Alibaba-NLP

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gte-Qwen2-7B-instruct

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我使用了第三方的rerankers库，效果还不错。

class RagServer:
    def __init__(self, model: str, db_dir: str, embed_func: str, res_num=5, reranker_num=25):
        vector_store = langchain_chroma.Chroma(persist_directory=db_dir, 
                                               embedding_function=langchain_ollama.OllamaEmbeddings(model=embed_func, num_gpu=0))
        retriever = vector_store.as_retriever(search_type="mmr", search_kwargs={"k": reranker_num, "score_threshold": 0.3})
        self.model = model
        ranker = rerankers.Reranker('Alibaba-NLP/gte-multilingual-reranker-base', 'zh', model_type='cross-encoder', device='cuda:0', dtype=torch.float16, 
                                    model_kwargs={'cache_dir':'cache_dir', 'trust_remote_code': True, 'local_files_only': True},
                                    tokenizer_kwargs={'cache_dir':'cache_dir', 'local_files_only': True})
        compressor = ranker.as_langchain_compressor(res_num)
        self.compression_retriever = ContextualCompressionRetriever(base_compressor=compressor, base_retriever=retriever)

然后使用self.compression_retriever.invoke(question)调用
注意根据自己的显存和网络环境，调整num_gpu，device='cuda:0'，'local_files_only': True几个参数。