What data in this category looks like
Data for this category comes from the ecological environment online monitoring platform, pollution source automatic monitoring system, law enforcement case handling system, and reports submitted by third-party testing institutions. Daily documents include on-site inspection records, original sampling records, official monitoring reports, order for correction decisions, and administrative penalty decisions. Data is updated daily. Each individual document contains structured fields such as law enforcement time, sampling location, pollutant concentration, rectification requirements, and unstructured text content. Fields are linked to operation traces and monitoring results across all stages of the law enforcement workflow.
What constraints these characteristics impose on the vector models and indexing link
Data for this category is updated daily and comes from multiple dispersed sources. Vector indexes must adapt to the incremental update rhythm to avoid data lag. Documents contain both structured monitoring fields and unstructured law enforcement text. When chunking, the field association logic must be retained to avoid losing business-related information after splitting. Law enforcement documents have fixed process structures. Chunk boundaries must match the logical paragraphs of the documents to prevent damage to the integrity of the evidence chain. Multi-source data requires unified field mapping rules to ensure metadata consistency for vector indexes and avoid field confusion during subsequent searches.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the single-paragraph length of on-site inspection records and monitoring reports, avoids splitting logical units of law enforcement documents |
| Overlap Length | 100–150 characters | Retains contextual association between adjacent chunks, prevents critical monitoring data from being split across two chunks |
| Similarity Threshold | Calibrated based on actual testing | Adapts to business scenarios such as pollutant concentration comparison and law enforcement process matching, avoids false matches or missed matches |
| Single Search Limit | Top 8–12 results | Covers the number of associated documents for a single law enforcement matter, ensures sufficient recall of on-site records and monitoring data |
| Maximum Context | 3500–4000 tokens | Carries associated content from multiple law enforcement documents, adapts to the context carrying capacity of FastGPT v4.17.0 |
defaultToken | 8192 | Matches the system's default token limit, prevents law enforcement evidence from being lost due to content truncation |
Three Common Misconfigurations
- Phenomenon: The similarity values in search return results are abnormally high, such as above 10000. Cause: No similarity threshold filter is configured, or the threshold setting exceeds the reasonable range for the business scenario, leading to recall of irrelevant documents.
- Phenomenon: No index progress is displayed after uploading documents to the knowledge base, or the index status remains failed. Cause: The field format of documents in this category is not matched, or the incremental index update frequency setting does not match the daily update rhythm of the data, leading to index task blocking.
- Phenomenon: A prompt indicating no available channels is displayed when calling the vector model. Cause: Access parameters for the corresponding embedding model are not configured according to official documentation, or channel permissions are not correctly bound to the current workspace.
How to Verify Proper Configuration
- Upload a single on-site inspection record, check whether the index progress completes normally, and confirm that the chunking logic does not split key law enforcement process paragraphs.
- Input a simulated law enforcement inquiry question, retrieve associated documents, verify the business relevance of the recall results, and adjust the similarity threshold to a range that meets business requirements.
- View the metadata logs of the vector database, confirm that the field mapping rules for multi-source data have been correctly applied, and there is no field confusion.
- Test the incremental index trigger logic, confirm that daily updated data can automatically trigger index updates without lag.
The values provided here are common starting points and should be evaluated 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.