Regulatory Compliance Reference Source and Traceability

What this category of data looks like

Regulatory compliance data primarily comes from official documents released by official financial regulatory authorities and industry associations. Update rhythm is irregular, synced with the release of new regulatory documents or revisions of existing ones. Single documents typically use a hierarchical chapter structure, with fixed fields including document number, issuing body, effective date, clause number, and specific text content. Character span varies widely, ranging from thousands to hundreds of thousands of characters, with no unified short text format.

What constraints do these characteristics impose on the "reference source and traceability" link

The official source attribute requires that traceability information clearly mark the issuing body and document number, to avoid vague references. The irregular update feature requires the knowledge base synchronization mechanism to support incremental updates and version tagging, preventing recall of expired old clauses. The chapterized structure requires recall units to accurately locate specific clauses, ensuring the reference scope does not exceed the complete content of a single clause. The multi-field document structure requires traceability output to include key information such as effective date and clause number, to facilitate verification against the original text.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`recall_chunk_size`	800–1200 characters	Regulatory compliance clauses are mostly coherent regulatory texts. This character range covers complete clause units and avoids split breaks
`recall_top_k`	Top 3–5 results	Compliance Q&A for regulatory documents usually focuses on single clauses or adjacent related clauses. Too many recall results will introduce irrelevant content
`source_display_fields`	`["file_name", "document_number", "publish_date", "chunk_content"]`	Traceability for regulatory documents requires clear file identification, clause location and specific text content. This configuration covers core traceability information
`knowledge_base_sync_mode`	Incremental sync mode	Updates to regulatory documents have no fixed cycle. Incremental sync reduces synchronization resource usage and improves update efficiency
`chunk_separator`	Regular expression `["第[0-9]+条", "第[0-9]+款"]`	Chapter division of regulatory documents takes clauses and sub-clauses as core units. Splitting by this ensures the integrity of recall units
`enable_version_control`	Enabled	Regulatory documents may be revised or repealed. Version control prevents recalling expired clauses

The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.

Three Common Configuration Errors

Returns invalid dataset id format error when called. Cause: Incorrectly uses variable reference in [{datasetId: xxx}] format, which does not match the variable binding rules or standard format required by FastGPT.
Recall results include content from non-target knowledge bases, and outputs do not strictly match the original text response. Cause: Fails to set an accurate value for recall_top_k and does not enable the strict_match parameter, leading to expanded recall scope.
Only displays file names when referencing source data, without clause numbers and effective dates. Cause: Does not configure document_number and publish_date fields in source_display_fields, only retaining the default file name display.

How to Confirm Correct Configuration

Upload a test regulatory document, check if parsed chunks are split by clauses, with no cross-clause chunk fragments.
Submit a query about specific regulatory clauses, verify that all fields configured in source_display_fields are included in the returned results.
Simulate an update to a regulatory document, perform an incremental sync, check if the version tag of the corresponding file in the knowledge base is updated.
Call the interface to test variable references, confirm that the datasetId parameter format meets FastGPT requirements, with no format errors.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.