What This Category of Data Looks Like
This category of data primarily comes from business systems of central enterprise internal control departments, audit departments, finance departments, subordinate enterprises, and external audit institutions. It covers documents including internal control self-evaluation reports, audit notices, audit working papers, issue lists, rectification ledgers, rectification closing certificates, and more. Data updates follow an annual fixed cycle, while data updates for ad-hoc special audit projects have no fixed rhythm. Documents include both structured fields and unstructured text. Structured fields include issue numbers, rectification responsible persons, completion deadlines, and more. Unstructured text includes details of audit findings and specific content of rectification supporting materials.
What Constraints Do These Data Characteristics Impose on the Vector Model and Indexing Workflow
The data characteristics of this category impose clear constraints on the vector model and indexing workflow. Low-frequency data updated annually does not require frequent incremental indexing, and a full rebuild mode can be used to reduce resource consumption. Documents that include both structured fields and unstructured text need to support both text chunk vectors and structured field indexing to adapt to multi-dimensional retrieval needs. Heterogeneous data formats from multiple source systems must first undergo format alignment before chunking processing, to avoid chaotic chunking logic. Audit-related documents involve sensitive business content, so vector storage needs to comply with enterprise internal data security management and control requirements.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the length of single-segment core content in documents such as audit working papers and rectification ledgers, avoiding fragmented chunks that lose context or overly long chunks that reduce vector accuracy |
| Overlap Length | 50–80 characters | Retains overlapping content between adjacent chunks, ensuring that cross-chunk audit issue descriptions can be fully matched |
maxToken | 4096 token | Adapts to the input length limits of most mainstream vector models, preventing indexing failures caused by exceeding thresholds |
| Similarity Threshold | 0.72–0.85 | Matches the semantic similarity requirements for audit issues, filtering low-relevant historical rectification records and issue lists |
| Single Search Limit | Top 10 entries | Controls the number of retrieval return results, adapting to the multi-result verification scenario of audit rectification tracking |
defaultToken | 1024 token | Serves as the maximum token quota for default chunks, balancing text completeness and vector storage efficiency |
Three Common Misconfigurations
- Phenomenon: The number of indexes in the dataset automatically increases over time. Originally, one set of data corresponds to one set of indexes, but multiple sets appear later. Cause: The incremental index switch was not turned off. During annual full updates, incremental synchronization is triggered repeatedly, leading to duplicate index generation.
- Phenomenon: After configuring
text-embedding-3-large, the indexing task stays in the "Indexing" state for a long time. Restarting the service after commenting out the model does not resolve the issue. Cause: No access key for the vector model is configured, or enterprise intranet network permissions are missing. This causes model invocation failures, and the task does not terminate correctly, leaving behind blocking processes. - Phenomenon: The question-and-answer pair extraction task stays in the "Indexing" state for a long time and cannot change to ready. Cause: The chunk length exceeds the maximum input limit of the vector model, causing chunked text to fail vectorization and the task to get stuck.
How to Verify the Configuration is Correctly Applied
- Check the vector model invocation logs under FastGPT v4.17.0 to confirm that the token count of chunked text does not exceed the
maxTokenlimit. - Manually trigger an indexing task for a small batch of source data, and check whether the task completion status is normal.
- Retrieve test audit issues to confirm that the returned results' similarity meets the matching requirements of the business scenario.
- Verify that the final number of indexes matches the number of source data, confirming that no duplicate indexes have been generated.
The values given 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.