c_splitter=CharacterTextSplitter(chunk_size=26,chunk_overlap=4,separator=' ')text3="a b c d e f g h i j k l m n o p q r s t u v w x y z"c_splitter.split_text(text3) 加入分隔符参数以后,字符串被分割成三段 当我们在CharacterTextSplitter的参数中增加一个separator = ' '的...
from langchain.document_loaders import PyPDFLoader loader = PyPDFLoader("../examples/layout-parser-paper.pdf") pages = loader.load_and_split() print(pages[0]) 第二种方式: from langchain.document_loaders import MathpixPDFLoader loader = MathpixPDFLoader("example_data/layout-parser-paper.pdf"...
loader = PyPDFLoader(pdf_file) docs = loader.load_and_split() chain = load_summarize_chain(llm, chain_type="map_reduce") summary = chain.run(docs) print("Summary for: ", pdf_file) print(summary) print("\n") summaries.append(summary) return summaries 将摘要保存为文本文件: with open(...
pages = loader.load_and_split() print(f"第0页:\n{pages[0]}") ## 也可通过 pages[0].page_content只获取本页内容 看下运行结果:pypdf将PDF分成了一个数组,数组中的每个元素包含本页内容、文件路径和名称以及所在页码。 1.2 加载在线PDF文件 LangChain竟然也能加载...
docs = loader.load_and_split() chain =load_summarize_chain(llm, chain_type="map_reduce") summary = chain.run(docs)print("Summary for: ", pdf_file)print(summary)print("\n") summaries.append(summary) return summaries 将摘要保存为文本文件: ...
def load(self) -> List[Document]: def load_and_split( self, text_splitter: Optional[TextSplitter] = None ) -> List[Document]: def lazy_load( self, ) -> Iterator[Document]: 分别是加载,加载然后分割和最后的懒加载。 BaseLoader只是一个抽象类,所有的方法都是在子类中实现的。langchain提供了...
pages = loader.load_and_split() 1. _将文本拆分为块_:接下来,我们将提取的文本拆分为更小的块,以便进一步处理。我们使用 langchain 中的 RecursiveCharacterTextSplitter 类来实现此目的。 # 将页面拆分为块 text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0) ...
load_and_split(text_splitter) from pprint import pprint pprint(docs) """ WARNING:langchain.text_splitter:Created a chunk of size 535, which is longer than the specified 512 文本长度: 1442 [Document(page_content='ChatGLM-6B 是一个开源的、支持中英双语的对话语言模型,基于 General Language...
from langchain.chains.summarize import load_summarize_chain from langchain import OpenAI from langchain.document_loaders import PyPDFLoader pdf_loader = PyPDFLoader(pdf_file_path) docs = pdf_loader.load_and_split() llm = OpenAI() chain = load_summarize_chain(llm, chain_type="map_reduce") ...
# Load a Notion databaseloader=NotionDirectoryLoader("docs/Notion_DB")notion_db=loader.load()docs=text_splitter.split_documents(notion_db)print("Pages in the original notion document: ",len(notion_db))print("Length of chunks after splitting pages: ",len(docs))# Pages in the original notio...