What the data for this category looks like
Data is sourced from enterprise contract management systems, compliance risk databases, legal and regulatory repositories, case management systems, and reporting platforms. The update frequency is monthly. Daily documents include compliance risk lists, contract legal review opinions, major issue legal opinions, compliance review forms, and violation report investigation results. Document fields include risk category, review subject, issue number, violation details, and other items. There is no unified fixed unit; values vary based on the specific document type.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Data comes from multiple scattered internal systems. Multi-turn dialogue must gradually integrate ledger data from different sources to avoid context fragmentation. The monthly update frequency requires prompts to regularly link to the latest compliance database content, ensuring timeliness of risk judgment. Diverse document types require prompts to adapt to extraction logic for different fields. For example, extract compliance clauses from contract review opinions, and sort out processing workflows from violation investigation reports. Severe consequences of major risk errors require multi-turn dialogue to clearly define context boundaries, preventing omission of key compliance information.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Fixed association with the session ID of the current compliance ledger | Ensures multi-turn dialogue context is bound to the corresponding compliance project, avoiding cross-project data confusion |
messages | Retain the first 10 rounds of dialogue context | Compliance ledger data volume is large. Excessive context increases processing load. 10 rounds cover the dialogue logic of conventional compliance reviews |
variables | Pass the current compliance issue number and document type tag | Allows prompts to accurately match the field rules of the corresponding ledger, adapting to extraction requirements for different documents |
| Question Optimization | Calibrate based on actual testing | Compliance-related questions require clear business scenarios. Optimization improves the accuracy of risk identification |
| Maximum Context | 8000–12000 token | Compliance documents contain multiple types of fields. Sufficient context ensures prompts fully obtain ledger information |
| Single Search Limit | Top 5 entries | Compliance risk data requires prioritizing high-priority entries. 5 entries cover conventional retrieval needs |
Three Common Misconfigurations
- Symptom: Dialogue nodes return empty content or error prompts. Cause: The
variablesparameter exclusive to the compliance issue is not passed, so the prompt cannot match the field rules of the corresponding ledger. - Symptom: Historical content remains after deleting dialogue records. Cause: The session cleanup interface corresponding to
chatIdis not called correctly, leading to residual context. - Symptom: Feishu online document links cannot be read normally. Cause: Permission adaptation for document reading is not configured, or the prompt does not specify the parsing logic for Feishu documents.
How to Confirm Proper Configuration
- Manually pass test
variablesparameters for compliance issues, check whether the dialogue can correctly extract field information from the corresponding ledger. - Call the session cleanup interface, verify whether the dialogue records bound to
chatIdare completely deleted. - Upload Feishu online document links, check whether the system can normally parse the document content and include it in the context.
- Enable the question optimization configuration, initiate compliance-related queries, check whether question suggestions corresponding to the business scenario are generated.
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 2026-09-28.