edge_types,
    rel_prop_names=rel_prop_names,
    tags=tags,
)


storage_context = StorageContext.from_defaults(graph_store=graph_store)


# NOTE: can take a while!
index = KnowledgeGraphIndex.from_documents(
    documents,
    storage_context=storage_context,
    max_triplets_per_chunk=2,
    space_name=space_name,
    edge_types=edge_types,
    rel_prop_names=rel_prop_names,
    tags=tags,
)

Querying the Knowledge Graph

Section titled “Querying the Knowledge Graph”
query_engine = index.as_query_engine()


response = query_engine.query("Tell me more about Interleaf")
INFO:llama_index.indices.knowledge_graph.retrievers:> Starting query: Tell me more about Interleaf
INFO:llama_index.indices.knowledge_graph.retrievers:> Query keywords: ['Interleaf', 'history', 'software', 'company']
ERROR:llama_index.indices.knowledge_graph.retrievers:Index was not constructed with embeddings, skipping embedding usage...
INFO:llama_index.indices.knowledge_graph.retrievers:> Querying with idx: 6aa6a716-7390-4783-955b-8169fab25bb1: worth trying.


Our teacher, professor Ulivi, was a nice guy. He could see I w...
INFO:llama_index.indices.knowledge_graph.retrievers:> Querying with idx: 79f2a1b4-80bb-416f-a259-ebfc3136b2fe: on a map of New York City: if you zoom in on the Upper East Side, there's a t...
INFO:llama_index.indices.knowledge_graph.retrievers:> Querying with idx: 1e707b8c-b62a-4c1a-a908-c79e77b9692b: buyers pay a lot for such work. [6]


There were plenty of earnest students to...
INFO:llama_index.indices.knowledge_graph.retrievers:> Querying with idx: 31c2f53c-928a-4ed0-88fc-df92dba47c33: for example, that the reason the color changes suddenly at a certain point is...
INFO:llama_index.indices.knowledge_graph.retrievers:> Querying with idx: f51d8a1c-06bc-45aa-bed1-1714ae4e5fb9: the software is an online store builder and you're hosting the stores, if you...
INFO:llama_index.indices.knowledge_graph.retrievers:> Querying with idx: 008052a0-a64b-4e3c-a2af-4963896bfc19: Engineering that seemed to be at least as big as the group that actually wrot...
INFO:llama_index.indices.knowledge_graph.retrievers:> Querying with idx: b1f5a610-9e0a-4e3e-ba96-514ae7d63a84: closures stored in a hash table on the server.


It helped to have studied art...
INFO:llama_index.indices.knowledge_graph.retrievers:> Querying with idx: f7cc82a7-76e0-4a06-9f50-d681404c5bce: of Robert's apartment in Cambridge. His roommate was away for big chunks of t...
INFO:llama_index.indices.knowledge_graph.retrievers:> Querying with idx: db626325-035a-4f67-87c0-1e770b80f4a6: want to be online, and still don't, not the fancy ones. That's not how they s...
INFO:llama_index.indices.knowledge_graph.retrievers:> Querying with idx: 73e76f4b-0ebe-4af6-9c2d-6affae81373b: But in the long term the growth rate takes care of the absolute number. If we...
INFO:llama_index.indices.knowledge_graph.retrievers:> Extracted relationships: The following are knowledge triplets in max depth 2 in the form of `subject [predicate, object, predicate_next_hop, object_next_hop ...]`
software ['is', 'web app', 'common', 'now']
software ['is', 'web app', "wasn't clear", 'it was possible']
software ['generate', 'web sites']
software ['worked', 'via web']
software ['is', 'web app']
software ['has', 'three main parts']
software ['is', 'online store builder']
Lisp ['has dialects', 'because']
Lisp ['rare', 'C++']
Lisp ['is', 'language']
Lisp ['has dialects', '']
Lisp ['has dialects', 'because one of the distinctive features of the language is that it has dialects']
Lisp ['was regarded as', 'language of AI']
Lisp ['defined by', 'writing an interpreter']
Lisp ['was meant to be', 'formal model of computation']
Interleaf ['added', 'scripting language']
Interleaf ['made software for', 'creating documents']
Interleaf ['was how I learned that', 'low end software tends to eat high end software']
Interleaf ['was', 'on the way down']
Interleaf ['on the way down', '1993']
RISD ['was', 'art school']
RISD ['counted me as', 'transfer sophomore']
RISD ['was', 'supposed to be the best art school in the country']
RISD ['was', 'the best art school in the country']
Robert ['wrote', 'shopping cart', 'written by', 'robert']
Robert ['wrote', 'shopping cart', 'written by', 'Robert']
Robert ['wrote', 'shopping cart']
Robert Morris ['offered', 'unsolicited advice']
Yorkville ['is', 'tiny corner']
Yorkville ["wasn't", 'rich']
online ['is not', 'publishing online']
online ['is not', 'publishing online', 'means', 'you treat the online version as the primary version']
web app ['common', 'now']
web app ["wasn't clear", 'it was possible']
editor ['written by', 'author']
shopping cart ['written by', 'Robert', 'wrote', 'shopping cart']
shopping cart ['written by', 'Robert']
shopping cart ['written by', 'robert', 'wrote', 'shopping cart']
shopping cart ['written by', 'robert']
Robert ['wrote', 'shopping cart', 'written by', 'Robert']
Robert ['wrote', 'shopping cart', 'written by', 'robert']
Robert ['wrote', 'shopping cart']
Lisp ['defined by', 'writing an interpreter']
Lisp ['has dialects', 'because']
Lisp ['was meant to be', 'formal model of computation']
Lisp ['rare', 'C++']
Lisp ['is', 'language']
Lisp ['has dialects', '']
Lisp ['has dialects', 'because one of the distinctive features of the language is that it has dialects']
Lisp ['was regarded as', 'language of AI']
Y Combinator ['would have said', 'Stop being so stressed out']
Y Combinator ['helps', 'founders']
Y Combinator ['is', 'investment firm']
company ['reaches breakeven', 'when yahoo buys it']
company ['gave', 'business advice']
company ['reaches breakeven', 'when Yahoo buys it']
software ['worked', 'via web']
software ['is', 'web app', "wasn't clear", 'it was possible']
software ['generate', 'web sites']
software ['has', 'three main parts']
software ['is', 'online store builder']
software ['is', 'web app']
software ['is', 'web app', 'common', 'now']
Y Combinator ['would have said', 'Stop being so stressed out']
Y Combinator ['is', 'investment firm']
Y Combinator ['helps', 'founders']
company ['gave', 'business advice']
company ['reaches breakeven', 'when Yahoo buys it']
company ['reaches breakeven', 'when yahoo buys it']
INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 5916 tokens
INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens
display(Markdown(f"<b>{response}</b>"))

Interleaf was a software company that made software for creating documents. Their software was inspired by Emacs, and included a scripting language that was a dialect of Lisp. The company was started in the 1990s, and eventually went out of business.

response = query_engine.query(
    "Tell me more about what the author worked on at Interleaf"
)
INFO:llama_index.indices.knowledge_graph.retrievers:> Starting query: Tell me more about what the author worked on at Interleaf
INFO:llama_index.indices.knowledge_graph.retrievers:> Query keywords: ['Interleaf', 'author', 'work']
ERROR:llama_index.indices.knowledge_graph.retrievers:Index was not constructed with embeddings, skipping embedding usage...
INFO:llama_index.indices.knowledge_graph.retrievers:> Querying with idx: 6aa6a716-7390-4783-955b-8169fab25bb1: worth trying.


Our teacher, professor Ulivi, was a nice guy. He could see I w...
INFO:llama_index.indices.knowledge_graph.retrievers:> Querying with idx: 79f2a1b4-80bb-416f-a259-ebfc3136b2fe: on a map of New York City: if you zoom in on the Upper East Side, there's a t...
INFO:llama_index.indices.knowledge_graph.retrievers:> Querying with idx: 1e707b8c-b62a-4c1a-a908-c79e77b9692b: buyers pay a lot for such work. [6]


There were plenty of earnest students to...
INFO:llama_index.indices.knowledge_graph.retrievers:> Querying with idx: 31c2f53c-928a-4ed0-88fc-df92dba47c33: for example, that the reason the color changes suddenly at a certain point is...
INFO:llama_index.indices.knowledge_graph.retrievers:> Querying with idx: b1f5a610-9e0a-4e3e-ba96-514ae7d63a84: closures stored in a hash table on the server.


It helped to have studied art...
INFO:llama_index.indices.knowledge_graph.retrievers:> Querying with idx: 6cda9196-dcdb-4441-8f27-ff3f18779c4c: so easy. And that implies that HN was a mistake. Surely the biggest source of...
INFO:llama_index.indices.knowledge_graph.retrievers:> Querying with idx: a467cf4c-19cf-490f-92ad-ce03c8d91231: I've noticed in my life is how well it has worked, for me at least, to work o...
INFO:llama_index.indices.knowledge_graph.retrievers:> Extracted relationships: The following are knowledge triplets in max depth 2 in the form of `subject [predicate, object, predicate_next_hop, object_next_hop ...]`
software ['is', 'web app', 'common', 'now']
software ['is', 'web app', "wasn't clear", 'it was possible']
software ['generate', 'web sites']
software ['worked', 'via web']
software ['is', 'web app']
software ['has', 'three main parts']
software ['is', 'online store builder']
Lisp ['has dialects', 'because']
Lisp ['rare', 'C++']
Lisp ['is', 'language']
Lisp ['has dialects', '']
Lisp ['has dialects', 'because one of the distinctive features of the language is that it has dialects']
Lisp ['was regarded as', 'language of AI']
Lisp ['defined by', 'writing an interpreter']
Lisp ['was meant to be', 'formal model of computation']
Interleaf ['added', 'scripting language']
Interleaf ['made software for', 'creating documents']
Interleaf ['was how I learned that', 'low end software tends to eat high end software']
Interleaf ['was', 'on the way down']
Interleaf ['on the way down', '1993']
RISD ['was', 'art school']
RISD ['counted me as', 'transfer sophomore']
RISD ['was', 'supposed to be the best art school in the country']
RISD ['was', 'the best art school in the country']
Robert ['wrote', 'shopping cart', 'written by', 'robert']
Robert ['wrote', 'shopping cart', 'written by', 'Robert']
Robert ['wrote', 'shopping cart']
Robert Morris ['offered', 'unsolicited advice']
Yorkville ['is', 'tiny corner']
Yorkville ["wasn't", 'rich']
shopping cart ['written by', 'Robert', 'wrote', 'shopping cart']
shopping cart ['written by', 'robert', 'wrote', 'shopping cart']
shopping cart ['written by', 'Robert']
shopping cart ['written by', 'robert']
online ['is not', 'publishing online', 'means', 'you treat the online version as the primary version']
online ['is not', 'publishing online']
software ['has', 'three main parts']
software ['generate', 'web sites']
software ['is', 'web app', 'common', 'now']
software ['is', 'online store builder']
software ['is', 'web app']
software ['is', 'web app', "wasn't clear", 'it was possible']
software ['worked', 'via web']
editor ['written by', 'author']
YC ['is', 'work', 'is unprestigious', '']
YC ['grew', 'more exciting']
YC ['founded in', 'Berkeley']
YC ['founded in', '2005']
YC ['founded in', '1982']
YC ['is', 'full-time job']
YC ['is', 'engaging work']
YC ['is', 'batch model']
YC ['is', 'Summer Founders Program']
YC ['was', 'coffee shop']
YC ['invests in', 'startups']
YC ['is', 'fund']
YC ['started to notice', 'other advantages']
YC ['grew', 'quickly']
YC ['controlled by', 'founders']
YC ['is', 'work']
YC ['became', 'full-time job']
YC ['is self-funded', 'by Heroku']
YC ['is', 'hard work']
YC ['funds', 'startups']
YC ['controlled by', 'LLC']
Robert ['wrote', 'shopping cart']
Robert ['wrote', 'shopping cart', 'written by', 'Robert']
Robert ['wrote', 'shopping cart', 'written by', 'robert']
Lisp ['was meant to be', 'formal model of computation']
Lisp ['defined by', 'writing an interpreter']
Lisp ['was regarded as', 'language of AI']
Lisp ['has dialects', 'because']
Lisp ['has dialects', '']
Lisp ['has dialects', 'because one of the distinctive features of the language is that it has dialects']
Lisp ['rare', 'C++']
Lisp ['is', 'language']
party ['was', 'clever idea']
Y Combinator ['would have said', 'Stop being so stressed out']
Y Combinator ['is', 'investment firm']
Y Combinator ['helps', 'founders']
Robert Morris ['offered', 'unsolicited advice']
work ['is unprestigious', '']
Jessica Livingston ['is', 'woman']
Jessica Livingston ['decided', 'compile book']
HN ['edge case', 'bizarre']
HN ['edge case', 'when you both write essays and run a forum']
INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 4651 tokens
INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens
display(Markdown(f"<b>{response}</b>"))

The author worked on a software that allowed users to create documents, which was inspired by Emacs. The software had a scripting language that was a dialect of Lisp, and the author was responsible for writing things in this language.

The author also worked on a software that allowed users to generate web sites. This software was a web app and was written in a dialect of Lisp. The author was also responsible for writing things in this language.

Visualizing the Graph RAG

Section titled “Visualizing the Graph RAG”

If we visualize the Graph based RAG, starting from the term ['Interleaf', 'history', 'Software', 'Company'] , we could see how those connected context looks like, and it’s a different form of Info./Knowledge:

  • Refined and Concise Form
  • Fine-grained Segmentation
  • Interconnected-sturcutred nature
%pip install ipython-ngql networkx pyvis
%load_ext ngql
%ngql --address 127.0.0.1 --port 9669 --user root --password <password>
Connection Pool Created
INFO:nebula3.logger:Get connection to ('127.0.0.1', 9669)
Get connection to ('127.0.0.1', 9669)
.dataframe tbody tr th {
    vertical-align: top;
}


.dataframe thead th {
    text-align: right;
}
Name
0 Apple_Vision_Pro
1 basketballplayer
2 demo_ai_ops
3 demo_basketballplayer
4 demo_data_lineage
5 demo_fifa_2022
6 demo_fraud_detection
7 demo_identity_resolution
8 demo_movie_recommendation
9 demo_sns
10 guardians
11 k8s
12 langchain
13 llamaindex
14 paul_graham_essay
15 squid_game
16 test 
%%ngql
USE paul_graham_essay;
MATCH p=(n)-[*1..2]-()
  WHERE id(n) IN ['Interleaf', 'history', 'Software', 'Company']
RETURN p LIMIT 100;
INFO:nebula3.logger:Get connection to ('127.0.0.1', 9669)
Get connection to ('127.0.0.1', 9669)
.dataframe tbody tr th {
    vertical-align: top;
}


.dataframe thead th {
    text-align: right;
}
p
0 ("Interleaf" :entity{name: "Interleaf"})-[:rel...
1 ("Interleaf" :entity{name: "Interleaf"})-[:rel...
2 ("Interleaf" :entity{name: "Interleaf"})-[:rel...
3 ("Interleaf" :entity{name: "Interleaf"})-[:rel... 
%ng_draw
nebulagraph_draw.html

Query with embeddings

Section titled “Query with embeddings”
# NOTE: can take a while!


index = KnowledgeGraphIndex.from_documents(
    documents,
    storage_context=storage_context,
    max_triplets_per_chunk=2,
    space_name=space_name,
    edge_types=edge_types,
    rel_prop_names=rel_prop_names,
    tags=tags,
    include_embeddings=True,
)


query_engine = index.as_query_engine(
    include_text=True,
    response_mode="tree_summarize",
    embedding_mode="hybrid",
    similarity_top_k=5,
)
# query using top 3 triplets plus keywords (duplicate triplets are removed)
response = query_engine.query(
    "Tell me more about what the author worked on at Interleaf"
)
display(Markdown(f"<b>{response}</b>"))

Query with more global(cross node) context

Section titled “Query with more global(cross node) context”
query_engine = index.as_query_engine(
    include_text=True,
    response_mode="tree_summarize",
    embedding_mode="hybrid",
    similarity_top_k=5,
    explore_global_knowledge=True,
)


response = query_engine.query("Tell me more about what the author and Lisp")

Visualizing the Graph

Section titled “Visualizing the Graph”
## create graph
from pyvis.network import Network


g = index.get_networkx_graph()
net = Network(notebook=True, cdn_resources="in_line", directed=True)
net.from_nx(g)
net.show("example.html")

[Optional] Try building the graph and manually add triplets!

Section titled “[Optional] Try building the graph and manually add triplets!”
from llama_index.core.node_parser import SentenceSplitter
node_parser = SentenceSplitter()
nodes = node_parser.get_nodes_from_documents(documents)
# not yet implemented


# initialize an empty index for now
index = KnowledgeGraphIndex.from_documents([], storage_context=storage_context)
# add keyword mappings and nodes manually
# add triplets (subject, relationship, object)


# for node 0
node_0_tups = [
    ("author", "worked on", "writing"),
    ("author", "worked on", "programming"),
]
for tup in node_0_tups:
    index.upsert_triplet_and_node(tup, nodes[0])


# for node 1
node_1_tups = [
    ("Interleaf", "made software for", "creating documents"),
    ("Interleaf", "added", "scripting language"),
    ("software", "generate", "web sites"),
]
for tup in node_1_tups:
    index.upsert_triplet_and_node(tup, nodes[1])
query_engine = index.as_query_engine(
    include_text=False, response_mode="tree_summarize"
)


response = query_engine.query("Tell me more about Interleaf")
str(response)