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37 lines
1.4 KiB
Python
37 lines
1.4 KiB
Python
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import os
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import faiss
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from llama_index.core import StorageContext, VectorStoreIndex, load_index_from_storage
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from llama_index.legacy.vector_stores import FaissVectorStore
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from models.embedding import GLMEmbeddings
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from tqdm import tqdm
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from utils.data import split_into_chunks
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embed_model = GLMEmbeddings()
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def save_vectors(files: list[str], args):
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# split file into chunks
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nodes = []
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for file in tqdm(files, desc="文件切分"):
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nodes.extend(split_into_chunks(file, args.lines_per_chunk, args.lines_overlap, args.max_chars))
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# initialize vector store
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vector_store = FaissVectorStore(faiss_index=faiss.IndexFlatL2(embed_model.embedding_size))
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storage_context = StorageContext.from_defaults(vector_store=vector_store)
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# translate to vectors
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index = VectorStoreIndex(nodes=nodes, storage_context=storage_context, embed_model=embed_model)
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# save embedded vectors
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output_path = args.output_path
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os.makedirs(output_path, exist_ok=True)
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index.storage_context.persist(persist_dir=output_path)
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print(f"文件向量化完成，已保存至{output_path}")
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def load_vectors(vector_path: str):
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vector_store = FaissVectorStore.from_persist_dir(vector_path)
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storage_context = StorageContext.from_defaults(vector_store=vector_store, persist_dir=vector_path)
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return load_index_from_storage(storage_context=storage_context)
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