使用 LangChain 从文本数据构建知识图

pip install -q langchain openai pyvis gradio==3.39.0

from langchain.prompts import PromptTemplate
from langchain.llms.openai import OpenAI
from langchain.chains import LLMChain
from langchain.graphs.networkx_graph import KG_TRIPLE_DELIMITER
from pprint import pprint
from pyvis.network import Network
import networkx as nx
import gradio as gr

from google.colab import userdata
OPENAI_API_KEY = userdata.get('OPENAI_API_KEY')

# 用于知识三元组提取的提示模板
_DEFAULT_KNOWLEDGE_TRIPLE_EXTRACTION_TEMPLATE = (
    "You are a networked intelligence helping a human track knowledge triples"
    " about all relevant people, things, concepts, etc. and integrating"
    " them with your knowledge stored within your weights"
    " as well as that stored in a knowledge graph."
    " Extract all of the knowledge triples from the text."
    " A knowledge triple is a clause that contains a subject, a predicate,"
    " and an object. The subject is the entity being described,"
    " the predicate is the property of the subject that is being"
    " described, and the object is the value of the property.\\n\\n"
    "EXAMPLE\\n"
    "It's a state in the US. It's also the number 1 producer of gold in the US.\\n\\n"
    f"Output: (Nevada, is a, state){KG_TRIPLE_DELIMITER}(Nevada, is in, US)"
    f"{KG_TRIPLE_DELIMITER}(Nevada, is the number 1 producer of, gold)\\n"
    "END OF EXAMPLE\\n\\n"
    "EXAMPLE\\n"
    "I'm going to the store.\\n\\n"
    "Output: NONE\\n"
    "END OF EXAMPLE\\n\\n"
    "EXAMPLE\\n"
    "Oh huh. I know Descartes likes to drive antique scooters and play the mandolin.\\n"
    f"Output: (Descartes, likes to drive, antique scooters){KG_TRIPLE_DELIMITER}(Descartes, plays, mandolin)\\n"
    "END OF EXAMPLE\\n\\n"
    "EXAMPLE\\n"
    "{text}"
    "Output:"
)


KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT = PromptTemplate(
    input_variables=["text"],
    template=_DEFAULT_KNOWLEDGE_TRIPLE_EXTRACTION_TEMPLATE,
)

llm = OpenAI(
    api_key=OPENAI_API_KEY,
    temperature=0.9
    )


# 使用知识三元组提取提示创建一个LLMChain
chain = LLMChain(llm=llm, prompt=KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT)

# Run the chain with the specified text
text = "The city of Paris is the capital and most populous city of France. The Eiffel Tower is a famous landmark in Paris."
triples = chain.invoke(
    {'text' : text}
).get('text')

def parse_triples(response, delimiter=KG_TRIPLE_DELIMITER):
    if not response:
        return []
    return response.split(delimiter)


triples_list = parse_triples(triples)


pprint(triples_list)

[' (Paris, is the capital of, France)',
 '(Paris, is the most populous city in, France)',
 '(Eiffel Tower, is a, famous landmark)',
 '(Eiffel Tower, is in, Paris)']

def create_graph_from_triplets(triplets):
    G = nx.DiGraph()
    for triplet in triplets:
        subject, predicate, obj = triplet.strip().split(',')
        G.add_edge(subject.strip(), obj.strip(), label=predicate.strip())
    return G


def nx_to_pyvis(networkx_graph):
    pyvis_graph = Network(notebook=True, cdn_resources='remote')
    for node in networkx_graph.nodes():
        pyvis_graph.add_node(node)
    for edge in networkx_graph.edges(data=True):
        pyvis_graph.add_edge(edge[0], edge[1], label=edge[2]["label"])
    return pyvis_graph


def generateGraph():
    triplets = [t.strip() for t in triples_list if t.strip()]
    graph = create_graph_from_triplets(triplets)
    pyvis_network = nx_to_pyvis(graph)


    pyvis_network.toggle_hide_edges_on_drag(True)
    pyvis_network.toggle_physics(False)
    pyvis_network.set_edge_smooth('discrete')


    html = pyvis_network.generate_html()
    html = html.replace("'", "\\"")


    return f"""<iframe style="width: 100%; height: 600px;margin:0 auto" name="result" allow="midi; geolocation; microphone; camera;
    display-capture; encrypted-media;" sandbox="allow-modals allow-forms
    allow-scripts allow-same-origin allow-popups
    allow-top-navigation-by-user-activation allow-downloads" allowfullscreen=""
    allowpaymentrequest="" frameborder="0" srcdoc='{html}'></iframe>"""

demo = gr.Interface( 
    generateGraph, 
    inputs= None , 
    outputs=gr.outputs.HTML(), 
    title= “知识图谱” , 
    allow_flagging= 'never' , 
    live= True , 
) 


demo.launch( 
    height= 800 , 
    width= “100%”
）