NetworkX

NetworkX 是一個 Python 套件，用於建立、操作和研究複雜網路的結構、動態和功能。

本筆記本說明如何對圖形資料結構進行問答。

設定

我們必須安裝一個 Python 套件。

%pip install --upgrade --quiet  networkx

建立圖形

在本節中，我們建構一個範例圖形。目前，這最適合用於小段文字。

from langchain_community.graphs.index_creator import GraphIndexCreator
from langchain_openai import OpenAI

API 參考：GraphIndexCreator | OpenAI

index_creator = GraphIndexCreator(llm=OpenAI(temperature=0))

with open("../../../how_to/state_of_the_union.txt") as f:
    all_text = f.read()

我們將僅使用一小段程式碼片段，因為目前擷取知識三元組有點耗費資源。

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)

我們可以檢查建立的圖形。

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')]

查詢圖形

我們現在可以使用圖形 QA 鏈來詢問有關圖形的問題

from langchain.chains import GraphQAChain

API 參考：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.'

儲存圖形

我們也可以儲存和載入圖形。

graph.write_to_gml("graph.gml")

from langchain_community.graphs import NetworkxEntityGraph

API 參考：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()