What this category of data looks like
Data sources include the centralized treasury payment system, budget management integrated system, electronic invoice platform, and contract management system. Data is updated daily. Commonly processed documents include payment applications, fund appropriation plans, invoices, contracts, acceptance documents, reimbursement approval forms, and payment vouchers. Each type of document has dedicated business fields: payment applications include application numbers and budget subject codes; invoices include invoice numbers and invoice amounts; contracts include contract numbers and performance amounts. All data revolves around the full-process audit and execution of single fund payments.
Constraints Imposed on Vector Models and Indexing
Multi-source data access requires indexes to support unified mapping of cross-system data, to prevent field mismatches. The daily update frequency requires configuring incremental indexing strategies to reduce repeated full index computation overhead. Diverse document types with large length differences require adapting chunking rules for different documents, to avoid disrupting business logic. The strict requirements for single fund payment audits mean the similarity matching threshold must be tightly controlled, to avoid compliance risks from incorrect matches. The relevance of full-process data requires indexes to cover core audit fields such as budget indicators, economic classification codes, and approval nodes, to ensure retrieval results support complete compliance verification.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the typical length of single payment vouchers and invoices, avoid damaging business-related information during chunking |
| Overlap Length | 50–80 characters | Retain key identifiers such as contract numbers and payment order numbers across chunks, ensuring contextual integrity during retrieval |
| Similarity Threshold | 0.75–0.85 | Meet the rigor requirements for financial data, reduce mismatch probability, and comply with treasury audit compliance standards. Adjust this range based on the output accuracy of the vector model if using a domestic vector model |
| Single Search Limit | Top 10 entries | Control the number of returned results, adapting to the single verification need for single fund payment audits |
defaultToken | 2000 | Meet the context length requirement for single-batch ledger summary, ensuring core audit fields are fully transmitted |
maxToken | 8000 | Adapt to the total token consumption limit during report generation, avoiding truncation errors during generation |
Three Common Mistakes
- Attempting to manage the vector database directly via MongoDB fails to synchronize update data from business systems. Modifying underlying storage directly without using FastGPT’s official vector database management interface will destroy index consistency and integrity.
- A 403 rejection error is returned when calling the vector model, while curl testing operates normally. This occurs because the FastGPT vector model access whitelist is not configured, or the port deployed for Ollama is not open to the network where the FastGPT service runs.
- Duplicate payment voucher records remain after knowledge base index merging. This happens because index deduplication configuration is not enabled, or the deduplication threshold is set too high to identify duplicate entries.
How to Confirm Correct Configuration
Reference only; verify against the target FastGPT release and deployment documentation.
- Import a single payment voucher document, check that the chunking results cover all complete business fields with no critical information truncated.
- Initiate a reimbursement audit retrieval request, verify that the number of returned results matches the single search limit setting.
- View the vector model call logs, confirm that the consumption of
defaultTokenandmaxTokendoes not trigger truncation or errors. - Perform an index merging operation, check that duplicate data identification results match the expected threshold.
The values provided are common starting points and should be validated using sample data specific to the target deployment.
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.