example.py

from RAG.VectorBase import VectorStore
from RAG.utils import ReadFiles
from RAG.LLM import OpenAIChat, InternLMChat
from RAG.Embeddings import JinaEmbedding, ZhipuEmbedding


# 没有保存数据库
# docs = ReadFiles('./data').get_content(max_token_len=600, cover_content=150) # 获得data目录下的所有文件内容并分割
# vector = VectorStore(docs)
# embedding = ZhipuEmbedding() # 创建EmbeddingModel
# vector.get_vector(EmbeddingModel=embedding)
# vector.persist(path='storage') # 将向量和文档内容保存到storage目录下，下次再用就可以直接加载本地的数据库

# # vector.load_vector('./storage') # 加载本地的数据库

# question = '正向扫描的原理是什么？'

# content = vector.query(question, model='zhipu', k=1)[0]
# chat = OpenAIChat(model='gpt-3.5-turbo-1106')
# print(chat.chat(question, [], content))


# 保存数据库之后
vector = VectorStore()

vector.load_vector('./storage') # 加载本地的数据库

question = '逆向纠错的原理是什么？'

embedding = ZhipuEmbedding() # 创建EmbeddingModel

content = vector.query(question, EmbeddingModel=embedding, k=1)[0]
chat = OpenAIChat(model='gpt-3.5-turbo-1106')
print(chat.chat(question, [], content))