@@ -89,8 +89,6 @@ def get_secrets_from_kv(kv_name, secret_name):
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)
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# Function: Get Embeddings
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def get_embeddings (text , openai_api_base , openai_api_version , openai_api_key ):
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model_id = "text-embedding-ada-002"
@@ -203,7 +201,36 @@ def prepare_search_doc(content, document_id):
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# conn = pymssql.connect(server, username, password, database)
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cursor = conn .cursor ()
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print ("Connected to the database" )
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- cursor .execute ("DROP TABLE IF EXISTS processed_data" )
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+ cursor .execute ("DROP TABLE IF EXISTS vprocessed_data" )
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+ conn .commit ()
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+
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+ create_processed_data_sql = """CREATE TABLE vprocessed_data (
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+ ConversationId varchar(255) NOT NULL PRIMARY KEY,
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+ EndTime varchar(255),
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+ StartTime varchar(255),
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+ Content varchar(max),
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+ summary varchar(3000),
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+ satisfied varchar(255),
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+ sentiment varchar(255),
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+ topic varchar(255),
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+ key_phrases nvarchar(max),
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+ complaint varchar(255),
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+ mined_topic varchar(255)
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+ );"""
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+ cursor .execute (create_processed_data_sql )
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+ conn .commit ()
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+
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+ cursor .execute ('DROP TABLE IF EXISTS vprocessed_data_key_phrases' )
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+ conn .commit ()
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+
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+ create_processed_data_sql = """CREATE TABLE vprocessed_data_key_phrases (
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+ ConversationId varchar(255),
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+ key_phrase varchar(500),
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+ sentiment varchar(255),
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+ topic varchar(255),
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+ StartTime varchar(255),
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+ );"""
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+ cursor .execute (create_processed_data_sql )
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conn .commit ()
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file_system_client = service_client .get_file_system_client (file_system_client_name )
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