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，我们可以方便地从 Llama Hub 支持的任何数据源创建相对有效的知识图谱。

此外，查询知识图谱通常需要与存储系统相关的领域特定知识，例如 Cypher。但是，借助 LLM 和 LlamaIndex KnowledgeGraphQueryEngine，这可以使用自然语言完成！

在此演示中，我们将指导您完成以下步骤：

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

如果您正在 colab 上打开此 Notebook，您可能需要安装 LlamaIndex 🦙。

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%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

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

首先我们为 Llama Index 做一些基本准备。

OpenAI¶

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# 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
# 对于 OpenAI import os os.environ["OPENAI_API_KEY"] = "sk-..." import logging import sys logging.basicConfig( stream=sys.stdout, level=logging.INFO ) # logging.DEBUG 用于更详细的输出 # 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¶

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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, ) # 您还需要部署自己的嵌入模型以及聊天补全模型 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, )

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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

准备 NebulaGraph¶

在下一步创建知识图谱之前，请确保您已安装并运行 NebulaGraph，并且已定义数据模式。

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# 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
# 创建一个 NebulaGraph 集群（3.5.0 版本或更新版本），方法如下： # 选项 0：对于使用 Docker 的机器：`curl -fsSL nebula-up.siwei.io/install.sh | bash` # 选项 1：对于桌面版：NebulaGraph Docker Extension https://hub.docker.com/extensions/weygu/nebulagraph-dd-ext # 如果不是，请使用 NebulaGraph 控制台中的以下命令创建它： # 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" # 默认是 "nebula" os.environ[ "NEBULA_ADDRESS" ] = "127.0.0.1:9669" # 假设我们在本地安装了 NebulaGraph space_name = "llamaindex" edge_types, rel_prop_names = ["relationship"], [ "relationship" ] # 默认，如果从空的知识图谱创建可以省略 tags = ["entity"] # 默认，如果从空的知识图谱创建可以省略

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准备 StorageContext，将 graph_store 设置为 NebulaGraphStore

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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¶

在已定义 Llama Index 和 LLM 的帮助下，我们可以从给定文档构建知识图谱。

如果 NebulaGraphStore 中已有知识图谱，则可以跳过此步骤

步骤 1：从 Wikipedia 加载“银河护卫队 3”的数据¶

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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=["Guardians of the Galaxy Vol. 3"], auto_suggest=False )

步骤 2：生成以 NebulaGraph 作为 graph_store 的 KnowledgeGraphIndex¶

然后，我们将创建一个 KnowledgeGraphIndex 以启用基于图的 RAG，详情请参见此处，除此之外，我们还有一个正在运行的知识图谱用于其他目的！

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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 集群的 llamaindex 空间中拥有了一个关于电影“银河护卫队 3”的知识图谱，让我们来玩一下。

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# 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>
# 安装相关包，默认密码是 nebula %pip install ipython-ngql networkx pyvis %load_ext ngql %ngql --address 127.0.0.1 --port 9669 --user root --password

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Connection Pool Created
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	Name
0	llamaindex

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

INFO:nebula3.logger:Get connection to ('127.0.0.1', 9669)
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	e
0	("A second trailer for the film")-[: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	("Bradley Cooper")-[:relationship@138630731832...
9	("Bradley Cooper")-[:relationship@838402633192...

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# draw the result

%ng_draw
# 绘制结果 %ng_draw

nebulagraph_draw.html

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查询知识图谱¶

最后，让我们演示如何使用自然语言查询知识图谱！

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

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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 storage_context = StorageContext.from_defaults(graph_store=NebulaGraphStore( space_name=space_name, edge_types=edge_types, rel_prop_names=rel_prop_names, tags=tags, )) query_engine = KnowledgeGraphQueryEngine( storage_context=storage_context, llm=Settings.llm, verbose=True, )

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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.

Peter Quill 是漫威宇宙中的一个角色。他是 Meredith Quill 和 Ego the Living Planet 的儿子。

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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( "Tell me about Peter Quill?", ) graph_query = graph_query.replace("WHERE", "\n WHERE").replace( "RETURN", "\nRETURN" ) display( Markdown( f""" ```cypher {graph_query} ``` """ ) )

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

RETURN p.entity.name;

我们可以看到它帮助生成了图查询

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

并根据其结果合成答案

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

当然，我们仍然可以查询它！而且这个查询引擎可以是我们的最佳图查询语言学习助手 :)

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%%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)

Out[ ]

	p.entity.name	e.relationship	m.entity.name
0	Peter Quill	会回到 MCU	2021年5月
1	Peter Quill	从地球被绑架	小时候
2	Peter Quill	是领袖	银河护卫队
3	Peter Quill	由...抚养长大	一群外星盗贼和走私犯
4	Peter Quill	半人类	半天神

并更改查询以进行渲染

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%%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)

Out[ ]

	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...	("a group of alien thieves and smugglers" :ent...
4	("Peter Quill" :entity{name: "Peter Quill"})	("Peter Quill")-[:relationship@555553046209276...	("half-Celestial" :entity{name: "half-Celestia...

In [ ]

已复制！

%ng_draw
%ng_draw

nebulagraph_draw.html

Out[ ]

通过渲染的图，此知识获取查询的结果再清晰不过了。