What this category of data looks like
Data sources for this category include the regulatory and rule database, higher-level law database, departmental policy document database, and administrative normative document filing system. Update frequency is irregular. Daily processed documents include normative document drafts, drafting explanations, legality review opinions, solicited opinion summaries, and filing reports. Document fields cover formulating entities, clause content, review conclusions, filing status, and other details. The length of individual documents varies significantly.
Constraints Imposed on Conversation Logs and Auditing
Decentralized data sources and irregular updates require full recording of the time, parameters, and return results of each data source call in conversation logs. This enables tracing the timeliness of review basis. Wide variation in document length requires limiting the context length of single-turn conversations. This prevents log storage overflow. Core review basis must be separately marked in log fields. This allows quick location of original legality comparison data during audits. Errors in this process affect policy release and filing. Logs must retain full-process interaction records, including user questions, system-generated results, and review modification traces. Intermediate steps cannot be omitted.
Reference only; verify against the target FastGPT release and deployment documentation.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Log storage threshold | Single log no more than 20000 characters | Matches the maximum length of individual documents in this category, to avoid storage overflow |
chatId binding rule | Bind the unique identifier of the normative document review task | Ensure that review conversations for the same document are grouped under the same task log, to facilitate auditing by task |
detail log switch | Enable full-field log recording | Cover core fields such as review basis and comparison results, to meet audit tracing requirements |
| Context recall limit | Top 10 historical conversations | Adapt to the short-turn interaction scenario of this category's review, to avoid log redundancy |
| Log retention period | Retain for 6–12 months per administrative compliance requirements | Comply with audit tracing specifications of government departments |
responseChatItemId association rule | Bind the unique identifier of official review opinions | Associate conversation logs with final review documents, to facilitate cross-checking |
Three Common Misconfigurations
- Symptom: AI thinking process content appears in conversation logs. Cause: Non-essential field output for the
detailparameter was not disabled, leading to intermediate thinking steps being written to logs. - Symptom: Core review conversations are not recorded after calling cross-workflow applications or plugins. Cause: The
chatIdfrom cross-calls was not synchronized to the target application, so logs are not associated with the current review task. - Symptom: When viewing historical records via an interface, the returned source field cannot match local review document identifiers. Cause: The association rule between
responseChatItemIdand local review documents was not configured, resulting in missing log source identifiers.
How to Confirm Proper Configuration
- Access the FastGPT v4.17.0 system log page, search for the
chatIdof the target review task, and confirm the log includes complete interaction records and data source call information. - Launch a complete normative document review conversation, view the stored log content, and confirm no redundant AI thinking process information is present.
- Call the historical record interface, verify the returned source field can be associated with the official document identifier of the current review task.
- Check the time range of retained logs, and confirm it meets the preset compliance retention period requirements.
Additional Note
The configuration values listed here are common starting points. They should be evaluated 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 2026-09-28.