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Session

User
sukh.kalsi@dext.com · Sukh Kalsi
Started
2026-08-04 10:44
Last activity
2026-08-04 10:46
Questions
2
Grouping
backfill — reconstructed from timing by migration 011
session_id
f4aac3c8-dcbf-4b88-8f47-fd466be84674

Transcript

  1. 2026-08-04 10:44 ask success $1.0484 229.3 s full detail →
    Can you give me, for every accountant practice (partner account), the number of client accounts under it (grouped by parent_account_crn) and how many of those clients have a paid subscription? I need this as a full table across all ~1,800+ practices — is that something you can return, or is there a row limit?
    No answer recorded.
    5,000 rows Product Usage - Accounts
  2. 2026-08-04 10:46 ask success $0.5815 111.8 s full detail →
    That practice client-count table hit a 5,000-row limit and I need full coverage (~40k+ parent accounts, or at minimum all accountant practices). Please rerun the same query (client accounts grouped by parent_account_crn, count of distinct accounts) with the limit raised high enough to return every parent_account_crn, sorted by client count descending. Also: your query filtered number_of_days = 90 — does that mean the count is only clients active in the last 90 days, or is it the full current client book? I want the full current client book per practice.
    No answer recorded.
    43,454 rows Product Usage - Accounts

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.