What this category of data looks like
This category of data mainly comes from enterprise contract management systems, compliance risk databases, legal and regulatory repositories, case management systems, and reporting platforms. Daily processed documents include compliance risk lists, contract legal review opinions, major issue legal opinions, compliance review forms, and violation report investigation results. Data updates occur monthly. Most individual documents include fields such as business subject, risk type, processing progress, and compliance basis. There is no unified fixed unit for these documents. Some documents include structured approval nodes and associated case numbers.
Constraints on the vector models and indexes link
The monthly update rhythm of this category requires the indexing process to use a combination of periodic full reconstruction and incremental updates. This avoids excessive single reconstruction time that would disrupt business support. Multi-source, heterogeneous document types include structured approval forms and unstructured legal documents. This requires the indexing link to support mixed-format text splitting and vector mapping. Data from multiple systems has inconsistent field formats. Indexing must support custom field association to ensure retrieval matches the compliance review logic of business handling. Business errors have severe consequences, so index retrieval results need high confidence to avoid missing key compliance basis.
Configuration settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Fits the typical paragraph length of compliance documents, preventing single chunks from containing too many irrelevant compliance clauses |
| Overlap Length | 100–150 characters | Preserves cross-chunk compliance association information, avoiding key review basis from being split apart |
| Similarity Threshold | 0.75–0.85 | Aligns with legal review requirements for compliance basis accuracy, filtering low-relevance non-compliant content |
| Single Search Limit | Top 8 entries | Controls the volume of retrieval results, adapting to rapid compliance verification during business handling |
defaultToken | 4096 token | Matches the average text length of a single compliance document, meeting basic vector encoding needs |
maxToken | 8192 token | Covers the total length of multiple associated documents, supporting context retrieval for major issue reviews |
Three common configuration errors
- Sustained high server read-write resource usage after a full index rebuild. This occurs when no batch reconstruction strategy is set for monthly updated batch data. Loading too many compliance documents and regulatory repository data at once causes memory overflow.
- Index interface calls return a 400 status code with no response body. This happens when the special field format of legal documents is not supported, or vector model parameter configuration exceeds the
maxTokenlimit, leading to abnormal request body formatting. - Uploading a compliance review form occasionally gets stuck at the 1st or 2nd index group. This is caused by the locally deployed m3e-large model not having enough allocated video memory quota, or chunk length being set too large, leading to single-chunk vector generation timeouts.
How to confirm correct configuration
- Manually upload a typical compliance risk list. Check that index generation completes within the expected time, with no freezes or errors.
- Initiate a compliance review-related query. Verify that the number and similarity of retrieval results fall within the preset configuration range.
- Trigger an incremental index update. Confirm that only newly added monthly compliance data is added to the index, and historical data is not reprocessed.
- Check system logs. Confirm that resource usage during the vector generation phase does not exceed the server's configured rated threshold.
All the values listed above are common starting points. Adjust them based on your own specific samples and environment.
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.