What Data for This Category Looks Like
Data for this category primarily originates from code repositories, test framework reports, requirement specifications, and interface definitions. Stored documentation includes standardized test cases, coverage statistics reports, Mock interface descriptions, and defect tracking sheets. Data updates run daily, or trigger when code is committed. Each data entry typically includes fields including test scenario descriptions, expected results, associated code snippets, and execution status. Some report-type data includes statistical values and timestamps.
Constraints on Vector Models and Indexing
This category has high data update frequency and scattered trigger timings. Indexes must support incremental synchronization and on-demand refreshing to avoid retrieving expired test cases or reports. Data includes multiple formats: short scenario descriptions, long defect tracking sheets, structured code snippets, and more. Vector models must adapt to text inputs of varying lengths to ensure consistent vector representations across different data types. Test scenarios often involve boundary conditions and abnormal logic. Retrieval results must accurately match keywords and context. Index recall rules must prioritize associating tests with their associated code and requirement documents to avoid irrelevant results interfering with test case generation and defect localization.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the average text length of test cases and defect sheets, balances vector representation accuracy and retrieval efficiency |
| Overlap Length | 100–200 characters | Prevents disruption of test scenario context across chunks, preserves coherence of associated logic |
| Similarity Threshold | 0.75–0.85 | Adapts to the precise matching requirements of test scenarios, filters low-correlation redundant data |
| Single Search Limit | Top 5 entries | Focuses on core associated test cases and reports, avoids excessive results interfering with test logic judgment |
| Maximum Context | 4000–6000 tokens | Matches the total length limit of test-related documents, ensures complete loading of associated context |
Three Common Misconfigurations
- Symptom: Document indexing takes far longer than expected, and the indexing progress displayed in the interface stalls. Cause: Failed to match the update frequency of test data, used full indexing instead of incremental indexing. This causes each synchronization to process a large number of historical test reports and cases.
- Symptom: Retrieval results return multiple duplicate test case entries, or completely unrelated documents. Cause: Failed to correctly set the similarity threshold and single search limit, resulting in recall of low-correlation or duplicate index data.
- Symptom: Unable to properly call the specified vector model to generate document vectors. Cause: Confused the AI Proxy administrator Token (
AIPROXY_API_TOKEN) with model vendor credentials and application API Keys. Incorrectly configured AI Proxy environment variables into model channels or application call credentials. Failed to configure the correct upstream model channel address and vendor credentials through the model channel, resulting in model call failures.
How to Verify Configurations Are Correct
- Check the environment variables of the App/Admin server. Confirm that
AIPROXY_API_ENDPOINT(root address of the AI Proxy service) and non-emptyAIPROXY_API_TOKEN(AI Proxy administrator Token) are correctly configured, with no additional path suffixes. This aligns with the deployment requirements of FastGPT v4.17.0 and later versions. - Manually trigger an incremental index for a test case. Review server logs for no errors, and confirm that newly added test documents appear in the retrieval interface after indexing completes.
- Enter test scenario keywords to run a retrieval. Verify that the number of returned results matches the single search limit configuration, and that result relevance aligns with the similarity threshold setting.
- Confirm that the upstream channel address and vendor credentials of the target vector model have been correctly added in the model channel configuration, ensuring the vector generation process operates normally.
The values provided are common starting points and should be evaluated 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.