What the data for this category looks like
Data for this category originates from the centralized treasury payment system, unified budget management system, electronic invoice platform, and contract management system. Daily documents include payment applications, fund use plans, invoices, contracts, acceptance receipts, reimbursement approval forms, and payment vouchers. The data update frequency is daily. Included fields cover budget indicators, economic classification, payment amount, approval nodes, contract numbers, and more. Each single record is associated with multiple types of business documents, covering the full chain of information for a single fund payment.
Constraints on vector models and indexing workflows
Heterogeneous data from multiple systems requires index configurations to adapt to format conversion rules for different data sources, to avoid field parsing errors. The daily update rhythm requires an incremental indexing mechanism to match batch synchronization frequency, reducing resource usage from full indexing. Significant structural differences exist across multiple types of business documents. Documents such as invoices have structured fields, while contracts are long-form text. This requires configuring differentiated chunking rules for different document types. The characteristic that a single payment is associated with multiple types of documents requires indexes to support cross-document associated recall. Because errors directly relate to fund compliance, strict control of the accuracy boundary of recall results is necessary.
Configuration settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the length of multiple types of business documents, balance context completeness and chunk granularity, avoid truncating key review-related information |
| Overlap Length | 200–300 characters | Reduce information gaps between chunks, ensure semantic coherence for long-form text such as contracts and acceptance receipts |
| Similarity Threshold | 0.75–0.85 | Balance recall accuracy and coverage, meet compliance requirements for fiscal review, avoid interference from irrelevant documents |
maxToken | 8192 | Adapt to the input length limit of mainstream vector models, meet the complete embedding requirements for long-form contract text |
defaultToken | Calibrated based on actual testing | Adapt to differences in token consumption across documents from different data sources, match the system's global token quota rules |
| Single Search Limit | Top 5 entries | Focus on core associated documents, avoid excessive redundant results affecting review efficiency |
Three common misconfigurations
- Enabling the Doubao-embedding-large indexing model, filling in a custom request address and apikey, then testing directly results in an error. The cause is failure to confirm compatibility of the custom request address, or the apikey permissions not covering vector model calls.
- Uploading table-format business data results in no retrievable table content. The cause is failure to enable the multi-vector support configuration for table data, or failure to correctly associate table fields with vector embedding rules.
- The number of retrieval results does not meet expectations, and recalled documents are unrelated to the current review task. The cause is the similarity threshold setting not adapting to the field matching characteristics of fiscal data, or the chunking rules not covering core review fields.
How to verify proper configuration
- Access the vector model configuration interface, verify the selected vector model, filled custom request address and apikey, and click test connection to confirm no errors.
- Upload a single typical business document such as a reimbursement approval form, wait for indexing to complete, input core fields from the document for retrieval, and confirm the corresponding document can be recalled.
- Check the indexing task logs, confirm that the daily incremental synchronization task executes normally at the set frequency, with no failed records.
- After enabling multi-vector support for table data, upload a structured table document, input key fields from the table for retrieval, and confirm table content can be recalled.
The values listed here 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.