# DashScope Embeddings

If you’re opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.

%pip install llama-index-core
%pip install llama-index-embeddings-dashscope

# Set API key
%env DASHSCOPE_API_KEY=YOUR_DASHSCOPE_API_KEY

# you can set API key parameter DashScopeTextEmbedding(model=DashScopeTextEmbeddingModels.TEXT_EMBEDDING_V2, api_key=api_key)

# imports
from llama_index.embeddings.dashscope import (
    DashScopeEmbedding,
    DashScopeTextEmbeddingModels,
    DashScopeTextEmbeddingType,
)

# Create embeddings
# text_type=`document` to build index
embedder = DashScopeEmbedding(
    model_name=DashScopeTextEmbeddingModels.TEXT_EMBEDDING_V2,
    text_type=DashScopeTextEmbeddingType.TEXT_TYPE_DOCUMENT,
)
# Call text Embedding
embedding = embedder.get_text_embedding("衣服的质量杠杠的，很漂亮，不枉我等了这么久啊，喜欢，以后还来这里买")
print(f"Dimension of embeddings: {len(embedding)}")
print(embedding[:5])

Dimension of embeddings: 1536
[-0.00838587212517078, 0.01004877272531103, 0.0015754734226650637, -0.04273583173235969, -0.05209946086276315]

# imports
from llama_index.embeddings.dashscope import (
    DashScopeEmbedding,
    DashScopeTextEmbeddingModels,
    DashScopeTextEmbeddingType,
)

# Create embeddings
# text_type=`query` to retrive relevant context.
embedder = DashScopeEmbedding(
    model_name=DashScopeTextEmbeddingModels.TEXT_EMBEDDING_V2,
    text_type=DashScopeTextEmbeddingType.TEXT_TYPE_QUERY,
)
# Call text Embedding
embedding = embedder.get_text_embedding("衣服的质量杠杠的，很漂亮，不枉我等了这么久啊，喜欢，以后还来这里买")
print(f"Dimension of embeddings: {len(embedding)}")
print(embedding[:5])

Dimension of embeddings: 1536
[-0.00838587212517078, 0.01004877272531103, 0.0015754734226650637, -0.04273583173235969, -0.05209946086276315]

# call batch text embedding
from llama_index.embeddings.dashscope import (
    DashScopeEmbedding,
    DashScopeBatchTextEmbeddingModels,
    DashScopeTextEmbeddingType,
)

embedder = DashScopeEmbedding(
    model_name=DashScopeBatchTextEmbeddingModels.TEXT_EMBEDDING_ASYNC_V2,
    text_type=DashScopeTextEmbeddingType.TEXT_TYPE_DOCUMENT,
)

embedding_result_file_url = embedder.get_batch_text_embedding(
    embedding_file_url="https://dashscope.oss-cn-beijing.aliyuncs.com/samples/text/text-embedding-test.txt"
)
print(embedding_result_file_url)

https://dashscope-result-bj.oss-cn-beijing.aliyuncs.com/5fc5c860/2024-01-29/644ccedb-0b14-481c-a975-16bb5249282d_output_1706517940902.txt.gz?Expires=1706777144&OSSAccessKeyId=LTAI5tQZd8AEcZX6KZV4G8qL&Signature=g%2B0qcmOSwxEj8Cb2zXlvBbA6Fas%3D

# call multimodal embedding service
from llama_index.embeddings.dashscope import (
    DashScopeEmbedding,
    DashScopeMultiModalEmbeddingModels,
)

embedder = DashScopeEmbedding(
    model_name=DashScopeMultiModalEmbeddingModels.MULTIMODAL_EMBEDDING_ONE_PEACE_V1,
)

embedding = embedder.get_image_embedding(
    img_file_path="https://dashscope.oss-cn-beijing.aliyuncs.com/images/256_1.png"
)
print(f"Dimension of embeddings: {len(embedding)}")
print(embedding[:5])

Dimension of embeddings: 1536
[-0.03515625, 0.05035400390625, 0.008087158203125, 0.0163116455078125, 0.01064300537109375]

# call multimodal embedding service
from llama_index.embeddings.dashscope import (
    DashScopeEmbedding,
    DashScopeMultiModalEmbeddingModels,
)

embedder = DashScopeEmbedding(
    model_name=DashScopeMultiModalEmbeddingModels.MULTIMODAL_EMBEDDING_ONE_PEACE_V1,
)

input = [
    {"factor": 1, "text": "你好"},
    {
        "factor": 2,
        "audio": "https://dashscope.oss-cn-beijing.aliyuncs.com/audios/cow.flac",
    },
    {
        "factor": 3,
        "image": "https://dashscope.oss-cn-beijing.aliyuncs.com/images/256_1.png",
    },
]

embedding = embedder.get_multimodal_embedding(input=input)
print(f"Dimension of embeddings: {len(embedding)}")
print(embedding[:5])

Dimension of embeddings: 1536
[-0.0200169887393713, 0.041749317198991776, 0.01004155445843935, 0.03983306884765625, -0.006652673240751028]