使用 Jina Embeddings V3 进行嵌入

源代码 examples/offline_inference/embed_jina_embeddings_v3.py。

# SPDX-License-Identifier: Apache-2.0

from argparse import Namespace

from vllm import LLM, EngineArgs
from vllm.utils import FlexibleArgumentParser


def parse_args():
    parser = FlexibleArgumentParser()
    parser = EngineArgs.add_cli_args(parser)
    # Set example specific arguments
    parser.set_defaults(
        model="jinaai/jina-embeddings-v3", task="embed", trust_remote_code=True
    )
    return parser.parse_args()


def main(args: Namespace):
    # Sample prompts.
    prompts = [
        "Follow the white rabbit.",  # English
        "Sigue al conejo blanco.",  # Spanish
        "Suis le lapin blanc.",  # French
        "跟着白兔走。",  # Chinese
        "اتبع الأرنب الأبيض.",  # Arabic
        "Folge dem weißen Kaninchen.",  # German
    ]

    # Create an LLM.
    # You should pass task="embed" for embedding models
    model = LLM(**vars(args))

    # Generate embedding. The output is a list of EmbeddingRequestOutputs.
    # Only text matching task is supported for now. See #16120
    outputs = model.embed(prompts)

    # Print the outputs.
    print("\nGenerated Outputs:")
    print("Only text matching task is supported for now. See #16120")
    print("-" * 60)
    for prompt, output in zip(prompts, outputs):
        embeds = output.outputs.embedding
        embeds_trimmed = (
            (str(embeds[:16])[:-1] + ", ...]") if len(embeds) > 16 else embeds
        )
        print(
            f"Prompt: {prompt!r} \n"
            f"Embeddings for text matching: {embeds_trimmed} "
            f"(size={len(embeds)})"
        )
        print("-" * 60)


if __name__ == "__main__":
    args = parse_args()
    main(args)