langchain/docs/modules/chains/index_examples/graph_qa.ipynb

Graph QA

This notebook goes over how to do question answering over a graph data structure.

Create the graph

In this section, we construct an example graph. At the moment, this works best for small pieces of text.

In [1]:

from langchain.indexes import GraphIndexCreator
from langchain.llms import OpenAI
from langchain.document_loaders import TextLoader

In [2]:

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

In [3]:

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

We will use just a small snippet, because extracting the knowledge triplets is a bit intensive at the moment.

In [4]:

text = "\n".join(all_text.split("\n\n")[105:108])

In [5]:

text

Out [5]:

'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. '

In [6]:

graph = index_creator.from_text(text)

We can inspect the created graph.

In [7]:

graph.get_triples()

Out [7]:

[('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')]

Querying the graph

We can now use the graph QA chain to ask question of the graph

In [8]:

from langchain.chains import GraphQAChain

In [9]:

chain = GraphQAChain.from_llm(OpenAI(temperature=0), graph=graph, verbose=True)

In [10]:

chain.run("what is Intel going to build?")

Out [10]:

> 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.'

Save the graph

We can also save and load the graph.

In [7]:

graph.write_to_gml("graph.gml")

In [8]:

from langchain.indexes.graph import NetworkxEntityGraph

In [9]:

loaded_graph = NetworkxEntityGraph.from_gml("graph.gml")

In [10]:

loaded_graph.get_triples()

Out [10]:

[('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')]

In [ ]: