What the data for this category looks like
This category's data mainly comes from engineering quality and safety supervision systems, on-site construction site real-name platforms, supervision logs, and test reports. Commonly processed documents include supervision plans, supervision inspection records, hidden danger rectification notices, quality problem handling forms, and completion acceptance supervision reports. A single document usually contains fields such as main entity unit identifiers, inspection time, rectification requirements, and acceptance conclusions. The data update frequency is once per week. The volume of documents imported in a single batch fluctuates with the scale of supervision projects. The text length of each document varies widely, ranging from several hundred-word rectification notices to tens of thousands-word completion acceptance reports.
What constraints do these characteristics impose on the vector models and indexes link
The weekly data update feature requires the index configuration to support incremental synchronization mechanisms to avoid excessive computing resource usage from full reindexing. The large variation in document length requires chunking rules to adapt to different text volumes, preventing long documents from being over-split and destroying the contextual integrity of regulatory clauses. The field differences across multi-source data require basic field standardization during the indexing phase to ensure uniform input format for vector generation. Additionally, the business scenario is tied to quality and safety accident risks, requiring the accuracy of vector recall to meet the rigor requirements of regulatory decision-making.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the varying document lengths of this category, balancing contextual integrity of regulatory clauses and vector generation efficiency |
| Overlap Length | 100–150 characters | Prevents cross-chunk regulatory clauses from being split, ensuring coherent context during retrieval |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Balances recall accuracy and retrieval efficiency, meeting the demand for precise matching of regulatory basis in supervision scenarios |
| Single Search Limit | Top 10 results | Controls the volume of returned results, avoiding context overflow |
defaultToken | 4096 | Adapts to the input length limits of most open-source vector models, matching the average chunk length of documents in this category |
maxToken | 8192 | Supports complete vector generation for long documents, covering scenarios such as completion acceptance supervision reports |
Three common mistakes
- A 401 error is returned when calling the vector model, but the curl test works normally. The cause is that the vector model API key configured in FastGPT was not filled in correctly, or the key permissions do not cover the vector generation requests for this category.
- Duplicate document chunks appear after the knowledge base index is merged, and the custom split index order does not match the stored order. The cause is that the system's built-in deduplication function was not turned off, or the text hash value from custom splitting has a high matching degree with the hash value generated by the system, causing the system to automatically delete duplicate chunks.
- Retrieval results are empty after incremental index updates. The cause is that the trigger frequency of incremental updates does not match the data update frequency, and incremental synchronization is not performed at the weekly interval, causing newly uploaded supervision inspection records to not be properly indexed.
How to confirm the configuration is correct
- Upload a typical hidden danger rectification notice, perform vector retrieval on the FastGPT v4.17.0 platform, and verify that the returned results include the relevant regulatory basis for this document.
- View the vector model call logs, confirm that the configured values of
defaultTokenandmaxTokenmatch the set values, and there are no parameter overflow errors. - Manually import a new supervision plan document, wait for the indexing to complete, then perform retrieval, and confirm that the content of the new document is correctly recalled.
- Check the knowledge base deduplication settings, confirm that they match the requirements of custom splitting, to avoid unnecessary chunk deletion.
The values provided here 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.