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转换

转换是一种以节点列表作为输入,并返回节点列表的操作。每个实现 Transformation 基类的组件都包含同步的 __call__() 定义和异步的 acall() 定义。

目前,以下组件是 Transformation 对象:

虽然转换功能最好与 IngestionPipeline 配合使用,但也可以直接使用。

from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.extractors import TitleExtractor
node_parser = SentenceSplitter(chunk_size=512)
extractor = TitleExtractor()
# use transforms directly
nodes = node_parser(documents)
# or use a transformation in async
nodes = await extractor.acall(nodes)

转换可以传递到索引或整体全局设置中,并在调用索引上的 from_documents()insert() 时使用。

from llama_index.core import VectorStoreIndex
from llama_index.core.extractors import (
TitleExtractor,
QuestionsAnsweredExtractor,
)
from llama_index.core.ingestion import IngestionPipeline
from llama_index.core.node_parser import TokenTextSplitter
transformations = [
TokenTextSplitter(chunk_size=512, chunk_overlap=128),
TitleExtractor(nodes=5),
QuestionsAnsweredExtractor(questions=3),
]
# global
from llama_index.core import Settings
Settings.transformations = [text_splitter, title_extractor, qa_extractor]
# per-index
index = VectorStoreIndex.from_documents(
documents, transformations=transformations
)

您可以通过实现基类来自行实现任何转换。

以下自定义转换将移除文本中的任何特殊字符或标点符号。

import re
from llama_index.core import Document
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.ingestion import IngestionPipeline
from llama_index.core.schema import TransformComponent
class TextCleaner(TransformComponent):
def __call__(self, nodes, **kwargs):
for node in nodes:
node.text = re.sub(r"[^0-9A-Za-z ]", "", node.text)
return nodes

这些可以直接使用或用于任何 IngestionPipeline 中。

# use in a pipeline
pipeline = IngestionPipeline(
transformations=[
SentenceSplitter(chunk_size=25, chunk_overlap=0),
TextCleaner(),
OpenAIEmbedding(),
],
)
nodes = pipeline.run(documents=[Document.example()])