What Data Looks Like for This Category
Data for this category primarily originates from source code repositories, dependency lists, Static Application Security Testing (SAST) tools, and public vulnerability databases. Updates follow project version iterations or the completion of individual audit tasks. Daily output documents include code audit reports, vulnerability proof materials, repair recommendation documents, and retest records. Common data fields include associated code paths, vulnerability severity levels, impact scopes, repair verification markers, and more. No unified mandatory format exists, but content must align with the project dimension of the audit task. The character count of a single audit document fluctuates with project scale. Unlike static code scanning, this category of data also includes manually reviewed business logic and attack path analysis content.
What Constraints These Characteristics Impose on Context and Token Processing
First, data updates dynamically with project versions. This requires the context to support refresh triggered by version commits, to avoid referencing outdated vulnerability information or older code audit results. Second, documents contain multiple types of audit materials. Teams must distinguish content in different formats such as code snippets, vulnerability descriptions, and repair recommendations, and allocate token resources reasonably to prevent a single type of data from occupying too much context space. Third, the data volume of a single audit task grows with project scale. Teams must set an upper limit on overall context token usage, while ensuring core audit data such as dangerous function localization and data flow tracking is not truncated, to avoid affecting the completeness of audit conclusions. Unlike static code scanning with a single data structure, this category integrates tool scan results and manual analysis content. This requires the context to support input in different formats, to avoid unbalanced token allocation.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum Context Length | 8000–16000 token (calibrated via actual testing) | Adapts to the token capacity limit of quantized models such as qwen2.5-14b-int4, and matches the average data volume of code audit documents |
| Maximum Reference Token Count | 2000–4000 characters | Limits the token usage of a single audit report snippet, to avoid overall context overflow |
| Context Refresh Timing | Triggered by project version commits | Aligns with the rhythm of data updates by version, to ensure the context always associates with the latest audit data |
| Single Recall Result Limit | Top 10 entries | Focuses on high-severity vulnerabilities and core repair recommendations, reducing token consumption from non-critical data |
| Context Filter Threshold | Calibrated via actual testing | Filters low-priority test records and non-core audit logs, focusing on key audit content |
Three Common Misconfigurations
- The symptom is a
token validation failederror displayed in the chat interface. The cause is incorrect configuration of maximum context length, causing the total token count of uploaded audit documents to exceed the model's supported range. - The symptom is knowledge base responses being truncated, returning only partial repair recommendation content. The cause is the maximum reference token count being set too low, truncating long-form vulnerability proof materials or detailed repair plans.
- The symptom is an
InternalError.Algo.InvalidParameter: Range of max_tokens shoerror being returned. The cause is the maximum context length value exceeding the token interval supported by the model, and not matching the actual capacity of the quantized model.
How to Verify Proper Configuration
- Upload a typical code audit report, and check in the context preview module of FastGPT v4.17.0 to confirm all key content loads completely without truncation.
- Initiate a test conversation containing multiple vulnerability descriptions, and verify that the returned results cover all recalled audit data without missing core content.
- Simulate a project version update to trigger a context refresh, and verify that the associated latest audit records are correctly loaded into the context scope.
- Check system operation logs to confirm there are no token-related parameter errors or context overflow-related error messages.
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.