What the data for this category looks like
This category of data comes from ecological environment law enforcement detachments, monitoring stations, pollutant discharge units, and third-party testing institutions. Daily documents include on-site inspection records, sampling records, monitoring reports, order for correction decisions, and administrative penalty decisions. Data sources cover online monitoring platforms, automatic pollutant source monitoring data, ecological environment law enforcement systems, and third-party test reports. The update frequency is daily. Document formats include both structured numerical values such as pollutant concentrations and sampling point coordinates, and unstructured text such as law enforcement process descriptions and rectification requirements. Fields include core business information such as law enforcement time, monitoring parameters, and penalty basis.
What constraints do these characteristics impose on the vector models and indexes link
Data sources updated daily require indexes to support incremental synchronization, avoiding time-consuming issues caused by full reconstruction. The mixed characteristics of multiple types of documents require vector models to adapt to the vectorization of structured and unstructured content, ensuring that data in different formats can generate vectors of uniform dimensions. The rigor of law enforcement evidence requires index recall to retain the original associated information of documents, avoiding truncation of key content such as law enforcement procedures and sampling timelines. Differences in data formats from multiple sources require a unified preprocessing workflow, ensuring that documents exported from different systems complete field mapping and format standardization before vectorization, preventing retrieval failures caused by inconsistent vector dimensions.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the single-paragraph text length of on-site inspection records and monitoring reports, avoiding inclusion of excessive unrelated law enforcement process descriptions in a single chunk |
| Overlap Length | 100–200 characters | Retains cross-chunk sampling timeline and law enforcement process association information, preventing key evidence from being truncated |
maxToken | 4096 | Adapts to the input limits of mainstream vector models, ensuring that long monitoring reports can be fully vectorized |
defaultToken | 2048 | Used as the default vectorization length for short documents such as order for correction decisions, balancing accuracy and performance |
| Similarity Threshold | 0.75–0.85 | Distinguishes law enforcement evidence from unrelated documents, avoiding recall of non-associated monitoring data or law enforcement records |
| Single Search Limit | Top 10 entries | Controls the number of retrieval results, adapting to the business scenario where law enforcement personnel quickly access core evidence |
Three Common Misconfigurations
- A 401 unauthorized status code is returned when connecting to the m3e vector model. Cause: The API key for the vector model is not configured correctly, or the key is not granted call permissions for the vector embedding service.
- After custom-split document chunks are stored in the knowledge base, duplicate chunks are automatically cleared, causing the original index order to not match the split order. Cause: The knowledge base enables document chunk deduplication by default, and chunk order metadata after splitting is not retained.
- After deploying multiple replica instances, the knowledge base index speed does not improve or even times out. Cause: The distributed index strategy of the vector database is not configured, and multiple replicas do not share index cache, leading to repeated calculations.
How to Confirm Proper Configuration
- A standard on-site inspection record document is uploaded. The vector embedding task status in the FastGPT v4.17.0 backend is checked to confirm it displays as successful, with no error logs.
- Monitoring reports containing specific pollutant concentrations are retrieved. The similarity scores of the recall results are verified to fall within the preset threshold range.
- Daily incremental data synchronization is simulated. The index is checked to confirm it only updates newly added documents and does not trigger a full reconstruction process.
- Custom-split document chunks are tested for storage in the knowledge base. It is confirmed that duplicate chunks are not automatically deleted, and the original split order is retained.
The values provided here are common starting points and should be measured against respective 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.