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spring-ai/batch-llm/output/04-skip-and-ledger.txt
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# 3,000 tickets through the real job
job status COMPLETED
step: read=3000 write=2987 processSkip=13 commits=30 rollbacks=0
TICKET_RESULT rows 2987
SKIPPED_TICKET rows 13
classified + skipped 3000 (tickets: 3000)
Skipped tickets and why (first one of each kind):
ticket 46 category not in schema raw reply: {"category":"Account & Payments","priority":1,"confidence":0.74}
ticket 234 not JSON raw reply: Sure! Here is the classification you asked for: {"category":"BILLING","priority":4,"confidence":0.7}
ticket 491 missing field raw reply: {"category":"OTHER","priority":2}
First 3 ledger rows (one per committed chunk):
tickets written skipped attempts 429s 503s bad promptTok complTok cost
1-100 99 1 104 1 1 3 10831 1469 0.007619000
101-200 100 0 100 0 0 0 10574 1453 0.007466500
201-300 99 1 105 2 1 3 10736 1495 0.007610500
Ledger totals vs the provider's own meter (they must agree when nothing was lost):
ledger provider
calls received 3,104 3,104
429 rejections 39 39
503 rejections 14 14
billed calls 3,051 3,051
prompt tokens 324,790 324,790
completion tokens 44,107 44,107
Cost = prompt tokens x input price + completion tokens x output price, per million tokens.
table 'EXAMPLE-NOT-A-REAL-PRICE' (input 0.50, output 1.50): 0.228555500 <- the ledger's total
same tokens under table 'OTHER-EXAMPLE' (input 2.00, output 6.00): 0.914222000
Both tables are invented inputs. Tokens are the measurement; money is tokens x a table you supply.
Tokens spent on replies that were then skipped: bad replies billed = 64 of 3051 billed calls.