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%pip install llama-index-llms-premai
%pip install llama-index-llms-premai
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from llama_index.embeddings.premai import PremAIEmbeddings
from llama_index.embeddings.premai import PremAIEmbeddings
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import os
import getpass
if os.environ.get("PREMAI_API_KEY") is None:
os.environ["PREMAI_API_KEY"] = getpass.getpass("PremAI API Key:")
prem_embedding = PremAIEmbeddings(
project_id=8, model_name="text-embedding-3-large"
)
import os
import getpass
if os.environ.get("PREMAI_API_KEY") is None:
os.environ["PREMAI_API_KEY"] = getpass.getpass("PremAI API Key:")
prem_embedding = PremAIEmbeddings(
project_id=8, model_name="text-embedding-3-large"
)
调用嵌入模型¶
现在你已经准备就绪了。现在让我们开始使用我们的嵌入模型,首先是单个查询,然后是多个查询(也称为文档)。
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query = "Hello, this is a test query"
query_result = prem_embedding.get_text_embedding(query)
query = "Hello, this is a test query"
query_result = prem_embedding.get_text_embedding(query)
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print(f"Dimension of embeddings: {len(query_result)}")
print(f"Dimension of embeddings: {len(query_result)}")
Dimension of embeddings: 3072
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query_result[:5]
query_result[:5]
Out[ ]:
[-0.02129288576543331, 0.0008162345038726926, -0.004556538071483374, 0.02918623760342598, -0.02547479420900345]