Upstage 嵌入¶
如果您在 colab 上打开此 Notebook,您可能需要安装 LlamaIndex 🦙。
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%pip install llama-index-embeddings-upstage==0.2.1
%pip install llama-index-embeddings-upstage==0.2.1
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!pip install llama-index
!pip install llama-index
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import os
os.environ["UPSTAGE_API_KEY"] = "YOUR_API_KEY"
import os os.environ["UPSTAGE_API_KEY"] = "YOUR_API_KEY"
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from llama_index.embeddings.upstage import UpstageEmbedding
from llama_index.core import Settings
embed_model = UpstageEmbedding()
Settings.embed_model = embed_model
from llama_index.embeddings.upstage import UpstageEmbedding from llama_index.core import Settings embed_model = UpstageEmbedding() Settings.embed_model = embed_model
使用 Upstage 嵌入¶
注意,您可能需要更新您的 openai 客户端:pip install -U openai
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# get API key and create embeddings
from llama_index.embeddings.upstage import UpstageEmbedding
embed_model = UpstageEmbedding()
embeddings = embed_model.get_text_embedding(
"Upstage new Embeddings models is great."
)
# 获取 API 密钥并创建嵌入 from llama_index.embeddings.upstage import UpstageEmbedding embed_model = UpstageEmbedding() embeddings = embed_model.get_text_embedding( "Upstage new Embeddings models is great." )
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print(embeddings[:5])
print(embeddings[:5])
[0.02535058930516243, 0.007272760849446058, 0.015372460708022118, -0.007840132340788841, 0.0017625312320888042]
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print(len(embeddings))
print(len(embeddings))
4096
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embeddings = embed_model.get_query_embedding(
"What are some great Embeddings model?"
)
embeddings = embed_model.get_query_embedding( "What are some great Embeddings model?" )
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print(embeddings[:5])
print(embeddings[:5])
[0.03518765792250633, 0.01018011849373579, 0.013282101601362228, -0.008568626828491688, -0.005505830980837345]
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print(len(embeddings))
print(len(embeddings))
4096
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# embed documents
embeddings = embed_model.get_text_embedding_batch(
[
"Upstage new Embeddings models is awesome.",
"Upstage LLM is also awesome.",
]
)
# 嵌入文档 embeddings = embed_model.get_text_embedding_batch( [ "Upstage new Embeddings models is awesome.", "Upstage LLM is also awesome.", ] )
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print(len(embeddings))
print(len(embeddings))
2
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print(embeddings[0][:5])
print(embeddings[0][:5])
[0.028246860951185226, 0.008945596404373646, 0.01719627156853676, -0.005711239762604237, 0.0016300849383696914]