What the data for this category looks like
This category of data primarily comes from object storage, data warehouses, annotation platforms, and customer acceptance feedback. Updates occur per batch or per version. Daily used documents include dataset descriptions, version records, delivery checklists, and acceptance reports. Core fields include version number, total sample count, storage path, delivery date, and acceptance status. These documents support document drafting and verification work for data project managers, data engineers, and customer algorithm teams.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Multi-source data sources require multi-turn dialogue to associate dataset metadata from different storage channels, to avoid mixing data. Batch or version-based update cycles require prompt engineering to track version change information, ensuring document content matches the current version. Fixed document structures require multi-turn dialogue to extract fields such as version numbers and sample differences, to assist with version management and delivery verification. Time-consuming tasks including version number management, sample difference descriptions, and delivery completeness verification require prompt engineering to automatically complete field verification and information aggregation, reducing manual operation costs.
How to set configurations
These configurations apply to FastGPT v4.17.0 and later versions:
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to the unique identifier of the dataset version | Used to associate dataset metadata for the corresponding version, avoiding cross-version data mixing |
messages | Concatenate system prompts, user requirements, and dataset document content | Adapts to the document structure of dataset delivery and version management, ensuring context includes content such as version records and delivery checklists |
variables | Pass in fields including version number, delivery batch, and total sample count | Used to transfer category-specific data fields during multi-turn dialogue, assisting with version management and verification |
| Maximum context length | 2000–4000 characters | Adapts to the content length of single-batch dataset documents, preventing truncation of critical version description information |
| Temperature coefficient | 0.3–0.7 | Balances accuracy and flexibility in document information extraction, ensuring accurate output of fields such as version numbers and storage paths |
Three common mistakes
- Multi-turn dialogue returns empty version record fields. The cause is failure to pass dataset-specific fields such as version number in
variables, preventing the prompt from extracting corresponding information. - Slow dialogue response. The cause is failure to reasonably limit the context length in
messages, leading the model to process excessive redundant dataset document content. - API call returns 400 invalid image error. The cause is failure to correctly configure AI Proxy environment variables, leading to abnormal access to image resources.
How to confirm configurations are set correctly
- Check if
chatIdis uniquely bound to the currently processed dataset version, ensuring no cross-version data calls. - Verify if
messagesincludes core document content such as dataset descriptions, version records, and delivery checklists, confirming context covers category-specific data. - Confirm that fields such as version number and delivery batch passed in
variablesare correct, ensuring the prompt can accurately extract corresponding information. - Initiate a test dialogue, and check if the returned content includes dataset version information and delivery completeness description.
The values provided are common starting points, and should be evaluated 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.