Data Overview for This Category
This category serves central enterprise internal control departments, audit departments, finance departments, subordinate enterprises, and external audit institutions. Data sources include enterprise internal control systems, ERP, financial sharing systems, audit management systems, and procurement contract project systems. Data is updated once per year. Daily documents cover internal control self-evaluation reports, audit notices, audit working papers, issue lists, rectification ledgers, and rectification closure certificates. These documents include structured fields such as issue number, rectification responsible entity, and closure status, as well as unstructured supporting material text. Overall data volume varies based on enterprise hierarchy and business scope.
Constraints for Vector Models and Indexing
Annually updated batch data requires indexes to support flexible switching between incremental synchronization and full reconstruction, adapting to the batch import rhythm of annual audit data. Heterogeneous data from multiple system sources must adapt to text parsing for different formats, avoiding embedding bias between structured fields and unstructured supporting materials. The high rigor of audit-related documents requires vector models to meet semantic recognition accuracy standards for professional terms such as internal control deficiencies and rectification closed-loop. Configure separate indexes for structured fields of rectification ledgers to support precise matching, avoiding insufficient accuracy caused by relying solely on full-text vector retrieval. Limit the concurrency of single indexing when single batch data volume is large, to avoid system resource exhaustion that disrupts the overall process.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the text length of audit working papers and rectification ledgers, balancing semantic completeness and indexing granularity |
| Overlap Length | 50–80 characters | Avoid semantic breaks across chunks, covering long sentence cohesion in rectification supporting materials |
defaultToken | 4096 | Match the average text token volume of audit documents from central state-owned enterprises, ensuring complete embedding of professional content |
maxToken | 8192 | Adapt to long documents such as rectification closure certificates imported in batches, preventing truncation of key supporting information |
| Similarity Threshold | 0.72–0.80, calibrated via actual testing | Filter low-match irrelevant issue lists, avoiding repeated identification of audit issues |
| Single Index Limit | 200 records | Adapt to the batch import rhythm of annual data, avoiding system resource overload |
Three Common Misconfigurations
- Phenomenon: Indexing tasks remain stuck on the last set of data, with no progress updates or timeout prompts. Cause: No breakpoint resume mechanism is configured, and the single batch data volume during full import exceeds the system's carrying capacity, causing process blocking.
- Phenomenon: Search tests return empty results or structured field information is missing. Cause: No separate index mapping is configured for structured fields such as issue numbers and rectification responsible entities in rectification ledgers, relying solely on full-text vector retrieval leading to loss of structured information.
- Phenomenon: A search error is triggered after adding a custom embedding model and completing indexing. Cause: The input token range of the custom model is not verified to match the
maxTokenconfiguration, causing format exceptions during embedding.
How to Verify Correct Configuration
- Import a single typical audit working paper, review chunked text fragments, and confirm semantics are not excessively truncated.
- Submit a batch data indexing task, review progress logs, and confirm there are no continuous blocking or abnormal exit records.
- After importing a custom embedding model, run a small-batch test indexing, and verify the search function can normally return matching results.
- Review structured field index configuration, confirm fields such as issue numbers and rectification responsible entities have been separately mapped to retrieval rules.
The values provided are common starting points and should be validated against in-house test 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.