What this category of data looks like
Data for this category primarily comes from central enterprise internal control systems, ERP systems, financial sharing systems, audit management systems, and procurement contract project systems. Daily generated documents include internal control self-evaluation reports, audit notices, audit working papers, issue lists, rectification ledgers, and rectification closure certificates. Data is updated annually. Documents contain structured fields and unstructured text, with some documents linked to specific details such as business voucher numbers, rectification responsible persons, and closure times. Overall content focuses on business records related to post-event inspection and rectification closure.
What constraints do these characteristics impose on the vector models and indexes stage?
Data sources are scattered across multiple heterogeneous systems, requiring adaptation to document import and parsing rules for different formats. The annual update cycle means indexes do not need frequent refreshing, but must support full reindexing to cover the latest policy and rectification content. Documents contain both structured fields and unstructured text, so both field matching and semantic recall must be supported to enable precise queries. Documents such as rectification ledgers and closure certificates are linked to specific business identifiers, so indexes must retain these associations to avoid recalling irrelevant historical records. Audit-related Q&A has high traceability accuracy requirements, so recalled content must be directly related to the current audit project.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Match the text length of audit working papers and rectification ledgers, avoid single chunks spanning business logic |
| Overlap Length | 100–200 characters | Retain cross-chunk business association information, such as preceding and following descriptions of rectification issues and policy basis |
| maxToken | 4000–6000 tokens | Match the long-text context requirements of policy documents for central state-owned enterprises, avoid truncating key policy clauses |
| Single Search Limit | Top 8 entries | Cover the policy basis and rectification cases required for a single round of Q&A, while controlling redundant returned content |
| Reranking | Enable semantic reranking | Improve the accuracy of recalling policy basis related to audit issues, filter irrelevant documents |
| defaultToken | Calibrate based on actual testing | Adapt to context calls for knowledge bases of different sizes, avoid exceeding model call limits |
Three Common Misconfigurations
- Symptom: Search results include historical rectification records unrelated to the current audit project. Cause: No business identifier-based filtering rules are configured, and relying solely on semantic recall leads to redundant results.
- Symptom: The index model selection interface fails to load the optional model list, displaying a load failure prompt. Cause: The deployment path of the local index model is not configured correctly, so FastGPT cannot pull the model list.
- Symptom: Long-text Q&A results truncate key policy clauses. Cause: The maxToken parameter is not adjusted to the range suitable for long documents, leading to forced truncation of context.
How to Confirm the Configuration is Correct
- Upload a typical audit notice and corresponding rectification ledger, initiate the query "What are the policy basis corresponding to this audit project", and verify whether the returned results include the core content of the corresponding documents.
- Enter the index management page of FastGPT v4.17.0, trigger a full reindex, and check whether the task status is completed within a reasonable duration without generating error logs.
- Test a query that includes a business number, such as "View the rectification closure certificate for project XX", and confirm that the returned results only relate to documents for this project, with no irrelevant content included.
- Check the model call logs to confirm that the actual value of the maxToken parameter matches the configured range, with no abnormal truncation.
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.