知识图谱查询引擎¶

创建知识图谱通常涉及专业且复杂的任务。然而，通过利用Llama Index (LLM)、KnowledgeGraphIndex和GraphStore，我们可以从Llama Hub支持的任何数据源中相对高效地构建知识图谱。

此外，查询知识图谱通常需要与存储系统相关的领域专业知识，例如Cypher。但是，在LLM和LlamaIndex KnowledgeGraphQueryEngine的帮助下，这可以通过自然语言实现！

在本演示中，我们将引导您完成以下步骤：

使用Llama Index提取并设置知识图谱
使用Cypher查询知识图谱
使用自然语言查询知识图谱

如果你在Colab上打开这个Notebook，你可能需要安装LlamaIndex 🦙。

In [ ]:

Copied!





%pip install llama-index-readers-wikipedia
%pip install llama-index-llms-azure-openai
%pip install llama-index-graph-stores-nebula
%pip install llama-index-llms-openai
%pip install llama-index-embeddings-azure-openai
%pip install llama-index-readers-wikipedia
%pip install llama-index-llms-azure-openai
%pip install llama-index-graph-stores-nebula
%pip install llama-index-llms-openai
%pip install llama-index-embeddings-azure-openai

In [ ]:

Copied!

!pip install llama-index
!pip install llama-index

首先让我们为Llama Index的基础准备工作做好准备。

OpenAI¶

In [ ]:

Copied!

# For OpenAI

import os

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

import logging
import sys

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

# define LLM
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings

Settings.llm = OpenAI(temperature=0, model="gpt-3.5-turbo")
Settings.chunk_size = 512
# 针对OpenAI

import os

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

import logging
import sys

logging.basicConfig(
    stream=sys.stdout, level=logging.INFO
)  # 更详细的输出可使用logging.DEBUG

# 定义LLM
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings

Settings.llm = OpenAI(temperature=0, model="gpt-3.5-turbo")
Settings.chunk_size = 512

Azure¶

In [ ]:

Copied!





from llama_index.llms.azure_openai import AzureOpenAI
from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding

# For Azure OpenAI
api_key = "<api-key>"
azure_endpoint = "https://<your-resource-name>.openai.azure.com/"
api_version = "2023-07-01-preview"

llm = AzureOpenAI(
    model="gpt-35-turbo-16k",
    deployment_name="my-custom-llm",
    api_key=api_key,
    azure_endpoint=azure_endpoint,
    api_version=api_version,
)

# You need to deploy your own embedding model as well as your own chat completion model
embed_model = AzureOpenAIEmbedding(
    model="text-embedding-ada-002",
    deployment_name="my-custom-embedding",
    api_key=api_key,
    azure_endpoint=azure_endpoint,
    api_version=api_version,
)
from llama_index.llms.azure_openai import AzureOpenAI
from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding

# For Azure OpenAI
api_key = ""
azure_endpoint = "https://.openai.azure.com/"
api_version = "2023-07-01-preview"

llm = AzureOpenAI(
    model="gpt-35-turbo-16k",
    deployment_name="my-custom-llm",
    api_key=api_key,
    azure_endpoint=azure_endpoint,
    api_version=api_version,
)

# You need to deploy your own embedding model as well as your own chat completion model
embed_model = AzureOpenAIEmbedding(
    model="text-embedding-ada-002",
    deployment_name="my-custom-embedding",
    api_key=api_key,
    azure_endpoint=azure_endpoint,
    api_version=api_version,
)

In [ ]:

Copied!

from llama_index.core import Settings

Settings.llm = llm
Settings.embed_model = embed_model
Settings.chunk_size = 512
from llama_index.core import Settings

Settings.llm = llm
Settings.embed_model = embed_model
Settings.chunk_size = 512

为NebulaGraph做准备¶

在下一步创建知识图谱之前，让我们确保已启动NebulaGraph并定义了数据模式。

In [ ]:

Copied!





# Create a NebulaGraph (version 3.5.0 or newer) cluster with:
# Option 0 for machines with Docker: `curl -fsSL nebula-up.siwei.io/install.sh | bash`
# Option 1 for Desktop: NebulaGraph Docker Extension https://hub.docker.com/extensions/weygu/nebulagraph-dd-ext

# If not, create it with the following commands from NebulaGraph's console:
# CREATE SPACE llamaindex(vid_type=FIXED_STRING(256), partition_num=1, replica_factor=1);
# :sleep 10;
# USE llamaindex;
# CREATE TAG entity(name string);
# CREATE EDGE relationship(relationship string);
# :sleep 10;
# CREATE TAG INDEX entity_index ON entity(name(256));

%pip install ipython-ngql nebula3-python

os.environ["NEBULA_USER"] = "root"
os.environ["NEBULA_PASSWORD"] = "nebula"  # default is "nebula"
os.environ[
    "NEBULA_ADDRESS"
] = "127.0.0.1:9669"  # assumed we have NebulaGraph installed locally

space_name = "llamaindex"
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
# 创建NebulaGraph（3.5.0或更新版本）集群的方法：
# 选项0（适用于已安装Docker的机器）：`curl -fsSL nebula-up.siwei.io/install.sh | bash`
# 选项1（桌面版）：NebulaGraph Docker扩展 https://hub.docker.com/extensions/weygu/nebulagraph-dd-ext

# 如果没有，可以通过NebulaGraph控制台执行以下命令创建：
# CREATE SPACE llamaindex(vid_type=FIXED_STRING(256), partition_num=1, replica_factor=1);
# :sleep 10;
# USE llamaindex;
# CREATE TAG entity(name string);
# CREATE EDGE relationship(relationship string);
# :sleep 10;
# CREATE TAG INDEX entity_index ON entity(name(256));

%pip install ipython-ngql nebula3-python

os.environ["NEBULA_USER"] = "root"
os.environ["NEBULA_PASSWORD"] = "nebula"  # 默认为"nebula"
os.environ[
    "NEBULA_ADDRESS"
] = "127.0.0.1:9669"  # 假设我们已本地安装NebulaGraph

space_name = "llamaindex"
edge_types, rel_prop_names = ["relationship"], [
    "relationship"
]  # 默认值，如果从空知识图谱创建可省略
tags = ["entity"]  # 默认值，如果从空知识图谱创建可省略

Requirement already satisfied: ipython-ngql in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (0.5)
Requirement already satisfied: nebula3-python in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (3.4.0)
Requirement already satisfied: pandas in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from ipython-ngql) (2.0.3)
Requirement already satisfied: Jinja2 in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from ipython-ngql) (3.1.2)
Requirement already satisfied: pytz>=2021.1 in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from nebula3-python) (2023.3)
Requirement already satisfied: future>=0.18.0 in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from nebula3-python) (0.18.3)
Requirement already satisfied: httplib2>=0.20.0 in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from nebula3-python) (0.22.0)
Requirement already satisfied: six>=1.16.0 in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from nebula3-python) (1.16.0)
Requirement already satisfied: pyparsing!=3.0.0,!=3.0.1,!=3.0.2,!=3.0.3,<4,>=2.4.2 in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from httplib2>=0.20.0->nebula3-python) (3.0.9)
Requirement already satisfied: MarkupSafe>=2.0 in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from Jinja2->ipython-ngql) (2.1.3)
Requirement already satisfied: tzdata>=2022.1 in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from pandas->ipython-ngql) (2023.3)
Requirement already satisfied: numpy>=1.20.3 in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from pandas->ipython-ngql) (1.25.2)
Requirement already satisfied: python-dateutil>=2.8.2 in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from pandas->ipython-ngql) (2.8.2)
WARNING: You are using pip version 21.2.4; however, version 23.2.1 is available.
You should consider upgrading via the '/Users/loganmarkewich/llama_index/llama-index/bin/python -m pip install --upgrade pip' command.
Note: you may need to restart the kernel to use updated packages.

准备将graph_store配置为NebulaGraphStore的StorageContext

In [ ]:

Copied!





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

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

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)

(可选)使用LlamaIndex构建知识图谱¶

在Llama Index和LLM的帮助下，我们可以从给定的文档构建知识图谱。

如果我们已经在NebulaGraphStore上有了知识图谱，这一步可以跳过

第一步，从维基百科加载《银河护卫队3》的数据¶

In [ ]:

Copied!

from llama_index.core import download_loader

from llama_index.readers.wikipedia import WikipediaReader

loader = WikipediaReader()

documents = loader.load_data(
    pages=["Guardians of the Galaxy Vol. 3"], auto_suggest=False
)
from llama_index.core import download_loader

from llama_index.readers.wikipedia import WikipediaReader

loader = WikipediaReader()

documents = loader.load_data(
    pages=["银河护卫队3"], auto_suggest=False
)

步骤2，使用NebulaGraph作为图存储生成KnowledgeGraphIndex¶

接下来，我们将创建一个KnowledgeGraphIndex来实现基于图的RAG（检索增强生成），详情请参阅此处。除此之外，我们还运行着一个知识图谱用于其他用途！

In [ ]:

Copied!





from llama_index.core import KnowledgeGraphIndex

kg_index = KnowledgeGraphIndex.from_documents(
    documents,
    storage_context=storage_context,
    max_triplets_per_chunk=10,
    space_name=space_name,
    edge_types=edge_types,
    rel_prop_names=rel_prop_names,
    tags=tags,
    include_embeddings=True,
)
from llama_index.core import KnowledgeGraphIndex

kg_index = KnowledgeGraphIndex.from_documents(
    documents,
    storage_context=storage_context,
    max_triplets_per_chunk=10,
    space_name=space_name,
    edge_types=edge_types,
    rel_prop_names=rel_prop_names,
    tags=tags,
    include_embeddings=True,
)

现在我们在NebulaGraph集群上有一个名为llamaindex的空间，其中存储了关于《银河护卫队3》电影的知识图谱，让我们来探索一下。

In [ ]:

Copied!





# install related packages, password is nebula by default
%pip install ipython-ngql networkx pyvis
%load_ext ngql
%ngql --address 127.0.0.1 --port 9669 --user root --password <password>
# install related packages, password is nebula by default
%pip install ipython-ngql networkx pyvis
%load_ext ngql
%ngql --address 127.0.0.1 --port 9669 --user root --password 

Requirement already satisfied: ipython-ngql in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (0.5)
Requirement already satisfied: networkx in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (3.1)
Requirement already satisfied: pyvis in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (0.3.2)
Requirement already satisfied: Jinja2 in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from ipython-ngql) (3.1.2)
Requirement already satisfied: pandas in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from ipython-ngql) (2.0.3)
Requirement already satisfied: nebula3-python in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from ipython-ngql) (3.4.0)
Requirement already satisfied: jsonpickle>=1.4.1 in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from pyvis) (3.0.1)
Requirement already satisfied: ipython>=5.3.0 in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from pyvis) (8.10.0)
Requirement already satisfied: backcall in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from ipython>=5.3.0->pyvis) (0.2.0)
Requirement already satisfied: pickleshare in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from ipython>=5.3.0->pyvis) (0.7.5)
Requirement already satisfied: prompt-toolkit<3.1.0,>=3.0.30 in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from ipython>=5.3.0->pyvis) (3.0.39)
Requirement already satisfied: appnope in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from ipython>=5.3.0->pyvis) (0.1.3)
Requirement already satisfied: pygments>=2.4.0 in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from ipython>=5.3.0->pyvis) (2.15.1)
Requirement already satisfied: traitlets>=5 in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from ipython>=5.3.0->pyvis) (5.9.0)
Requirement already satisfied: pexpect>4.3 in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from ipython>=5.3.0->pyvis) (4.8.0)
Requirement already satisfied: stack-data in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from ipython>=5.3.0->pyvis) (0.6.2)
Requirement already satisfied: decorator in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from ipython>=5.3.0->pyvis) (5.1.1)
Requirement already satisfied: jedi>=0.16 in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from ipython>=5.3.0->pyvis) (0.18.2)
Requirement already satisfied: matplotlib-inline in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from ipython>=5.3.0->pyvis) (0.1.6)
Requirement already satisfied: parso<0.9.0,>=0.8.0 in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from jedi>=0.16->ipython>=5.3.0->pyvis) (0.8.3)
Requirement already satisfied: MarkupSafe>=2.0 in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from Jinja2->ipython-ngql) (2.1.3)
Requirement already satisfied: ptyprocess>=0.5 in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from pexpect>4.3->ipython>=5.3.0->pyvis) (0.7.0)
Requirement already satisfied: wcwidth in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from prompt-toolkit<3.1.0,>=3.0.30->ipython>=5.3.0->pyvis) (0.2.6)
Requirement already satisfied: six>=1.16.0 in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from nebula3-python->ipython-ngql) (1.16.0)
Requirement already satisfied: pytz>=2021.1 in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from nebula3-python->ipython-ngql) (2023.3)
Requirement already satisfied: future>=0.18.0 in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from nebula3-python->ipython-ngql) (0.18.3)
Requirement already satisfied: httplib2>=0.20.0 in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from nebula3-python->ipython-ngql) (0.22.0)
Requirement already satisfied: pyparsing!=3.0.0,!=3.0.1,!=3.0.2,!=3.0.3,<4,>=2.4.2 in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from httplib2>=0.20.0->nebula3-python->ipython-ngql) (3.0.9)
Requirement already satisfied: python-dateutil>=2.8.2 in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from pandas->ipython-ngql) (2.8.2)
Requirement already satisfied: numpy>=1.20.3 in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from pandas->ipython-ngql) (1.25.2)
Requirement already satisfied: tzdata>=2022.1 in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from pandas->ipython-ngql) (2023.3)
Requirement already satisfied: executing>=1.2.0 in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from stack-data->ipython>=5.3.0->pyvis) (1.2.0)
Requirement already satisfied: pure-eval in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from stack-data->ipython>=5.3.0->pyvis) (0.2.2)
Requirement already satisfied: asttokens>=2.1.0 in /Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages (from stack-data->ipython>=5.3.0->pyvis) (2.2.1)
WARNING: You are using pip version 21.2.4; however, version 23.2.1 is available.
You should consider upgrading via the '/Users/loganmarkewich/llama_index/llama-index/bin/python -m pip install --upgrade pip' command.
Note: you may need to restart the kernel to use updated packages.
Connection Pool Created
INFO:nebula3.logger:Get connection to ('127.0.0.1', 9669)
[ERROR]:
 'IPythonNGQL' object has no attribute '_decode_value'

输出[ ]:

	名称
0	llamaindex

In [ ]:

Copied!

# Query some random Relationships with Cypher
%ngql USE llamaindex;
%ngql MATCH ()-[e]->() RETURN e LIMIT 10
# 使用Cypher查询一些随机关系
%ngql USE llamaindex;
%ngql MATCH ()-[e]->() RETURN e LIMIT 10

INFO:nebula3.logger:Get connection to ('127.0.0.1', 9669)
INFO:nebula3.logger:Get connection to ('127.0.0.1', 9669)

输出[ ]:

	e
0	("该电影的第二支预告片")-[:relationsh...
1	("Adam McKay")-[:relationship@-442854342936029...
2	("Adam McKay")-[:relationship@8513344855738553...
3	("Asim Chaudhry")-[:relationship@-803614038978...
4	("Bakalova")-[:relationship@-25325064520311626...
5	("Bautista")-[:relationship@-90386029986457371...
6	("Bautista")-[:relationship@-90386029986457371...
7	("Beth Mickle")-[:relationship@716197657641767...
8	("布拉德利·库珀")-[:relationship@138630731832...
9	("Bradley Cooper")-[:relationship@838402633192...

In [ ]:

Copied!

# draw the result

%ng_draw
# 绘制结果

%ng_draw

nebulagraph_draw.html

输出[ ]:

查询知识图谱¶

最后，让我们演示如何用自然语言查询知识图谱！

在这里，我们将利用KnowledgeGraphQueryEngine，并将NebulaGraphStore作为storage_context.graph_store。

In [ ]:

Copied!





from llama_index.core.query_engine import KnowledgeGraphQueryEngine

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

query_engine = KnowledgeGraphQueryEngine(
    storage_context=storage_context,
    llm=llm,
    verbose=True,
)
from llama_index.core.query_engine import KnowledgeGraphQueryEngine

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

query_engine = KnowledgeGraphQueryEngine(
    storage_context=storage_context,
    llm=llm,
    verbose=True,
)

In [ ]:

Copied!





response = query_engine.query(
    "Tell me about Peter Quill?",
)
display(Markdown(f"<b>{response}</b>"))
response = query_engine.query(
    "Tell me about Peter Quill?",
)
display(Markdown(f"{response}"))

Graph Store Query:
```
MATCH (p:`entity`)-[:relationship]->(m:`entity`) WHERE p.`entity`.`name` == 'Peter Quill'
RETURN p.`entity`.`name`;
```
Graph Store Response:
{'p.entity.name': ['Peter Quill', 'Peter Quill', 'Peter Quill', 'Peter Quill', 'Peter Quill']}
Final Response: 

Peter Quill is a character in the Marvel Universe. He is the son of Meredith Quill and Ego the Living Planet.

彼得·奎尔是漫威宇宙中的一个角色。他是梅瑞狄斯·奎尔和活体行星Ego的儿子。

In [ ]:

Copied!





graph_query = query_engine.generate_query(
    "Tell me about Peter Quill?",
)

graph_query = graph_query.replace("WHERE", "\n  WHERE").replace(
    "RETURN", "\nRETURN"
)

display(
    Markdown(
        f"""
```cypher
{graph_query}
```
"""
    )
)
graph_query = query_engine.generate_query(
    "告诉我关于彼得·奎尔的信息？",
)

graph_query = graph_query.replace("WHERE", "\n  WHERE").replace(
    "RETURN", "\nRETURN"
)

display(
    Markdown(
        f"""
```cypher
{graph_query}
```
"""
    )
)

匹配 (p:entity)-[:relationship]->(m:entity) 当 p.entity.name == 'Peter Quill'

RETURN p.entity.name;

我们可以看到它有助于生成图查询:

MATCH (p:`entity`)-[:relationship]->(e:`entity`) 
  WHERE p.`entity`.`name` == 'Peter Quill' 
RETURN e.`entity`.`name`;

并根据其结果综合问题：

{'e2.entity.name': ['grandfather', 'alternate version of Gamora', 'Guardians of the Galaxy']}

当然我们仍然可以查询它！那么这个查询引擎可以成为我们最好的图查询语言学习机器人了 :)。

In [ ]:

Copied!





%%ngql 
MATCH (p:`entity`)-[e:relationship]->(m:`entity`)
  WHERE p.`entity`.`name` == 'Peter Quill'
RETURN p.`entity`.`name`, e.relationship, m.`entity`.`name`;
%%ngql 
MATCH (p:`entity`)-[e:relationship]->(m:`entity`)
  WHERE p.`entity`.`name` == 'Peter Quill'
RETURN p.`entity`.`name`, e.relationship, m.`entity`.`name`;

INFO:nebula3.logger:Get connection to ('127.0.0.1', 9669)

输出[ ]:

	p.entity.name	e.relationship	m.entity.name
0	彼得·奎尔	将回归漫威电影宇宙	2021年5月
1	彼得·奎尔	从小就被从地球绑架	当他还是个孩子时
2	彼得·奎尔	是	银河护卫队	的领导者
3	彼得·奎尔	由	一群外星盗贼和走私者抚养长大
4	彼得·奎尔	是半人类	半天神族

并更改要渲染的查询

In [ ]:

Copied!





%%ngql
MATCH (p:`entity`)-[e:relationship]->(m:`entity`)
  WHERE p.`entity`.`name` == 'Peter Quill'
RETURN p, e, m;
%%ngql
MATCH (p:`entity`)-[e:relationship]->(m:`entity`)
  WHERE p.`entity`.`name` == 'Peter Quill'
RETURN p, e, m;

INFO:nebula3.logger:Get connection to ('127.0.0.1', 9669)

输出[ ]:

	p	e	m
0	("Peter Quill" :entity{name: "Peter Quill"})	("Peter Quill")-[:relationship@-84437522554765...	("May 2021" :entity{name: "May 2021"})
1	("Peter Quill" :entity{name: "Peter Quill"})	("Peter Quill")-[:relationship@-11770408155938...	("as a child" :entity{name: "as a child"})
2	("Peter Quill" :entity{name: "Peter Quill"})	("Peter Quill")-[:relationship@-79394488349732...	("Guardians of the Galaxy" :entity{name: "Guar...
3	("Peter Quill" :entity{name: "Peter Quill"})	("Peter Quill")-[:relationship@325695233021653...	("一群外星盗贼和走私者" :ent...
4	("Peter Quill" :entity{name: "Peter Quill"})	("Peter Quill")-[:relationship@555553046209276...	("half-Celestial" :entity{name: "half-Celestia...

In [ ]:

Copied!

%ng_draw
%ng_draw

nebulagraph_draw.html

输出[ ]:

从渲染的图表来看，这个知识获取查询的结果再清晰不过了。