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from configs.model_config import *
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
import nltk
from vectorstores import MyFAISS
from chains.local_doc_qa import load_file
nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
if __name__ == "__main__":
filepath = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))),
"knowledge_base", "samples", "content", "test.txt")
embeddings = HuggingFaceEmbeddings(model_name=embedding_model_dict[EMBEDDING_MODEL],
model_kwargs={'device': EMBEDDING_DEVICE})
docs = load_file(filepath, using_zh_title_enhance=True)
vector_store = MyFAISS.from_documents(docs, embeddings)
query = "指令提示技术有什么示例"
search_result = vector_store.similarity_search(query)
print(search_result)
pass
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