Python Code Splitter 编写代码 PYTHON_CODE = """ def hello_world(): print("Hello, World!") # Call the function hello_world() """ python_splitter = RecursiveCharacterTextSplitter.from_language( language=Language.PYTHON, chunk_size=50, chunk_overlap=0 ) python_docs = python_splitter.create_...
create_documents([PYTHON_CODE]) print(python_docs) JavaScript Code Splitter 编写代码 JS_CODE = """ function helloWorld() { console.log("Hello, World!"); } // Call the function helloWorld(); """ js_splitter = RecursiveCharacterTextSplitter.from_language( language=Language.JS, chunk_size=...
RecursiveCharacterTextSplitter 是一个用于将文本递归地分割成指定大小的块的Python类。它的核心思想是根据一组分隔符(separators)逐步分割文本,直到每个块的大小都符合预设的 chunk_size。如果某个块仍然过大,它会继续递归地分割,直到满足条件为止。 3. RecursiveCharacterTextSplitter 方法的基本实现代码 以下是 Recursive...
Python Code Splitter 编写代码 PYTHON_CODE = """ def hello_world(): print("Hello, World!") # Call the function hello_world() """ python_splitter = RecursiveCharacterTextSplitter.from_language( language=Language.PYTHON, chunk_size=50, chunk_overlap=0 ) python_docs = python_splitter.create_...
先来看看CharacterTextSplitter,这个separator是一个字符串,说明是根据一个字符串进行分割,之后进行合并和chunk size 第一步就是_split_text_with_regex,其实没有什么说的,就是根据用户输入的符号, 切分成小块 第二步就是比较核心的_merge_splits,这个_merge_splits比较核心。大概 ...
Split text into semantic chunks, up to a desired chunk size. Supports calculating length by characters and tokens, and is callable from Rust and Python. - spiceai/text-splitter
text_splitter=RecursiveCharacterTextSplitter(chunk_size=500,chunk_overlap=50,length_function=len,add_start_index=True,)# or text_splitter=RecursiveCharacterTextSplitter(chunk_size=1000,chunk_overlap=100,length_function=len,add_start_index=True,) ...
Langchain-Chatchat(原Langchain-ChatGLM)基于 Langchain 与 ChatGLM 等语言模型的本地知识库问答 | Langchain-Chatchat (formerly langchain-ChatGLM), local knowledge based LLM (like ChatGLM) QA app with langchain - Langchain-Chatchat/text_splitter/ali_text_s
texts = text_splitter.split_documents(documents) local_model_name ='shibing624_text2vec-base-chinese'embeddings = HuggingFaceEmbeddings(model_name=local_model_name) db = FAISS.from_documents(texts, embeddings) faiss_index ="vectors_db/hln_tb_faiss_index"db.save_local(faiss_index)# db = FAISS...
PYTHON\\大模型\\gpt3.5\\data', glob='**/*.txt')# 将数据转成 document 对象,每个文件会作为一个 documentdocuments = loader.load()# 初始化加载器text_splitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=0)# 切割加载的 documentsplit_docs = text_splitter.split_documents(documents...