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Prompt

how much ACV do we have

Outcome
success
Cost
$0.4449
excludes embeddings
Latency
24.3 s
Query attempts
1
Rows returned
1
Tokens
167,119 / 980
in / out

Request

request_id
2f8711ba-4132-461e-9448-0e8c1eb376d0
When
2026-07-24 10:00
Tool
ask
User
yordan@dext.com · Yordan Todorov Ivanov
Identity
google
Session
8a82b645-a3e8-41e0-b917-b8a0c12ada1c
Model
gpt-5.5

Route taken

Topic
Bookings
Omni model
e407bba6-4fae-4079-ac4e-cd6cf8fdf6f8
Match method
agent loop
Nudge shown
—
Question shape
not classified
Caveat raised
—
Sharper question offered
—

Per-step detail — each LLM call, each Omni request, each rejected draft — is traced to stdout only and is not stored, so it cannot be shown here. Query attempts counts run_query executions, not model turns.

Omni query that ran (legacy)

{
  "calculations": [],
  "fields": [
    "omni_dbt__eds_bookings.total_prepare_bookings_monthly_acv",
    "omni_dbt__eds_all_accounts.total_distinct_accounts"
  ],
  "filters": {
    "omni_dbt__eds_all_accounts.finance_account_type": {
      "kind": "EQUALS",
      "type": "string",
      "values": [
        "Accountant"
      ]
    },
    "omni_dbt__eds_all_accounts.is_dext_demo": {
      "is_negative": true,
      "treat_nulls_as_false": true,
      "type": "boolean"
    },
    "omni_dbt__eds_all_accounts.salesforce_account_type": {
      "is_negative": true,
      "kind": "EQUALS",
      "type": "string",
      "values": [
        "Reseller"
      ]
    },
    "omni_dbt__eds_bookings.has_prepare_paid_subscription": {
      "is_negative": false,
      "type": "boolean"
    },
    "omni_dbt__eds_calendar_day.is_latest_available_date": {
      "is_negative": false,
      "type": "boolean"
    }
  },
  "limit": 1000,
  "modelId": "e407bba6-4fae-4079-ac4e-cd6cf8fdf6f8",
  "pivots": [],
  "sorts": [],
  "table": "omni_dbt__eds_all_accounts"
}

Feedback

No feedback linked to this request.

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.