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Session

User
silviya.chomakova@dext.com
Started
2026-07-17 12:05
Last activity
2026-07-17 12:06
Questions
2
Grouping
backfill — reconstructed from timing by migration 011
session_id
d22d3fd2-8519-430d-b3b1-e87855aa24c2

Transcript

  1. 2026-07-17 12:05 analyze success $0.4208 102.5 s full detail →
    List individual closed conversation IDs (Conversation ID), Support Team Name, and Conversation Closed At Timestamp Date for Customer Support Product Group = Commerce, Closed Date in April 2026, excluding Support Team Name = 'DC - Deflected by Fin'
    No answer recorded.
    316 rows Customer Support
  2. 2026-07-17 12:06 analyze success $0.4781 90.9 s full detail →
    List closed conversation IDs for Commerce, April 2026, Support Team Name is DC - Solutions Team, DC - Support Team Main, DC Onboarding Calls, DC Special (excluding DC - Deflected by Fin)
    No answer recorded.
    296 rows Customer Support

Cost is the LLM completion spend LiteLLM priced for each call, summed per request. Chandler is running on the Codex CLI, which draws ChatGPT plan quota rather than per-token API billing, so this figure is not money paid — it is what the same traffic would have cost on the API, priced from the token counts Codex reports. Read it as the size of the bill avoided. Two known limits: embedding spend is not recorded, so retrieval and matching cost is missing, and because the figure is one sum per request it cannot be split by model within a request — a request's classifier and agent calls can use different models while llm_model holds only one name. Only authenticated calls are logged, USAGE_LOG_ENABLED can switch logging off, and log writes are fail-soft — this is not a complete record of traffic. Questions are grouped into sessions: a session is exact when the caller echoed its id back to us and otherwise inferred from a 30-minute gap in that user’s activity, so a grouping is only as good as the source shown on the session itself. A call with no attributable user gets no session at all; those questions are listed separately rather than dropped.