What Data for This Category Looks Like
Data for this category comes from the unified budget management system, centralized treasury payment system, accounting system, and asset management system. The update frequency is annual. Daily documents include department final account statements, balance sheets, income and expenditure final account statements, final account explanations, and government financial reports. Fields include income and expenditure details, asset and liability balances, budget execution difference explanations, and more. Units are mostly yuan or ten thousand yuan. Report structures are fixed and include multi-level summary data.
What Constraints Do These Characteristics Impose on the Vector Models and Indexing Link
Annual updated bulk data requires indexes to support efficient bulk import and incremental update adaptation. Heterogeneous data from multiple systems needs unified field mapping to avoid data bias during vector embedding. Fixed-structure report documents require targeted chunking rules to prevent unrelated content from different reports from being mixed into the same chunk. Multi-level summary data must retain hierarchical association information to ensure retrieval matches content at the corresponding summary level. Large single processing data volume requires limiting token consumption per batch to avoid timeout issues.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Department final account statement pages have moderate content length. This avoids including logically disconnected report items in a single chunk, while reducing the number of chunks to lower retrieval pressure |
| Overlap Length | 50–80 characters | Difference explanation paragraphs in final account explanations may span chunks. Overlap preserves contextual association to ensure complete matching of difference explanations during retrieval |
| Similarity Threshold | 0.72–0.85 | Final account data has high accuracy requirements. This filters low-match irrelevant data and avoids retrieving incorrect income and expenditure details |
| Single Search Limit | Top 10 results | Summary reports have many associated items. This returns enough associated data for difference verification, while avoiding excessive content exceeding context limits |
defaultToken | 4096 | Token consumption for single-batch imports of department final account documents must be controlled within a reasonable range, adapting to the base model context length of FastGPT |
maxToken | 8192 | Context spliced after retrieval must include multi-level summary data and difference explanations. This requires sufficient tokens to accommodate complete content |
Three Common Mistakes
- Symptom: Indexing tasks run for a long time and eventually fail with a timeout error. Cause: The single-batch import scale was not adjusted to match the annual bulk data characteristics, leading to single-batch token consumption exceeding the
maxTokenlimit. - Symptom: Retrieval results include final account data from non-target budget units. Fields match but the main entity is incorrect. Cause: No metadata filtering for budget unit dimension was added to indexed data, leading to irrelevant cross-entity data being included in the vector index.
- Symptom: Specific budget execution difference explanation content cannot be retrieved. Cause: Chunk length was set too small, splitting difference explanations into multiple incomplete chunks, leading to loss of contextual association during vector embedding.
How to Confirm the Configuration Is Correct
- View the indexing task logs in FastGPT v4.17.0 to confirm that single-batch import token consumption does not exceed the set
maxTokenvalue, and no timeout errors occur. - Initiate a retrieval for a specific budget execution difference issue, and verify that the returned result chunks include complete difference explanations and corresponding income and expenditure details.
- Check the indexed metadata fields to confirm that each entry is bound to the correct budget unit and system source identifier.
- Simulate the annual bulk import process to confirm that all documents complete indexing within a reasonable time frame, with no failed tasks.
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.