What data from this category looks like
Daily data for text annotation projects comes from customer corpora, sample packages produced by annotation platforms, annotation specification documents, quality inspection sampling results, and problem feedback forms. Data is typically updated daily or after each batch of annotation tasks is completed. A single data entry includes fields such as text content, corresponding annotation tags, quality inspection results, annotator, and timestamp. Some batch documents like sample packages contain dozens to hundreds of associated texts. The overall content focuses on semantic information, with emphasis on associated descriptions of entities, intents, and tag boundaries.
What constraints these characteristics impose on the context and token link
Text annotation projects have batch long text and high-frequency update characteristics that impose multiple constraints on context and token management. First, use sufficient context tokens to carry complete semantics for long text from batch sample packages and quality inspection reports. This avoids losing tag boundary and entity association information due to truncation. Second, refresh context regularly for high-frequency updated annotation specifications and problem feedback forms to ensure timeliness. Limit the total tokens passed in a single request to stay below the model's upper limit. Third, split single ultra-long annotation samples precisely. This prevents abnormal token consumption and maintains stability for large model requests.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total semantic length of batch sample packages and quality inspection reports for text annotation projects, avoiding truncation of critical tag association information |
quoteMaxToken | 1000–2000 token/entry | Limits the context occupancy of a single annotation sample or quality inspection result, balancing recall completeness and token consumption |
| Single search upper limit | Top 8 recall results | Covers associated samples, annotation specifications, and historical quality inspection results required for text annotation tasks, avoiding semantic gaps caused by too few recalls |
| Context recall overlap rate | 15–20% | Ensures continuity of semantic boundaries and entity associations for text annotation, preventing deviation in understanding of annotation rules due to fragmented recall |
| Context refresh cycle | Every 24 hours | Matches the daily or per-batch update frequency of annotation specifications and sample packages, ensuring the timeliness of context content |
Three common mistakes
- Phenomenon: After configuring
quoteMaxToken, the context of ultra-long annotation samples is not correctly truncated. This causes timeout errors in large model requests. Cause: The system does not automatically split ultra-long text according to the configured token upper limit. Directly passing the full content exceeds the model's token limit. - Phenomenon: After setting a higher
maxContext, large model requests fail or have no response. Cause: TheAIPROXY_API_ENDPOINT(AI Proxy service root address, for example http://aiproxy:3000, do not append paths such as /v1) andAIPROXY_API_TOKEN(AI Proxy administrator Token, must be consistent with the server configuration and non-empty) environment variables of the App/Admin server are not correctly configured. This prevents normal connection to the AI Proxy service and forwarding of large model requests. - Phenomenon: Abnormal peaks appear in context token consumption in the operation report of batch annotation tasks. Cause: The total context tokens passed in a single request are not limited. Context superposition for batch samples exceeds the model's upper limit, triggering request failure.
How to confirm the configuration is correct
- Check the App/Admin server environment variables of FastGPT v4.17.0. Confirm that
AIPROXY_API_ENDPOINT(AI Proxy service root address, for example http://aiproxy:3000, do not append paths such as /v1) andAIPROXY_API_TOKEN(AI Proxy administrator Token, must be consistent with the server configuration and non-empty) are correctly configured and non-empty. - Initiate a test request for a single ultra-long annotation sample. Verify that the context parameters of the large model request match the configured values of
maxContextandquoteMaxToken. - Batch import annotation sample packages. Check that the number of recall results matches the Single search upper limit configuration.
- Wait for configuration to refresh. Review context token consumption data in the operation report, confirm no unexpected peaks appear.
The values provided are common starting points and should be validated against local 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.