What the data for this category looks like
The core data for this category comes from enterprise contract management systems, compliance risk databases, legal and regulatory repositories, case management systems, and reporting platforms. The update frequency is once per month. Commonly generated and used documents include compliance risk lists, contract legal review opinions, major issue legal opinions, compliance review forms, and violation report investigation reports. Data fields include business-specific fields such as compliance risk level, review opinion number, and reporting clue number. Document structures typically include structured review opinions with approval nodes, risk lists grouped by category, and legal opinions containing facts and legal basis.
What Constraints These Characteristics Impose on the Multi-turn Dialogue and Prompt Engineering Link
Multiple data sources mean multi-turn dialogue must associate context content from different systems, avoiding fragmented compliance risk judgments. The monthly update frequency means prompt engineering must regularly sync the latest legal regulations and case data to ensure referenced content is timely. The wide variation in document length, from short lists to lengthy legal opinions, requires chunking logic tailored to different document structures. Errors in compliance-related content carry serious consequences, so multi-turn dialogue must include strict context verification to avoid incorrect regulatory references or non-compliant response generation. Additionally, the specificity of business fields requires prompt engineering to bind dedicated business variables, ensuring responses align with internal enterprise compliance standards.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the paragraph structure of documents such as compliance review forms and legal opinions |
| Overlap Length | 150 characters | Preserves context association between chunks, avoiding logical breaks after long document segmentation |
| Single Search Limit | Top 5 entries | Matches the entry volume of compliance risk databases and case management systems |
variables | Bind compliance risk level, review opinion number | Enables prompt engineering to accurately call business fields for this category |
chatId | Bind legal department session identifier | Distinguishes conversation contexts across departments, avoiding cross-departmental information confusion |
messages | Format context in conversation order | Ensures logical consistency of context for multi-turn dialogue, meeting interface verification requirements |
Three Common Configuration Errors
- When the agent returns a 422 status code while replying with system operation guidance, the cause is that the multi-turn dialogue
messagesparameter fails to correctly format compliance document context, leading to interface verification failure. - When retrieval results are inaccurate using headings as long document separators, the cause is that compliance documents have multiple heading levels and some paragraphs lack clear headings, resulting in a mismatch between chunking logic and business logic.
- When the default prompt box cannot be hidden when starting a new conversation, the cause is that the initial pop-up trigger switch has not been disabled in the front-end configuration.
How to Verify Successful Configuration
- Upload a compliance review form and a legal opinion, confirm that chunked content retains complete business logic.
- Initiate a test conversation including compliance risk level and review opinion number, verify that bound
variablescorrectly participate in prompt generation. - Initiate multiple consecutive conversations, check that context correctly associates compliance document content from different sources.
- Test the department identifier bound to
chatId, confirm that conversation contexts across different legal departments do not mix.
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.