What data for this category includes
Daily documents for this category include stress testing plans, capacity evaluation reports, stress testing data records, and optimization suggestion sheets. Data primarily originates from stress testing platforms, APM monitoring tools, database slow query logs, and production traffic baselines. Data is updated once monthly or upon major version releases. Common document fields include throughput rate, latency values, resource occupancy rates, and capacity thresholds, with typical units being requests per second, milliseconds, percentage, GB, and similar units. Core scenarios associated with this data include stress testing model design, bottleneck indicator identification, and capacity conclusion review. Potential abnormal consequences include peak-hour service timeouts, unbalanced resource procurement, or wasted capacity expansion.
What constraints these characteristics impose on the knowledge base retrieval and recall workflow
Stress testing-related documents have data sources scattered across multiple monitoring and platform systems. Retrieval must cover both structured and unstructured data from multiple sources, so cross-dataset recall support is required. Data updates occur monthly or during major version releases, so the knowledge base’s incremental synchronization cycle must match this update rhythm to avoid retrieving outdated stress testing parameters. Document fields include professional indicators such as throughput rate and latency, so retrieval must support filtering by indicator dimensions. Recalled content must also include clear unit and scenario association information to ensure the accuracy of technical support and pre-sales Q&A. Additionally, abnormal consequences of stress testing scenarios impact business resources and service stability, so recalled retrieval results must have high confidence levels to avoid misleading decision-making.
How to configure settings
The following configurations are based on the interface and parameter logic of FastGPT v4.17.0:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Matches semantic similarity of professional terms and indicators in stress testing scenarios, preventing recall of irrelevant general testing documents |
limit | Top 8 entries | Controls the number of results returned per recall, adapts to the relatively long content of individual stress testing documents, and reduces context stitching time |
searchMode | hybrid | Combines keyword matching and semantic retrieval to cover specific steps in stress testing plans and indicator descriptions in capacity evaluation reports |
usingReRank | Enabled | Re-ranks recalled results by relevance to the query, prioritizing core content such as bottleneck identification and capacity conclusions |
datasetSearchUsingExtensionQuery | Enabled | Automatically expands query terms to supplement stress testing-related synonyms, such as expanding "latency" to "response latency" and "request latency" |
collectionIds | Specify knowledge base collection IDs for the stress testing domain and APM monitoring domain | Limits the retrieval scope to documents related to performance testing and capacity evaluation, avoiding redundant content from non-target domains |
Three common configuration errors
- Symptom: After completing knowledge base retrieval, the final AI conversation output takes an extended period, with the interface showing a wait time exceeding 30 seconds. Cause: The retrieval scope is not limited via
collectionIds, and redundant documents outside the performance testing domain are recalled, increasing context stitching and inference time. - Symptom: After using the locally deployed m3e-large model as an embedding tool via the API, retrieval recall results are empty or have poor matching accuracy. Cause:
collectionIdsassociated with the knowledge base collection for the corresponding embedding model are not specified, or vector dimensions are not aligned with the embedding model. - Symptom: Capacity-related data recalled from the knowledge base does not match the current production baseline. Cause: Incremental synchronization is not configured according to the monthly or major version update frequency, leading to retrieval of outdated stress testing data.
How to confirm the configuration is correctly applied
- Execute a stress testing bottleneck identification query, verify that recalled results only include stress testing plans and capacity evaluation report documents, and confirm that the
collectionIdsconfiguration takes effect. - Check the retrieval logs in FastGPT v4.17.0, verify that the number of results returned per recall matches the
limitsetting, and confirm that retrieval scope and quantity control function normally. - Compare the sorting of recalled results with and without
usingReRankenabled, and confirm that the re-ranking logic operates correctly. - Simulate a stress testing data update, check whether the knowledge base completes incremental synchronization within the set cycle, and confirm that the update frequency matches business requirements.
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 2026-10-19.