What this category of data looks like
Data sources for this category include raw datasets, ETL logs, annotation platforms, and sampling results. Update frequency is daily or per task batch. Daily produced documents include cleaning rules, quality inspection sampling sheets, abnormal data lists, and delivery reports. Each document’s fields correspond to unique data identifiers, processing rule configurations, sampling batch numbers, abnormal item details, and similar content. There are no unified fixed units. Field content is flexibly adjusted based on specific data types and business requirements. Core data processing steps focus on duplicate data identification, outlier handling, and sampling quality inspection. Each step relies on structured batch-related information to ensure traceability of data processing.
Constraints on HTTP Interfaces and External Systems From These Characteristics
Dispersed data sources and inconsistent update rhythms require HTTP interfaces to support adaptive logic for multi-source data pulling and batch uploads. Daily documents contain various structured and unstructured content, so interfaces must support different format parameter transfer rules. Abnormal data lists and delivery reports require accurate association with data batches, so interfaces must support querying and callbacks based on batch identifiers. Duplicate data identification and sampling quality inspection steps take extended time, so interfaces must set reasonable timeout mechanisms to prevent mid-task interruptions. The risk of customer rejection requires interfaces to return clear status identifiers and error details to facilitate quick troubleshooting of issues in data processing.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Authorization | Carry a token in Bearer {API_KEY} format | Complies with general interface authentication specifications to ensure data transmission security |
| Single request data volume | 1000–2000 entries | Adapts to the batch processing scale of raw datasets to avoid request timeouts |
| Timeout threshold | 600 seconds | Matches the typical processing duration of duplicate data identification and sampling quality inspection |
responseData parsing rule | Filter returned content by data batch fields | Facilitates quick association of processing results for a single batch task |
variables binding parameters | Associate cleaning rule IDs and sampling batch numbers | Ensures one-to-one correspondence between interface calls and business document parameters |
This guide applies to FastGPT v4.17.0.
Three Common Misconfigurations
- Long-term "Indexing" status after interface call: Phenomenon: After calling the
responseDatainterface to upload data, the interface continuously displays the "Indexing" status without updates. Cause: Callback verification is not configured per batch, or the single request data volume exceeds the interface’s carrying limit, resulting in backlog of the processing queue. - Only GPT series models displayed in model selection: Phenomenon: No custom model options appear in the model dropdown menu of the text extraction module. Cause: The correct model identifier parameter is not bound via
variables, or interface authentication does not cover call permissions for custom models. - Interface returns empty abnormal data list: Phenomenon: After calling the abnormal data query interface, the
responseDatafield returns an empty array. Cause: Correct cleaning rule ID or sampling batch number is not passed, resulting in the interface being unable to match corresponding data.
How to Verify Proper Configuration
- The authentication interface is called to verify the validity of the
Authorizationtoken, and the returned status code is confirmed to be 200. - A small batch of test data is submitted, and
responseDatais checked to confirm it returns correct processing batch identifiers and result entries. - After binding
variablesparameters, parameter mapping is checked to confirm it matches cleaning rules and sampling batch numbers. - An abnormal request is simulated, and the interface is confirmed to return clear error messages and status codes to facilitate troubleshooting.
The values provided are common starting points and should be evaluated against matching 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.