33 LlamaIndex中的元数据提取：增强文档处理_llamaindex summaryextractor-优快云博客

LlamaIndex中的元数据提取：增强文档处理

在处理长文档时，文本块可能缺乏必要的上下文来区分与其他相似的文本块。为了解决这个问题，我们可以利用大型语言模型（LLMs）提取与文档相关的某些上下文信息，以更好地帮助检索和语言模型区分相似的段落。

使用方法

首先，我们定义一个元数据提取器，它接受一系列特征提取器，这些提取器将按顺序处理。然后，我们将这个元数据提取器传递给节点解析器，节点解析器将为每个节点添加额外的元数据。

from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.extractors import (
    SummaryExtractor,
    QuestionsAnsweredExtractor,
    TitleExtractor,
    KeywordExtractor,
)
from llama_index.extractors.entity import EntityExtractor

transformations = [
    SentenceSplitter(),
    TitleExtractor(nodes=5),
    QuestionsAnsweredExtractor(questions=3),
    SummaryExtractor(summaries=["prev", "self"]),
    KeywordExtractor(keywords=10),
    EntityExtractor(prediction_threshold=0.5),
]

然后，我们可以在输入文档或节点上运行这些转换：

from llama_index.core.ingestion import IngestionPipeline

pipeline = IngestionPipeline(transformations=transformations)

nodes = pipeline.run(documents=documents)

以下是一个提取的元数据示例：

{'page_label': '2',
 'file_name': '10k-132.pdf',
 'document_title': 'Uber Technologies, Inc. 2019 Annual Report: Revolutionizing Mobility and Logistics Across 69 Countries and 111 Million MAPCs with $65 Billion in Gross Bookings',
 'questions_this_excerpt_can_answer': '\n\n1. How many countries does Uber Technologies, Inc. operate in?\n2. What is the total number of MAPCs served by Uber Technologies, Inc.?\n3. How much gross bookings did Uber Technologies, Inc. generate in 2019?',
 'prev_section_summary': "\n\nThe 2019 Annual Report provides an overview of the key topics and entities that have been important to the organization over the past year. These include financial performance, operational highlights, customer satisfaction, employee engagement, and sustainability initiatives. It also provides an overview of the organization's strategic objectives and goals for the upcoming year.",
 'section_summary': '\nThis section discusses a global tech platform that serves multiple multi-trillion dollar markets with products leveraging core technology and infrastructure. It enables consumers and drivers to tap a button and get a ride or work. The platform has revolutionized personal mobility with ridesharing and is now leveraging its platform to redefine the massive meal delivery and logistics industries. The foundation of the platform is its massive network, leading technology, operational excellence, and product expertise.',
 'excerpt_keywords': '\nRidesharing, Mobility, Meal Delivery, Logistics, Network, Technology, Operational Excellence, Product Expertise, Point A, Point B'}

自定义提取器

如果提供的提取器不符合你的需求，你也可以定义一个自定义提取器，如下所示：

from llama_index.core.extractors import BaseExtractor

class CustomExtractor(BaseExtractor):
    async def aextract(self, nodes) -> List[Dict]:
        metadata_list = [
            {
                "custom": node.metadata["document_title"]
                + "\n"
                + node.metadata["excerpt_keywords"]
            }
            for node in nodes
        ]
        return metadata_list