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 is updated once per year. Daily documents include department final account statements, balance sheets, income and expenditure final account statements, final account explanations, and government financial reports. Most documents are structured tables and formatted explanatory text. Fields include income and expenditure subject codes, amounts, filling instructions, and others. The basic accounting unit is the yuan.
What constraints these characteristics impose on the vector model and indexing workflow
The annual centralized data update requirement means the indexing workflow must support batch import and incremental update adaptation. A high proportion of structured table documents requires chunking strategies that preserve the integrity of core fields such as subjects and amounts, to avoid semantic breaks caused by splitting key rows. Data from multiple systems has format differences, so unified formatting processing must be completed before indexing. The business need to accurately check accounts and statements and explain differences requires high relevance of recall results, which imposes constraints on the accuracy of similarity matching.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the combined length of subject rows and explanatory text in final account statements, avoiding splitting core accounting units |
| Overlap Length | 50–100 characters | Retains subject association information across chunks, ensuring contextual coherence during account and statement checks |
defaultToken | 4000 token | Matches the typical length of a single final account explanation document, meeting the contextual needs of basic recall |
| Similarity Threshold | 0.75–0.85 | Ensures recall accuracy for core queries such as account and statement differences and income and expenditure subjects, avoiding interference from irrelevant results |
| Single Search Limit | Top 8 entries | Covers the multi-dimensional associated data requirements when summarizing reports from subordinate units |
maxToken | 16000 token | Adapts to full contextual recall of annual final account reports, meeting full data reference needs for difference explanations |
Three common mistakes
- Symptom: After indexing, searching for relevant subjects returns no results or a "no matching content found" prompt. Cause: The chunking strategy was not adjusted for structured table documents, and the association unit between subject codes and amounts was split, resulting in vector semantic breaks.
- Symptom: Knowledge base query takes too long, exceeding the preset threshold. Cause: The actual call limit for
maxTokenwas not restricted, and recalling excessive annual data in a single query caused excessive contextual transmission pressure. - Symptom: Search results are not updated after updating documents. Cause: The incremental indexing trigger rule for annual data was not enabled, and only full indexing was performed, resulting in update delays.
How to confirm the configuration is correct
- Upload a single department final account statement, check the chunk preview interface, and confirm that core subject rows are not split.
- Initiate an account and statement check query, and verify that the similarity scores of returned results fall within the preset range.
- Simulate batch import of annual data, and verify that the index task progress bar and completion status are normal.
- Modify a single final account explanation text, perform incremental indexing, then search for the modified content, and confirm that the results are updated synchronously.
The values listed here are common starting points, and should be evaluated 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.