What Data for This Category Looks Like
Data for this category comes primarily from pressure testing platforms, APM monitoring tools, database slow logs, and production traffic baselines. Four types of documents are regularly produced: pressure testing plans, capacity evaluation reports, pressure testing data records, and optimization suggestion sheets. Unlike integrated testing scenarios, this category focuses on data related to throughput, latency, and resource surplus. Data is updated monthly or when major versions are released. Core fields include throughput rate, latency value, resource occupancy rate, peak traffic, and more. Common units are requests per second, milliseconds, percentages, GB, and others. Core steps in data processing focus on pressure testing model design, bottleneck indicator identification, and capacity conclusion review. Abnormal data or incorrect configuration may lead to issues such as peak-hour timeouts and unbalanced resource procurement.
Constraints Imposed on Vector Models and Indexes
Data for this category mixes structured performance metrics and unstructured technical document text. This requires vector models to support embedding logic for both types of data. The monthly update frequency eliminates the need for high-frequency incremental indexing. A periodic full-update indexing strategy can be used instead. Units and value ranges for core fields are relatively fixed. Indexes must support filtering and recall based on specific indicator fields. This prevents irrelevant data from interfering with retrieval results. Hardware resources for performance testing scenarios are mostly general-purpose servers or consumer-grade graphics cards. Index sharding and cache configurations must match existing resource conditions. This avoids occupying too much computing power and disrupting business operations. This configuration guide is based on FastGPT v4.17.0.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the single-block text length of pressure testing reports and technical documents, avoids semantic fragmentation caused by overly small chunks |
| Similarity Threshold | 0.75–0.85 | Filters low-match non-performance-related documents, focuses on core pressure testing indicator content |
| Single Search Limit | Top 10 entries | Matches the small-batch precise retrieval needs of performance testing scenarios, avoids returning excessive redundant data |
| maxToken | 8192 token | Adapts to the large model context window, accommodates complete pressure testing plans and evaluation report content |
| defaultToken | Calibrated via actual testing | Adapts to the input length limit of self-built embedding models, avoids model call errors |
Three Common Misconfigurations
- Symptom: An error stating "This token does not have permission to use the model" is returned when calling an embedding model, or a prompt indicates no available channels. Cause: Access parameters for the self-built embedding model are not correctly configured, the model interface is not directly connected after skipping one API forwarding, or channel permission configurations are incorrect.
- Symptom: Knowledge base search latency is too high, with a single retrieval taking more than 30 seconds. Cause: The chunk length is set too large, increasing embedding time for single-block text, or the index is not optimized via sharding based on performance indicator fields, leading to full data scans during retrieval.
- Symptom: Retrieval results do not include core pressure testing indicator content, and a large number of irrelevant documents are returned. Cause: The similarity threshold is set too low, failing to filter low-match non-performance-related data, or retrieval keywords are not optimized for performance testing scenarios.
How to Verify Configurations Are Correct
- Run a small-batch retrieval of pressure testing data. Check if returned results include core performance indicator fields, to confirm the retrieval range matches preset goals.
- Review embedding model call logs. Confirm there are no errors related to token permission issues or no available channels, to verify model access configurations are effective.
- Test retrieval latency across different chunk lengths. Adjust to a reasonable range that matches team hardware resources, to confirm configurations are effective.
- Compare the number of retrieval results before and after configuration adjustments. Confirm that the single search limit and similarity threshold configurations match scenario requirements.
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.