Adding samples on embeddings architectures #2
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added 7 commits
February 26, 2023 11:10
| import azure.functions as func | ||
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| account_name = os.environ["azure_table_account_name"] | ||
| account_key = os.environ["azure_table_key"] |
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Not table key, rather "Azure_Storage_key" or "Storage_Account_Key"
| import openai | ||
| import azure.functions as func | ||
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| account_name = os.environ["azure_table_account_name"] |
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"Azure_Storage_Name" or "Storage_Account_Name"
| "source": [ | ||
| "for i, row in documents_df.iloc[0:100].iterrows(): # for simplicity, only processing the first 100 documents\n", | ||
| "\n", | ||
| " embedding = get_embedding(row['content'], TEXT_SEARCH_EMBEDDING_ENGINE)\n", |
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Limit embedding to some specific float values otherwise it exceeds the 32KB limit of the table column
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@paragagrawal11 @dereklegenzoff : This PR seems stuck. @gojira Can you please review and approve? |
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