What data in this category looks like
Data sources for this category include original datasets, ETL logs, annotation platform outputs, and sampling results. Updates occur daily or are triggered per batch task. Daily produced documents include cleaning rules, quality inspection sampling sheets, abnormal data lists, and delivery reports. Fields across these documents include data entry count, anomaly type, processing status, sampling batch number, and similar items. There are no unified fixed units; values adjust based on the dataset specifications of specific tasks. This content is written based on FastGPT v4.17.0.
What constraints these characteristics impose on multi-turn dialogue and prompts
Decentralized data sources require dialogue to associate context across multiple data types. Unstable update frequency requires dialogue context to support dynamic loading of the latest cleaning and quality inspection data. Diverse daily document structures require prompts to adapt to different document field formats, avoiding adaptation mismatches from fixed templates. Multi-turn dialogue must distinguish between different roles of business personnel: data engineers focus on cleaning rule adjustments, quality inspectors focus on sampling result verification, and project managers focus on delivery report progress. Context memory must accurately match role-specific needs.
How to set configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Generate a unique identifier per user session, retain 24-hour validity | Distinguish chat records of different business users, avoid cross-session context confusion |
messages | Retain the most recent 8–12 rounds of dialogue context, filter redundant content from specified reply plugins | Adapt to the concise dialogue needs of data cleaning and quality inspection scenarios, reduce interference from invalid information |
variables | Pass in the current task's dataset ID and sampling batch number | Bind specific business data, allowing prompts to accurately associate with the current cleaning or quality inspection task |
| Maximum context length | 8000–12000 characters | Adapt to the conventional length of documents such as cleaning rules and abnormal data lists |
| History memory toggle | Calibrate based on actual testing | Context complexity varies significantly across different data batches, requiring flexible adjustment |
Three common configuration errors
- Phenomenon: Chat history contains redundant content generated by specified reply plugins, causing AI-generated cleaning suggestions to deviate from business requirements. Cause: Failed to filter non-business dialogue fragments generated by plugins in the
messagesparameter configuration. - Phenomenon: Unable to initiate a dialogue after deployment, with the interface returning an invalid API key error. Cause: Failed to correctly fill in the AI key on the system configuration page, or the key does not have corresponding service permissions enabled.
- Phenomenon: Chat records of different business users are visible to each other, leading to leakage of data task information. Cause: Failed to generate a unique session identifier for each user via
chatId, and failed to isolate session contexts.
How to confirm proper configuration
- Initiate a test dialogue, enter a summary of the current task's cleaning rules, and verify whether the AI reply associates with the business identifier information in the passed
variablesparameter. - Conduct multi-session testing, initiate dialogues using different
chatIdvalues, and confirm that historical records of each session do not interfere with each other. - Send test content containing specified reply plugins, check whether the chat history retains business-related dialogue fragments and filters out redundant content generated by plugins.
- View system operation logs, confirm that the context of each dialogue is not abnormally truncated, and meets the configured length requirements.
The values provided here 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.