本示例介绍如何在 LangChain 中使用 AI21SemanticTextSplitter。
安装
pip install langchain-ai21环境设置
我们需要获取 AI21 API 密钥并设置 AI21_API_KEY 环境变量：
from getpass import getpass
if "AI21_API_KEY" not in os.environ:
os.environ["AI21_API_KEY"] = getpass()示例用法
按语义分割文本
本示例展示如何使用 AI21SemanticTextSplitter 基于语义意义将文本分割成块。
from langchain_ai21 import AI21SemanticTextSplitter
TEXT = (
"We’ve all experienced reading long, tedious, and boring pieces of text - financial reports, "
"legal documents, or terms and conditions (though, who actually reads those terms and conditions to be honest?).\n"
"Imagine a company that employs hundreds of thousands of employees. In today's information "
"overload age, nearly 30% of the workday is spent dealing with documents. There's no surprise "
"here, given that some of these documents are long and convoluted on purpose (did you know that "
"reading through all your privacy policies would take almost a quarter of a year?). Aside from "
"inefficiency, workers may simply refrain from reading some documents (for example, Only 16% of "
"Employees Read Their Employment Contracts Entirely Before Signing!).\nThis is where AI-driven summarization "
"tools can be helpful: instead of reading entire documents, which is tedious and time-consuming, "
"users can (ideally) quickly extract relevant information from a text. With large language models, "
"the development of those tools is easier than ever, and you can offer your users a summary that is "
"specifically tailored to their preferences.\nLarge language models naturally follow patterns in input "
"(prompt), and provide coherent completion that follows the same patterns. For that, we want to feed "
'them with several examples in the input ("few-shot prompt"), so they can follow through. '
"The process of creating the correct prompt for your problem is called prompt engineering, "
"and you can read more about it here."
)
semantic_text_splitter = AI21SemanticTextSplitter()
chunks = semantic_text_splitter.split_text(TEXT)
print(f"The text has been split into {len(chunks)} chunks.")
for chunk in chunks:
print(chunk)
print("====")基于语义意义合并分割文本
本示例展示如何使用 AI21SemanticTextSplitter 基于语义意义将文本分割成块，然后基于 chunk_size.
from langchain_ai21 import AI21SemanticTextSplitter
TEXT = (
"We’ve all experienced reading long, tedious, and boring pieces of text - financial reports, "
"legal documents, or terms and conditions (though, who actually reads those terms and conditions to be honest?).\n"
"Imagine a company that employs hundreds of thousands of employees. In today's information "
"overload age, nearly 30% of the workday is spent dealing with documents. There's no surprise "
"here, given that some of these documents are long and convoluted on purpose (did you know that "
"reading through all your privacy policies would take almost a quarter of a year?). Aside from "
"inefficiency, workers may simply refrain from reading some documents (for example, Only 16% of "
"Employees Read Their Employment Contracts Entirely Before Signing!).\nThis is where AI-driven summarization "
"tools can be helpful: instead of reading entire documents, which is tedious and time-consuming, "
"users can (ideally) quickly extract relevant information from a text. With large language models, "
"the development of those tools is easier than ever, and you can offer your users a summary that is "
"specifically tailored to their preferences.\nLarge language models naturally follow patterns in input "
"(prompt), and provide coherent completion that follows the same patterns. For that, we want to feed "
'them with several examples in the input ("few-shot prompt"), so they can follow through. '
"The process of creating the correct prompt for your problem is called prompt engineering, "
"and you can read more about it here."
)
semantic_text_splitter_chunks = AI21SemanticTextSplitter(chunk_size=1000)
chunks = semantic_text_splitter_chunks.split_text(TEXT)
print(f"The text has been split into {len(chunks)} chunks.")
for chunk in chunks:
print(chunk)
print("====")将文本分割为文档
本示例展示如何使用 AI21SemanticTextSplitter 基于语义意义将文本分割成文档。元数据将为每个文档包含一种类型。
from langchain_ai21 import AI21SemanticTextSplitter
TEXT = (
"We’ve all experienced reading long, tedious, and boring pieces of text - financial reports, "
"legal documents, or terms and conditions (though, who actually reads those terms and conditions to be honest?).\n"
"Imagine a company that employs hundreds of thousands of employees. In today's information "
"overload age, nearly 30% of the workday is spent dealing with documents. There's no surprise "
"here, given that some of these documents are long and convoluted on purpose (did you know that "
"reading through all your privacy policies would take almost a quarter of a year?). Aside from "
"inefficiency, workers may simply refrain from reading some documents (for example, Only 16% of "
"Employees Read Their Employment Contracts Entirely Before Signing!).\nThis is where AI-driven summarization "
"tools can be helpful: instead of reading entire documents, which is tedious and time-consuming, "
"users can (ideally) quickly extract relevant information from a text. With large language models, "
"the development of those tools is easier than ever, and you can offer your users a summary that is "
"specifically tailored to their preferences.\nLarge language models naturally follow patterns in input "
"(prompt), and provide coherent completion that follows the same patterns. For that, we want to feed "
'them with several examples in the input ("few-shot prompt"), so they can follow through. '
"The process of creating the correct prompt for your problem is called prompt engineering, "
"and you can read more about it here."
)
semantic_text_splitter = AI21SemanticTextSplitter()
documents = semantic_text_splitter.split_text_to_documents(TEXT)
print(f"The text has been split into {len(documents)} Documents.")
for doc in documents:
print(f"type: {doc.metadata['source_type']}")
print(f"text: {doc.page_content}")
print("====")创建带有元数据的文档
本示例展示如何使用 AI21SemanticTextSplitter 从文本创建文档，并为每个文档添加自定义元数据。
from langchain_ai21 import AI21SemanticTextSplitter
TEXT = (
"We’ve all experienced reading long, tedious, and boring pieces of text - financial reports, "
"legal documents, or terms and conditions (though, who actually reads those terms and conditions to be honest?).\n"
"Imagine a company that employs hundreds of thousands of employees. In today's information "
"overload age, nearly 30% of the workday is spent dealing with documents. There's no surprise "
"here, given that some of these documents are long and convoluted on purpose (did you know that "
"reading through all your privacy policies would take almost a quarter of a year?). Aside from "
"inefficiency, workers may simply refrain from reading some documents (for example, Only 16% of "
"Employees Read Their Employment Contracts Entirely Before Signing!).\nThis is where AI-driven summarization "
"tools can be helpful: instead of reading entire documents, which is tedious and time-consuming, "
"users can (ideally) quickly extract relevant information from a text. With large language models, "
"the development of those tools is easier than ever, and you can offer your users a summary that is "
"specifically tailored to their preferences.\nLarge language models naturally follow patterns in input "
"(prompt), and provide coherent completion that follows the same patterns. For that, we want to feed "
'them with several examples in the input ("few-shot prompt"), so they can follow through. '
"The process of creating the correct prompt for your problem is called prompt engineering, "
"and you can read more about it here."
)
semantic_text_splitter = AI21SemanticTextSplitter()
texts = [TEXT]
documents = semantic_text_splitter.create_documents(
texts=texts, metadatas=[{"pikachu": "pika pika"}]
)
print(f"The text has been split into {len(documents)} Documents.")
for doc in documents:
print(f"metadata: {doc.metadata}")
print(f"text: {doc.page_content}")
print("====")使用起始索引将文本分割为文档
本示例展示如何使用 AI21SemanticTextSplitter 基于语义意义将文本分割成文档。元数据将为每个文档包含一个起始索引。 **注意** 起始索引提供的是块顺序的指示，而不是每个块的实际起始索引。
from langchain_ai21 import AI21SemanticTextSplitter
TEXT = (
"We’ve all experienced reading long, tedious, and boring pieces of text - financial reports, "
"legal documents, or terms and conditions (though, who actually reads those terms and conditions to be honest?).\n"
"Imagine a company that employs hundreds of thousands of employees. In today's information "
"overload age, nearly 30% of the workday is spent dealing with documents. There's no surprise "
"here, given that some of these documents are long and convoluted on purpose (did you know that "
"reading through all your privacy policies would take almost a quarter of a year?). Aside from "
"inefficiency, workers may simply refrain from reading some documents (for example, Only 16% of "
"Employees Read Their Employment Contracts Entirely Before Signing!).\nThis is where AI-driven summarization "
"tools can be helpful: instead of reading entire documents, which is tedious and time-consuming, "
"users can (ideally) quickly extract relevant information from a text. With large language models, "
"the development of those tools is easier than ever, and you can offer your users a summary that is "
"specifically tailored to their preferences.\nLarge language models naturally follow patterns in input "
"(prompt), and provide coherent completion that follows the same patterns. For that, we want to feed "
'them with several examples in the input ("few-shot prompt"), so they can follow through. '
"The process of creating the correct prompt for your problem is called prompt engineering, "
"and you can read more about it here."
)
semantic_text_splitter = AI21SemanticTextSplitter(add_start_index=True)
documents = semantic_text_splitter.create_documents(texts=[TEXT])
print(f"The text has been split into {len(documents)} Documents.")
for doc in documents:
print(f"start_index: {doc.metadata['start_index']}")
print(f"text: {doc.page_content}")
print("====")分割文档
本示例展示如何使用 AI21SemanticTextSplitter 基于语义意义将文档列表分割成块。
from langchain_ai21 import AI21SemanticTextSplitter
from langchain_core.documents import Document
TEXT = (
"We’ve all experienced reading long, tedious, and boring pieces of text - financial reports, "
"legal documents, or terms and conditions (though, who actually reads those terms and conditions to be honest?).\n"
"Imagine a company that employs hundreds of thousands of employees. In today's information "
"overload age, nearly 30% of the workday is spent dealing with documents. There's no surprise "
"here, given that some of these documents are long and convoluted on purpose (did you know that "
"reading through all your privacy policies would take almost a quarter of a year?). Aside from "
"inefficiency, workers may simply refrain from reading some documents (for example, Only 16% of "
"Employees Read Their Employment Contracts Entirely Before Signing!).\nThis is where AI-driven summarization "
"tools can be helpful: instead of reading entire documents, which is tedious and time-consuming, "
"users can (ideally) quickly extract relevant information from a text. With large language models, "
"the development of those tools is easier than ever, and you can offer your users a summary that is "
"specifically tailored to their preferences.\nLarge language models naturally follow patterns in input "
"(prompt), and provide coherent completion that follows the same patterns. For that, we want to feed "
'them with several examples in the input ("few-shot prompt"), so they can follow through. '
"The process of creating the correct prompt for your problem is called prompt engineering, "
"and you can read more about it here."
)
semantic_text_splitter = AI21SemanticTextSplitter()
document = Document(page_content=TEXT, metadata={"hello": "goodbye"})
documents = semantic_text_splitter.split_documents([document])
print(f"The document list has been split into {len(documents)} Documents.")
for doc in documents:
print(f"text: {doc.page_content}")
print(f"metadata: {doc.metadata}")
print("====")