What the data for this use case looks like
Data sources for customer requirement pool management typically include ticket systems, customer interview records, CRM systems, and product roadmaps. Update frequency is weekly or tied to product release cycles. Daily associated documents include requirement pool lists, requirement evaluation forms, customer feedback records, and version planning descriptions. Common data fields include requirement ID, associated customer identifier, feedback content, priority tags, scheduling status, and response time limits. Data flows naturally through business processes, with no additional custom statistical definitions.
What constraints these characteristics impose on conversation logs and auditing
Decentralized data sources require conversation logs to integrate multi-source records from API calls, page interactions, and workflow executions. This prevents missing cross-system requirement communication. Unfixed update frequencies mean audit cycles must match weekly or release cycles. This ensures full coverage of requirement iteration workflows. Multiple associated documents require log fields to bind unique identifiers such as requirement IDs and customer identifiers. This enables quick backtracking of evaluation and scheduling records. The risk of distorted requirement commitments requires audits to cover the full conversation chain from collection to scheduling communication. This ensures every business commitment has traceable conversation records. FastGPT v4.17.0 audit configurations must adapt to these characteristics to avoid disconnect between logs and business data.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId binding rule | Bind to requirement ID | Customer requirement pool data uses requirement ID as the unique identifier. Binding enables quick association with the full conversation chain for the corresponding requirement |
responseChatItemId mapping rule | Align with the requirement ID field in requirement evaluation forms | Requirement evaluation forms contain core requirement information. Alignment enables direct matching of evaluation communication content in conversations |
| Conversation log retention period | 30–90 days | Requirement update frequency follows weekly or per release cycles. Retention period must cover at least one full release cycle |
| Single log export count | 500–1000 entries | Matches single retrieval volume for requirement pool data, prevents interface timeouts |
| Audit log cache size | 200–500 MB | Matches storage volume for requirement pool data, prevents disk overflow |
| Workflow log output path | Specify a dedicated requirement pool directory | Customer requirement pool data requires separate archiving. A dedicated directory enables quick retrieval and auditing |
detail field collection scope | Include requirement ID and customer identifier | Must match core fields of requirement pool documents, ensures logs can directly associate with business documents |
| Multi-user conversation isolation | Isolate by customer identifier | Requirement communication for different customers must be recorded separately to avoid data mixing |
Common Mistakes
- Symptom: Conversation records created via API cannot be viewed in page logs. Cause:
chatIdis not bound to the requirement ID, so logs are not associated with the corresponding requirement pool data entry. - Symptom: console.log logs from code run modules in workflows cannot be found in the audit panel. Cause: Workflow log collection for the
detailfield is not enabled, so console output is not included in the audit scope. - Symptom: The Human field in conversation previews displays as null. Cause: Context information related to requirement identifiers is not included in request parameters, so log parsing cannot match corresponding content.
- Symptom: Historical requirement communication records cannot be retrieved after manual conversation log cleanup. Cause: Retention period is set shorter than the requirement update cycle, leading to premature deletion of cross-version audit records.
How to Verify Correct Configuration
- Send a single API request to create a conversation, include the requirement ID parameter. Check if the log contains associated records for the bound
chatIdand requirement ID. - Run a workflow that includes a code module. Check if the audit panel includes the corresponding console.log output.
- Send multiple user conversation requests. Check if conversation logs for different users are isolated by permissions, and only their own interaction records are visible.
- Check that the log retention period covers at least one full requirement update cycle. This ensures cross-version audit records are not deleted prematurely.
The values provided are common starting points and should be measured against the reader's own samples.
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.