What the data for this category looks like
This category's data originates from regulatory and rule databases, higher-level law databases, departmental policy document repositories, and administrative regulatory document filing systems. Update frequency is irregular. Daily processed documents include regulatory document drafts, drafting explanatory statements, legality review opinions, solicited opinion summaries, and filing reports. Each individual document contains fields such as document provisions, issuing authority, drafting basis, solicited opinion feedback, and review conclusions. There are no unified fixed quantitative unit indicators. Only specific business information is carried alongside the document content.
What constraints these characteristics impose on the multi-turn dialogue and prompt engineering link
Decentralized data sources and irregular updates require multi-turn dialogue workflows to support dynamic calls to different types of compliance databases. This ensures the latest higher-level law and policy basis is obtained for every review. Diverse document types and unstructured provision content require prompts to adapt to different document formats, accurately extracting core fields such as draft provisions, review opinions, and solicited opinions. The core review link requires comparing document authority and provision legality item by item. Multi-turn dialogue must gradually break down review steps to avoid single-context overload. At the same time, review errors may lead to filing corrections or reconsideration risks. Prompts must clearly bind compliance verification rules to enhance review rigor.
How to configure settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the single-paragraph length of regulatory document provisions, avoiding chunking that breaks legal provision logic |
| Overlap Length | 100–200 characters | Retains contextual association between adjacent chunks, ensuring continuity of provision review |
chatId | Bound to the unique identifier of the current review task | Isolates dialogue data from different review tasks, avoiding cross-task contextual confusion |
messages | Arranged core content in the order of review steps | Ensures multi-turn dialogue context unfolds according to business logic, facilitating prompt recognition of the current review node, and adapting to the multi-turn dialogue context management logic of FastGPT v4.17.0 |
variables | Preset fixed parameters such as review subject and document type | Reduces repeated input and unifies review baseline rules |
| Single Search Limit | Top 5 matching results | Limits the number of returned compliance basis documents, avoiding redundant information that interferes with review judgments |
Three common configuration errors
- Phenomenon: An unselected knowledge base prompt pops up on the officially released dialogue interface, but compliance database content can be retrieved normally in debug preview mode. Cause: The data source permission for the corresponding regulatory database is not bound in the application configuration, and the preview mode does not verify data source binding status.
- Phenomenon: After a certain number of multi-turn dialogue rounds, the core provision content of the specified review document cannot be correctly extracted. Cause: Maximum context length is not limited, and old dialogue fragments overwrite the core document data of the current review.
- Phenomenon: After calling the dialogue interface via API, historical chat records for the specified user cannot be obtained, only the current session fragment is returned. Cause: The unique identifier of the corresponding review task is not bound using
chatId, resulting in historical dialogue data not being associatively stored by user dimension.
How to confirm successful configuration
- Initiate a simulated review dialogue, check whether the preset compliance knowledge base data source can be selected on the interface, and confirm consistent behavior between debug and formal environments.
- Initiate a multi-step progressive review dialogue, verify that the current dialogue context only contains relevant documents and step content for this review task.
- Call the dialogue interface for the specified
chatIdvia API, confirm that returned results include historical dialogue data for the corresponding review task. - Switch between different test accounts to initiate dialogues, confirm that historical dialogue data for each account is not displayed across accounts.
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.