What This Category’s Data Looks Like
This category’s data typically comes from search ad backends, search term reports, bidding ranking data, and landing page data. Updates run on a daily or weekly cycle. Daily output documents include keyword lists, negative keyword tables, bid records, and search term reports. Most data uses structured fields: keyword text, match type, bid amount, clicks, conversions, negative keyword rule entries, and more. Units include yuan, impressions, cost per thousand impressions, and others. Most document formats are tables or editable lists to support batch management and retrieval.
Constraints Imposed on Conversation Logs and Auditing
Data sources for this category are scattered, with inconsistent formats. Conversation logs must uniformly collect keyword operations, bid adjustments, and negative keyword change records across all systems, and link them to unique identifiers of business documents. Data update frequencies vary between daily and weekly cycles. Auditing cycles must match the update rhythm of the corresponding data to avoid missing cross-cycle logs. Daily documents have many fields and cover core advertising delivery operations. Logs must fully retain fields including operator, time, and modification content, with no arbitrary truncation. Logs must also support searching specific operation details via the detail field to quickly locate the conversation chain linked to a fault.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to the unique identifier of the target search ad account | Tie conversation logs to keyword operations for a specific ad account, preventing cross-account log confusion |
responseChatItemId | Retain the most recent 8 conversation context entries | Cover the full interaction chain for a single fault troubleshooting, avoiding loss of context for critical adjustment steps |
detail | Record keyword text, pre- and post-bid change values, and negative keyword rule adjustments | Preserve all operational details required for auditing, meeting core compliance traceability field requirements |
| Log storage period | 180 days | Align with standard advertising industry compliant auditing cycles, covering monthly and quarterly retrospective timeframes |
| Retrieval trigger threshold | Calibrate based on actual testing | Match the data volume scale of this category, preventing excessive invalid logs from being returned during searches |
Three Common Configuration Errors
- Symptom: After adding a text content extraction node to a workflow, multi-turn conversation context fails to load correctly. Cause: The
chatIdparameter was not bound properly, so conversation context does not match the current ad account’s keyword task. - Symptom: When a workflow’s code run node inputs a field with historical records, validation fails and the workflow cannot start. Cause: The association range for historical records was not limited per configuration requirements, triggering an exception in the field validation logic.
- Symptom: Occasional empty model responses occur, and the workflow stalls for 10 seconds before throwing an error. Cause: Conversation logs were not configured to fully record the
detailfield, making it impossible to identify input parameter exceptions during model calls.
How to Verify Proper Configuration
- Access the log management page in FastGPT v4.17.0, enter the
chatIdof the target ad account, and confirm that conversation records for keyword adjustments are returned. - Open a single log entry’s details, and verify that the
detailfield includes core operational information such as keyword text and pre- and post-bid change values. - Trigger a keyword bid adjustment workflow, and check that the log retains all audit-required fields including operator, time, and modification content.
- On the workflow configuration page, confirm the historical record binding parameters, and verify that no errors are reported during the validation phase.
The values listed here are common starting points, and should be evaluated against your own specific samples and use cases.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.