What this category of data looks like
The data for this category primarily comes from source code repositories, dependency lists, SAST tool outputs, and public vulnerability databases. Daily generated business documents include code audit reports, vulnerability proofs, repair suggestions, and retest records. Data updates are triggered by project version iterations or the completion of a single audit task. Each project or version corresponds to an independent dataset. Individual data entries include fields such as code snippets, vulnerability descriptions, risk levels, and repair solutions. There are no unified fixed units; values adjust flexibly based on document type and audit content.
What constraints do these characteristics impose on the vector model and indexing workflow
The data characteristics of this category impose multiple constraints on the vector model and indexing workflow. First, the data includes mixed-format content such as code snippets, vulnerability descriptions, and repair solutions. This requires indexes to adapt to vector generation logic for multiple text types, preventing single models from failing to cover semantic features of different fields. Second, data updates are triggered by project versions or single audit tasks. This requires support for incremental index configuration, eliminating the need for full dataset reconstruction each time to reduce computing resource usage. Third, the field structure of individual data entries adjusts flexibly based on audit content. This allows customization of target fields for vector generation, avoiding irrelevant content from participating in vector calculations. Finally, the business timeliness requirement of security audits requires that index query response speeds match task progress rhythms.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
defaultToken | 2048 token | Adapts to the vector generation logic of FastGPT v4.17.0, covering the mixed input length of code snippets and text descriptions |
| Chunk Length | 800–1200 characters | Matches the length of single-segment vulnerability descriptions and code snippets in code audit reports, avoiding semantic loss from overly fragmented or overly long chunks |
| Overlap Length | 100–200 characters | Ensures cross-chunk code logic and vulnerability context are not fragmented, supporting semantic association for data flow tracing |
| Similarity Threshold | 0.75–0.85 | Filters low-match vulnerability information, avoiding false positives or irrelevant results interfering with security audit judgments |
| Single Search Limit | Top 8 entries | Balances query efficiency and result completeness, covering common vulnerability association and repair solution matching needs |
| Maximum Context | 6000 characters | Adapts to the integration needs of multiple linked data segments in security audits, supporting semantic analysis of complete attack paths |
Three common configuration errors
- Issue: Unfiltered comments and blank line content is included in the vector index when importing source code repository data. Cause: No text cleaning rules were configured before chunking, so non-core audit content was included in the vector generation process.
- Issue: Timeout errors occur during incremental index updates. Cause: Incremental index mode was not enabled, and full index reconstruction was executed for each update trigger, exceeding hardware performance limits.
- Issue: A large number of low-match entries are returned when searching for vulnerability repair solutions. Cause: The similarity threshold was set too low, failing to filter irrelevant data with weak semantic association.
How to confirm the configuration is correct
- Upload a single core audit document, check the logs of the vector generation process, and confirm that only the preset target fields participated in vector calculations.
- Trigger an incremental index update, verify that the update duration matches hardware performance, and confirm that no full dataset reconstruction was executed.
- Input a test query containing vulnerability descriptions, check the quantity and relevance of returned results, and adjust relevant configurations to meet business judgment standards.
- Verify the connection and read/write status of the vector database, and confirm that index data can be normally retrieved and called.
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 2026-10-19.