What Data for This Category Looks Like
Data for this category comes from regulatory and rule databases, higher-level law databases, departmental policy document libraries, and administrative regulatory document filing systems. Update frequency is irregular. Daily processed documents include regulatory document drafts, drafting explanations, legality review opinions, solicited opinion summaries, and filing reports. Most documents are a mix of structured and semi-structured content, with fields including document number, issuing entity, effective date, article content, review opinions, etc. The content length of a single document ranges from thousands to tens of thousands of characters, with no fixed uniform single-document length limit.
Constraints Imposed on Vector Models and Indexes
This category has scattered data sources and irregular update cycles. The indexing system must support unified vector mapping across heterogeneous data sources and incremental synchronization. Daily processed documents mix structured fields and long-text articles, with wide variations in single-document content length. Chunking strategies must adapt to different content types. Overly fine chunking creates context redundancy. Overly coarse chunking loses key supporting basis. Legality review requires precise matching of higher-level laws and authority basis. Vector recall precision takes priority over recall volume. This avoids irrelevant content interfering with review judgments.
How to Set Configurations
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the article length of daily regulatory documents, preserves contextual association while avoiding excessive single-block content that impacts vector generation precision |
| Overlap Length | 100–150 characters | Retains article connection information between chunks, ensuring that review can reference supporting content from adjacent clauses during legality review |
maxToken | 4096 token | Adapts to the context range of mainstream embedding models for FastGPT v4.17.0, meeting the vector splicing needs of single review documents |
| Similarity Threshold | 0.85–0.90 | Prioritizes ensuring matching precision between recalled content and higher-level laws or authority basis, avoiding low-relevance content interfering with reviews |
| Single Search Limit | Top 8 results | Focuses on core review basis, reducing the processing burden of non-essential content in subsequent steps |
| Maximum Context | 3000–4000 characters | Retains complete contextual association between drafts and supporting basis, ensuring accuracy of review judgments |
Three Common Mistakes
- Phenomenon: Vector recall results include large amounts of non-higher-level law content unrelated to the review. Valid supporting basis cannot be accurately located. Cause: A reasonable similarity threshold is not set for the precise requirements of legality review. Low-matching content is incorrectly added to recall results.
- Phenomenon: In a locally deployed FastGPT v4.17.0 instance, after indexing hundreds of thousands of regulatory documents, search tests return no matching results. Cause: Chunk length is set too large. Core articles are truncated and cannot generate valid vectors. Or the index mapping of vector storage does not cover all data source fields.
- Phenomenon: Review cannot reference supporting content from adjacent clauses, leading to judgment deviations caused by broken context. Cause: Overlap length is set too small. Connection information between chunks is not retained. Supporting basis for long articles is split and lost during review.
How to Confirm Configurations Are Correct
- Manually extract one regulatory document draft and its corresponding higher-level law supporting basis. Run a search test, and verify that recall results include the target supporting content.
- Check indexing task running logs. Confirm that incremental synchronization or full indexing progress is normal, with no error fields or timeout records.
- Adjust chunk length and overlap length parameters. Compare recall results under different configurations. Confirm that contextual connection and precision meet review requirements.
- Verify that vector storage field mapping includes metadata required for review, such as document number and issuing entity. Ensure that recall results can be linked to specific supporting documents.
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.