Zdao032
39 lines
1.5 KiB
Python
39 lines
1.5 KiB
Python
import os
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from langchain_community.vectorstores import FAISS
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from langchain_huggingface import HuggingFaceEmbeddings
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with open("./data/data.txt", 'r', encoding='utf-8') as file:
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txt_list = [line.strip() for line in file]
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embedding_path = "/data/Z_LLM_data/Embed_data/bge-m3"
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embeddings = HuggingFaceEmbeddings(model_name=embedding_path)
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faiss_archived = "./data/faiss_data/data"
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vectorstore_txt_faiss = FAISS.from_texts(txt_list, embeddings)
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vectorstore_txt_faiss.save_local(faiss_archived)
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retriever_txt_faiss1 = vectorstore_txt_faiss.as_retriever(search_kwargs={"k":3})
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retriever_txt_faiss2 = vectorstore_txt_faiss.as_retriever(
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search_type="mmr",
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search_kwargs={"k": 3, # 检索结果
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"fetch_k": 1, # 候选结果数量
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"lambda_mult": 0.5} # 平衡指数，1为相关性；0为多样性
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)
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retriever_txt_faiss3 = vectorstore_txt_faiss.as_retriever(
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search_type="similarity_score_threshold",
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search_kwargs={"score_threshold": 0.5}
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)
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def intersection_of_three_lists(input_str):
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list1 = retriever_txt_faiss1.invoke(input_str)
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list2 = retriever_txt_faiss2.invoke(input_str)
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list3 = retriever_txt_faiss3.invoke(input_str)
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def _intersection_of_three_lists(retrieval_results):
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return [doc.page_content for doc in retrieval_results]
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list11 = _intersection_of_three_lists(list1)
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list22 = _intersection_of_three_lists(list2)
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list33 = _intersection_of_three_lists(list3)
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return list(set(list11) & set(list22) & set(list33)) |