Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
Data for this category primarily comes from enterprise contract management systems, compliance risk databases, legal and regulatory repositories, case management systems, and reporting platforms. Daily documents include compliance risk lists, contract legal review opinions, major issue legal opinions, compliance review forms, and violation report investigation reports. These documents contain fields such as risk level, review opinion number, reporting item description, and processing workflow nodes. Data updates occur monthly. Individual document lengths vary widely, ranging from hundreds of characters for compliance review forms to thousands of characters for major issue legal opinions.
What constraints these characteristics impose on vector models and indexes
Decentralized data sources create inconsistent formats. Some data comes from system-exported Excel files, while other data comes as structured documents. The platform must support multi-format parsing. Monthly data updates require index construction to support incremental synchronization. This avoids excessive time spent on full index reconstruction. Wide variation in document lengths mixes short and long texts. The setup must adapt to different chunking strategies. Fields include structured information such as compliance risk levels and review opinion numbers. Configurations must preserve field association relationships. This prevents business logic from being broken during chunking. Error consequences are severe. Index recall accuracy must match the strict requirements of compliance reviews.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the wide variation in document lengths for this category, balances semantic completeness and chunk granularity |
| Chunk Overlap Length | 100–200 characters | Avoids cutting off complete compliance risk descriptions or logical connections of review opinions |
defaultToken | 4096 | Matches the embedded text length requirements for most compliance documents in this category |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Meets the strict requirements of compliance reviews, filters low-relevance recall results |
| Single Search Limit | Top 8 results | Balances recall comprehensiveness and context length limits |
| Maximum Context | 8000 characters | Adapts to context requirements for long-document reviews |
Three Common Configuration Mistakes
- Phenomenon: After importing Excel-format compliance review forms, chunked content includes redundant headers or irrelevant columns. Non-business-related fields appear in recall results. Cause: Operators do not configure header filtering and column selection rules for Excel source data. Default chunking does not preserve business field associations.
- Phenomenon: Timeout errors occur when fully rebuilding indexes for monthly updated compliance data. Cause: Teams do not configure incremental index synchronization. Full processing exceeds system resource limits.
- Phenomenon: Semantic connections break after chunking long-text major issue legal opinions. Recall results miss key compliance risk points. Cause: Chunk length exceeds the token limit supported by the vector model, or insufficient overlap length causes context breaks.
How to Verify Correct Configuration
- Import a single compliance review form. Review the chunk preview. Confirm chunks do not cut off core business fields and risk descriptions.
- Trigger an incremental synchronization task. Confirm the recall range of updated data matches the monthly update frequency.
- Run a simulated compliance risk query. Confirm recall result relevance meets preset threshold requirements.
- Check system logs. Confirm no format parsing or timeout errors occur during index construction and recall processes.
The values provided are common starting points. They should be evaluated against samples from the relevant deployment context.
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.