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from langchain.llms import Ollama
from langchain.embeddings import OllamaEmbeddings
from langchain.chains import RetrievalQA
from langchain.vectorstores import FAISS
from langchain.text_splitter import CharacterTextSplitter
from langchain.document_loaders import TextLoader

# 加载本地模型
llm = Ollama(model="llama2")
embeddings = OllamaEmbeddings(model="llama2")

# 加载文档
loader = TextLoader("your_knowledge_base.txt")
documents = loader.load()

# 切分文本
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)

# 创建向量数据库
db = FAISS.from_documents(texts, embeddings)

# 创建问答链
qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=db.as_retriever())

# 提问
query = "你的问题是什么？"
result = qa.run(query)

print(result)