What this category of data looks like
Data primarily comes from inbound and outbound shipment records, on-site inspection forms, third-party test reports, and emergency response ledgers from the hazardous chemicals storage and transportation management system. Single transport or storage records are generated in real time, with full inventory data summarized daily. Documents include structured fields and unstructured text. Structured fields include hazardous chemical name, CAS number, storage location, inventory (unit: ton or liter), transport batch, inspection date. Unstructured content includes inspection descriptions, hidden danger rectification notes, and similar content. Single structured ledger length ranges from hundreds to thousands of characters. Longest unstructured test reports can reach tens of thousands of characters.
What constraints do these characteristics impose on the vector models and indexing link?
The precision requirement for structured fields means the indexing process must retain field associations. It must avoid splitting chunks that break the integrity of key information such as CAS numbers and storage locations. The wide range of unstructured text lengths requires adapting chunking strategies of different lengths. This prevents too short chunks from losing semantic connections or too long chunks exceeding model processing limits. High-frequency real-time updated data requires configuring incremental indexing mechanisms. This avoids excessive resource usage from full indexing. Numeric fields such as inventory and batch must be processed in coordination with text vectors. They cannot rely solely on semantic retrieval matching.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the typical length of hazardous chemical inspection reports and ledger records, retains complete semantic units |
| Overlap Length | 100–200 characters | Prevents chunk truncation of key field associations, ensures cross-chunk information coherence |
maxToken | 8192 token | Matches the processing needs of long-document test reports, aligns with the mainstream configuration standard for FastGPT v4.17.0 |
defaultToken | 2048 token | Adapts to the conventional context length of single-batch storage and transportation ledgers, balances retrieval efficiency and information integrity |
| Similarity Threshold | 0.75–0.85 | Filters low-match irrelevant ledgers, accurately hits key queries such as hazardous chemical names and CAS numbers |
| Single Search Limit | Top 10 entries | Balances retrieval recall scope and result display efficiency, adapts to the batch processing needs of ledger summary |
Three common mistakes
- Symptom: Indexing tasks remain in the "indexing" state with no progress. Cause: Incremental indexing trigger rules are not configured. Every indexing start performs a full scan of the massive daily updated storage and transportation data, resulting in excessive resource occupation.
- Symptom: CAS number matching accuracy in retrieval results does not meet expectations, and some correct results are not recalled. Cause: Vector mapping is not configured separately for structured fields. Only full-text chunking is performed, and the matching weight of precise fields is lost.
Reference only; verify against the target FastGPT release and deployment documentation.
How to confirm the configuration is correct
- View the detailed logs of the indexing task, confirm that the chunked documents retain structured fields such as CAS numbers and storage locations, with no forced truncation.
- Enter a query containing a specific hazardous chemical CAS number, verify whether the field matching degree of the returned results falls within the preset similarity threshold range.
- Check the system configuration page of FastGPT v4.17.0, confirm that the values of
defaultTokenandmaxTokenare consistent with the table recommendations. - Simulate importing a single batch of newly added storage and transportation ledgers, confirm that the indexing task can be completed within the preset time without timeout errors.
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.