What this category of data looks like
This category of data primarily comes from the ecological environment online monitoring platform, pollution source automatic monitoring system, ecological environment law enforcement case handling system, and reports submitted by third-party testing institutions. Daily generated documents include on-site inspection records, original sampling records, official monitoring reports, order for rectification decisions, and administrative penalty decisions. Fields include monitoring point number, pollutant concentration value, law enforcement time, rectification requirements, penalty reasons, and more. Units include mg/m³, mg/L, hours, and others. Data updates daily, and the content volume of individual documents varies.
Constraints on vector models and indexes
Multi-source mixed data access demands that the index system support structured automatic monitoring data, semi-structured law enforcement records, and unstructured official reports. It must support unified vectorization processing for multi-format data. Daily update frequency requires the index to support incremental synchronization, avoiding resource consumption from daily full reconstruction. Large differences in the content span of individual documents require flexible text chunking rules. This ensures key information such as excessive pollutant concentrations and law enforcement times is not split or truncated. Law enforcement evidence relevance demands that the index retain field-level associations. Recall results must be traceable to corresponding monitoring points or law enforcement links, supporting logical associations for subsequent ledger summary and report generation.
How to configure settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | This category includes short on-site inspection records and long official monitoring reports. This range balances information integrity and vector matching accuracy |
| Overlap Length | 100–150 characters | Prevents key information such as monitoring point numbers and law enforcement officer names from being split and lost across chunks |
| Similarity Threshold | 0.75–0.85 | Precise recall of relevant data for corresponding law enforcement periods or monitoring points is required, avoiding irrelevant content being included in the ledger summary process |
defaultToken | 4096 token | Most core law enforcement and monitoring documents fall within this total token count, allowing complete loading of individual key documents |
maxToken | 8192 token | Ledger summary requires integrating content from multiple associated documents. This value supports cross-document context association |
| Single Recall Limit | Top 8 entries | Law enforcement scenarios require precise matching. Excessive recall increases subsequent manual screening costs |
Three common misconfigurations
- Symptom: Index creation fails, and background logs show file parsing errors. Cause: Multi-format data parsing adaptation is not enabled. This category includes Excel-format monitoring ledgers and law enforcement records in multi-dimensional document formats, and corresponding parsing rules are not configured.
- Symptom: The number of vector recall results does not match expectations, and some key monitoring data is not recalled. Cause: The similarity threshold setting is not calibrated through actual testing based on the field characteristics of this category of data. Settings that are too high or too low will affect recall accuracy.
- Symptom: Embedding model call errors occur, and vectors cannot be generated. Cause: A dedicated index model is not configured separately. The general embedding interface is called through cross-service access directly, leading to call link timeouts or permission verification failures.
How to confirm proper configuration
- Upload a single on-site inspection record and monitoring report, check the index generation progress, and confirm there are no parsing error prompts.
- Enter precise queries such as "COD concentration at XX point on May 10, 2024", and verify that the recall results include relevant data for the corresponding period and point.
- Enter the index configuration page, confirm that the values of
defaultTokenandmaxTokenmatch the preset configurations. - Import batch daily updated data, verify that incremental indexes can synchronize normally, and no full index reconstruction is triggered.
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.