What this category of data looks like
Typical data for this category comes from central enterprise internal control departments, audit departments, subordinate enterprises, and external audit institutions. Daily documents include internal control self-evaluation reports, audit notices, audit working papers, issue lists, rectification ledgers, and rectification closing certificates. Data sources cover enterprise internal control systems, ERP, financial sharing systems, audit management systems, and procurement contract project systems. Data is updated annually. The data structure combines structured fields and unstructured text, including exclusive business fields such as issue number, rectification requirements, and supporting material number.
What constraints do these characteristics impose on the vector models and indexes link
The annual update frequency does not require high-frequency incremental indexing, and a full regular update indexing strategy can be adopted. Mixed storage of multiple types of documents requires a chunking strategy adapted to different document lengths, to avoid splitting short paragraphs from audit working papers and long entries from rectification ledgers. Data from multiple systems requires unified vector encoding formats to ensure retrieval consistency across system documents. The strong association between audit issues and rectification records requires vector recall to prioritize matching the semantic association between issues and rectifications, reducing manual screening costs. Strict error consequences require retrieval results to have high confidence, so the recall range and similarity threshold need to be precisely controlled.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the typical paragraph length of internal control self-evaluation reports and audit working papers, avoid splitting audit issue descriptions across chunks |
| Overlap Length | 100–150 characters | Retain associated rectification requirement information across chunks, ensure complete rectification logic across paragraphs is not missed during retrieval |
| Similarity Threshold | 0.75–0.85 | Match semantic consistency between audit issues and rectification records, filter irrelevant general documents |
| Single Search Limit | Top 8 entries | Cover associated rectification ledgers and supporting materials for common audit issues, meet audit verification needs |
defaultToken | 4096 | Adapt to the text length of single documents such as audit notices and rectification closing certificates |
maxToken | 8192 | Carry context information for multiple associated documents, meet the complete evidence requirements for audit verification |
Three common mistakes
- Phenomenon: Custom vector database connection fails, and the system returns a configuration error prompt. Cause: The database access key and port are not configured according to system requirements, and a non-vector type database is mistakenly connected.
- Phenomenon: The number of vector recall results is far lower than expected, with only a small number of documents returned. Cause: Filter rules are not set for the exclusive fields of audit issue lists and rectification ledgers, leading to overly restricted recall range.
- Phenomenon: Model call times out, and task execution fails. Cause: Reasonable
defaultTokenandmaxTokenparameters are not set, causing the text length processed in a single run to exceed system limits.
How to confirm configuration is complete
- Upload a single internal control self-evaluation report, trigger the vector indexing task, and check the indexing generation log to confirm that the chunking parameters match the actual text.
- Input simulated audit questions, call the retrieval function, and verify whether the returned results include the associated issue list and rectification ledger.
- Test the custom vector database connection, check the connection status prompt returned by the system to confirm the configuration is correct.
- Adjust the similarity threshold, observe the quantity change of recall results, and confirm that the threshold 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.