【AI大模型应用开发】教你用AI轻松将你的知识库变为知识图谱（附完整代码，直接运行）

0. 实现效果

先来看下最终的实现效果：输入知识，大模型自动将知识中的实体、关系、属性等提取出来，并自动生成知识图谱。为了方便查看，代码中还对创建的知识谱图进行了可视化展示。

（1）对于某个知识提取出的知识图谱结构：

（2）可视化知识谱图的展示：

1. 代码实现过程

1.1 知识图谱结构的识别

将知识转化为知识图谱，最重要的步骤是将知识中的实体、关系、属性等提取出来。这也是代码的主要部分。这部分是通过Prompt来实现的。

（1）Prompt部分

# Prompt template for knowledge triple extraction  
_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:"  
)

这个Prompt主要的任务是让大模型从语句中提取出知识三元组，即实体、关系、属性。看着有点像识别句子中的主谓宾。Prompt中给了几个示例，Few-shot的方式可以让大模型更好地理解用户的需求。

（2）调用大模型的基本流程

KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT = PromptTemplate(  
    input_variables=["text"],  
    template=_DEFAULT_KNOWLEDGE_TRIPLE_EXTRACTION_TEMPLATE,  
)  
  
llm = ChatOpenAI(temperature=0.9)  
  
# Create an LLMChain using the knowledge triple extraction prompt  
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')

（3）结果的解析

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

（4）执行结果示例：

1.2 知识图谱的可视化

示例代码中使用gradio框架进行了可视化界面的搭建。可视化不是本文重点，这里就不详细介绍了。可以看完整代码里的实现。大体是使用了 pyvis 和 networkx 来使用前面提取的三元组进行图结构的构建。

运行之后，打开提示中的链接，就可以看到可视化界面了。

这里有小小的疑问：为什么要同时使用pyvis和networkx？看代码中是先用三元组构建了networkx的图结构，然后将networkx结构转化成了pyvis结构，然后再使用pyvis进行可视化。这个过程有必要？不能直接利用三元组构建pyvis结构吗？期待各位大佬的解答！

2. 完整代码

下面将可直接运行的完整代码奉上（当然，缺依赖库的话还是要自己装一装的了）：

from langchain.prompts import PromptTemplate  
from langchain_openai import ChatOpenAI  
from langchain.chains import LLMChain  
from langchain.graphs.networkx_graph import KG_TRIPLE_DELIMITER  
from pprint import pprint  
  
# Prompt template for knowledge triple extraction  
_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 = ChatOpenAI(temperature=0.9)  
  
# Create an LLMChain using the knowledge triple extraction prompt  
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')  
  
pprint(triples)  
  
def parse_triples(response, delimiter=KG_TRIPLE_DELIMITER):  
    if not response:  
        return []  
    return response.split(delimiter)  
  
triples_list = parse_triples(triples)  
  
pprint(triples_list)  
  
from pyvis.network import Network  
import networkx as nx  
  
  
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>"""  
      
import gradio as gr  
  
demo = gr.Interface(  
    generateGraph,  
    inputs=None,  
    outputs='html',  
    title="Knowledge Graph",  
    allow_flagging='never',  
    live=True,  
)  
  
demo.launch(  
    height=800,  
    width="100%"  
)

3. 可能遇到的坑

如果你使用的是参考链接中的原代码，则很可能会遇到下面的问题。

（1）报错：module gradio has no attribute outputs. gradio版本4.16

解决：outputs=gr.outputs.HTML 改为 outputs='html'

4. 总结

本文我们主要是学习了如何利用AI将知识转化为知识图谱的结构。其中最主要的，就是从知识中提取出三元组，这就强依赖Prompt和大模型的能力了。然后，锦上添花的，代码实现了知识图谱结构的可视化。尽管简单，但思路绝对值得借鉴。

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