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https://github.com/JasonYANG170/CodeGeeX4.git
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43 lines
1.4 KiB
Python
43 lines
1.4 KiB
Python
import os
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from langchain_community.docstore import InMemoryDocstore
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from langchain_community.vectorstores.faiss import FAISS, dependable_faiss_import
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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 vectorize(files: list[str], args):
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# split file into chunks
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chunks = []
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for file in tqdm(files, desc="文件切分"):
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chunks.extend(split_into_chunks(file, args.chunk_size, args.overlap_size))
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# initialize the vector store
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vector_store = FAISS(
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embedding_function=embed_model,
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index=dependable_faiss_import().IndexFlatL2(embed_model.embedding_size),
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docstore=InMemoryDocstore(),
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index_to_docstore_id={},
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)
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# translate to vectors
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batch_size = args.batch_size
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for i in tqdm(range(0, len(chunks), batch_size), desc="向量化"):
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try:
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vector_store.add_documents(chunks[i:i + batch_size])
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except Exception as e:
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print(f"文件向量化失败，{e}")
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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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vector_store.save_local(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_vector_store(vector_path: str):
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return FAISS.load_local(vector_path, embed_model, allow_dangerous_deserialization=True)
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