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让我们加载SelfHostedEmbeddings、SelfHostedHuggingFaceEmbeddings和SelfHostedHuggingFaceInstructEmbeddings类。

import runhouse as rh
from langchain_community.embeddings import (
    SelfHostedEmbeddings,
    SelfHostedHuggingFaceEmbeddings,
    SelfHostedHuggingFaceInstructEmbeddings,
)

API 参考：SelfHostedEmbeddings | SelfHostedHuggingFaceEmbeddings | SelfHostedHuggingFaceInstructEmbeddings

# For an on-demand A100 with GCP, Azure, or Lambda
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1", use_spot=False)

# For an on-demand A10G with AWS (no single A100s on AWS)
# gpu = rh.cluster(name='rh-a10x', instance_type='g5.2xlarge', provider='aws')

# For an existing cluster
# gpu = rh.cluster(ips=['<ip of the cluster>'],
#                  ssh_creds={'ssh_user': '...', 'ssh_private_key':'<path_to_key>'},
#                  name='my-cluster')

embeddings = SelfHostedHuggingFaceEmbeddings(hardware=gpu)

text = "This is a test document."

query_result = embeddings.embed_query(text)

以及类似的 SelfHostedHuggingFaceInstructEmbeddings

embeddings = SelfHostedHuggingFaceInstructEmbeddings(hardware=gpu)

现在让我们使用自定义加载函数加载嵌入模型

def get_pipeline():
    from transformers import (
        AutoModelForCausalLM,
        AutoTokenizer,
        pipeline,
    )

    model_id = "facebook/bart-base"
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    model = AutoModelForCausalLM.from_pretrained(model_id)
    return pipeline("feature-extraction", model=model, tokenizer=tokenizer)


def inference_fn(pipeline, prompt):
    # Return last hidden state of the model
    if isinstance(prompt, list):
        return [emb[0][-1] for emb in pipeline(prompt)]
    return pipeline(prompt)[0][-1]

embeddings = SelfHostedEmbeddings(
    model_load_fn=get_pipeline,
    hardware=gpu,
    model_reqs=["./", "torch", "transformers"],
    inference_fn=inference_fn,
)

query_result = embeddings.embed_query(text)

相关

• 嵌入模型 概念指南
• 嵌入模型 操作指南