forked from phoenix/litellm-mirror
72 lines
1.7 KiB
Python
72 lines
1.7 KiB
Python
import clickhouse_connect
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# client = clickhouse_connect.get_client(
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# host='hjs1z7j37j.us-east1.gcp.clickhouse.cloud',
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# port=8443,
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# username='default',
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# password='M~PimRs~c3Z6b',
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# secure=False
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# )
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import clickhouse_connect
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client = clickhouse_connect.get_client()
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row1 = [
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"123456", # request_id
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"GET", # call_type
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"api_key_123", # api_key
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50.00, # spend
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1000, # total_tokens
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800, # prompt_tokens
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200, # completion_tokens
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"2024-02-24 12:00", # startTime (replace with the actual timestamp)
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"2024-02-24 13:00", # endTime (replace with the actual timestamp)
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"gpt-3.5", # model
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"user123", # user
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'{"key": "value"}', # metadata (replace with valid JSON)
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True, # cache_hit
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"cache_key_123", # cache_key
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"tag1,tag2", # request_tags
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]
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row2 = [
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"789012", # request_id
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"POST", # call_type
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"api_key_456", # api_key
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30.50, # spend
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800, # total_tokens
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600, # prompt_tokens
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200, # completion_tokens
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"2024-02-24 14:00", # startTime (replace with the actual timestamp)
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"2024-02-24 15:00", # endTime (replace with the actual timestamp)
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"gpt-4.0", # model
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"user456", # user
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'{"key": "value"}', # metadata (replace with valid JSON)
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False, # cache_hit
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"cache_key_789", # cache_key
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"tag3,tag4", # request_tags
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]
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data = [row1, row2]
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client.insert(
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"spend_logs",
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data,
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column_names=[
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"request_id",
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"call_type",
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"api_key",
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"spend",
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"total_tokens",
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"prompt_tokens",
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"completion_tokens",
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"startTime",
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"endTime",
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"model",
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"user",
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"metadata",
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"cache_hit",
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"cache_key",
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"request_tags",
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],
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)
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