通过 DashScope API 调用将多种模态转换为向量

import dashscopefrom dashscope import MultiModalEmbeddingfrom dashvector import Client

dashscope.api_key = '{your-dashscope-api-key}'

# 调用DashScope ONE-PEACE模型，将各种模态素材embedding为向量def generate_embeddings(text: str = None, image: str = None, audio: str = None):    input = []    if text:        input.append({'text': text})    if image:        input.append({'image': image})    if audio:        input.append({'audio': audio})    result = MultiModalEmbedding.call(        model=MultiModalEmbedding.Models.multimodal_embedding_one_peace_v1,        input=input,        auto_truncation=True    )    if result.status_code != 200:        raise Exception(f"ONE-PEACE failed to generate embedding of {input}, result: {result}")    return result.output["embedding"]

# 创建DashVector Clientclient = Client(    api_key='{your-dashvector-api-key}',    endpoint='{your-dashvector-cluster-endpoint}')
# 创建DashVector Collectionrsp = client.create('one-peace-embedding', 1536)assert rspcollection = client.get('one-peace-embedding')assert collection
# 向量入库DashVectorcollection.insert(    [        ('ID1', generate_embeddings(text='阿里云向量检索服务DashVector是性能、性价比具佳的向量数据库之一')),        ('ID2', generate_embeddings(image='https://dashscope.oss-cn-beijing.aliyuncs.com/images/256_1.png')),        ('ID3', generate_embeddings(audio='https://dashscope.oss-cn-beijing.aliyuncs.com/audios/cow.flac')),        ('ID4', generate_embeddings(            text='阿里云向量检索服务DashVector是性能、性价比具佳的向量数据库之一',            image='https://dashscope.oss-cn-beijing.aliyuncs.com/images/256_1.png',            audio='https://dashscope.oss-cn-beijing.aliyuncs.com/audios/cow.flac'        ))    ])
# 向量检索docs = collection.query(    generate_embeddings(text='The best vector database'))print(docs)