What the data for this category looks like
Data for this category comes from the unified budget management system, project declaration materials, fund payment data, business system ledgers, and satisfaction surveys. Daily documents include performance target forms, self-assessment forms, performance evaluation reports, supporting material lists, and rectification reports. Data updates occur annually. Individual documents contain fields such as performance quantitative indicators, fund allocation details, and supporting association numbers. There is no real-time high-frequency updated content. The batch data volume changes with the number of projects in the current year.
What constraints do these characteristics impose on the vector models and indexing link
The annual update frequency means high-frequency incremental indexing is unnecessary. A full-rebuild indexing update process can be used. The mixed format of multiple document types requires chunking parameters to adapt to text length differences, from short fields to long reports. Non-standardized fields from multiple sources require basic format alignment before indexing, to avoid vector mapping bias. The coexistence of supporting unstructured materials and performance quantitative indicators requires indexing to support vector association between structured fields and unstructured text.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Covers individual performance indicator descriptions or short report paragraphs, avoids cross-semantic chunking |
| Overlap Length | 100–150 characters | Connects semantic connections between adjacent chunks, adapts to the paragraph logic of performance reports |
| Similarity Threshold | 0.75–0.85 | Distinguishes matching degrees between performance indicators and irrelevant text, avoids false recalls |
| Single Search Limit | Top 10 entries | Matches the limited number of supporting materials for current year projects, controls retrieval overhead |
defaultToken | 16384 | Adapts to the complete context of long-text performance evaluation reports; FastGPT v4.17.0 supports this parameter |
maxToken | 4096 | Limits the context length of a single round of retrieval, avoids exceeding the model's processing limits |
Three Common Configuration Errors
- Retrieval results are empty or have incorrect counts after switching indexes. The cause is failing to set the new index as the default active state, or switching to use it before index rebuilding is complete.
- Interface returns parameter errors when adding indexes in batches. The cause is failing to correctly specify the associated performance project number field, resulting in failure to match business data during vector mapping.
- Too many index chunks generated for a single document cause duplicate retrieval. The cause is failing to unify chunking rules, splitting a single complete performance evaluation report into meaningless multiple chunks.
How to Confirm Proper Configuration
- Check the active status in the index management interface, confirm that the currently used index matches the current business cycle.
- Submit a retrieval test for a single performance self-assessment form, verify that the returned associated project number matches the test data.
- Import a small amount of test data in batches, check whether the index generation progress matches the configured chunking parameters.
- View the vector model call logs, confirm that the actual values of
defaultTokenandmaxTokenmeet the configuration requirements.
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.