Dashscope
DashScopeEmbedding #
Bases: MultiModalEmbedding
DashScope class for text embedding.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
model_name |
str
|
Model name for embedding. Defaults to DashScopeTextEmbeddingModels.TEXT_EMBEDDING_V2. Options are:
|
TEXT_EMBEDDING_V2
|
text_type |
str
|
The input type, ['query', 'document'], For asymmetric tasks such as retrieval, in order to achieve better retrieval results, it is recommended to distinguish between query text (query) and base text (document) types, clustering Symmetric tasks such as classification and classification do not need to be specially specified, and the system default value "document" can be used. |
'document'
|
api_key |
str
|
The DashScope api key. |
None
|
Source code in llama-index-integrations/embeddings/llama-index-embeddings-dashscope/llama_index/embeddings/dashscope/base.py
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get_batch_query_embedding #
get_batch_query_embedding(embedding_file_url: str) -> Optional[str]
Get batch query embeddings.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
embedding_file_url |
str
|
The url of the file to embedding which with lines of text to embedding. |
required |
Returns:
Name | Type | Description |
---|---|---|
str |
Optional[str]
|
The url of the embedding result, format ref: https://help.aliyun.com/zh/dashscope/developer-reference/text-embedding-async-api-details. |
Source code in llama-index-integrations/embeddings/llama-index-embeddings-dashscope/llama_index/embeddings/dashscope/base.py
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get_batch_text_embedding #
get_batch_text_embedding(embedding_file_url: str) -> Optional[str]
Get batch text embeddings.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
embedding_file_url |
str
|
The url of the file to embedding which with lines of text to embedding. |
required |
Returns:
Name | Type | Description |
---|---|---|
str |
Optional[str]
|
The url of the embedding result, format ref: https://help.aliyun.com/zh/dashscope/developer-reference/text-embedding-async-api-details. |
Source code in llama-index-integrations/embeddings/llama-index-embeddings-dashscope/llama_index/embeddings/dashscope/base.py
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get_multimodal_embedding #
get_multimodal_embedding(input: List[Dict], auto_truncation: bool = False) -> List[float]
Call DashScope multimodal embedding. ref: https://help.aliyun.com/zh/dashscope/developer-reference/one-peace-multimodal-embedding-api-details.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
input |
str
|
The input of the multimodal embedding, eg: [{'factor': 1, 'text': 'ä½ å¥½'}, {'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'}] |
required |
Raises:
Type | Description |
---|---|
ImportError
|
Need install dashscope package. |
Returns:
Type | Description |
---|---|
List[float]
|
List[float]: The embedding result |
Source code in llama-index-integrations/embeddings/llama-index-embeddings-dashscope/llama_index/embeddings/dashscope/base.py
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