What This Category of Data Looks Like
This category of data mainly comes from the ecological environment law enforcement system, automatic pollution source monitoring platform, and test reports submitted by third-party testing institutions. Daily generated documents include on-site inspection records, sampling records, official monitoring reports, order for rectification decisions, and administrative penalty decisions. The data is updated daily. Each individual document contains fields such as on-site operating conditions, monitoring values, law enforcement procedures, and rectification requirements. Some monitoring data has fixed units such as concentration and flow. The field structures of different documents vary.
Constraints on Vector Models and Indexes
High-frequency daily updated data requires the index to support incremental synchronization to avoid performance loss caused by full reconstruction. Mixed storage of multiple types of documents requires adjusting chunking rules for different document structures to ensure that core fields such as monitoring values and law enforcement procedures are fully retained. Some documents contain details of law enforcement processes. The index link must ensure the accuracy of retrieval results to support law enforcement scenario Q&A in party building learning, while avoiding interference from irrelevant information. Data sources are scattered, so a unified index entry is needed to integrate documents from different systems, ensuring that party building learning Q&A covers all law enforcement and monitoring related content and avoids retrieval omissions.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the typical length of on-site inspection records and monitoring reports, avoiding splitting single content that would disrupt law enforcement process logic |
| Overlap Length | 100–150 characters | Retains contextual connections between adjacent chunks, ensuring that retrieval can fully cover the associated content of monitoring data and rectification requirements |
defaultToken | 4096 token | Matches the context length requirements of most party building learning Q&A, covering the combined display of law enforcement documents and policy content |
maxToken | 8192 token | Adapts to long document splicing scenarios, meeting the complete analysis needs of multiple law enforcement cases |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Balances the recall accuracy of party building learning Q&A, avoiding accidental recall of irrelevant law enforcement documents |
| Single Search Limit | Top 8 results | Controls the number of retrieval results, avoiding information overload in party building learning scenarios |
Three Common Misconfigurations
- Retrieval results include expired online monitoring data, causing the law enforcement content referenced in party building learning Q&A to be out of date. This is caused by not configuring incremental index synchronization rules and not filtering expired data after daily updates.
- A single sampling record is split into multiple unrelated chunks, making it impossible to fully match sampling data and overproof determination basis during retrieval, leading to conclusions with insufficient evidence in Q&A. This is caused by setting the chunk length too small, which disrupts the complete logic of law enforcement documents.
- The number of retrieval results is far lower than expected, failing to cover the law enforcement cases required for party building learning. This is caused by setting the single search limit too low, not matching the recall needs of the scenario.
How to Verify Proper Configuration
- Upload a single typical document (such as an on-site inspection record) and check the chunk preview results to confirm that core fields are not split.
- Trigger an incremental synchronization task and check the index update log to confirm that newly added monitoring data for the day has been correctly included in the index.
- Input test questions related to party building learning, check the number and relevance of retrieval results, and adjust the values of corresponding configuration items.
- Test long context splicing scenarios to confirm that the spliced content of multiple law enforcement documents meets Q&A requirements.
The values listed above 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.