通过DashScope API调用将多种模态转换为向量 - IT评测·应用市场-qidao123.com技术社区

import dashscope
from dashscope import MultiModalEmbedding
from 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 Client
client = Client(
api_key='{your-dashvector-api-key}',
endpoint='{your-dashvector-cluster-endpoint}'
)
# 创建DashVector Collection
rsp = client.create('one-peace-embedding', 1536)
assert rsp
collection = client.get('one-peace-embedding')
assert collection
# 向量入库DashVector
collection.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)

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