This configuration is compatible with FastGPT v4.17.0.
What Data for This Category Looks Like
Data for this category primarily originates from package managers, code repositories, vulnerability databases, and license databases, with an update frequency of daily or per version. Daily generated documents include SBOM lists, dependency risk reports, license lists, and repair tickets. Common data fields include dependency package names, version numbers, vulnerability CVE IDs, license types, repair suggestions, and similar items. No additional custom statistical metrics or percentage-based data are included.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
The data sources for this category are scattered and require cross-document correlation. Multi-turn dialogue must sequentially associate dependency information in SBOM lists, risk data from vulnerability databases, and compliance requirements from license lists. Prompts must clearly specify the calling order of different data sources. The data update frequency is daily or per version, so dialogue must regularly refresh the latest versions of dependencies and vulnerability information. Prompts must include verification logic for data update times. Additionally, daily generated structured documents must limit field scope. Prompts must avoid returning irrelevant content. The most time-consuming dependency identification and vulnerability matching steps must be guided step-by-step via multi-turn dialogue to avoid overload from single requests.
How to Set Configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to the unique review ID of a code change | Associate dialogue with specific code changes to facilitate subsequent auditing and traceability |
messages | Retain the first 8 rounds of dialogue context | Control context length to avoid exceeding processing limits, while covering historical review logic |
variables | Configure as exclusive calling prefixes for SBOM lists and vulnerability reports | Limit dialogue to calling structured documents exclusive to this category, avoiding interference from irrelevant data |
| Dialogue record retention period | 7 days | Match the daily/per version update frequency of supply chain data, and align with the standard audit cycle of security teams |
| Knowledge base permission control | Divide access scope by team roles | Differentiate access permissions for sensitive data among entities such as security teams and developer efficiency teams |
Three Common Misconfigurations
- Phenomenon: Dialogue records are lost or exceed the expected retention period. Cause: The dialogue record retention time parameter is not correctly configured, or data association fails because the corresponding review ID is not bound.
- Phenomenon: The knowledge base returns irrelevant non-supply chain dependency data. Cause: The
variablessetting is not used to limit the exclusive document prefix for calls, resulting in the dialogue calling content from the general knowledge base. - Phenomenon: Multi-turn dialogue context overflow causes response timeouts. Cause: The number of dialogue rounds retained in
messagesis not limited, leading to excessive historical context being passed to the processing workflow.
How to Verify Successful Configuration
- Initiate a dialogue for a code change review, check if the unique ID of the corresponding change is associated in the dialogue list, to confirm the
chatIdconfiguration is effective. - Check the retention period of dialogue records, verify that it matches the preset 7-day cycle, to confirm the configuration item is effective.
- Initiate a query containing sensitive vulnerability information, confirm that only authorized roles can view the corresponding content, to verify that knowledge base permission control is effective.
- Test consecutive multi-turn dialogue, check if the returned context only includes the first 8 rounds of historical dialogue, to confirm the
messagesconfiguration is effective.
The values provided are common starting points and 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 2026-10-19.