temp001.py

from langchain_community.vectorstores import FAISS
# from langchain.embeddings.openai import OpenAIEmbeddings
from langchain_openai import OpenAIEmbeddings
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
# from langchain_openai import embeddings

# embeddings = HuggingFaceEmbeddings(model_name='/Users/yunshi/Downloads/360Data/Data Center/Working-On Task/演讲与培训/2023ChatGPT/Coding/RAG/bge-large-zh') ## 切换成BGE的embedding。
# embeddings = HuggingFaceEmbeddings(model_name='/Users/yunshi/Downloads/360Data/Data Center/Working-On Task/演讲与培训/2023ChatGPT/RAG/bge-large-zh/') ## 切换成BGE的embedding。
import os
import openai
from dotenv import load_dotenv
from openai import embeddings


load_dotenv()
### 设置openai的API key
os.environ["OPENAI_API_KEY"] = os.environ['user_token']
openai.api_key = os.environ['user_token']
bing_search_api_key = os.environ['bing_api_key']


# embeddings = OpenAIEmbeddings(show_progress_bar=True)  ## 这里是联网情况下，部署在Huggingface上后使用。
embeddings = HuggingFaceEmbeddings(model_name='GanymedeNil/text2vec-large-chinese') ## 这里是联网情况下，部署在Huggingface上后使用。
# embeddings = HuggingFaceEmbeddings(model_name='/Users/yunshi/Downloads/360Data/Data Center/Working-On Task/演讲与培训/2023ChatGPT/Coding/RAG/bge-large-zh') ## 切换成BGE的embedding。
# embeddings = OpenAIEmbeddings(disallowed_special=())  ## 这里是联网情况下，部署在Huggingface上后使用。
vector_store = FAISS.load_local("./faiss_index/", embeddings=embeddings, allow_dangerous_deserialization=True) ## 加载vector store到本地。

user_input = """
我是一个企业主，我需要关注哪些“存货”相关的数据资源规则？
"""

docs = vector_store.similarity_search(user_input)
print(docs)