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NetworkX

NetworkX 是一个用于创建、操作和研究复杂网络结构、动态和功能的Python包。

本笔记本介绍了如何在图数据结构上进行问答。

设置​

我们需要安装一个Python包。

%pip install --upgrade --quiet  networkx

创建图表​

在本节中，我们构建了一个示例图。目前，这种方法最适合处理小段文本。

from langchain_community.graphs.index_creator import GraphIndexCreator
from langchain_openai import OpenAI
API Reference:GraphIndexCreator | OpenAI
index_creator = GraphIndexCreator(llm=OpenAI(temperature=0))
with open("../../../how_to/state_of_the_union.txt") as f:
    all_text = f.read()

我们将只使用一小段代码，因为目前提取知识三元组有点密集。

text = "\n".join(all_text.split("\n\n")[105:108])
text
'It won’t look like much, but if you stop and look closely, you’ll see a “Field of dreams,” the ground on which America’s future will be built. \nThis is where Intel, the American company that helped build Silicon Valley, is going to build its $20 billion semiconductor “mega site”. \nUp to eight state-of-the-art factories in one place. 10,000 new good-paying jobs. '
graph = index_creator.from_text(text)

我们可以检查创建的图表。

graph.get_triples()
[('Intel', '$20 billion semiconductor "mega site"', 'is going to build'),
 ('Intel', 'state-of-the-art factories', 'is building'),
 ('Intel', '10,000 new good-paying jobs', 'is creating'),
 ('Intel', 'Silicon Valley', 'is helping build'),
 ('Field of dreams',
  "America's future will be built",
  'is the ground on which')]

查询图​

我们现在可以使用图问答链来对图提出问题

from langchain.chains import GraphQAChain
API Reference:GraphQAChain
chain = GraphQAChain.from_llm(OpenAI(temperature=0), graph=graph, verbose=True)
chain.run("what is Intel going to build?")


> Entering new GraphQAChain chain...
Entities Extracted:
 Intel
Full Context:
Intel is going to build $20 billion semiconductor "mega site"
Intel is building state-of-the-art factories
Intel is creating 10,000 new good-paying jobs
Intel is helping build Silicon Valley

> Finished chain.
' Intel is going to build a $20 billion semiconductor "mega site" with state-of-the-art factories, creating 10,000 new good-paying jobs and helping to build Silicon Valley.'

保存图表​

我们也可以保存和加载图形。

graph.write_to_gml("graph.gml")
from langchain_community.graphs import NetworkxEntityGraph
API Reference:NetworkxEntityGraph
loaded_graph = NetworkxEntityGraph.from_gml("graph.gml")
loaded_graph.get_triples()
[('Intel', '$20 billion semiconductor "mega site"', 'is going to build'),
 ('Intel', 'state-of-the-art factories', 'is building'),
 ('Intel', '10,000 new good-paying jobs', 'is creating'),
 ('Intel', 'Silicon Valley', 'is helping build'),
 ('Field of dreams',
  "America's future will be built",
  'is the ground on which')]
loaded_graph.get_number_of_nodes()
loaded_graph.add_node("NewNode")
loaded_graph.has_node("NewNode")
loaded_graph.remove_node("NewNode")
loaded_graph.get_neighbors("Intel")
loaded_graph.has_edge("Intel", "Silicon Valley")
loaded_graph.remove_edge("Intel", "Silicon Valley")
loaded_graph.clear_edges()
loaded_graph.clear()

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