法律文档的实体消歧与可视化

在本指南中，我们将演示如何从示例法律合同中提取和消歧实体。然后，我们将这些实体及其依赖关系可视化为一个实体图。这种方法对于法律科技应用非常有用，有助于理解复杂的文档。

动机

法律合同充满了复杂的细节和相互关联的条款。自动提取和可视化这些元素可以更容易地理解文档的整体结构和条款。

定义数据结构

Entity 和 Property 类对提取的实体及其属性进行建模。DocumentExtraction 封装了这些实体的列表。

from pydantic import BaseModel, Field
from typing import List


class Property(BaseModel):
    key: str
    value: str
    resolved_absolute_value: str


class Entity(BaseModel):
    id: int = Field(
        ...,
        description="Unique identifier for the entity, used for deduplication, design a scheme allows multiple entities",
    )
    subquote_string: List[str] = Field(
        ...,
        description="Correctly resolved value of the entity, if the entity is a reference to another entity, this should be the id of the referenced entity, include a few more words before and after the value to allow for some context to be used in the resolution",
    )
    entity_title: str
    properties: List[Property] = Field(
        ..., description="List of properties of the entity"
    )
    dependencies: List[int] = Field(
        ...,
        description="List of entity ids that this entity depends  or relies on to resolve it",
    )


class DocumentExtraction(BaseModel):
    entities: List[Entity] = Field(
        ...,
        description="Body of the answer, each fact should be a separate object with a body and a list of sources",
    )

实体提取与消歧

ask_ai 函数利用 OpenAI 的 API 从输入内容中提取和消歧实体。

import instructor
from openai import OpenAI

# Apply the patch to the OpenAI client
# enables response_model keyword
client = instructor.from_openai(OpenAI())


def ask_ai(content) -> DocumentExtraction:
    return client.chat.completions.create(
        model="gpt-4",
        response_model=DocumentExtraction,
        messages=[
            {
                "role": "system",
                "content": "Extract and resolve a list of entities from the following document:",
            },
            {
                "role": "user",
                "content": content,
            },
        ],
    )  # type: ignore

图可视化

generate_graph 接受提取的实体并使用 Graphviz 将其可视化。它为每个实体创建节点，并为其依赖关系创建边。

from graphviz import Digraph

def generate_html_label(entity: Entity) -> str:
    rows = [
        f"<tr><td>{prop.key}</td><td>{prop.resolved_absolute_value}</td></tr>"
        for prop in entity.properties
    ]
    table_rows = "".join(rows)
    return f"<<table border='0' cellborder='1' cellspacing='0'><tr><td colspan='2'><b>{entity.entity_title}</b></td></tr>{table_rows}</table>>"


def generate_graph(data: DocumentExtraction):
    dot = Digraph(comment="Entity Graph", node_attr={"shape": "plaintext"})

    for entity in data.entities:
        label = generate_html_label(entity)
        dot.node(str(entity.id), label)

    for entity in data.entities:
        for dep_id in entity.dependencies:
            dot.edge(str(entity.id), str(dep_id))

    dot.render("entity.gv", view=True)

执行

最后，执行代码以可视化示例法律合同的实体图。

content = """
Sample Legal Contract
Agreement Contract

This Agreement is made and entered into on 2020-01-01 by and between Company A ("the Client") and Company B ("the Service Provider").

Article 1: Scope of Work

The Service Provider will deliver the software product to the Client 30 days after the agreement date.

Article 2: Payment Terms

The total payment for the service is $50,000.
An initial payment of $10,000 will be made within 7 days of the the signed date.
The final payment will be due 45 days after [SignDate].

Article 3: Confidentiality

The parties agree not to disclose any confidential information received from the other party for 3 months after the final payment date.

Article 4: Termination

The contract can be terminated with a 30-day notice, unless there are outstanding obligations that must be fulfilled after the [DeliveryDate].
"""  # Your legal contract here
model = ask_ai(content)
generate_graph(model)

这将生成实体及其依赖关系的图形表示，存储为 "entity.gv"。