What the Data for This Category Looks Like
Data for text annotation projects comes primarily from customer-provided corpora, annotation results from internal annotation platforms, specialized vocabularies, and quality inspection sampling results. Core daily documents include annotation specifications, sample packages, quality inspection reports, and problem feedback forms. Data update frequency is adjusted daily or per batch of annotation tasks. Each data entry includes fields such as original corpus text, annotation tags, and quality inspection markers. There is no unified fixed storage unit. Data is classified and stored only by annotation batch or corpus length. This category focuses on semantic boundary, entity, and intent recognition. It differs from image annotation projects, which use visual feature processing logic.
What Constraints Do These Characteristics Impose on Context and Token Processing
Customer corpora and annotation samples have wide length variation, ranging from tens of characters to thousands of characters. This increases the difficulty of adapting to context windows. High-frequency daily or per-batch updates require context processing to support quick loading of the latest annotation specifications and vocabulary data. This prevents annotation deviations caused by outdated data. Quality inspection sampling results and problem feedback forms contain many semantically ambiguous cases. Context recall must prioritize associating similar difficult samples. At the same time, token consumption must be controlled to avoid exceeding the model's supported upper limit. High-frequency demands for tag boundary explanations and consistency inspections require context to retain association records of multiple rounds of annotation and quality inspection. It also supports context retention for multi-turn conversations, matching review workflow requirements. This further increases the complexity of token management.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the average token length of multi-round annotation records and quality inspection reports for text annotation projects, avoiding exceeding the context window upper limit of mainstream large models |
quoteMaxToken | 1500–2000 characters | Prioritizes recalling sample packages and vocabulary fragments related to the current annotation task, controlling token consumption for a single reference |
| Context update interval | Every 6 hours | Matches the daily or per-batch data update rhythm, ensuring that annotation specifications and vocabulary associated with the context are the latest versions |
| Difficult sample recall limit | Top 3 entries | Focuses on high-frequency scenarios of tag boundary explanations and consistency inspections, reducing irrelevant context token usage |
| Automatic truncation threshold | Calibrated based on actual testing | Adapts to corpus length differences across different annotation tasks, preventing token overflow caused by overly long corpora |
Three Common Mistakes
- A 422 status code is returned during calls, with an error message indicating that the token length does not meet requirements. This occurs because FastGPT calling specifications are not followed, the
maxContextvalue is not controlled, the total incoming context token count exceeds the model's supported range, and no adaptations are made based on the corpus length characteristics of text annotation projects. - The annotation specifications and vocabulary associated with the context are outdated, leading to deviations in tag boundary explanations. This happens because a reasonable context update interval is not set, outdated data beyond the update cycle is used, and the daily or per-batch update rhythm of text annotation projects is not matched.
- Recalled context contains too many irrelevant samples, leading to redundant review results and high token consumption. This occurs because the difficult sample recall limit is not enforced, and core scenarios of tag boundary explanations and consistency inspections are not prioritized.
How to Confirm the Configuration Is Correct
- Upload a batch of annotation corpora and sample packages of typical lengths. Check the token consumption value in the context preview interface of FastGPT v4.17.0. Confirm that the value does not exceed the preset
maxContextrange. - Modify the annotation specification, then wait for the preset context update interval. Verify that the associated specification content in the context is the latest version.
- Submit a review task containing difficult samples. Check the number of recalled context entries. Confirm that the number matches the preset recall limit.
- Trigger the review process for overly long corpora. Confirm that the system automatically triggers the truncation logic, and no token overflow error occurs.
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.