What this category of data looks like
Data sources include centralized treasury payment systems, unified budget management systems, electronic invoice platforms, and contract management systems. Update frequency is daily. Daily business documents cover payment applications, fund use plans, invoices, contracts, acceptance reports, reimbursement approval forms, and payment vouchers. Each individual document includes standard financial business fields such as budget indicator code, economic classification code, payment amount, and approval node status. No custom special units are used, and all fields follow general financial business formats.
Constraints on the knowledge base retrieval and recall link
Heterogeneous data formats from multiple system sources require the retrieval link to adapt to document structures and field rules of different systems. The daily update frequency requires the incremental recall synchronization cycle to align with the daily business update rhythm, preventing data lag from affecting audit efficiency. Diverse business document types require differentiated recall weights for different document types such as payment applications and reimbursement approval forms. Compliance audit requirements for individual payments demand that retrieval results accurately match core fields like budget indicators and economic classification, avoiding generalized recall that disrupts audit workflows. High-risk error consequences require the recall result confidence threshold to match the strict audit standards of financial business.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Adapts to the field accuracy matching requirements of financial business documents, avoids low-confidence results interfering with compliance audits |
limit | Top 8–12 results | Covers associated documents such as contracts, invoices, and approval forms required for individual payment audits, avoids excessive results increasing audit workload |
searchMode | Hybrid semantic retrieval + keyword retrieval | Adapts to accurate matching of structured fields such as budget indicators and economic classification, while covering full-text semantic relevance of documents |
usingReRank | Enabled | Improves the sorting priority of high-confidence results, reduces mismatch risks |
datasetSearchUsingExtensionQuery | Enabled | Generates extended query terms for structured fields such as budget indicator codes, improves accurate recall rate |
collectionIds | Bind corresponding financial business document collections | Limits the retrieval scope to only documents related to centralized treasury payment and reimbursement audit, avoids interference from irrelevant data |
Three Common Configuration Errors
- Phenomenon: Retrieval results include non-target business documents. Cause:
collectionIdsis not configured to limit the retrieval scope, leading to cross-category documents being recalled. - Phenomenon: Retrieval latency exceeds the allowable business threshold. Cause:
limitis set too high andusingReRankis not enabled, resulting in excessive low-correlation results participating in sorting calculations. - Phenomenon: Failed to accurately match budget indicator code fields. Cause:
datasetSearchUsingExtensionQueryis not enabled, so no extended query terms are generated for structured fields, causing semantic matching to fail.
How to Verify Successful Configuration
- Review the knowledge base collection binding configuration to confirm it only includes documents related to centralized treasury payment and reimbursement audit.
- Submit a test retrieval request, and verify that the confidence scores of all returned results fall within the preset threshold range.
- Input the budget indicator code as a query term, and confirm that the recall results include the core business fields of the corresponding documents.
- Run a retrieval in a simulated batch audit scenario, and confirm that single-response latency meets business requirements.
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.