What the data for this category looks like
The data for text annotation projects mainly comes from unannotated corpora provided by clients, annotation results produced by internal annotation platforms, industry-standard vocabularies, and feedback documents from quality inspection sampling. Daily generated documents include annotation execution specifications, batch sample packages, monthly quality inspection reports, and annotation issue feedback ledgers. Data is updated daily or per single-batch annotation task. A single data entry includes original text fragments, annotation tags, quality inspection verification results, and other content, focusing on annotation information related to semantic boundaries, entity classification, and intent recognition.
What constraints these characteristics impose on the "workflow orchestration" link
Because data sources are scattered and updated in batches, the workflow must support batch access to multiple data sources and scheduled trigger configuration. Because daily documents include annotation specifications and quality inspection reports, the workflow needs to embed document parsing nodes to read tag rules and quality inspection standards. Because annotation data focuses on semantic details, the workflow needs to configure entity boundary verification and tag consistency check steps. At the same time, data anomalies will directly lead to model training deviations or delivery delays, so failure retry and exception alarm configuration items must be added.
How to set the configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Define per annotation batch, include general tag rules, external interface authentication token, batch ID | Text annotation projects require unified tag verification rules and cross-node authentication information to avoid repeated configuration |
nodeId | Bind to data source update events, trigger the workflow per batch | Data is updated per annotation batch; triggering via the data source node ID avoids invalid execution |
| Timeout setting | 600 seconds | Document parsing and external interface calls for text annotation usually require long processing times, avoiding mid-run interruptions |
| Failure retry count | 3 times | Temporary network fluctuations may occur during external interface calls for annotation data; retries can reduce exception rates |
| Context window | 800–1200 characters | The text length of a single annotation sample usually falls within this range, avoiding context overflow that affects parsing |
Three common mistakes
- Symptom: The workflow cannot call the preset global authentication token, resulting in failed external interface calls. Cause: The global variable field was not added to the
variablesconfiguration item, or the configuration was not saved to take effect. - Symptom: The workflow runs normally within the platform, but only returns the results of the first node when called via an external interface. Cause: No context passing node was configured, resulting in session state not being synchronized to subsequent links.
- Symptom: The workflow cannot read the annotation sample list in the uploaded Excel file. Cause: No file parsing node was added, or the text column reading path for Excel was not specified.
How to confirm the configuration is complete
- Enter the workflow configuration page of FastGPT v4.17.0, check the configuration content of
variablesandnodeId, and confirm they have been saved and take effect. - Trigger a test run, and check whether there are normal records of global variable loading and node triggering in the platform logs.
- Conduct a test using an external interface, confirm that the passed authentication parameters match the content of the preset
variables. - Upload a small test sample package, check whether the file parsing node can correctly extract text content and pass it to subsequent links.
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.