What the data for this category looks like
Typical entities include judicial administrative departments, departmental legal affairs offices, and policy and legal positions in government offices. Daily processed documents include normative document drafts, drafting explanations, legality review opinions, solicited comment summaries, and filing reports. Data sources include regulatory and rule databases, upper-level law databases, departmental policy document databases, and administrative normative document filing systems. Update frequency is irregular. Documents contain fields such as formulation entity, effective date, rights and obligation clauses, and review basis association fields. There are no fixed standardized unit fields.
What constraints these characteristics impose on the knowledge base retrieval and recall link
Data sources are scattered across multiple official databases. The retrieval pipeline must support cross-source linked recall to avoid missing cross-database associated review basis. Update frequency is irregular. An incremental synchronization mechanism must be configured to ensure recalled content is always the latest effective regulations and documents. Document structures vary, and review requirements are strict. Precise matching of upper-level law provisions and authority basis is required, and irrelevant content must not be included. The length of individual documents varies widely. The context length of retrieval results must be controlled to avoid exceeding the model's processing limit.
Configuration settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Legality review of normative documents requires strict matching of upper-level law provisions to avoid low-confidence associated content interfering with review judgments |
limit | Top 10 entries | Must cover multi-dimensional review basis while controlling context length to avoid exceeding the model's processing limit |
searchMode | Cross-database linked retrieval | Data sources are scattered across multiple databases such as regulatory databases and filing systems, so linked recall of relevant basis is required |
usingReRank | Enabled | Review requires precise matching of provision content, and reranking can filter low-relevance recall results to improve accuracy |
datasetSearchUsingExtensionQuery | Enabled | Review questions often require expanding associated upper-level law provisions, and optimized queries can cover more potential basis |
collectionIds | Bind exclusive datasets based on review scenarios | Avoid mixing irrelevant files into retrieval results, ensuring recalled content is only compliant basis required for current review |
Three common mistakes
- Symptom: A 400 status code is returned when calling the retrieval interface, with a prompt about parameter format errors. Cause: The platform-approved
collectionIdsparameter format was not used, and unauthorized dataset IDs were passed, causing interface verification failure. - Symptom: After multiple consecutive review conversations, the accuracy of retrieval results for the same upper-level law basis decreases. Cause: The session context was not reset, and redundant historical conversation information interfered with vector generation for the current query, leading to retrieval matching deviations.
- Symptom: A large number of low-confidence associated content appears in retrieval results, failing to meet the strict review requirements. Cause: A reasonable
similaritythreshold was not set, and documents with insufficient relevance to the review basis were recalled, reducing accuracy.
How to verify the configuration is correct
- Verify the dataset binding configuration. Confirm that
collectionIdsonly associates data sources such as the regulatory and rule database and filing system required for the current review scenario. - Initiate a single review query. Check whether the number of returned results matches the preset
limitvalue, with no additional irrelevant content included. - Compare retrieval results with
usingReRankenabled and disabled. Confirm that high-confidence upper-level law basis is recalled first. - Call the retrieval interface. Verify that returned results have no abnormal status codes, and parameter formats comply with platform specifications.
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.