参考：https://python.langchain.com/docs/modules/data_connection/document_loaders/
https://github.com/thomas-yanxin/LangChain-ChatGLM-Webui/blob/master/app.py

代码

可以支持pdf、md、doc、txt等格式

from langchain.document_loaders import UnstructuredFileLoader
import re
from typing import List

from langchain.text_splitter import CharacterTextSplitter


class ChineseTextSplitter(CharacterTextSplitter):
    def __init__(self, pdf: bool = False, **kwargs):
        super().__init__(**kwargs)
        self.pdf = pdf

    def split_text(self, text: str) -> List[str]:
        if self.pdf:
            text = re.sub(r"\n{3,}", "\n", text)
            text = re.sub('\s', ' ', text)
            text = text.replace("\n\n", "")
        sent_sep_pattern = re.compile(
            '([﹒﹔﹖﹗．。！？]["’”」』]{0,2}|(?=["‘“「『]{1,2}|$))') 
        sent_list = []
        for ele in sent_sep_pattern.split(text):
            if sent_sep_pattern.match(ele) and sent_list:
                sent_list[-1] += ele
            elif ele:
                sent_list.append(ele)
        return sent_list



def load_file(filepath):
    if filepath.lower().endswith(".md"):
        loader = UnstructuredFileLoader(filepath, mode="elements")
        docs = loader.load()
    elif filepath.lower().endswith(".pdf"):
        loader = UnstructuredFileLoader(filepath)
        textsplitter = ChineseTextSplitter(pdf=True)
        docs = loader.load_and_split(textsplitter)
    else:
        loader = UnstructuredFileLoader(filepath, mode="elements")
        textsplitter = ChineseTextSplitter(pdf=False)
        docs = loader.load_and_split(text_splitter=textsplitter)
    return docs

继续把上面切分数据保存到FAISS
参考：https://blog.csdn.net/weixin_42357472/article/details/133778618?spm=1001.2014.3001.5502

from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings

embedding = HuggingFaceEmbeddings(model_name=r"C:\Users\loong\.cache\huggingface\hub\models--shibing624--text2vec-base-chinese\snapshots\2642****1812248")  ##HuggingFace离线模型，用加载HuggingFaceEmbeddings

##保存向量
docs = load_file(r"C:\Users\loong\D***资料.txt")
vector_store = FAISS.from_documents(docs, embedding)
vector_store.save_local('faiss_index')

## query 向量库搜索测试

query = "公司地址"
vector_store.similarity_search(query)