What Data for This Category Looks Like
Typical participants in this category include implementation consultants, test managers, and customer business representatives. Commonly produced and used documents include UAT test cases, acceptance issue tickets, acceptance reports, and signed confirmation forms. Data sources cover requirement specification documents, test environment configurations, customer business samples, and defect tracking systems. Data is updated once per phase or version. The most time-consuming work steps are test case preparation, issue reproduction and categorization, and acceptance conclusion confirmation. Consequences of errors include acceptance delays, delayed final payments, and requirement scope disputes.
What Constraints These Characteristics Impose on the Context and Token Link
The core content of documents such as UAT test cases and acceptance issue tickets is business scenarios and acceptance criteria. Context retrieval must prioritize matching specific business scenarios to avoid interference from irrelevant information. Data is updated per phase or version, so context caching must be refreshed synchronously to ensure retrieved content uses the latest version. Individual document lengths vary widely, from short issue tickets to complete UAT test case suites. Token allocation must be managed appropriately to avoid truncating core acceptance basis materials. The most time-consuming test case preparation step relies on accurate context retrieval, reducing the workload of repeatedly organizing documents. Serious error consequences require that context must not lose key acceptance basis fields, and token thresholds must adapt to large model input limits.
How to Set Configurations
These configurations are adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to batch concatenation of multiple UAT documents, supporting large model understanding of complete acceptance scenarios |
quoteMaxToken | 1500 token | Limits token consumption per single reference, balancing context completeness and model input limits |
| Single retrieval upper limit | Top 8 entries | Covers core test cases and issues for a single phase of acceptance, avoiding context overload |
| Context refresh timing | Triggered after each version update | Matches the data update rhythm per phase or version, ensuring retrieved content uses the latest acceptance basis |
| Maximum length per single reference | 1200–1800 characters | Matches typical length of UAT test case steps and issue descriptions, preserving complete business scenarios |
Three Common Misconfigurations
- If
maxContextexceeds the input limit of the current large model, the full context cannot be sent to the model. This manifests as the knowledge base preview showing existing context, but the model output not associating relevant content. This corresponds to the issue where context is not sent due to an excessively large configured reference limit. - In offline deployment environments where a local tokenizer is not configured, calls attempt to request the external cl100k.tiktoken resource, triggering a network connection error. This manifests as curl requests returning resource not found or timeout errors.
- Failing to update the context cache per phase or version retrieves old versions of UAT test cases or defect records. This manifests as matching closed old entries during issue reproduction and categorization, leading to deviations in acceptance conclusions.
How to Confirm Configurations Are Correct
- Upload a complete UAT test case document. Check whether the context preview includes core fields such as test case steps and expected results, confirming retrieved content matches the business scenario.
- Initiate a query related to the acceptance scenario. Check whether the model output associates the corresponding acceptance issue ticket or defect record, confirming the context is correctly passed.
- Simulate an offline deployment environment. Check running logs to confirm the tokenizer call does not request external network resources, with no relevant error messages.
- Adjust
quoteMaxTokento a smaller value. Verify whether single reference content is truncated, confirming token allocation meets expectations.
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.