What Data Looks Like for This Category
This category’s data comes primarily from assessment tool export results, project interview records, assessed party system inventories, and security device logs. Daily produced documents include classification reports, gap analysis reports, remediation plans, and assessment reports. Each document includes structured and unstructured content such as control item details, remediation requirements, and evidence lists. Data updates follow individual project cycles. The same batch of data will not be updated repeatedly after a single project is completed. Data from different projects has no correlation.
What Constraints These Characteristics Impose on Vector Models and Indexes
Data sources include structured tables and unstructured text. Mixed formats require indexing chunking strategies to balance structured field extraction and long text segmentation. Single-project data update cycles are fixed, and no cross-project reusability exists. Index construction must support full or incremental updates at the project level. Documents contain a large volume of cybersecurity rating-specific terminology. Vector models must adapt to domain semantic understanding to avoid semantic deviation of terminology. The data sorting phase accounts for a high share of total work time. Index recall must accurately match control items and remediation requirements to reduce manual verification workload.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the segmentation granularity of long texts such as classification reports and remediation plans, balances semantic integrity and indexing efficiency |
| Overlap Length | 100–200 characters | Prevents chunk truncation of professional terminology or complete control item descriptions, ensures cross-chunk semantic coherence |
| Similarity Threshold | 0.75–0.85 | Accurately matches cybersecurity rating control items and remediation evidence, reduces manual verification costs caused by false recalls |
| maxToken | 4096 | Adapts to the context recall length of long assessment reports, complies with general large model input limits, and matches the parameter upper limit of FastGPT v4.17.0 |
| defaultToken | 8192 | Ensures complete semantic input during vector calculation, covers long text requirements for professional documents |
| Single Search Limit | Top 10 entries | Balances recall coverage and retrieval latency, adapts to medium data volume scenarios within a single project |
Three Common Misconfigurations
- Abnormally high vector calculation scores and multiple identical scores across results during indexing. This occurs when chunk length is set too large, leading to excessive semantic overlap between single chunks and converged vector features.
- Unusually slow vectorization processing speed. This occurs when no separate field extraction rules are configured for structured table data. Mixed processing of structured and unstructured content leads to excessive computational resource usage.
- After importing a CSV-format system inventory, the number of index entries after vectorization is less than the original entry count. This occurs when the CSV file contains empty fields or rows with abnormal formatting, and no abnormal row filtering configuration is enabled. Some data is not included in the index.
How to Confirm Proper Configuration
- Upload a single classification report, enter control item keywords to run a retrieval test, verify the match between recall results and query content, and adjust the similarity threshold to meet business requirements.
- View the index construction log to confirm the final number of index entries matches the number of entries in the original imported data, with no abnormal data loss.
- Test vectorization processing for a single remediation plan, verify that processing latency matches the expected configuration, and adjust chunk length or parallel processing parameters to optimize efficiency.
- Verify the score distribution of vector calculation results, confirm no abnormally high or converged scores, and validate the rationality of chunk and overlap length settings.
The values provided here 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 2026-10-19.