What Data for This Category Includes
Data for this category comes from the unified budget management system, centralized treasury payment system, accounting system, and asset management system. Updates occur 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. Most documents use a structure of structured tables paired with explanatory text. Fields cover revenue and expenditure details, asset and liability balances, budget execution difference explanations, and other fiscal-related items. Units use standard fiscal accounting units such as yuan and ten thousand yuan.
What Constraints Do These Characteristics Impose on Vector Models and Indexes
The low-frequency annual update requirement means indexes must support a combination of full reconstruction and incremental updates, to avoid recalculating historical data. Heterogeneous data imported from multiple source systems requires unified field formatting, to prevent vector embedding bias. The mixed document structure of structured tables and text requires an adaptive chunking strategy that balances table integrity and text semantics. The professional nature of fiscal accounting fields requires vector models to adapt to specialized terminology, to avoid semantic confusion.
How to Configure Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the mixed table and text structure of department final account documents, avoiding semantic breaks |
| Overlap Length | 50–100 characters | Retains cross-chunk context association, preventing table rows from being split across chunks |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Matches the semantic similarity range for fiscal specialized terminology, filtering low-relevance results |
| Single Search Limit | Top 8 results | Covers the multi-document association requirement for summary reports from subordinate units |
defaultToken | 4096 | Adapts to the total text length of final account documents, ensuring complete loading of a single statement |
maxToken | 8192 | Supports context length for combined queries of multiple statements, meeting summary analysis requirements |
Three Common Misconfigurations
- Issue: Index tasks throw an error stating "Text too long to embed". Cause:
maxTokenis not set to limit the input length of a single document, causing extremely large final account statements to exceed the model's capacity limit. - Issue: Search results are chaotic after mixing different index models. Cause: The embedding dimensions of the vector model are not unified. Mixing index models from different domains leads to inconsistent semantic spaces.
- Issue: Custom RAG queries return empty results or fail to maintain hierarchical associations. Cause: Associated indexes are not configured for the superior-subordinate summary logic of department final accounts. Using only flat entity indexes fails to reflect the hierarchical relationships between data.
How to Confirm Configurations Are Correct
- Upload a typical single final account statement, check that index chunks retain complete table rows and core semantic paragraphs, confirming that chunk length and overlap length settings match the document structure.
- Enter a fiscal specialized query term, verify that the similarity range of search results aligns with business expectations, adjust the similarity threshold to a reasonable range.
- Import heterogeneous data from multiple sources, check that index tasks complete normally, confirm that no embedding failure errors occur after unifying data formats.
- Initiate a summary query for cross-unit statements, verify that search results cover relevant documents for target units, confirming that the single search limit setting is reasonable.
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.