What this category of data looks like
This category of data originates from the unified budget management system, centralized treasury payment system, accounting system, and asset management system. It updates on an annual cycle. Daily documents include department final account statements, balance sheets, income and expenditure final account statements, final account explanations, and government financial reports. Data fields cover revenue and expenditure details, asset and liability items, budget execution difference explanations, and more. Units match corresponding measurement standards based on specific report items, and there is no unified fixed format.
What constraints do these characteristics impose on the vector models and indexes link
The annual update feature requires the indexing process to support batch full updates. Reserve operating resource space for annual full indexing. A mix of multiple types of structured statements and unstructured explanatory documents requires a chunking strategy that balances the accuracy of structured fields and the semantic integrity of unstructured text. Homologous data from multiple systems has field naming differences. Complete basic field alignment before indexing to avoid deviations in semantic vector generation. The single indexing data volume is large. Set a reasonable chunking threshold to prevent indexing timeouts.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the standard text length of department final account statements and explanatory documents, avoids semantic fragmentation |
| Overlap Length | 50–80 characters | Retains cross-chunk context connections, covers cross-paragraph content such as budget execution difference explanations |
maxToken | 8192 | Adapts to the vector generation limit for long texts such as government financial reports, aligns with the model support scope of FastGPT v4.17.0 |
| Similarity Threshold | Calibrated based on actual testing | Adapts to the semantic matching requirements of party building Q&A, avoids mismatches or insufficient recall |
| Single Search Limit | Top 10 entries | Balances recall accuracy and response speed for party building Q&A |
| Index Timeout | 600 seconds | Aligns with the operating duration requirements of annual batch full indexing |
Three Common Misconfigurations
- Symptom: The semantic similarity value displays as 10000+, which exceeds the normal matching range. Cause: The value rules for the similarity threshold are not configured correctly. The original output value of the vector model is used, and the system's standardized matching logic is not followed.
- Symptom: The knowledge base remains in the indexing state for a long time after importing the dataset, with no progress updates. Cause: The resource configuration for annual batch indexing is not adapted, or homologous data from multiple systems is not aligned in advance, leading to indexing blocking.
- Symptom: The vector model channel shows no available options, and the specified model cannot be selected. Cause: The channel configuration for the corresponding model is not completed in the system backend, or the access permission for the corresponding service is not bound.
How to Verify Correct Configuration
- Check the chunk configuration items in the knowledge base, confirm that the chunk length and overlap length match the text structure of department final account documents.
- Trigger an indexing test for a small batch dataset, check whether the indexing status is completed within the preset duration and there are no abnormal errors.
- Initiate a financial final account Q&A related to party building learning, verify the semantic matching logic of the recall results, and adjust the similarity threshold to a range that meets business requirements.
- Check the vector model call records, confirm that the value of
maxTokenaligns with the model support scope of FastGPT v4.17.0.
The values listed here are common starting points and should be evaluated against the specific deployment’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.