如何构建知识图谱

在本指南中，我们将介绍基于非结构化文本构建知识图谱的基本方法。构建好的图谱可用作 RAG 应用中的知识库。

⚠️ 安全提示 ⚠️

构建知识图谱需要执行对数据库的写入操作。这样做存在固有风险。在导入数据之前，请务必验证和确认数据的有效性。有关一般安全最佳实践的更多信息，请参见此处。

架构

从高层次来看，从文本构建知识图的步骤如下：

从文本中提取结构化信息: 模型用于从文本中提取结构化的图信息。
存储到图数据库: 将提取的结构化图信息存储到图数据库中，可支持下游的 RAG 应用

设置

首先，获取所需的软件包并设置环境变量。在本示例中，我们将使用 Neo4j 图数据库。

%pip install --upgrade --quiet  langchain langchain-neo4j langchain-openai langchain-experimental neo4j

[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m A new release of pip is available: [0m[31;49m24.0[0m[39;49m -> [0m[32;49m24.3.1[0m
[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m To update, run: [0m[32;49mpip install --upgrade pip[0m
Note: you may need to restart the kernel to use updated packages.

本指南默认使用 OpenAI 模型。

import getpass
import os

os.environ["OPENAI_API_KEY"] = getpass.getpass()

# Uncomment the below to use LangSmith. Not required.
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass()
# os.environ["LANGSMITH_TRACING"] = "true"

 ········

接下来，我们需要定义 Neo4j 凭据和连接。请遵循这些安装步骤来设置 Neo4j 数据库。

import os

from langchain_neo4j import Neo4jGraph

os.environ["NEO4J_URI"] = "bolt://localhost:7687"
os.environ["NEO4J_USERNAME"] = "neo4j"
os.environ["NEO4J_PASSWORD"] = "password"

graph = Neo4jGraph(refresh_schema=False)

API 参考:Neo4jGraph

LLM 图转换器

从文本中提取图谱数据能够将非结构化信息转换为结构化格式，从而促进更深入的洞察，并更高效地导航复杂的关系和模式。LLMGraphTransformer通过利用大语言模型（LLM）来解析和分类实体及其关系，将文本文档转换为结构化的图谱文档。所选用的 LLM 模型会显著影响输出结果，因为它决定了所提取图谱数据的准确性和细微差别。

import os

from langchain_experimental.graph_transformers import LLMGraphTransformer
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(temperature=0, model_name="gpt-4-turbo")

llm_transformer = LLMGraphTransformer(llm=llm)

API 参考:LLMGraphTransformer |ChatOpenAI

现在我们可以传入示例文本并检查结果。

from langchain_core.documents import Document

text = """
Marie Curie, born in 1867, was a Polish and naturalised-French physicist and chemist who conducted pioneering research on radioactivity.
She was the first woman to win a Nobel Prize, the first person to win a Nobel Prize twice, and the only person to win a Nobel Prize in two scientific fields.
Her husband, Pierre Curie, was a co-winner of her first Nobel Prize, making them the first-ever married couple to win the Nobel Prize and launching the Curie family legacy of five Nobel Prizes.
She was, in 1906, the first woman to become a professor at the University of Paris.
"""
documents = [Document(page_content=text)]
graph_documents = await llm_transformer.aconvert_to_graph_documents(documents)
print(f"Nodes:{graph_documents[0].nodes}")
print(f"Relationships:{graph_documents[0].relationships}")

API 参考:文档

Nodes:[Node(id='Marie Curie', type='Person', properties={}), Node(id='Pierre Curie', type='Person', properties={}), Node(id='University Of Paris', type='Organization', properties={})]
Relationships:[Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Pierre Curie', type='Person', properties={}), type='MARRIED', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='University Of Paris', type='Organization', properties={}), type='PROFESSOR', properties={})]

检查以下图像以更好地理解生成知识图谱的结构。

请注意，由于我们使用了大语言模型（LLM），图构建过程是非确定性的。因此，每次执行时您可能会得到略有不同的结果。

此外，您还可以根据您的需求定义特定类型的节点和关系以进行提取。

llm_transformer_filtered = LLMGraphTransformer(
    llm=llm,
    allowed_nodes=["Person", "Country", "Organization"],
    allowed_relationships=["NATIONALITY", "LOCATED_IN", "WORKED_AT", "SPOUSE"],
)
graph_documents_filtered = await llm_transformer_filtered.aconvert_to_graph_documents(
    documents
)
print(f"Nodes:{graph_documents_filtered[0].nodes}")
print(f"Relationships:{graph_documents_filtered[0].relationships}")

Nodes:[Node(id='Marie Curie', type='Person', properties={}), Node(id='Pierre Curie', type='Person', properties={}), Node(id='University Of Paris', type='Organization', properties={})]
Relationships:[Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Pierre Curie', type='Person', properties={}), type='SPOUSE', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='University Of Paris', type='Organization', properties={}), type='WORKED_AT', properties={})]

为了更精确地定义图模式，可以考虑使用三元组方法来描述关系。在这种方法中，每个三元组包含三个元素：源节点、关系类型和目标节点。

allowed_relationships = [
    ("Person", "SPOUSE", "Person"),
    ("Person", "NATIONALITY", "Country"),
    ("Person", "WORKED_AT", "Organization"),
]

llm_transformer_tuple = LLMGraphTransformer(
    llm=llm,
    allowed_nodes=["Person", "Country", "Organization"],
    allowed_relationships=allowed_relationships,
)
graph_documents_filtered = await llm_transformer_tuple.aconvert_to_graph_documents(
    documents
)
print(f"Nodes:{graph_documents_filtered[0].nodes}")
print(f"Relationships:{graph_documents_filtered[0].relationships}")

Nodes:[Node(id='Marie Curie', type='Person', properties={}), Node(id='Pierre Curie', type='Person', properties={}), Node(id='University Of Paris', type='Organization', properties={})]
Relationships:[Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Pierre Curie', type='Person', properties={}), type='SPOUSE', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='University Of Paris', type='Organization', properties={}), type='WORKED_AT', properties={})]

为了更好地理解生成的图，我们可以再次对其进行可视化。

node_properties 参数启用节点属性的提取，从而允许创建更详细的图。当设置为 True 时，LLM 会自动识别并提取相关节点属性。相反，如果将 node_properties 定义为字符串列表，则 LLM 仅从文本中选择性地检索指定的属性。

llm_transformer_props = LLMGraphTransformer(
    llm=llm,
    allowed_nodes=["Person", "Country", "Organization"],
    allowed_relationships=["NATIONALITY", "LOCATED_IN", "WORKED_AT", "SPOUSE"],
    node_properties=["born_year"],
)
graph_documents_props = await llm_transformer_props.aconvert_to_graph_documents(
    documents
)
print(f"Nodes:{graph_documents_props[0].nodes}")
print(f"Relationships:{graph_documents_props[0].relationships}")

Nodes:[Node(id='Marie Curie', type='Person', properties={'born_year': '1867'}), Node(id='Pierre Curie', type='Person', properties={}), Node(id='University Of Paris', type='Organization', properties={}), Node(id='Poland', type='Country', properties={}), Node(id='France', type='Country', properties={})]
Relationships:[Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Poland', type='Country', properties={}), type='NATIONALITY', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='France', type='Country', properties={}), type='NATIONALITY', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Pierre Curie', type='Person', properties={}), type='SPOUSE', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='University Of Paris', type='Organization', properties={}), type='WORKED_AT', properties={})]

存储到图数据库

生成的图文档可以使用 add_graph_documents 方法存储到图数据库中。

graph.add_graph_documents(graph_documents_props)

大多数图数据库支持索引以优化数据导入和检索。由于我们可能无法预先知道所有的节点标签，我们可以通过使用 baseEntityLabel 参数为每个节点添加一个次要基础标签来处理此问题。

graph.add_graph_documents(graph_documents, baseEntityLabel=True)

结果将如下所示:

最后的选项是同时导入提取节点和关系的源文档。这种方法允许我们追踪每个实体出现在哪些文档中。

graph.add_graph_documents(graph_documents, include_source=True)

图将具有以下结构：

在此可视化中，源文档以蓝色高亮显示，从中提取的所有实体通过MENTIONS关系连接。

⚠️ 安全提示 ⚠️​

架构​

设置​

LLM 图转换器​

存储到图数据库​

⚠️ 安全提示 ⚠️

架构

设置

LLM 图转换器

存储到图数据库