What data for this category looks like
Data sources for this category include requirement specification documents, test environment configurations, customer business sample data, and defect system records. Daily used and maintained documents include UAT use cases, acceptance issue tickets, acceptance reports, and signed confirmation forms. Maintenance subjects are implementation consultants, test managers, and customer business representatives. Data updates occur once per project phase or per version iteration. Document fields include use case steps, expected results, issue descriptions, reproduction environments, and more. There are no unified fixed units; values adjust based on document type and business scenario.
What constraints these characteristics impose on vector models and indexes
Dispersed data sources covering multiple types of business documents require indexes to support associated recall of cross-source heterogeneous data. Updates are triggered in batches per phase or version, with no need for real-time incremental indexing, making full-index construction strategies suitable. Daily documents have large structural differences, so chunking strategies must adapt to the semantic unit lengths of different documents. Acceptance scenarios have high requirements for matching accuracy, so strong association between vector recall and business scenarios must be ensured to avoid low-confidence invalid matches.
How to set configurations
This content is written for FastGPT v4.17.0, adapted to the business data characteristics of this category:
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the semantic integrity of single segments in UAT use cases and acceptance issue tickets, avoiding excessive semantic splitting |
| Overlap Length | 100–150 characters | Retains contextual association between adjacent chunks, adapting to the step continuity of acceptance documents |
| Similarity Threshold | 0.75–0.85 | Ensures matching accuracy for business scenarios, reducing false recall rates for acceptance issues |
| Single Search Limit | Top 6–10 results | Covers the typical number of acceptance issues per project phase, balancing recall efficiency and recall scope |
| maxToken | 3500–4500 token | Adapts to the contextual length of acceptance-related documents, meeting semantic recall requirements for complete use cases or issue tickets |
| defaultToken | 120–180 token | Controls storage costs per vector, adapting to index scale built in batches per phase |
Three common mistakes
- The exported knowledge base dataset.csv only includes the index field and no content field. This occurs because the post-chunk content extraction rule is not configured correctly, and only index metadata is synchronized without associating original text content.
- Custom index models fail to load after an intranet Docker deployment. This occurs because model files are not mounted to the specified path within the container, or the model call address is not correctly declared in the configuration file.
- Default chunking parameters truncate complete business content when importing text. This occurs because chunking settings are not adjusted based on the semantic length of acceptance documents, causing vector matching to fail to associate complete use case steps or issue descriptions.
How to confirm configurations are set correctly
- Run an index build for a single UAT use case document, and check if there are any chunking failure or vector generation failure errors in the task log.
- Submit an acceptance-related retrieval request, verify that returned results include matching business documents, and that sorting follows expected confidence rules.
- View the knowledge base index statistics page, confirm that the number of generated vectors matches the number of imported documents.
- Adjust the similarity threshold, verify that the number of retrieval results changes as expected based on the configuration adjustment.
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 2026-10-19.